3D Bioprinted Lung Cancer Models for Biomedical Research
vorgelegt von
M. Sc.
Yikun Mei
ORCID:0000-0003-1881-753X
an der Fakultät III – Prozesswissenschaften
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften
- Dr. rer. nat. -
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Lorenz Adrian
Gutachter: Prof. Jens Kurreck
Gutachter: Prof. Felicitas Escher
Tag der wissenschaftlichen Aussprache: 21. October 2024
Berlin 2024
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
II
Abstract
Lung cancer remains one of the most prevalent and lethal forms of cancer worldwide,
underscoring the pressing necessity for the development of novel therapeutic
approaches. Despite the development of some targeted therapies, the incidence of lung
cancer continues to rise in developing countries, creating a need for additional treatment
options. It is therefore evident that research on test models for various treatment
methods, including chemotherapy and radiotherapy, is of great importance. Recent
advances in three-dimensional bioprinting technology offer potential solutions to this
problem. Three-dimensional (3D) bioprinting techniques have been employed to
construct models for drug and toxicology testing, as well as radiation therapy for non-
small cell lung cancer.
The majority of current research on 3D lung cancer models is focused on static models.
The first part of this thesis describes the development of a perfusable lung cancer model
that can be used for drug testing. The model was generated using digital light processing
(DLP) printing, incorporates simulated human blood vessels, and can be connected to
a peristaltic pump for long-term perfusion culture. This configuration allows for the
examination of drug candidates under dynamic conditions. In a proof-of-concept study
with gemcitabine, the 3D bioprinted model demonstrated an IC50 value that was
approximately 1000 times higher than that of two-dimensional (2D) cell cultures.
Compared to the static 3D model, dynamic culture in a perfusion system significantly
increased cell viability by about 60% and enhanced the cytotoxic effects of the tested
drug on tumor cells. Additionally, the expression of apoptosis markers, including
Cleaved caspase-3 and Cleaved poly (ADP-ribose) polymerase-1 (PARP-1), was
upregulated in response to drug treatment.
The second part of the thesis evaluated the suitability of standardized 3D-printed human
lung cancer models for radiotherapy studies. Radiotherapy is an important part of the
treatment of lung cancer. However, preclinical radiotherapy testing is often performed
in animal models, which have several drawbacks, including species-specific differences
and ethical concerns. To replace animal models, bioprinted 3D human lung cancer
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
III
models consisting of lung tumor cells embedded in human primary lung fibroblast were
developed. The models were placed in a mouse phantom made of materials that mimic
the X-ray radiation attenuation rates found in mice. This setup was found to be capable
of simulating the selective killing effect and induction of apoptosis of X-rays in A549
lung tumor epithelial cells, while healthy primary lung fibroblasts were protected. In
comparison to 2D cell irradiation experiments, the 3D model was found to more
accurately reflect the actual in vivo treatment outcomes.
In conclusion, a range of printing techniques to fabricate disease models for lung cancer
chemotherapy and radiotherapy were employed in this thesis. This provides novel
options for the development of enhanced testing platforms for prospective lung cancer
treatment modalities.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
IV
Zusammenfassung
Lungenkrebs ist nach wie vor eine der häufigsten und tödlichsten Krebsarten weltweit.
Trotz der Entwicklung einiger zielgerichteter Therapien nimmt die Inzidenz von
Lungenkrebs in den Entwicklungsländern weiter zu, was die Dringlichkeit der
Entwicklung neuer therapeutischer Ansätze unterstreicht. Es liegt daher auf der Hand,
dass die Entwicklung von Modellen für verschiedene Behandlungsmethoden,
einschließlich Chemo- und Strahlentherapie, von großer Bedeutung ist. Die jüngsten
Fortschritte in der dreidimensionalen (3D) Biodruck-Technologie bieten Lösungen für
dieses Problem. Durch 3D-Biodruck hergestellte Tumormodelle können für
Arzneimittelstudien, toxikologische Studien und Strahlentherapiestudien bei nicht-
kleinzelligem Lungenkrebs eingesetzt werden.
Der Großteil der aktuellen Forschung zu 3D-gedruckten Lungenkrebsmodellen
konzentriert sich auf statische Modelle. Der erste Teil dieser Arbeit beschreibt die
Entwicklung eines perfundierten Lungenkrebsmodells, das für Wirkstofftests
verwendet werden kann. Das Modell wurde mit Hilfe des Digital Light Processing
(DLP) Druckverfahrens hergestellt, enthält simulierte menschliche Blutgefäße und
kann für eine Langzeit-Perfusionskultur an eine Peristaltikpumpe angeschlossen
werden. Diese Konfiguration ermöglicht die Untersuchung von Wirkstoffkandidaten
unter dynamischen Bedingungen. In einer Proof-of-Concept-Studie mit Gemcitabin
zeigte das 3D-Modell einen etwa tausendmal höheren IC50-Wert als zweidimensionale
(2D) Zellkulturen. Im Vergleich zum statischen 3D-Modell erhöhte die dynamische
Kultur in einem Perfusionssystem die Viabilität der Zellen um etwa 60% und verstärkte
die zytotoxische Wirkung des getesteten Medikaments auf Tumorzellen. Zusätzlich
wurde die Expression von Apoptosemarkern, einschließlich gespaltenem Caspase-3
und gespaltenem Poly (ADP-Ribose)-Polymerase-1 (PARP-1), infolge der
Medikamentenbehandlung hochreguliert.
Im zweiten Teil der Arbeit wurde die Eignung von standardisierten 3D-gedruckten
menschlichen Lungenkrebsmodellen für Strahlentherapiestudien untersucht. Die
Strahlentherapie ist ein wichtiger Bestandteil der Behandlung von Lungenkrebs.
Präklinische Tests zur Strahlentherapie werden jedoch häufig an Tiermodellen
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
V
durchgeführt, was mehrere Nachteile mit sich bringt, darunter speziesspezifische
Unterschiede und ethische Bedenken. Um Tiermodelle zu ersetzen, wurden 3D-
gedruckte Lungenkrebsmodelle entwickelt, die aus Lungentumorzellen bestehen, die in
gesunde primäre Lungenfibroblasten eingebettet sind. Die Modelle wurden in ein
Mausphantom platziert, das aus Materialien besteht, die die Abschwächung von
Röntgenstrahlen in Mäusen nachahmen. Es konnte gezeigt werden, dass dieser
experimentelle Aufbau in der Lage ist, die selektive zelltoxische Wirkung und die
Induktion von Apoptose durch Röntgenstrahlung in A549 Lungentumor-Epithelzellen
zu simulieren, während gesunde primäre Lungenfibroblasten verschont blieben. Im
Vergleich zu 2D-Zellbestrahlungsexperimenten wurde festgestellt, dass das 3D-Modell
die tatsächlichen in vivo Behandlungsergebnisse genauer wiedergibt.
Zusammenfassend konnte in dieser Arbeit gezeigt werden, dass verschiedene
Drucktechniken zur Herstellung von 3D-Lungenkrebsmodellen für Chemo- und
Strahlentherapiestudien eingesetzt werden können. Dies eröffnet neue Möglichkeiten
für die Entwicklung verbesserter Testplattformen für zukünftige
Behandlungsmodalitäten von Lungenkrebs.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
VI
Table of contents
Abstract....................................................................................................................... II
Zusammenfassung...................................................................................................... Ⅳ
List of contents........................................................................................................... Ⅵ
Acknowledgements................................................................................................... Ⅹ
List of publications contributing to this work............................................................ XIⅤ
Rights and Permission……………………………………………………………. ⅩⅤ
List of Abbreviations…………………………………………………..……….....ⅩⅥ
1. Introduction ....................................................................................................1
1.1. Lung cancer epidemiology and treatment options..................................................1
1.2. Advantages of 3D bioprinted tumor models compared to 2D cell
culture…………………………………………………………………………….…....2
1.3. Limitations of animal models……………………………………………………..4
1.3.1 The introduction of mouse phantoms in radiotherapy research…………….……5
1. 4. 3D bioprinting technologies for human organ models….………………………..6
1. 4. 1. 3D Bioprinting Method……………………………………………………..…7
1. 4. 1. 1. Droplet printing………………………………………………………….….7
1. 4. 1. 2. Extrusion printing…………………………………………………………...8
1. 4. 1. 3. Laser-Assisted printing……………………………………….…..………….9
1. 4. 1. 4. Digital light processing (DLP)………………………………………….....10
1. 4. 1. 5. Suspension printing……………………………………………………..…11
1. 4. 2. 3D Bioprinting materials………………………………………………….….13
1. 5. 3D printed tumor organ models……………………………………………....…17
1. 5. 1 Tumor extracellular matrix………………………………………...…..……...17
1. 5. 2 Tumor vasculature…………………………………………………….…..…..18
1. 5. 3 Tumor immune microenvironment……………………………………………19
1. 5. 4 Application of 3D printed tumor models……………………….……………..20
2. Objectives of this work .................................................................................22
3. Materials and methods..................................................................................23
3.1. Materials……………………………………………………………………..…..23
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
VII
3.1.1. Cultured cells…………………………………………………….…………….23
3.1.2. Laboratory equipment…………………………………………………………23
3.1.3. Consumables…………………………………………………………………..24
3.1.4. Biopolymeres………………………………………………………………….24
3.1.5. Chemicals……………………………………………………………………...25
3.1.6. Kits…………………………………………………………………………….25
3.1.7. Buffers…………………………………………………………………………26
3.1.8. Mediums……………………………………………………………………….26
3.1.9. Medium additives………………………………..………………………….…26
3.1.10. Antibodies……………………………………………………………….……26
3.1.11. Enzyme....……………………………………………………………….……27
3.1.12. Software……………………………………………...……………….……...27
3.2. Methods…………………………………………………………….……………29
3.2.1. Cell culture……………………………………………………….……………29
3.2.1.1 Resuscitation Method for Cells (Rapid Thawing)………………………..…..29
3.2.1.2 Cell passage…………………………………………………………..………29
3.2.1.3 Cell cryopreservation (slow freezing)………………………………..………30
3.2.2. Life-dead staining……………………………………………………..……….30
3.2.3. XTT assay……………………………………………………………..……….31
3.2.4. Cryosectioning of the 3D models…………………………………..………….31
3.2.5. Paraffin sectioning of the 3D models……………………………..…………...31
3.2.6. Immunohistochemistry……………………………………………..………….33
3.2.7. Western blot………………………………………………………..…………..33
3.2.8. Extraction of cells’ RNA and qPCR…………………………………..……….34
3.2.9. Cell proliferation assay with EdU kit………………………………...……..…35
3.2.10. LDH assay……………………………………………………………………36
3.2.11. Preparation of printing ink and printing of 3D models………………………36
3.2.11.1 LumenX digital light printing………………………………………...…….36
3.2.11.2 BioX extrusion printing……………………………………………………..37
3.2.12. Irradiation…………………………………………………………….………37
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
VIII
3.2.13. Radiotherapy Model System Setup and Treatment Planning………….……..38
3.2.14. Statistics………………………………………………………...…….………38
4. Results and discussion...................................................................................40
4.1. Generation of a Perfusable 3D Lung Cancer Model by Digital Light
Processing………………………………………………………………………….…40
4.1.1 Gemcitabine Demonstrates Significant Cytotoxic Effects on H358 2D Cell
Culture………………………………………………………………………………..40
4.1.2. Cell Survival after 3D Printing……………………………………………...…42
4.1.3 Treatment of Bioprinted 3D Constructs with Gemcitabine…………………….44
4.1.4. IC50 of Gemcitabine for H358 Cells in 2D and 3D Culture…….…………….46
4.1.5. Establishment of a Perfused Model…………………………….……………...47
4.1.6. Application of Gemcitabine by the Perfusion System………………………....49
4.1.7. Endothelial cells lining the walls of blood vessels………………………….…52
4.2. 3D Printing for Lung Cancer Models in Radiotherapy Testing……...………….54
4.2.1. Operational arrangement of radiotherapy equipment and setting of radiotherapy
instrument parameters………………………………………………………………..54
4.2.2. Comparison between 3D phantom and in vivo mouse model revealed comparable
dose distribution………...……………………………………………………………56
4.2.3 Cytotoxicity and cell viability of 2D cells and 3D models after
radiotherapy……………………………………………………………………….…57
4.2.4. Radiotherapy Induces DNA Double-Strand Breaks in 2D Cells and 3D
models…………………………………………………………………..……………61
4.2.5. Radiotherapy exerts its effects by inducing apoptosis in 2D cells and 3D
models………………………………………………………………..………………63
4.2.6. Radiotherapy-induced LDH release in 2D cells and 3D models………………64
4.2.7. Comparison of radiotherapy with and without phantom………………...….…65
5. Discussion………………………….………………………………………..68
5.1. Perfusable lung cancer model…………………………….…………………..…68
5.2. 3D Printed Lung Cancer Radiotherapy Model………………………..………...72
5.3. Outlook………………………………………………………………….………75
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
IX
6. Conclusion…………………………………………………..……………...78
6.1. Perfusable lung cancer model…………………………………………….…..…78
6.2. 3D Printed Lung Cancer Radiotherapy Model………………………….……....78
References……………………………………………………………………………79
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
X
Acknowledgements
I would like to express my gratitude to everyone who supported me during my time as
a Ph.D. student, including the numerous colleagues who were involved, as well as the
head of the Department of Applied Biochemistry, my supervisor Prof. Dr. Jens Kurreck:
• Prof. Dr. Jens Kurreck, I would like to express my sincere gratitude to him for the
trust and appreciation he showed me during the time of my Ph.D. application and for
welcoming me to pursue a doctoral degree in Germany. The mentorship provided, both
in terms of research guidance and assistance in my professional and personal life, has
proven to be immensely beneficial. Particularly noteworthy is Prof. Kurreck's
willingness to make time for one-on-one meetings, a gesture that was of great
importance during the most demanding phases of my doctoral studies and served as a
crucial means of alleviating my psychological burden.
• Dr. Beatrice Tolksdorf, I would like to thank her for her guidance on my research
project, as well as her instruction on various experimental methods, such as paraffin
sectioning. From the third year on, she took over the responsibility to supervise the
progress of my project and to discuss the experimental results on a regular basis. During
the collaboration process with the Charité Hospital laboratory, she actively participated
in each meeting, facilitating communication and collaboration with the Charité Hospital
physicians.
• Dr. Dongwei Wu, as a senior colleague one year ahead of me, especially as the only
Chinese colleague in the group besides myself, Brother Wu has been of tremendous
help. When I first arrived in Germany and was unfamiliar with various aspects of life
and faced numerous bureaucratic procedures, Brother Wu's support helped me navigate
through these inconveniences. In the field of research, Brother Wu taught me various
3D printing techniques. He is an absolute expert in the field, and has demonstrated
ingenious ideas in both bioprinting and printing various components. His expertise has
greatly benefited my academic journey.
• Matthias Ziersch, to be honest, he is working on his Ph.D., but also seems to be doing
some postdoctoral work and some lab management. He really is a multi-talent. Matze
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XI
has a good sense of humor, and in addition to giving me valuable research advice, his
witty remarks create a relaxed atmosphere in the lab.
• Viola Röhrs and Bernd Krostitz, as lab technicians, their help has been invaluable to
me. Bernd provided training for my cell experiments when I first joined the lab, and he
facilitated the procurement of various consumables used in our daily activities. He also
helped me arrange my medical checkup at the school clinic. Through our daily
interactions, he introduced me to many aspects of German cultural traditions, for which
I am truly grateful.
Viola has a thorough understanding of the protocols for all laboratory experiments. As
a result, she taught me many of the methodologies, as many methodologies I had
learned in China were different here, making adaptation a challenge. Her patient
guidance was indispensable. In addition, she takes care of the procurement of various
reagents, which provides us with great convenience in our research efforts.
• Ahmed Samir Mohamed，he is an exceptionally talented and enthusiastic scientist
with an outstanding capability for research. His scientific insights often demonstrate
remarkable foresight, and I have gained significant benefits from our daily discussions.
During our group meetings, his contributions are consistently constructive and valuable.
Furthermore, he played a pivotal role in introducing the confocal microscope to our lab,
a contribution that has proven invaluable to everyone's experiments.
• Dr. Thomas Hiller, Ida Shaef, and Dr. Johanna Berg, although they all left our research
group midway, I am deeply grateful for their assistance. Thomas and Ida, representing
the Pramo Company, played a pivotal role in ordering many of the instruments and
reagents essential to my initial project. Thomas, who also became my first German
friend, was of great help in our daily interactions. Ida guided me through my first qPCR
experiment.
Johanna, our former postdoctoral fellow, possessed formidable research skills. During
my first two years, she often proactively answered my questions when I was in doubt
and acted as a timely source of guidance. With her extensive experience, she had
excellent solutions for many subtle challenges in experiments that might go unnoticed.
Her expertise was of great benefit to me.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XII
• Dr. med. Dani Hakimeh, when my second project seemed insurmountable, Dani came
to my rescue. My research required the use of radiation therapy equipment, but for
various reasons, the equipment I'd originally planned to use was not available, causing
a significant delay. Dani stepped in and put me in touch with the Oncology Radiation
Department at the Charité, which opened up the possibility of success once again. I am
sincerely grateful for Dani's help.
• Dr. med. Franziska Hausmann and Elena Lakotsenina, they are both medical
practitioners employed at the Charité Hospital Oncology Radiation Therapy
Department. Dani facilitated my communication with the aforementioned individuals.
Dr. Franziska is a leading expert in the field of radiotherapy, consistently offering a
multitude of professional suggestions during each meeting. Elena, a doctoral candidate,
works in close collaboration with me on radiotherapy experiments. She displays
remarkable patience and consistently responds in a timely manner. Additionally, they
are both co-authors of my second project.
• To my beloved parents and other relatives, the support of my parents is obvious and
invaluable. As someone in my thirties who is still pursuing a Ph.D., it is considered
relatively late in China, which puts a great burden on my parents. In fact, the decision
to leave my job in China and embark on this academic journey was a monumental and
difficult decision for our entire family. Without my parents' unwavering support, it
would have been impossible for me to come to the other side of the world. Due to the
pandemic, I have only been able to return home once in the past four years, and I can
only communicate with my family online during every Chinese festival. These years
have been challenging for them, and I am truly grateful for their continued support.
DYF has also been with me for a long time and has given me some good suggestions
and help, ZJJ has ever helped me draw some parts of schematics.
• Dr. Henny Fechner, Babette Dieringer, Dr. Anja Geisler, Lesile Elsner, as colleagues
in the same larger group, provide valuable feedback and suggestions on my project
progress during the group meetings every Monday and Wednesday, which is very
important to me.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XIII
• China Scholarship Council, I express my gratitude for the financial support provided
by the Council for my Ph.D. studies.
• Einstein Center 3R, Dr. Hoenzke, in addition to the supervisors, fellow PHD students,
relatives and friends who have helped me as mentioned above, I must also mention a
special aspect - since arriving in Germany, my research work has not only been
conducted in Prof. Jens Kurreck's laboratory, but upon the professor's recommendation,
I have also joined the Einstein Center 3R organization in Berlin. This organization is
dedicated to improving unavoidable animal testing and developing alternative methods
for widespread use to build trust in relevant scientific activities. The 3Rs principle -
Replacement, Reduction, Refinement - is an important task in academic biomedical
research. Before coming to Germany, I was not aware of the importance of animal
welfare in medical experiments, perhaps because it was not given enough attention in
my previous work environment. However, animal experimentation has been criticized
by the German public. Therefore, the efforts of this organization have also given new
importance to our 3R work. In addition, the organization pays great attention to the
training of graduate students and assigns each of us a mentor tutor. Dr. Hoenzke is my
mentor tutor and keeps in close contact with me. She is an expert in lung disease
research and provides valuable advice on my research direction and future work plans.
I would like to express my gratitude to the Einstein Center 3R organization and Dr.
Hoenzke. I hope to maintain close contact and collaborate closely with them in the
future after returning to work in my home country.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XIV
List of publications contributing to this work
I. Mei, Y., Wu, D., Berg, J., Tolksdorf, B., Roehrs, V., Kurreck, A., Hiller, T., &
Kurreck, J. (2023). Generation of a Perfusable 3D Lung Cancer Model by
Digital Light Processing. Int J Mol Sci, 24(7), 6071.
https://doi.org/10.3390/ijms24076071
II. Mei, Y., Lakotsenina, E., Wegner, M., Hehne, T., Krause, D., Hakimeh, D., Wu,
D., Schültke, E., Hausmann, F., Kurreck, J., & Tolksdorf, B. (2024). Three-
Dimensional-Bioprinted Non-Small Cell Lung Cancer Models in a Mouse
Phantom for Radiotherapy Research. Int J Mol Sci, 25(19), 10268.
https://doi.org/10.3390/ijms251910268
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XV
Rights and Permission
Open access:
Tabel 1 in Introduction 1.4. is adapted and rearranged from Xie, et al.,2023
(https://doi.org/10.3390/gels9020088) used under a Creative Commons Attribution 4.0
International License: http://creativecommons.org/licenses/by/4.0/.
Tabel 2 in Introduction 1.4. is adapted and rearranged from Yu, et al.,2020
(https://doi.org/10.3390/polym12122958) used under a Creative Commons Attribution
4.0 International License: http://creativecommons.org/licenses/by/4.0/.
The figures in Result 4.1. are adapted and rearranged from Paper Ⅰ, Mei, et al., 2023
(https://doi.org/10.3390/ijms24076071) used under a Creative Commons Attribution
4.0 International License: http://creativecommons.org/licenses/by/4.0/.
The figures in Result 4.2. are adapted and rearranged from Paper Ⅱ, Mei, et al., 2024
(https://doi.org/10.3390/ijms251910268) used under a Creative Commons Attribution
4.0 International License: http://creativecommons.org/licenses/by/4.0/.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XVI
List of Abbreviations
2D Two-dimensional
3D Three-dimensional
5-FU 5-Fluorouracil
ALDH1 Aldehyde dehydrogenase 1
AM Additive manufacturing
CAD Computer-aided design
Calcein-AM Calcein-acetoxymethyl ester
CT Computed tomography
CTLs cytotoxic T lymphocytes
CTV Clinical target volume
DEPC Diethyl pyrocarbonate
DLP Digital light processing
DMSO Dimethyl sulfoxide
DOD Droplet-based
DPBS Dulbecco’s Phosphate Buffered Saline
ECM Extracellular matrix
EDTA Ethylenediaminetetraacetic acid
EdU 5-ethynyl-2'-deoxyuridine
FB Fibroblast
GBM Glioblastoma
GelMa Gelatin methacrylamide
GMHA Methacrylate-anhydride hyaluronic acid
HA Hyaluronic acid
hMSCs Human mesenchymal stem cells
hNDFs Human neonatal dermal fibroblasts
HP Hewlett-Packard
Huvec Human vascular endothelial cells
IC50 Half-maximal inhibitory concentration
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XVII
ICIs Immune checkpoint inhibitors
ILC2s Innate lymphoid cells
LDH Lactate dehydrogenase
LIFT Laser-Induced Forward Transfer
MDA-MB Metastatic breast cancer cells
MDSCs Myeloid-derived suppressor cells
MMP Matrix metalloproteinase
NSCLC Non-small cell lung cancer
NSCs Neural stem cells
OCT Optimal cutting temperature
OSL Optically-stimulated luminescence
PARP-1 Poly (ADP-ribose) polymerase-1
PBS Phosphate-buffered saline
PCL Polycaprolactone
PCR Polymerase chain reaction
PD-1 programmed death-1
PD-L1 Programmed death ligand-1
PEG Polyethylene glycol
PEGDA Polyethylene glycol diacrylate
PF127 Pluronic F127
PMS Phenazine methosulphate
PVA Polyvinyl alcohol
RT-PCR Reverse Transcription-Polymerase Chain Reaction
SDS-PAGE Sodium dodecyl-sulfate polyacrylamide gel electrophoresis
SEM Standard error of the mean
SLA stereolithography
TAMs Tumor-associated macrophages
TLDs Thermoluminescence dosimeters
TME Tumor microenvironment
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
XVIII
XTT 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-
carboxanilide)
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
1
1. Introduction
1. 1. Lung cancer epidemiology and treatment options
Despite a consistent decline in the incidence and mortality rates of lung cancer over the
past decade, lung cancer remains a primary contributor to malignant tumor-related
deaths (1). In terms of incidence, non-small cell lung cancer (NSCLC) ranks second
only to prostate cancer in men and breast cancer in women, representing the second
most common cancer in both genders. NSCLC represents the most common type of
lung cancer, accounting for approximately 80 % of cases. Over the past two decades,
there has been a continuous emergence of novel clinical treatment modalities for
NSCLC (2). The search for novel drugs with improved efficacy, capable of extending
survival and preventing or overcoming drug resistance, has prompted the discovery of
potential targets and the development of promising therapeutic options. In recent years,
the approval of immune checkpoint inhibitors (ICIs) targeting programmed death
ligand-1 (PD-L1), programmed death-1 (PD-1) or cytotoxic T-cell lymphocyte-4
(CTLA-4) as immune modulatory agents has significantly improved patient treatment
and prognosis (3). Chimeric Antigen Receptor T-cell (CAR-T) immunotherapy is a
treatment that involves genetically modifying a patient's autologous T cells to express
a CAR, which allows them to specifically recognize antigens on the surface of tumor
cells. These engineered CAR-T cells proliferate in vivo and eliminate tumor cells by
binding to the targeted antigens. CAR-T therapy has achieved significant progress in
the treatment of B-cell malignancies and has been approved for use in acute
lymphoblastic leukemia (ALL), lymphoma, and multiple myeloma. Its clinical
application is rapidly expanding, with ongoing trials exploring its efficacy across a
range of different cancers (4). These therapies have led to prolonged survival rates, even
in patients with advanced NSCLC. However, despite the notable durability of these
drugs, indicating the presence of persistent immune memory (5), some patients still
experience tumor recurrence and the development of metastasis, which are resistant to
the medications (6). Despite all the advancements lung cancer is associated with a
remarkably high case fatality rate, causing up to 20% of annual cancer deaths. This
surpasses the combined mortality burden of prostate, breast, and colon cancers. The
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
2
disease remains a significant global health burden (7). Therefore, there is an urgent need
to establish more advanced methods for the screening and testing of new drugs.
Figure 1. Schematic of Lung Cancer Epidemiology. Lung cancer maintains a high incidence and
mortality rate globally, remaining the most prevalent cancer and the leading cause of cancer-related
deaths in both men and women combined. Numerous risk factors contribute to lung cancer, including
smoking (both primary and secondhand), marijuana smoking, air pollution, occupational exposure, and
genetic predisposition (8). Icons/elements from BioRender were used in the creation of this image.
1. 2. Advantages of 3D bioprinted tumor models compared to 2D cell culture
The current research strategies encompass 2D cell culture and animal models. 2D cell
models are unable to replicate the intricacies of tumor tissue, given that tumor cells
inherently proliferate in a 3D environment (9). Although embedding human tumor cells
into an animal model creates a biologically relevant microenvironment (10), this
chimeric entity still falls short of accurately reflecting the authentic pathophysiological
conditions. Even tough numerous candidate drugs are very successfully tested in animal
experiments, a 97% failure rate is observed in subsequent clinical trials (11). However,
a promising novel methodology for investigating drugs is the use of 3D bioprinted
organ models. These models offer a better representation of the in vivo
microenvironment, with numerous examples suggesting that they are at least as
effective as 2D cell cultures for therapeutic efficacy testing (12). For example,
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
3
Sebestyén et al. employed alginate-based hydrogel bioinks for 3D bioprinting and long-
term cultivation of breast cancer cells (ATCC—CRL1500) in vitro. This approach was
used to assess the therapeutic efficacy of lapatinib, doxycycline, and doxorubicin, both
individually and in combination, on tumors. Their findings confirmed that the 3D
bioprinted breast cancer model demonstrated drug sensitivity and exhibited a similar
mTOR/metabolism protein expression profile compared to 2D cell models (13).
Furthermore, Song et al. successfully achieved 3D growth of drug-resistant breast
cancer spheroids in alginate-gelatin bioink using 3D bioprinting. Throughout the 3D
cultivation process, the drug-resistant breast cancer spheroids retained their phenotype,
characterized by elevated CD44, reduced CD24, and increased aldehyde
dehydrogenase 1 (ALDH1) expression (14). Further research has demonstrated that 3D
models can achieve drug therapeutic effects that are more closely aligned with in vivo
conditions than 2D cell culture models. For instance, in 2021, Mao et al. made a
significant contribution to the field by utilizing primary hepatocellular carcinoma cells
for in vitro 3D bioprinting. They demonstrated that using 3D models for drug research
may yield drug efficacy results closer to in vivo conditions (15). Another example is
that in 3D bioprinted models, HeLa cells exhibited an increased proliferation rate,
higher matrix metalloproteinase (MMP) protein expression, and enhanced
chemoresistance (16). Sun et al. isolated primary tumor cells from cholangiocarcinoma
patients and cultured them for expansion. They used a bioink composed of gelatin,
sodium alginate, and matrigel to generate a 3D bioprinted model. Compared to 2D
culture models, the 3D bioprinted model demonstrated markedly elevated tumor traits,
including malignancy, stemness potential, fibrosis, invasion, and metastatic potential.
The elevated drug resistance further validated its stem cell-like attributes (15). In
general, 3D bioprinted cell culture models demonstrate enhanced resistance to
radiotherapy and chemotherapy compared to their 2D counterparts. This phenomenon
partly explains why many treatments, initially effective in 2D experiments, later fail in
advanced testing stages. For instance, Mao et al. developed a multicellular 3D model
comprising SW480 cells, tumor-associated macrophages, and endothelial cells. In
comparison to single-cell 3D bioprinted models, this multicellular model demonstrated
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
4
enhanced resistance to chemotherapy drugs, including 5-FU, oxaliplatin, and irinotecan
(15).
1. 3. Limitations of animal models
Although animal models remain the preferred choice for preclinical evaluation, they
have a lot of drawbacks. The selection of an appropriate animal model requires the
consideration of a number of factors, including the methods used for disease induction,
the time and cost required to establish model systems, and the selection of specific
models for the symptoms of interest. Many in vivo model systems mimic protein
diseases or other pathological conditions, such as genetic alterations or specific disease
symptoms. However, they cannot fully replicate human pathophysiological conditions
or the whole human organism (17). An alternative for the use of animal models are 3D
bioprinted cancer models. For instance, a 3D bioprinted primary hepatocellular
carcinoma model developed by Greten et al. significantly shortened construction time
(several hours compared to several months) (18), exhibited uniform and controllable
shape and density, retained the original characteristics of liver cancer, demonstrated
high reliability and stability in the construction process, and did not rely on the inherent
proliferative capacity of primary tumors.
Aside from chemotherapy or immunotherapy, radiation therapy is also a crucial
component of treatment for many lung cancer patients. For patients with locally
advanced disease who are not eligible for or unsuitable for surgical resection, the
standard treatment for advanced NSCLC has, for several decades, solely comprised
cytotoxic chemotherapy. However, with the introduction of targeted therapy and
immunotherapy, the treatment possibilities have improved a lot (19). The emerging
techniques in radiation therapy similarly require enhanced preclinical evaluation,
holding the potential to improve tumor control under the same or even better normal
tissue tolerance (20). Efficacy of translating radiotherapy experiments into clinical
improvements depends on their accuracy in replicating human biology and their ability
to predict clinical outcomes. The process of accurately modeling radiotherapy response
in mice remains challenging due to factors such as the heterogeneity of cancer as well
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
5
as the complexity and variability of radiation responses in tumors and normal tissues.
Therefore, existing mouse radiation models are not optimal choices, as there are aspects
of human radiotherapy response that cannot be simulated in mice, not to mention the
need to navigate controversies in animal ethics (21). Furthermore, mouse tumor models
are beset by lengthy approval procedures and intricate operations (22). Such elevated
failure rates result in a substantial depletion of research funds and time.
1. 3. 1. The introduction of mouse phantoms in radiotherapy research
As mentioned above, recent technological advances in the field of small-animal
radiotherapy have led to the development of sophisticated irradiation platforms. Precise
dosimetry in radiobiology experiments is crucial for the reliability of these radiation
testing platforms (23,24). In 2016, several research institutions collaboratively
employed cylindrical acrylic small-animal phantoms capable of housing calibrated
thermoluminescence dosimeters (TLDs) to validate dose output. The results indicated
that the measured dose deviated from the expected dose by more than ± 5% (25). Seed
et al. improved this by conducting a similar investigation using a two-phase process.
Initially, they tested the use of optically-stimulated luminescence (OSL) dosimeters in
mouse cadavers, followed by the use of TLDs in cylindrical acrylic phantoms. Under
these conditions, four out of seven research institutions were able to deliver doses with
deviations of less than ±5% of the expected values (26).
In 2020, Gronberg et al. developed robust polystyrene phantoms that could be used to
calibrate small-animal irradiators. They invited three institutions using the same small-
animal irradiator to participate in collaborative testing, ultimately demonstrating the
feasibility of dose delivery within ± 7% of the prescribed value (23). These studies
indicate that even when employing the same methods, different research institutions
may experience discrepancies in radiotherapy testing. This underscores the need for
additional training to achieve high reproducibility and efficacy in preclinical
experiments, while adhering to the 3R principles of replacement, reduction, and
refinement.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
6
As radiation technologies and devices continue to evolve, physical phantoms play an
increasingly important role. The use of 3D printing and additive manufacturing (AM)
offers cost-effective solutions with high flexibility in anatomical details (such as body
shape and internal structures) and material selection (to mimic tissue characteristics like
X-ray absorption, light scattering, or magnetic resonance properties), enabling the
precise replication of multi-purpose models (27-29).
Figure 2 A schematic comparison of existing tumor models, including cells grown on the surface of
2D models, animal models, and several commonly used 3D models. Icons/elements from BioRender
were used in the creation of this image.
1. 4. 3D bioprinting technologies for human organ models
In recent years, the invention of 3D bioprinting organ models has emerged as a highly
promising strategy for the study of human pathophysiology (30). This advanced
technology allows for the precise arrangement of diverse types of human cells within
organs, thereby achieving remarkable spatial resolution (31,32). It is noteworthy that
bioprinting has demonstrated considerable potential in the dynamic field of cancer
biology (33). The technique's inherent flexibility allows for the precise manipulation of
extracellular environment stiffness, the amalgamation of diverse cell types, and the
tailored design of intricate 3D structures. Currently, this cutting-edge technology has
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
7
been successfully employed in the fabrication of a spectrum of human organ models,
encompassing skin, blood vessels, bones, liver, kidneys, spleen, and more (34).
In previous investigations, a sophisticated multicomponent bioink, comprising alginate,
diethylaminoethyl cellulose, gelatin, and collagen peptide, was employed for 3D
bioprinting. This bioink has been shown to be effective in creating models that can be
used for preliminary drug screening in the context of cancer research (35). The majority
of bioprinted cancer models have been developed using human cancer cells. Even when
non-cancerous cells are incorporated into the model, they typically originate from
human sources, thereby overcoming the limitations associated with conventional
animal models, in which human tumors are introduced into the microenvironment of
mice or other animals.
1. 4. 1. 3D bioprinting methods
3D bioprinting can be classified according to the printing techniques employed, which
include droplet printing, extrusion printing, laser-assisted printing, digital light
processing (DLP) printing, and suspension printing. This section will provide detailed
introductions to each 3D printing technology and discuss the most commonly used
bioprinting materials. The most common and widely used printing technologies, as well
as bioinks, will be listed and their respective advantages and disadvantages will be
presented in tabular form.
1. 4. 1. 1. Droplet printing
Droplet printing is the simplest printing method, essentially involving the deposition of
cell-containing droplets. Therefore, it offers the advantages of fast printing speed and
low cost. However, due to the required low viscosity of the bioink this technology has
many limitations. Klebe et al. reported this printing method for the first time in 1988,
using a commercially available Hewlett-Packard (HP) thermal drop-on-demand droplet
printer and a simple hydrogel composition for bioprinting (36). By allocating different
forces through printing modules, such as the incorporation of heating reservoirs or
piezoelectric actuators, cells or biological materials are deposited in the form of droplets.
Drawbacks of droplet bioprinting are that heat-based and piezoelectric-based printing
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
8
modules may cause cell damage and cell lysis during the printing process (37). However,
the utilisation of a thermal droplet printer has been demonstrated to preserve the
viability of printed cells at a rate of 89 %, with only a minimal number of cells
exhibiting damage. (38). Additionally, non-uniform droplet sizes and nozzle clogging
introduce additional complexities to the process.
In order to employ droplet-based printing technology for the printing of layered
constructs, additional techniques are required. These include droplet, acoustic droplet
ejection, and micro-valve bioprinting. The aim of these techniques is to enhance model
complexity. Droplet-based bioprinters, which are analogous to conventional two-
dimensional printers, employ printheads to deposit bioink in a sequential manner,
resulting in the gradual construction of tissues or organs. However, they may be
inadequate for achieving physiological cell density and still exhibit limitations in bioink
selection. Nevertheless, integrating multiple printheads can enhance the quality of the
printed material (39). Acoustic droplet ejection printers comprise either single or 2D
microfluidic channel arrays, wherein the bioink is maintained at the appropriate
position at the exit of the small channels due to surface tension. This method employs
gentle acoustic forces to eject droplets from an open liquid reservoir, thus eliminating
the necessity for nozzle-based droplet ejection. Furthermore, the process ensures that
neither the bioink nor the constituent living cells are subjected to potentially detrimental
stressors such as heat, high pressure, high voltage, or shear stresses during the droplet
ejection process (40). Micro-valve bioprinting typically employs employing
electromagnetic coils and plungers to regulate nozzle opening. Upon the application of
pressure to the bioink within the fluid chamber, the micro-valve closes the small orifice
of the nozzle, thereby halting the output. The mode of droplet generation is contingent
upon the applied pressure and the duration of valve closure. By adjusting parameters
such as pneumatic pressure, nozzle geometry, cell concentration, and bioink
composition, micro-valve bioprinting can uphold the viability of various cells (41).
1. 4. 1. 2. Extrusion printing
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
9
Extrusion-based printing is currently the most widely used printing method, and an
important reason for this is its capacity to utilize an array of biomaterials [35],
significantly enhancing the feasibility of composite printing. Extrusion-based 3D
bioprinting employs computer-controlled fluid distribution systems to accurately
deposit materials based on 3D computer-aided design (CAD) files. The printhead is
capable of movement in the x, y, and z axes, enabling the continuous deposition of
biomaterial onto the printing platform (42). For pneumatic systems, air pressure
extrudes highly viscous bioinks, forming seamless filaments that are then cross-linked
by light, enzymes, chemicals, or temperature to create mechanically durable structures.
Mechanical systems are typically controlled by a piston or screw. Compared to
dropwise printing methods, the continuous extrusion of bioink without interruptions
helps maintain the integrity of the printed constructs (43). This method enables the
printing of bioinks with high cell density. Furthermore, it facilitates the fabrication of
complex 3D structures composed of different cell types and materials (44). Despite the
numerous advantages of this method, the shear damage caused by the printing nozzle
through pressure or mechanical force can cause damage to the cells and therefore also
limits the resolution that can be reached. This aspect requires improvement (45).
1. 4. 1. 3. Laser-assisted printing
The earliest documented account of laser-based bioprinting technology was in 1999,
wherein a 2D cell pattern was successfully printed (46). A bioprinter based on laser-
induced forward transfer (LIFT) typically consists of a pulsed laser whose beam is
absorbed by a layer beneath the bioink in the donor substrate. When the focused laser
beam reaches the target location in the energy-absorbing layer, the supporting donor
layer at the corresponding position is vaporized, resulting in the ejection of a droplet of
bioink onto the receiving platform. The risk of contamination to the 3D model is
minimal during the printing process, as the dispenser and bioink do not come into direct
contact with one another (47). The resolution of the printed pattern is dependent upon
a number of factors, including the energy of the laser, the frequency of the pulses, the
thickness and viscosity of the biological material layer, the distance between the donor
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
10
and collector slides, and the wettability of the substrate slide (48). Laser-assisted
printing circumvents the nozzle-related issues inherent to the previous two printing
methods. However, the disadvantages include an increased risk of cell damage with
higher laser energy, a high cost of printing modules, and a lack of user-friendliness (49).
1. 4. 1. 4. Digital light processing (DLP)
Another very important laser-assisted printing technique is stereolithography (SLA),
that was first introduced by Charles W. Hull in 1986 (50). Amongst the various SLA
techniques, DLP is particularly noteworthy for bioprinting (51). In comparison to other
bioprinting techniques, such as droplet-based, extrusion-based, and LIFT, DLP employs
light to crosslink bioinks in a layer-by-layer manner within a reservoir. Therefore, this
technique is constrained to light-responsive bioinks, which typically include gelatin
methacrylamide (GelMa) and polyethylene glycol diacrylate (PEGDA) (52). Printing
is based on a laser beam cross-linking photosensitive bioinks by projecting a 2D pattern
of the plane of interest onto the bioink reservoir (53), ensuring that the model has an
extremely high resolution, higher than any of the previously mentioned printing
methods. Such high resolution is particularly suitable for printing some delicate
structures, such as vascular structures (54). Additionally, Xolography, as a novel
printing technology, also falls under the category of DLP printing. It uses two
intersecting light beams of different wavelengths to selectively polymerize a
photosensitive resin. The process involves a dual-wavelength photoinitiator, where the
first wavelength activates the photoinitiator into a latent state, and the second
wavelength solidifies the material. This method enables fast, high-resolution printing
without the need for support structures. Although it cannot currently print cell-
containing bio-materials directly, advancements in materials science and bio-printing
technologies may lead to the development of cell-friendly photoinitiators and printing
conditions, allowing Xolography to be directly applied to the printing of bio-materials
mixed with cells (55). However, DLP is not without drawbacks, including limited
options for bioinks and higher costs.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
11
1. 4. 1. 5. Suspension printing
Suspension bioprinting is primarily used to print cells or biomaterials within a
suspended medium, typically a liquid or gel with high density and viscosity, such as
hyaluronic acid, to form 3D tissues with specific shapes and structures. Hyaluronic acid
is a readily prepared, reusable material that exhibits self-healing and shear-thinning
properties. The osmotic pressure of hyaluronic acid can be used to regulate the
expansion and contraction of bioinks, thereby modifying the physical characteristics of
3D-printed scaffolds (56). A scaffold comprising human umbilical vein endothelial cells
with high resolution and cell viability has been produced using suspension bioprinting
technology, which may prove an effective method for meeting future scaffold
requirements (57).
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
12
Table 1. Types of 3D bioprinting technologies and their advantages and disadvantages. Table
modified after Zhang et al. (58).
3D Bioprinting
Technologies
Advantages Disadvantages
Droplet printing
Noncontact, easy, low cost, high
cell vitality, and high speed
Few
materials, low driving
pressure, low printing
accuracy, and small-size
structures
Extrusion
printing
Wide range of materials, low
cost, simple process and easy to
use, good printability and
fidelity, and large-size structures
with preferred shapes and forms
Nozzle blockages, longer print
times, low cell viability and
missing materials, and layer-
by-layer deposition limitations
Laser-Assisted
printing
No nozzles, high resolution,
automation, high cell vitality, and
high repeatability and efficiency
The workstation is complex
and requires a laser source
Digital light
processing
(DLP)
Higher spatial resolution, simple,
faster print times, better cell
viability, and can perform
noninvasive 3D biological
printing of tissue structures in
vivo
More complex workstations,
high-
precision instruments,
composite functional hydrogel
biological ink materials
Suspension
bioprinting
Stabilizes the gel form, maintains
cell viability, broadens the
application range of printing
materials, and can encapsulate
very-low-viscosity bioinks
The printing temperature is
determined by the temperature
of the suspension medium
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
13
1. 4. 2. 3D Bioprinting materials
The sources of hydrogel materials are mainly divided into polymers and modified
polymers. The former are natural or synthetic polymers that are capable of forming
hydrogels, while the latter are polymer composite inks constructed by adding functional
molecules to them. Over the years, various novel hydrogels suitable for a range of
printing technologies have emerged. A summary, compiled by JunHee Lee et al., listing
the advantages and disadvantages of various hydrogels as well as their respective
crosslinking methods can be found in table 2.
The most commonly used bioinks are cell-laden hydrogels, which are easy to formulate
and are widely utilized in all the bioprinting techniques that have been introduced in
1.4.2. Cell-laden hydrogels can have different components, including alginate, collagen,
gelatin, fibrin, chitosan, agarose, and more. Typically, they undergo a subsequent
crosslinking process after printing, forming self-supporting structures conducive to
shaping and cell engraftment. Crosslinking generally occurs in two forms: physical
crosslinking and chemical crosslinking. Physical crosslinking typically relies on ion
interactions and hydrogen bonding, while chemical crosslinking depends on the
formation of covalent bonds (59,60).
For instance, alginate-based inks are one of the most commonly used inks for extrusion-
based printing. It is printed in the form of a viscous solution (mixed with calcium sulfate,
collagen or matrigel), and then immersed in a calcium chloride solution to induce post-
printing cross-linking. Although alginate has minimal cytotoxicity, it lacks cell
adhesion properties. Therefore, it is typically blended with other natural polymers (such
as gelatin and fibrinogen) to facilitate cell adhesion and enhance the biological activity
of the hydrogel (61). An alginate and gelatin-based bioink was used in 2021 by Berg et
al. to develop a 3D lung model to study influenza virus infections (62).
Gelatin is a natural polymer extracted from animal connective tissues that exhibits a
gel-like state at low temperatures and a liquid state at high temperatures (63). Its
primary advantage lies in its biocompatibility, non-immunogenicity, and cell-friendly
binding domains. Therefore, gelatin is often blended with other hydrogel components
to enhance the biocompatibility of bioinks. For example, Yong Miao et al. used a
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
14
gelatin/alginate hydrogel ink to prepare multilayer scaffolds that simulated the in vivo
hair follicle microenvironment, including the stratum corneum and dermis (64). After
modification with methacrylamide and methacrylate groups, gelatin becomes
photocrosslinkable, known as gelatin methacrylamide (GelMA). GelMA bioinks can be
utilized in DLP printing (65-67). Another option suitable for digital light processing
technology is a methacrylic light-curable silk fibroin bioink (68). Silk fibroin is a
natural protein with excellent mechanical properties, biodegradability, biocompatibility,
and bio-absorbability. Printing with this material can achieve high mechanical stability
and good biocompatibility (69).
Collagen is the main protein component of the extracellular matrix in tissues and organs.
It is typically sourced from animals such as rats and pigs (70,71). Similar to gelatin,
collagen also exhibits excellent biocompatibility, non-immunogenicity, and cell-
friendly binding domains, providing superior microenvironments for cell growth (72).
In contrast to gelatin, collagen is in a pre-gel state at low temperatures and can undergo
thermal crosslinking at 37 °C (47). Collagen is typically not printed alone as a hydrogel
but is rather mixed with other bioink components for printing (73). Another natural
polymer with high biocompatibility is chitosan, whose solution remains stable and
viscous under physiological conditions, forming 3D printing scaffolds to support cell
proliferation and differentiation (74). For instance, a self-healing chitosan hydrogel for
injectable and printable inks was prepared using phenol-functionalized chitosan and
dibenzaldehyde-capped polyethylene glycol. The phenol functionalization of chitosan
introduces unique interactions, rendering the hydrogel with fast gelation rates, self-
repair capabilities, and secondary visible light crosslinking abilities (75). The
mentioned hydrogels, as well as numerous other novel hydrogels not mentioned,
demonstrate that the materials utilized for 3D printing hydrogel inks primarily originate
from natural polymer materials with high biocompatibility. Additionally, the
incorporation of artificial polymers or nanomaterials can enhance their mechanical
properties, printing fidelity, and other properties to fulfill diverse application
requirements.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
15
Table 2. Current natural and synthetic bioinks widely used for 3D bioprinting (50).
Bioink Crosslinking
Mechanism
Advantages Disadvantages
Alginate Ionic crosslinking
Biocompatibility, low toxicity,
low price
Absence of cell-
binding domains
Chitosan Genipin,
glutaraldehyde
Biocompatibility,
biodegradability,
antibacterial/fungal activity
Poor mechanical
strength and rapid
dissociation,
absence of cell-
binding domains
Gelatin Temperature,
glutaraldehyde,
transglutaminase,
HRP and H2O2,
carbodiimide,
genipin
Biocompatibility, non-
immunogenicity and cell-
friendly binding domains
Low viscosity and
poor mechanical
strength at 37 °C
Collagen
Temperature; UV,
glutaraldehyde,
carbodiimide and
genipin
Improved cell adhesion,
attachment, and growth
Low viscosity and
poor mechanical
strength
Silk Enzymatic
crosslinking
Nontoxicity, gradual
degradation, and low
immunogenicity; owning high
viscosity and shear thinning
Inducement of
nozzle clogging,
absence of cell
biding for cell
adherence, limited
cell growth and
function
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
16
Fibrin
Cytocompatibility, providing
binding sites for cell
attachment, proliferation, and
low immunogenicity
Rapid degradation,
too soft, low
mechanical
strength and fragile
Agarose Temperature High mechanical strength, low
price
Poor cell adhesion,
brittle
Hyaluronic
acid (HA)
Glutaraldehyde,
carbodiimide,
divinyl sulfone
Enhancement of chondrocyte
growth and chondrogenic
differentiation
Rapid degradation
and low
mechanical
strength
Matrigel Temperature
Promotes cell growth and
differentiation
Expensive and
unsuitable for
clinical translation
Polycaprolact
one (PCL)
Low melting
point and high
stability
Unsuitable for cell
encapsulation
Polyethylene
glycol (PEG)
Biocompatibility, non-
immunogenicity; widely used
sacrificial bio-ink
Low cell adhesion
Gelatin
methacrylami
de (GelMA)
UV Biocompatibility,
biodegradable
Negative effects on
cell viability in the
crosslinking
process
Pluronic
F127(PF127)
Temperature, UV
Commonly used as sacrificial
bio-ink
Poor mechanical
properties and
unsuitable for cell
culture
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
17
Polyvinyl
alcohol
(PVA)
Glutaraldehyde
Biodegradable, biocompatible,
thermostable, and water-
soluble
Low cell affinity
poly(lactic-
co-glycolic
acid
(PLA/PLGA)
Biodegradable, biocompatible Poor cell adhesion
dECM Temperature
Promotes cell growth and
differentiation
Low viscosity;
complicated
process of
decellularization
and costly; requires
complete
sterilization of
dECM
1. 5. 3D printed tumor models
The application of 3D bioprinting technology offers significant advantages for the
development of in vitro tumor models. This technology allows users to precisely deposit
various biomaterials, cells, and biomolecules within predefined structures. 3D
bioprinters can produce high-resolution microstructures that mimic the complexity of
the tumor microenvironment (TME). Therefore, 3D bioprinted models serve as
preclinical models for a variety of research applications in oncology and the
pharmaceutical industry. These models present an opportunity to develop high-
throughput drug screening platforms and can be further tailored to individual patient
needs, thereby driving advances in the field of personalized cancer therapy.
1. 5. 1 Tumor extracellular matrix
The role and significance of the tumor microenvironment (TME) were first addressed
as early as the 19th century and have been increasingly recognized (76). It is currently
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
18
believed that the TME influences cancer progression and the development of drug
resistance in tumor patients (77). Figure 3 briefly illustrates the various components of
the TME, including normal tissue cells, immune cells, tumor tissues, fibroblasts,
epithelial cells, and small blood vessels within the tissue. The interactions among these
components affect blood circulation and immune translation, thereby forming a tumor
niche (78). This tumor niche can initiate tumor invasion, metastasis, and the
development of drug resistance, ultimately leading to continuous progression and
allowing cancer cells to grow and spread continuously (79).
Figure 3. Illustration of a tumor niche with various cellular components in the tumor
microenvironment (TME). The tumor niche serves as the background for the dynamic interplay of
various TME components, including healthy cells, cancer cells, immune cells, extracellular matrix (ECM)
proteins and so on. Collectively, these components promote pro-tumor activities of cancer cells, such as
fibroblast activation, ECM modulation, immunosuppression, and angiogenesis, which drive the process
of tumor progression. Icons/elements from BioRender were used in the creation of this image.
1. 5. 2 Tumor vasculature
Blood perfusion is crucial not only for supplying oxygen and nutrients to normal tissues
and organs but also for the growth of tumor tissues. However, the vascular networks of
the two are distinct (80). In tumor tissues, due to the uncontrolled proliferation of tumor
cells, the formation and progression of blood vessels are forced, resulting in a slowing
of the blood flow (81). These vessels are also known as "leaky vessels" due to their
perforations. Due to of aberrant signaling, the blood vessels within tumors exhibit
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
19
disorganized bifurcation and heterogeneous lumen (82). Endothelial cells within the
blood vessels play a crucial role in the tumor microenvironment (TME), as the growth
factors secreted by the tumor can induce these endothelial cells to initiate the process
of angiogenesis (83).
1. 5. 3 Tumor immune microenvironment
The immune system is capable of detecting and eliminating tumor cells (84).
Nevertheless, tumors can gradually alter the tumor immune microenvironment (TIME)
into an immunosuppressive state, thereby counteracting host immunity. A growing body
of research indicates that the equilibrium between pro-tumor and anti-tumor
inflammatory mediators may influence the progression of tumors. Tumor cells can
evade immune surveillance through a number of mechanisms, including defective
antigen presentation, enhanced negative immune regulatory pathways, and the
recruitment of pro-tumor immune cells (85). This ultimately results in the impairment
of anti-tumor immune cells' function, preventing the immune response from completely
eradicating tumor cells. The efficacy of anti-tumor immunity is contingent upon the
equilibrium between anti-tumor and pro-tumor immune components (86). Despite the
considerable heterogeneity among different cancer types and individual patients, the
role of the tumor immune microenvironment (TIME) in tumor progression is similar.
The primary objective of immunotherapy is to reinstate the tumor-killing capacity of
anti-tumor immune cells, with a particular focus on cytotoxic T lymphocytes (CTLs).
However, pro-tumor immune cells, including myeloid-derived suppressor cells
(MDSCs), regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and type
2 innate lymphoid cells (ILC2s), play crucial roles in impeding anti-tumor immune
responses and shaping an immunosuppressive microenvironment (87). The study of the
functions and mechanisms of pro-tumor immune cells will facilitate the improvement
of the response rate of immunotherapy and the development of novel
immunotherapeutic strategies.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
20
1. 5. 4 Application of 3D printed tumor models
In recent years, 3D bioprinting technology has been increasingly utilized to construct
tumor tissues for the purpose of simulating the tumor microenvironment, blood
perfusion, and immune function conditions. Figure 4 illustrates the general process for
creating a tumor model, including cell acquisition, printing, post-printing processing,
and subsequent characterization testing. In a study by Tang et al. DLP printing
technology was employed to construct a glioblastoma (GBM) model that accurately
simulates the tumor microenvironment (TME). This model comprises a tumor region
with GBM cells, a vascular region containing HUVEC cells, and a cell-free stromal
region. For the DLP printing process methacrylate-anhydride hyaluronic acid (GMHA)
and GelMA as bioinks were used. The core of the model comprised glioblastoma stem
cells, which were surrounded by astrocytes and neural stem cells (NSCs). This model
was used to investigate the immune functions, with a specific focus on the role of
macrophages in GBM. The findings demonstrated that this model could predict patient
survival, stem cell homeostasis, invasion, and drug resistance (88). Another example of
constructing a GBM tumor model is the microbrain model with macrophages developed
by Heinrich et al. They co-cultured metastatic breast cancer cells (MDA-MB-231) with
HUVECs and found that co-culturing endothelial cells with tumor cells more accurately
represents the in vivo environment. Furthermore, the upregulation of tumor-related
genes with the addition of fibroblasts suggests the critical importance of multicellular
co-culture in the TME (89,90). Jiang et al. utilized extrusion-based 3D bioprinting
technology to construct a breast cancer tumor model. Similarly, they co-printed
fibroblasts and breast cancer cells, resulting in fibroblasts surrounding tumor cells to
form a spiral structure. Subsequent testing revealed fibroblast migration and interaction
with multicellular tumor spheroids, highlighting the stroma-dependent nature of
tumorigenesis (91).
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
21
Figure 4. A schematic diagram of different 3D bioprinting technologies used for the printing of 3D
tumor models. After completing the CAD file design, tumor cells are combined with the bioink and
printed using droplet printing, extrusion printing, laser-assisted printing, and DLP printing. The cell-
laden bioink is then printed into the desired tissue microstructure, thereby forming biomimetic structures
and functional constructs. Following the cultivation period, a series of biological characterization tests
are conducted to ensure the functionality of the tissue. Icons/elements from BioRender were used in the
creation of this image.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
22
2. Objectives of this work
The overall objective of this work was to develop two distinct 3D bioprinted tumor
models: one for NSCLC with a perfusion system, and another integrating tumor cells
and normal lung fibroblasts. These models aim to assess the efficacy of new drug
candidates and radiotherapy in preclinical experiments, providing insights into
therapeutic effects and mechanisms. The first project has already been published, and
the second project is under submission. The specific objectives of the work were:
Project 1: The objective of this study was to design a 3D lung model, utilize digital light
printing technology for high-resolution printing, and dynamically cultivate the model.
In addition, components of the perfusion system were designed and printed and
assembled with a peristaltic pump to finally realize a perfusion tumor model capable of
long-term cultivation. The model was subjected to toxicological and drug testing
experiments, including cell viability analysis, calculation of drug IC50 values,
assessment of cell proliferation capacity, immunohistochemical staining of the model,
and analysis of functional differences between the perfused 3D tumor models, static 3D
tumor models, and 2D cells.
Project 2：The objective of this study was to develop a 3D lung cancer model using
extrusion-based bioprinting, incorporating both healthy lung fibroblast cells and
NSCLC cells. To simulate realistic physiological conditions for radiation effects, the
model was placed in a mouse phantom that mimics the X-ray radiation attenuation rates
found in mice. After irradiation, the samples were subjected to a series of tests to assess
their activity and functionality. These included cell viability analysis, assessment of
metabolic activity, and immunohistochemical staining. The aim was to investigate the
mechanisms of radiation-induced cytotoxicity. The study explored and compared the
similarities and differences between irradiated 3D models and 2D cells.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
23
3. Materials and methods
The following section summarizes the experimental materials and equipment involved
in the thesis, and provides a detailed procedure for the experimental methods.
3.1. Materials
3.1.1. Cultured cells
Cell name Cell type Manufacturer
16HBE14o- Human bronchial epithelial cell line Merck
A549 Lung epithelial cancer cell ATCC
CFBE41o- CF human bronchial epithelial cell line Merck
H358 Bronchoalveolar carcinoma non-small cell lung
cancer cell
ATCC
Lung
fibroblasts
Primary lung fibroblast ATCC
3.1.2. Laboratory equipment
Laboratory equipment Manufacturer
Bio X Cellink
Centrifuge 3-18K Sigma-Aldrich
Centrifuge 5415 R Eppendorf
Centrifuge Fresco Thermo Scientific
Combi-Spin FVL-2400N Biosan
Compact paraffin embedding center SLEE
Duomax 1030 Heidolph
Female Luer thread style coupler Masterflex
Galaxy Mini Merck eurolab
CO2-Incubator Sanyo
Lucky Reptile incubator VEVOR
Lumen X Cellink
Magnetic Stirrer Heidolph MR3001K IKA
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
24
Microscope Fluorescence Zeiss Observer. Z1 Zeiss
Microscope Inverse Transmitted Light Primo Vert Zeiss
Nanodrop Thermo Scientific
Neubauer counting chamber Superior Marienfeld
Nitrogen tank GT38 AIR LIQUIDE
Paraffin stretching baths LAUDA
pfm Rotary 3004 M Pfm medical
Sterile workbench AFE 2020 Thermo Scientific
Sterile workbench MSC-advantage 1.8 Thermo Scientific
Sunrise™ absorbance reader Tecan
Thermocycler CFX96 Thermo Scientific
Thermomixer comfort Eppendorf
Vacuum pump system BVC 21 NT Vacuubrand
3.1.3. Consumables
3.1.4. Biopolymeres
Biopolymere Catalogue
number
Manufacturer
Alginate 9005-38-3 Sigma-Aldrich
Collagen 11179179001 Sigma-Aldrich
Consumable Manufacturer
2 mL cryo tube Roth
5 mL, 10 mL Disposable syringe B. Braun
96 well, 48 well, 24well, 12well, 6 well Cell Culture Plate CELLSTAR
Cell scraper NeoLabLine
Printer cartridges Cellink
Sterile Vat Cellink
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
25
Gelatin 9000-70-8 Sigma-Aldrich
GelMA 95% DS Stock Solution Cellink
3.1.5. Chemicals
Chemical Catalogue number Manufacturer
2-Propanol 67-63-0 Roth
99.8% and 96% Ethanol 20824365/85829460 VWR
Calcium chloride dihydrate (CaCl2·2H2O) 10035-04-8 Sigma-Aldrich
Calcium sulfate dihydrate (CaSO4·2H2O) 10101-41-4 Sigma-Aldrich
Dimethyl sulfoxide (DMSO) 67-68-5 Sigma-Aldrich
Formaldehyd (CH2O) 50-00-0 Sigma-Aldrich
Triton-X-100 9002-93-1 Roth
Tween 20 9005-65-6 Roth
XTT 111072-31-2 VWR
3.1.6. Kits
Kit Catalogue
number
Manufacturer
CyQUANT™ LDH Cytotoxicity Assay C20300 Thermo Scientific
EdU Cell Proliferation Kit C10337 Thermo Scientific
LIVE/DEAD Viability/Cytotoxicity Kit L32250 Thermo Scientific
RevertAid H Minus First Strand cDNA
Synthesis Kit
K1631 Thermo Scientific
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
26
3.1.7. Buffers
Buffer Catalogue
number
Manufacturer
Dulbecco’s Phosphate Buffered Saline
(DPBS); w/o Calcium, w/o Magnesium
L0615 Biowest
HBSS (10X), Calcium, Magnesium 14065056 Thermo
Scientific
3.1.8. Medias
Medium Catalogue
number
Manufacturer
DMEM High Glucose w/o L-Glutamine;
w/o Sodium Pyruvate
L0101 Biowest
DMEM Low Glucose; w/o L-Glutamine, w/
Sodium Pyruvate
L0064 Biowest
RPMI 1640; w/o L-Glutamine L0501 Biowest
3.1.9. Medium additives
3.1.10. Antibodies
Antibody Catalogue
number
Manufacturer
Anti-Cyclophilin B antibody ab16045 Abcam
Medium additive Catalogue
number
Manufacturer
FBS c.c.pro
L- Glutamine 100X, 200 mM X0550 Biowest
Penicillin – Streptomycin Solution 100X L0022 Biowest
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
27
Anti-gamma H2A.X (phospho S139)
antibody
ab81299 Abcam
Anti-
pan Cytokeratin antibody
[KRT/1877R]
ab234297 Abcam
Anti-Von Willebrand Factor antibody ab6994 Abcam
Bax Antibody (B-9) sc-7480 Santa Cruz
Bcl-2 Antibody (C-2) sc-7382 Santa Cruz
beta Actin Antibody (C4) sc-47778 Santa Cruz
CD31 (PECAM-1) (89C2) Mouse mAb #3528 Cell Signaling
CFTR (D6W6L) Rabbit mAb #78335 Cell Signaling
Cleaved caspase-3 (Asp175) (5A1E) Rabbit
mAb
#9664 Cell Signaling
Cleaved PARP (Asp214) (D64E10) XP®
Rabbit mAb
#5625 Cell Signaling
FAPA Polyclonal Antibody bs-5758R Bioss Antibodies
VE-cadherin Antibody (F-8) sc-9989 Santa Cruz
Vimentin (D21H3) XP® Rabbit mAb #5741 Cell Signaling
3.1.11. Enzymes
Enzyme Catalogue
number
Manufacturer
Trypsin-EDTA 1x L0940-100 Biowest
TrpLE Express 12604021 gibco
3.1.12. Software
Software Manufacturer
Adobe Illustrator CS3 Adobe
BaiduNetdisk Baidu
Bambu Studio Bambu Lab
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
28
GrapgPad Prism 7 GrapgPad Sofeware Inc.
ImageJ ImageJ
PreForm_3.29 Formlabs
Rhino 6 Robert McNeel &Associates
Slic3r Alessandro Ranellucci
SPSS 20 IBM
ZEN 2.3 pro Zeiss
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
29
3.2. Methods
3.2.1. Cell culture
3.2.1.1 Thawing of cells from liquid nitrogen
Upon retrieval of the cryopreserved cells from liquid nitrogen, an immediate transfer to
a water bath maintained at 37 °C was executed. The cryotube was gently agitated to
ensure complete thawing within a 1-minute timeframe. Subsequently, the thawed cells
were diluted with culture medium to a volume exceeding 10 times the original.
Thawed cells were then seeded directly into cell culture dishes containing complete
growth medium. Following a 24-hour incubation period, the medium was replaced to
eliminate residual dimethyl sulfoxide (DMSO).
Post-thawing, cells were centrifuged at 300 × g for 3 minutes and the supernatant
discarded to eliminate residual DMSO. The cells were then seeded into culture dishes
containing complete growth medium.
3.2.1.2 Cell passaging
The passaging of cells was conducted at room temperature, with suspended dead cells
removed by washing with phosphate-buffered saline (PBS) that was added along the
side wall to prevent dislodging of adherent cells. Subsequently, 0.25 % trypsin was
added (2.5 mL for a T75-flask, 3 mL for a T175-flask), distributed thoroughly, and then
incubated at 37 °C until the cells were detached. During the final minute of incubation,
gentle tapping was performed to ensure that adherent cells were detached. Subsequently
medium was added to terminate the digestion reaction. Cell clumps were carefully
dispersed, the supernatant was discarded after centrifugation at 300 × g for three
minutes, and the cell suspension was transferred into new culture flasks at the desired
ratio.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
30
Table 3. Reference for cell passaging conditions.
Cell line Temperature Digestion time Passage ratio
Time to
confluence
H358 37℃ 3 min 1:10 1 week
A549 37℃ 3 min 1:10 1 week
Lung
fibroblast
37℃ 5 min 1:3 2 weeks
CFBE41o- 37℃ 6 min 1:5 2 weeks
16HBE14o
-
37℃ 6 min 1:5 2 weeks
3.2.1.3 Cell cryopreservation
Cells were harvested as described in section 4.2.1.2 and resuspended in
cryopreservation solution (10 % DMSO in FCS) at a concentration of 2 × 106 cells/mL.
Subsequently, 1 mL aliquots were dispensed into cryovials and frozen in Mr. Frosty™
Freezing Container (Thermo Fisher Scientific, Waltham, MA, USA) in a -80 °C
refrigerator overnight. The following day, the cryovials were rapidly transferred to
long-term storage in liquid nitrogen.
3.2.2. Life-dead staining
The viability of cells in 2D and 3D culture was assessed using the viability/cytotoxicity
kit (Thermo Fisher Scientific, Waltham, MA, USA), in accordance with the
manufacturer's instructions. Following a one-hour incubation at 37 °C in phenol red-
free RPMI medium (Biowest) containing 2 µM calcein-AM (acetoxymethyl ester) and
2 µM ethidium homodimer-1, an analysis was conducted using an inverted fluorescence
microscope (Observer Z1, Zeiss, Jena, Germany). Green fluorescence indicated live
cells, while red fluorescence indicated dead cells.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
31
3.2.3. XTT assay
The metabolic activity was quantified using the XTT (2,3-bis-(2-methoxy-4-nitro-5-
sulfophenyl)-2H-tetrazolium-5-carboxanilide) assay (Alfa Aesar, Ward Hill, MA,
USA), which was conducted in accordance with the manufacturer's instructions. The
XTT reagent (1 mg/mL) and phenazine methosulphate (PMS, 3.83 mg/mL, AppliChem,
Darmstadt, Germany) were diluted in a 500:1 ratio in RPMI without phenol red
(Biowest). Subsequently, 100 μL of the aforementioned solution were added to each
well of a 96-well 2D cell culture plate. Subsequently, the plates were incubated for four
hours at 37 °C and 5 % CO₂ in a humidified atmosphere. The absorbance was
determined at 450 nm and 620 nm as a reference using a Sunrise microplate reader
(Tecan, Männedorf, Switzerland). In the case of 3D constructs, the XTT/PMS solution
was added in a fivefold excess to the bioink volume and incubated for four hours.
Subsequently, the absorbance of the supernatant was measured as described above.
Cells treated with 70% alcohol for 15 minutes were included as a background control.
The cell viability was calculated using the following formula:
Cell viability = Absorbance test wells - Absorbance background wells
The half-maximal inhibitory concentration (IC50) values of what were calculated from
nonlinear regression curves (dose-response curves) based on the measured viability
data using GraphPad Prism 7 (GraphPad, La Jolla, CA, USA).
3.2.4. Cryosectioning of the 3D models
Following washing with PBS, the models were placed into 12-well plates (Corning,
Glendale, AZ, USA) and fixed with 4% paraformaldehyde in PBS for 30 minutes.
Subsequently, the models were washed with PBS and then incubated overnight at 4 °C
in an infiltration solution (10% BSA in PBS). Thereafter, the models were removed
from the infiltration solution, washed once with PBS, and cut to the desired size. The
constructs were then transferred to a flat-side-down freezing mould. The mould was
filled with an optimal cutting temperature compound (OCT) layer until the models were
enveloped by OCT and the mould slots were filled. The moulds were then placed in
a -80 °C freezer for 16 hours. Following this, the samples were transferred to a -20 °C
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
32
freezer and stored until cryosectioning. The frozen blocks were sectioned using a
cryostat (Leica CM 1850, Wetzlar, Germany) at -20 °C. The prepared models were cut
into 14 µm slices, placed on glass slides, air-dried, and stored at -20 °C until staining.
3.2.5. Paraffin sectioning of the 3D models
The 3D models were washed with HBSS and fixed in 3.7 % formaldehyde in HBSS for
30 minutes. Subsequently, the samples were washed three times for 10 minutes with
HBSS. The dehydration of the samples was achieved through a series of ethanol
concentrations. For this, the samples were incubated in 50 % (v/v) ethanol diluted in
HBSS for one hour, followed by incubation in 70 % (v/v) ethanol, 80 % (v/v) ethanol,
90 % ethanol, and three rounds of 100 % ethanol for one hour each. Following this, the
samples were incubated in 100 % Roticlear for two hours. Following the removal of
Roticlear, paraffin was added and replaced at least four times, with each incubation
period lasting one hour at 65 °C. The samples were then transferred into metal
encapsulation molds containing a small quantity of warm paraffin, histocassettes were
placed on top, filled with liquid paraffin, and the samples were allowed to solidify on a
4 °C cooling pad. Subsequently, the embedded samples were stored at -20 °C.
The previously fixed and processed paraffin blocks were and sectioned at 16 µm using
a microtome (pfm Rotary 3004 M, pfm medical, Cologne, Germany). Subsequent
staining of the paraffin-embedded tissue sections was performed by melting the paraffin
at 65 °C for one hour. Thereafter, the slices were subjected to a series of ethanol
reduction steps, with the following concentrations and times: 100 % ethanol (15
minutes), fresh 100 % ethanol (1 minute), 90 % ethanol (10 minutes), 80 % ethanol (5
minutes), 70 % ethanol (5 minutes), and 50 % ethanol (5 minutes). Following a single
wash with HBSS, the tissue sections were immersed in a sodium citrate-EDTA antigen
retrieval buffer (10mM Sodium Citrate, pH 7.0) and heated by steam for 30 minutes.
Subsequently, the samples were left to cool down to room temperature, washed once
more with HBSS, and then proceeded to the next staining step.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
33
3.2.6. Immunohistochemistry
The slices used for staining were subjected to a five-minute wash in HBSS.
Subsequently, the samples were treated with 0.1 % (v/v) Triton X-100 (Carl Roth) and
5 % BSA in HBSS what as a blocking solution for one hour. Following the removal of
the blocking solution, the corresponding primary antibodies were diluted in the same
solution and added to each slice. The slices were then incubated at 4 °C overnight.
Subsequently, the slices were washed again for five minutes with the blocking solution
and the secondary antibodies were added dropwise to the samples and incubated in the
dark at room temperature for one hour. Subsequently, a five-minute wash in PBS was
conducted, after which Mount FluorCare DAPI (Art. No. HP20.1, Carl Roth) was added
dropwise to each slice, which was then covered with a coverslip. Following a 40-minute
incubation period, the samples were imaged using a fluorescence microscope (Observer
Z1, Carl Zeiss) and the signals were normalized according to the DAPI signal using
ImageJ software (1.53e, National Institutes of Health, Bethesda, MD, USA).
3.2.7. Western blot
The model was crushed into 1.5-mL centrifuge tubes and digested with Cell Collect G
(LR020000, Cellink) overnight at 4 °C to release the cells. The following day, the
supernatant was discarded after centrifugation at 14000 rpm for 10 min, and the cells
were lysed with Repa buffer (89900, Thermo Fisher) to extract protein. The protein
concentration was determined using the Bradford assay (Thermo Fisher Scientific),
which relies on the change in absorbance of the Bradford reagent from 465 nm to 595
nm upon binding with proteins, in accordance with the manufacturer's instructions.
Prior to sample application for Sodium dodecyl-sulfate polyacrylamide gel
electrophoresis (SDS-PAGE), 30 µg proteins were combined with 4X reducing sample
buffer (BioRad) and denatured at 95 °C for 5 minutes. Subsequently, 30 µg of the
sample was loaded into a 15 % SDS-PAGE gel (Biorad), with Precision Plus Protein
Standards Dual Color (Biorad) used as a molecular weight marker. Electrophoresis was
conducted at 100 V.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
34
Following the separation of proteins via SDS-PAGE, the proteins were transferred onto
a nitrocellulose membrane (Biorad) via Western blotting. ProSieve transfer buffer
(Biorad) was used for semi-dry blotting, with a voltage of 70 V applied for a duration
of 70 minutes. Subsequently, the membrane was incubated in blocking buffer, 5 % skim
milk in TBS-T for one hour, followed by an overnight incubation at 4 °C with the
respective primary antibody, diluted in blocking buffer. The following day, the
membrane was washed three times for five minutes each with TBS-T, followed by
incubation at room temperature for one hour with the secondary antibody diluted in
TBS-T containing 1 % skim milk. Following three additional washes with TBS-T,
proteins were visualized by incubating the membranes with detection solution and
detecting them using the VersaDoc imaging system (Biorad) with Quantity One
software (Biorad). ImageJ imaging software was then employed for subsequent
quantification of signals.
3.2.8. RNA extraction and quantitative PCR
The extraction of RNA samples was conducted using the TRIZOL reagent (Invitrogen)
according to the manufacturer's instructions. After incubation at room temperature for
five minutes, 0.2 mL of chloroform were added and the samples incubated at room
temperature for 15 minutes. Following centrifugation at 14,000 rpm for 15 minutes at
4 °C, the aqueous phase was carefully transferred to a clean, RNase-free centrifuge tube
for RNA precipitation. An equal volume of isopropanol was added to precipitate the
RNA, followed by thorough mixing, incubation at room temperature for 10 minutes,
and centrifugation at 14,000 rpm for 10 minutes at 4 °C. Subsequently, the supernatant
was discarded, and 75 % ethanol, prepared with DEPC (diethyl pyrocarbonate)-treated
water, was added in order to wash the RNA precipitate. Following thorough mixing, the
sampels were centrifuged at 14,000 rpm for 10 minutes at 4 °C. The RNA precipitate
was then air-dried for approximately ten minutes at room temperature. Lastly, the RNA
precipitate was dissolved in 40 μL of RNase-free water. RNA samples were stored at
-80 °C for further use.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
35
The quality of the RNA was evaluated by quantifying its concentration using a
Nanodrop spectrophotometer (Thermo Scientific). For quantitative PCR, genomic
DNA in the samples was removed using the RevertAid H Minus First Strand cDNA
Synthesis Kit (Thermo Scientific). For this, 20 μL of RNA were mixed with 3 μL of
RQ1 buffer, 1 μL of Ribolock inhibitor, 3 μL of DNAse RQ1, and 3 μL of DEPC-treated
water. The mixture was then incubated at 37 °C overnight. On the following day, 3 μL
of RQ1 stop solution were added to each sample, and the mixture was incubated at 6
5°C for ten minutes to stop the process. Subsequently, reverse transcription was carried
out using the RevertAid H Minus First Strand cDNA Synthesis Kit (Thermo Scientific).
In a 20 μL reaction volume 400 µg of RNA were mixed with 1 µL of random hexamer
primer and DEPC-treated water. The mixture was then incubated at 70 °C for 5 minutes.
Subsequently, 4 µL of reaction buffer, 1 µL of Ribolock, and 2 µL of dNTPs were added.
The mixture was incubated at 24 °C for one hour, after which it was heated to 70 °C for
10 minutes. In the negative control group, no reagents were added, and only RNA was
mixed with DEPC-treated water to a total volume of 20 µL. Following the completion
of the reverse transcription process, cDNA samples were used for the subsequent qPCR.
For the qPCR 3 µL of DEPC-treated water was mixed with 5 µL of SYBR green buffer
(company please add correct full name), 0.5 µL of forward primer (CFTR-F: 5’-
CCTCAGAAATGATCGAGAACATCC-3’, 18S-F: CGCGGTTCTATTTTGTTGGT),
0.5 µL of reverse primer (CFTR-R: 5’-CCTCCTCCCAGAAGGCTGTTACATTC-3’,
18S-R: AGTCGGCATCGTTTATGGTC), and 1 µL of cDNA. The RT-PCR reaction
program consisted of an initial incubation at 95°C for 3 minutes, followed by 40 cycles
of denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 30 seconds.
3.2.9. Cell proliferation assay with EdU kit
To detect cell proliferation, the EdU kit (Thermo Scientific) was used according to the
manufacturer's instructions. An appropriate number of cells were plated on coverslips
and incubated overnight. The following day, half of the medium was removed from the
cells and an equal volume of EdU labeling solution (final concentration of 10 µM) was
added. Cells were incubated for two hours under appropriate growth conditions. After
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
36
incubation, cells were immediately transferred to a 6-well plate and fixed with 1 mL of
3.7 % formaldehyde in PBS for 15 minutes (30 minutes for 3D models) at room
temperature. After fixation, cells were permeabilized with 1 mL of 0.5 % Triton® X-
100 in PBS for 20 minutes at room temperature (30 minutes for 3D models). EdU
detection was performed by incubating the cells with the Click-iT® Plus reaction
cocktail for 30 minutes (45 minutes for 3D models) at room temperature in the dark.
After incubation, cells were washed with PBS and stained with 1X Hoechst® 33342
solution for 30 minutes (45 minutes for 3D models) at room temperature in the dark.
Subsequently, cells were washed with PBS and the optional antibody labeling was
performed according to the manufacturer's recommendations, with samples protected
from light during incubation.
3.2.10. LDH assay
To measure LDH release, the LDH kit (Thermo Scientific) was used according to the
manufacturer's instructions. For 2D cultures, 10,000 cells were seeded into each well
of a 96-well plate with 100 µL of culture medium per well. For 3D models, the cells
were printed into 12-well plates and cultivated with 1 mL of culture medium per well.
Both 2D and 3D cultures were incubated overnight at 37 °C in a 5 % CO2 atmosphere.
For LDH activity measurement after radiation treatment, sample preparation involved
treating one well of each triplicate set with one-tenth of the total volume of sterile
ultrapure water to measure spontaneous LDH activity, control wells for maximum LDH
activity received 10 µL of 10X lysis buffer for 2D cultures and 50 µL of 10X lysis
buffer for 3D models, which retained 500 µL of culture medium, followed by gentle
shaking. After a 45-minute incubation, 50 µL of the culture medium from each sample
was transferred to three wells of a 96-well flat-bottom plate. Optionally, 50 µL of 1X
LDH positive control was added to three wells for positive control measurements. Fifty
microliters of the reaction mixture were added to each sample well and mixed
thoroughly, followed by protection from light at room temperature for 30 minutes. Then,
50 µL of stop solution was added to each sample well, gently mixed, and any bubbles
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
37
were removed before measuring absorbance at 490 nm and 680 nm. Cytotoxicity
percentage was calculated using the following formula:
3.2.11. Bioink preparation and 3D bioprinting of 3D models
3.2.11.1 LumenX digital light processing printing
The model was designed using Rhino 6 (Robert McNeel &Associates). Upon
completion of the design, the model was exported as an STL file and sliced using the
Lumen X digital light processing bioprinter (Cellink) slicing software. GelMa PhotoInk
(D16110022026, Cellink, Gothenburg, Sweden) was thawed in a 37 ℃ water bath in
the dark. The Lumen X digital light processing bioprinter was preheated to 65℃.
Hydroxyethyl cellulose gelatin ink (GelMA ink) was thoroughly mixed with the cells
and then added to a Polydimethylsiloxane (PDMS) culture dish (D16110020872,
Cellink). The cell concentration was 10,000 cells/µL. The printing parameters were set
as follows: 7.25 seconds exposure time, 100 µm resolution, and 54 % projector power
level. Following the printing process, the model was detached from the building
platform and immersed in PBS to remove residual ink from the surface. If the model
contained channel structures, PBS was aspirated into the channels using a 200 µL
pipette to ensure complete clearance. Lastly the models were transferred to X-well
plates, submerged in culture medium, and incubated at 37 °C in a 5 % CO2 atmosphere.
3.2.11.2 BioX extrusion printing
The model was designed using Rhino 6. Upon completion of the design, the model was
exported as an STL file and sliced using the BioX bioprinter (Cellink) slicing software.
The final bioink consisted of 3 % (w/v) alginate, 3 % (w/v) gelatin and 2 % (w/v) of
1.2 M CaSO4. The bioink was prepared by heating the alginate/gelatin blend to 37 ℃
and loading it into a syringe, while the appropriate volume of 1.2 M CaSO4 (2 % w/v)
and cells (10.000 cells/mL) was loaded into a second syringe. The two syringes were
then connected via a Luer-Lock adapter, and the contents were thoroughly mixed using
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
38
an automatic mixing device (92) at a mixing rate of 10 mm/s for 80 cycles. After mixing,
the bioink was cross-linked in a 23 ℃ constant temperature chamber for 15 minutes
before being transferred to the printing cartridge. The printing speed is approximately
7 mm/s, and the printing pressure is around 30 kPa.
3.2.12. Irradiation
The 2D cells were cultured for 24 hours after seeding and the 3D models were cultured
for one week after printing before being irradiated with and without the phantom at
doses of 4 and 8 Gy using the YXLON MaxiShot X-ray machine (YXLON
International X-Ray GmbH, Hamburg, Germany) with the following settings: 200 kV,
15 mA, 5.5 FOC, dose rate 0.89 Gy/min. Medium exchange was performed after
irradiation on day 2. Sham-irradiated ("0 Gy") models were used as negative controls
and were also transported to the irradiator to control for any changes in temperature and
atmosphere. The radiation therapy experiments were conducted with the support and
assistance of the Department of Radiation Oncology, Charité University Medicine
Berlin.
3.2.13. Radiotherapy model system setup and treatment planning
To maintain sterility while placing the 3D model in the mouse phantom, a resin box
was designed using Rhino 6 software. The box was printed using a Formlabs Form 2
3D printer (Formlabs, Somerville, MA, USA) with clear resin V4 material (Formlabs).
After printing, the box was cleaned to remove any residual resin and then soaked in
deionized water for one month, with the water changed daily.
A previously published mouse phantom mimicking real mice was used for the
irradiation experiments (93). Prior to the irradiation experiment, the 3D lung cancer
model was removed from the culture medium and placed in the resin box, which was
then placed in the chest slot of the mouse phantom (Figure 11e). The design and
production of the mouse phantom were carried out by the Department of Product
Development and Mechanical Engineering Design at Hamburg University of
Technology.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
39
The treatment planning system (TPS) used was the Eclipse External Beam Planning
software (version 15.6, Varian Medical Systems, Palo Alto, CA, US). First, the two CTs
of the 3D phantom and the in vivo mouse model were imported into the TPS. Next, the
CT of the in vivo mouse model was adjusted so that the position of the tissue box in the
3D phantom correlated with the position in the other CT so that the structure of the
tissue box could be transferred. In this way, both the density and the dose distribution
in the corresponding CT slices could be compared later.
3.2.14. Statistics
Results are presented as the mean ± standard deviation (SD) of at least three in-
dependent experiments. Statistical analysis was performed using GraphPad Prism 7
software (Dotmatics, Boston, MA, USA). For comparisons between two groups, an un-
paired two-sample t-test was used for analysis. One-way ANOVA was used for analy-
sis of variance to compare multiple groups. Statistical significance was accepted at the
levels of *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
40
4. Results
The main objective of this thesis was to develop three-dimensional NSCLC models for
testing chemotherapy and radiotherapy treatments. The results are presented in two
parts: generation of a perfusable 3D lung cancer model by digital light processing and
3D bioprinted lung cancer models for radiotherapy research.
4.1. Generation of a perfusable 3D lung cancer model by Digital Light Processing
4.1.1 Gemcitabine demonstrates significant cytotoxic effects on H358 2D cell
culture
In order to establish a perfusable lung cancer model, the NSCLC cell line H358 was
selected, and gemcitabine was chosen as the test drug. Gemcitabine, a cytidine analog,
is employed in the treatment of various cancer types. Initially, gemcitabine testing was
conducted on 2D cell cultures. Cytotoxicity assays revealed that in the absence of
gemcitabine, the majority of cells remained viable, indicated by green fluorescence
(Figure 5a). However, with an increasing concentration of gemcitabine, the proportion
of dead cells (red fluorescence) gradually increased. At the highest concentration used
(64 nM), dead cells constituted the majority in the field of view, as indicated by intense
red fluorescence.
In addition to its cytotoxic effects on cells, the impact of gemcitabine on cellular
proliferation was also investigated. Given its nature as a cytotoxic agent, its influence
on cell proliferation was assessed by staining for active DNA synthesis in EdU (5-
ethynyl-2'-deoxyuridine) assays. As shown in Figure 5b, the control group exhibited
active cell proliferation in the absence of gemcitabine treatment. However, at a
gemcitabine concentration of 1 nM, the proliferative capacity of the cells was
noticeably affected, and at concentrations exceeding 4 nM, proliferating cells were no
longer observed. Previous studies have indicated that the primary mode of action of
gemcitabine is the induction of apoptosis in cancer cells. Cleaved caspase-3 is a
commonly used marker for detecting apoptotic cells. As shown in Figure 5c, the number
of cells positive for Cleaved caspase-3 increased with rising concentrations of
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
41
gemcitabine. These preliminary experiments suggest that gemcitabine kills H358 cells
and inhibits proliferation by inducing apoptosis.
Figure 5. Effects of gemcitabine on H358 cells in a 2D monolayer culture. Increasing concentrations
of gemcitabine were added to the culture medium as indicated. After four days of treatment, the 2D
cultures were analyzed. (a) Cytotoxicity assays were performed by staining cells with calcein-AM and
ethidium homodimer-1. Live cells appear in green under the fluorescence microscope and dead cells
appear in red. (b) The effects of gemcitabine treatment on cell proliferation were determined by EdU
assays. (c) Immunofluorescence staining for Cleaved caspase-3 was used to identify apoptotic cells
following gemcitabine treatment. Representative data of three independent experiments are shown. Scale
bar: 200 μm. The figure is adapted from Paper 1, Mei, et al., 2023 (https://doi.org/10.3390/ijms24076071)
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
42
used under a Creative Commons Attribution 4.0 International License:
http://creativecommons.org/licenses/by/4.0/.
4.1.2. Cell survival after 3D bioprinting
Based on the findings of the 2D cell experiments, the aim was to advance the
development of a physiologically relevant 3D cancer model. H358 cells were combined
with GelMA bioink and subsequently printed in 0.75 mm thick square layers using the
Lumen X DLP printer. The printer facilitated crosslinking of GelMA by irradiation with
405 nm wavelength. Results from cell viability assays indicated that the majority of
cells survived the printing procedure, as indicated by green fluorescence, with only a
minimal number of deceased cells observed in the field of view after a 24-hour period,
as indicated by red fluorescence (Figure 6a). Metabolic activity assays revealed
sustained cellular metabolism over a two-week period, particularly at lower cell
densities (10,000 cells), with only marginal metabolic decline observed at higher cell
densities (Figure 6b). Furthermore, the bioprinted 3D model demonstrated normal
cellular proliferation, as assessed by EdU assays (Figure 6c). In conclusion, these
results demonstrate the high viability and proliferative capacity of H358 cells after DLP
printing.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
43
Figure 6. Cytotoxicity, cell viability and proliferation of H358 cells in 3D printed models. (a)
Cytotoxicity assays were performed by staining cells with calcein-AM and ethidium homodimer-1.
Living cells appear in green under the fluorescence microscope and dead cells appear red. (b) The
metabolic activity of H358 cells in 3D models measured by a tetrazolium hydroxide salt (XTT) assay.
As a negative control, the values for dead cells that were treated with 75% alcohol are given. Data from
three independent experiments are presented as mean ± standard deviation (c) Proliferation of H358
cells in the bioprinted 3D models was determined by EdU assays 24 h after printing. Proliferating cells
appear in green. Cell nuclei are blue after DAPI staining. Representative data of three independent
experiments are shown. Scale bar: 200 µM. The figure is adapted from Paper 1, Mei, et al., 2023
(https://doi.org/10.3390/ijms24076071) used under a Creative Commons Attribution 4.0 International
License: http://creativecommons.org/licenses/by/4.0/.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
44
4.1.3 Treatment of bioprinted 3D constructs with gemcitabine
In comparison to the previously observed effects of gemcitabine on 2D cell cultures,
drug treatment experiments were conducted on the bioprinted 3D model subsequent to
confirming the viability of H358 cells during both the printing process and extended
3D cultivation. To this end, structures with a total volume of 15 μL and a thickness of
0.75 mm were precisely printed using DLP technology. Given the necessity for the drug
to permeate the hydrogel within the 3D system in order to interact with the cells, higher
concentrations were required to induce cytotoxicity when compared to monolayer
cultures. As illustrated in Figure 7a, cells in the 3D model maintained viability in the
absence of gemcitabine or at the lowest concentration of 2 μM. However, starting at 4
μM and continuing up to 16 μM gemcitabine, a progressive increase in the proportion
of dead cells, as indicated by red fluorescence, was observed.
Similarly, as the concentration-dependent cytotoxicity increased, cell proliferation
capability was suppressed with escalating drug concentrations. Notably, at a
gemcitabine concentration of 4 μM, there were no actively proliferating cells in the
field of view (Figure 7b). In immunofluorescence assays, there was a concentration-
dependent increase in cells expressing Cleaved caspase-3 (Figure 7c). These results
confirm the suitability of the 3D bioprinted model to study the therapeutic effects of
drugs on H358 cancer cells. Despite the significant escalation of gemcitabine
concentrations from nanomolar to micromolar levels in the latter scenario, the results
obtained were comparable to those observed in 2D monolayer cultures.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
45
Figure 7. Effects of gemcitabine on H358 cells in 3D printed models. Increasing concentrations of
gemcitabine were added to the culture medium as indicated. After four days of treatment, the 3D
cultures were analyzed. (a) Cytotoxicity assays were performed by staining cells with calcein-AM and
ethidium homodimer-1. Living cells appear in green under the fluorescence microscope and dead cells
appear in red. (b) Proliferation of H358 cells in the bioprinted 3D models after treatment with increasing
concentrations of gemcitabine was determined by EdU assays. Proliferating cells appear in green. Cell
nuclei are shown in blue after DAPI staining. (c) Induction of apoptosis by gemcitabine treatment is
shown as detected by immunofluorescence staining for Cleaved caspase-3. Representative data of three
independent experiments are shown. Scale bar: 200 µM. The figure is adapted from Paper 1, Mei, et al.,
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
46
2023 (https://doi.org/10.3390/ijms24076071) used under a Creative Commons Attribution 4.0
International License: http://creativecommons.org/licenses/by/4.0/.
4.1.4. IC50 of gemcitabine for H358 cells in 2D and 3D culture
To quantitatively characterize the effect of gemcitabine on H358 cells and to establish
the experimental drug concentrations for subsequent assays, IC50 values were
determined for both 2D and 3D cultures. The XTT assay was employed for the
quantitative assessment of cell viability under varying drug concentrations (Figure 8).
For the 2D culture, the calculated IC50 was 2.0 ± 0.7 nM. In 3D culture, gemcitabine
failed to induce complete cell death even at the highest concentration. Notably,
gemcitabine, a commonly used clinical chemotherapy drug, is typically administered
in combination with other anti-tumor drugs due to its limited efficacy when used as a
monotherapy (94). Therefore, the IC50 value for the 3D structure can only be estimated
to be in the low micromolar range, which represents an increase of three orders of
magnitude compared to the IC50 in 2D culture. This quantitative characterization of
drug-induced cytotoxicity validates the qualitative findings obtained from the previous
fluorescent cytotoxicity assays.
Figure 8. Concentration dependence of gemcitabine-induced cytotoxicity as determined by XTT
assays. H358 cells in 2D (a) and 3D (b) cultures were treated with gemcitabine at nanomolar
concentrations for 2D and micromolar concentrations for 3D for 24 h. Data from three independent
experiments are presented as mean ± standard deviation. The figure is adapted from Paper 1, Mei, et al.,
2023 (https://doi.org/10.3390/ijms24076071) used under a Creative Commons Attribution 4.0
International License: http://creativecommons.org/licenses/by/4.0/.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
47
4.1.5. Establishment of a perfused model
Even when printing relatively complex perfusion models, its printing resolution
reaching up to 100 µm ensures there is no issue with poor repeatability. The model
exhibits strength and a degree of elasticity. Perfusion can be facilitated through tubing
insertion (Figure 9b). The model was designed to allow dynamic cultivation with a
perfusion system. The dimensions of the model were minimized in order to conserve
expensive bioinks and to ensure an even distribution of reagents throughout the model.
The model had an oval shape, with the main structure measuring 12 mm × 6 mm × 3.5
mm. Additionally, an inlet and an outlet were positioned on the top for easy connection
to the plastic tubing of the perfusion system. A curved channel with a diameter of 1.5
mm passed through the model (Figure 9a).
Upon completion of printing, the model was connected to a peristaltic pump. Figure
10b shows the perfusion setup. The spatiotemporal distribution of media within the
bioprinted model was monitored using a blue liquid (Figure 9c). Starting with a yellow
hue, the model gradually transitioned to a green tint upon perfusion with blue ink. After
21 hours, the entire model exhibited a uniform deep green color, confirming the
effective supply of cells throughout the entire construct via perfusion.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
48
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
49
Figure 9. The 3D model design and perfusion system (a) The 3D model was designed using the Rhino
6 program (left: top view of the model; middle: sectional view of the internal channel of the model; right:
front view of the model). The Lumen X DLP bioprinter was then used to produce the 3D model using a
GelMA-based bioink. (b) The entire perfusion system, including a peristaltic pump, connected to the 3D
lung cancer model. A side port allows a connection to a syringe. (c) The yellow models were perfused
with blue ink. After 21 h of perfusion with blue ink, the dark green color indicates a thorough mixing of
the yellow model and the blue ink. (d) Models were then cultured in regular orange-red media. Untreated
models are shown on the left, while the models on the right were incubated with XTT reagent, which
turns orange when converted by metabolically active cells. The top two models were cultured under static
conditions, while the bottom two models were perfused. (e) Viability of cells in the model after perfusion
for four days was quantified by XTT assays. The blank value of the untreated models was subtracted
from the models incubated with the reagent. Data from four independent experiments are presented as
mean ± standard deviation. **** p < 0.0001. The figure is adapted from Paper 1, Mei, et al., 2023
(https://doi.org/10.3390/ijms24076071) used under a Creative Commons Attribution 4.0 International
License: http://creativecommons.org/licenses/by/4.0/.
Continuous perfusion effectively eliminated the yellow tint of the GelMA bioink
(Figure 9d). The models were then treated with the XTT reagent. Notably, the perfused
models exhibited more pronounced staining than models cultured under static
conditions, indicating increased cell viability. This observation was supported by
quantitative analysis. Metabolic activity, as determined by the XTT assay, showed a
significant increase of 58.4% in models cultured under dynamic conditions compared
to their non-perfused counterparts (Figure 9e). These findings highlight the beneficial
effect of perfusion on cell viability within bioprinted 3D structures.
4.1.6. Application of gemcitabine by the perfusion system
After the assembly of the perfusion system, gemcitabine was administered to the lung
cancer model. To evaluate the efficacy of dynamic cultivation, a comparative analysis
was conducted between models cultured under perfusion conditions and those cultured
under static conditions. The latter were immersed in a medium containing 10 mM
gemcitabine, while the perfused models were submerged in a drug-free medium, with
gemcitabine delivered to the cell models only through the perfusion medium.
Drug-induced apoptosis in H358 cells was examined by immunohistochemistry.
Following four days of drug treatment, the models were fixed, embedded, and
subsequently sectioned using a cryotome. Figures 10a and 10b show the lack of Cleaved
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
50
caspase-3 and Cleaved PARP-1 signals in the absence of gemcitabine, with fluorescent
signals appearing after drug treatment. The signal density was notably higher in the
perfused models compared to those cultured under static conditions. Quantitative
analysis of fluorescence microscopy images was performed using the ImageJ program.
The Cleaved caspase-3 signal was approximately four times higher in the perfused
models than in the models cultured under static conditions (Figure 10c). Similarly, the
signal of Cleaved PARP-1 was approximately six times higher for dynamic cultivation
compared to static cultivation (Figure 10d).
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
51
Figure 10. Gemcitabine-induced apoptosis in H358 cells cultured in 3D bioprinted models under
static and dynamic conditions (a,b) Immunohistochemical analysis of Cleaved caspase-3 (a) and PARP-
1 (b) in cryosections of the bioprinted model. Models were treated with 10 μM gemcitabine for four days
and then fixed, embedded, and sliced by cryosectioning. Representative data from three independent
experiments are shown. Scale bar: 200 µm. (c, d) Quantification of fluorescent cells using ImageJ 1.53e.
Positive signals for Cleaved caspase-3 (c) and PARP-1 (d) were normalized to the total number of cells,
as determined by DAPI staining. The value for gemcitabine-treated models under static conditions was
set to 1 in order to present the perfusion data relative to it. Data from four independent experiments are
presented as mean ± standard deviation. **** p < 0.0001. The figure is adapted from Paper 1, Mei, et al.,
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
52
2023 (https://doi.org/10.3390/ijms24076071) used under a Creative Commons Attribution 4.0
International License: http://creativecommons.org/licenses/by/4.0/.
4.1.7. Endothelial cells lining the walls of blood vessels
A future aspect is to increase the complexity of the perfusion model to mimic the
complexity of authentic vascular structures. To this end, a suspension of Human
Umbilical Vein Endothelial Cells (HUVEC) added to the channel of the model and the
effect of rotation on HUVEC cell attachment after seeding was studied. Due to the
limited field of view of the microscope, it was not possible to obtain full cross-sectional
images. Therefore, two images - one above and one below - were seamlessly stitched
together to construct a full circular representation. As shown in Figure 11, slow rotation
of the model for 20 minutes significantly promoted cell attachment compared to its
static counterpart. In the absence of rotation, gravitational forces caused cells to settle
to the bottom of the channel. An endothelial cell lined channel will allow for more
physiological testing of compounds in the future.
In conclusion, the first part of the thesis outlines the development of a perfused lung
cancer model using DLP bioprinting technology. High cell viability after the printing
process was demonstrated and the utility of these models for cytostatic toxicity studies
was established. Furthermore, the incorporation of channels into the model enables the
connection to a perfusion system that simulates certain characteristics of the vascular
system in living organisms. In comparison to static cultivation, perfusion of the model
resulted in increased cell viability. In a proof-of-concept study, the aforementioned
setup was employed to investigate the activity of an approved anticancer drug and to
explore its mechanism of action. Thus, the model is suitable for testing new compounds
with potential antitumor activity.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
53
Figure 11. Fluorescence DAPI staining of cross-sections of the model after channel seeding with
vascular endothelial cells (HUVEC cells). (a) Static cultivation after HUVEC cell seeding, followed
by fixation, embedding, and DAPI staining. (b) Slow rotation of the model for 20 minutes after seeding
with HUVEC cells, followed by fixation, embedding and DAPI staining.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
54
4.2. 3D bioprinted lung cancer models for radiotherapy research
4.2.1. Operational arrangement of radiotherapy equipment and setting of
radiotherapy instrument parameters
In an earlier study, a basic 3D-printed model of human lung cancer for radiotherapy
research using broad-beam and microbeam methods was created by Al-Zeer et al. (95).
Building on these results, this study enhanced the 3D lung model and experimental
setup to more accurately simulate physiological conditions.
The improved model was designed using Rhino 6 3D design software, comprising a
central cancerous part containing A549 cells surrounded by normal tissue containing
normal human primary lung fibroblasts (Figure 12a).
The model measures 0.6 mm in height, 9 mm in diameter, and has a central circular
disk 4.5 mm in diameter. For microextrusion bioprinting, cells were embedded in a
hydrogel of alginate and gelatin. After printing, the models were cultured for one week
to recover from the printing process. On day 8, the models were placed in a
biocompatible resin box with internal dimensions of 11 mm x 11 mm x 2.4 mm (Figure
12b), which was then inserted into the thoracic slot of a previously published mouse
phantom (93) for irradiation (Figure 12c). CT images of the mouse phantom show the
internal structure (Figure 12d), with the purple area representing the lung cancer model,
positioned exactly where the lung would be in a real mouse. The upper lung corresponds
to the sternum region, with the white signal representing the calcium component
simulating bone, while the lower lung corresponds to the abdominal cavity, which is
empty. Together, these structures simulate the passage of radiation through skin, bone
and healthy lung tissue to target the lungs. The irradiation platform measures 17 cm x
13 cm, providing space for two well plates or one well plate and the mouse phantom
(Figure 12e).
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
55
Figure 12. Design and printing of the radiotherapy model and schematic diagram of the
radiotherapy device arrangement (a) The 3D model was designed using the Rhino 6 program. (b) The
3D lung cancer model was printed using the BioX printer. (c) The 3D lung cancer model was placed
inside a resin box. (d) A mouse phantom was used for model placement. (e) Top view, side view, and
front view of the mouse phantom under CT scan, the red area is the slot where the resin box is placed in
the mouse lung. (f) Simultaneous radiotherapy experiments on the 2D cell plate and the mouse phantom.
(g) The entire internal radiotherapy area of the radiotherapy device. This figure is adapted from Paper
Ⅱ, Mei, et al., 2024 (https://doi.org/10.3390/ijms251910268) used under a Creative Commons
Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
56
4.2.2 Comparison between 3D phantom and in vivo mouse model revealed
comparable dose distribution
For analysis of dose distributions, the tissue box was manually contoured as clinical
target volume (CTV) on simulation CT scan of the 3D phantom and transferred to the
in vivo mouse model. Planned dose to the CTV were 2 Gy with an ICRU conform dose
distribution (95 -107% of prescribed dose in target volume). Dose maximum for the
CTVs were 2.082 and 2.090 Gy for the 3D phantom and the in vivo mouse model,
respectively (Figure 13a, b). While Monitor units were kept similar in both irradiation
plans at 217.1, minimal dose was 1.88 Gy (3D phantom) and 0.181 Gy (in vivo mouse
model). Similar, mean dose was slightly lower in the in vivo mouse model compared to
the 3D phantom (1.77 Gy vs. 2.002 Gy).
For the 3D phantom the density histogram shows decreased density over the lungs,
comparable to the in vivo mouse model (Figure 13c, d). These results indicate, that
compared to a standard in vitro irradiation, irradiation of 3D models in the phantom can
better mimic differences in tissue density and so potentially dose distribution.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
57
Figure 13. Dose distribution (marked by coloured isodose curves) as well as mean irradiation doses
were comparable between the 3D phantom (a) and the in vivo mouse model(b). Investigation of density
histogramms showed differences according to anatomical localisation (e.g. lower Houndsfield units in
lung tissue) (c, d). Clinical target volume (pink contour) was delineated by hand on CT scan of 3D
phantom and manually transferred to CT scan of in vivo mouse model. This figure is adapted from Paper
Ⅱ, Mei, et al., 2024 (https://doi.org/10.3390/ijms251910268) used under a Creative Commons
Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/.
4.2.3 Cytotoxicity and cell viability of 2D cells and 3D models after radiotherapy
An initial assessment was conducted to evaluate the response of 2D cells seeded in well
plates to irradiation. To account for potential temporal variations in radiation effects,
assessments were conducted at 24 and 48 hours post-irradiation. In accordance with the
cellular composition of the 3D model, A549 cells, representing NSCLC cells, and
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
58
normal human primary lung fibroblasts (NHLFb), representing normal cells, were
irradiated with doses of 4 Gy and 8 Gy, respectively. This is in line with the commonly
used dose for a single fraction of radiotherapy in clinical practice, which typically
ranges from 2 to 10 Gy.
The viability of lung fibroblast cells did not differ significantly from that of the control
group at 24 hours and 48 hours post-radiotherapy (Figure 14a), maintaining consistently
high levels. In contrast, the viability of A549 cells decreased significantly following
radiotherapy, with a more pronounced effect observed at the 8 Gy radiation dose
compared to the 4 Gy dose. Furthermore, a slight difference in effect was observed
between the 48-hour and 24-hour post-radiotherapy assessments.
Figure 14c demonstrates the rapid proliferation of A549 cells, which cover the entire
field of view. The control group exhibited a few instances of cell death. In contrast, lung
fibroblast cells exhibited a relatively slow proliferation rate, with minimal intercellular
spacing and almost no evidence of cell death within the field of view. As illustrated in
Figures 13d and 13e, lung fibroblast cells exhibited minimal evidence of cell death
following irradiation. In stark contrast, a significant number of dead cell signals were
observed in the irradiated A549 cells. The quantification of dead cells by ImageJ
revealed a higher proportion of dead cells in the 8 Gy group, with a slight increase in
the number of dead cells at 48 hours compared to 24 hours (Figure 15). These findings
are in accordance with the results of the cell viability assays (Figure 14a and b).
In comparison with the direct irradiation of two-dimensional cells in well plates, the 3D
models irradiated within mouse phantoms were evaluated. Given that the therapeutic
effect on cells in this radiation mode may be less pronounced than that observed with
direct irradiation of cells in well plates, measurements were conducted at multiple time
points. As illustrated in Figure 14f, the overall cell viability exhibited a gradual decline
at 24 hours, 48 hours, and 5 days post-irradiation. This decline was observed to correlate
with the administered radiation doses, demonstrating a dose-response relationship. The
group that was exposed to 8 Gy exhibited the most pronounced decline in cell viability,
reaching a level below 50% of that observed in the control group. This result reflects
the collective cell viability of the two cell types within the model.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
59
To evaluate the two cell types in different regions of the model (the central tumor area
and the surrounding normal lung fibroblasts), cell cytotoxicity staining was conducted.
As illustrated in Figure 14g, the number of dead cell signals observed in both the central
and peripheral areas of the control group was minimal. In contrast, the central field of
view of both irradiated groups exhibited bright areas of dead cells, with a pronounced
difference between the central and peripheral areas observed in the 8 Gy dose group.
This evidence illustrates that radiation selectively damages tumor cells while minimally
affecting normal lung cells.
Figure 14. Cytotoxicity and cell viability of 2D cells and 3D models after radiotherapy (a) Following
radio-therapy at 24 hours and 48 hours, metabolic activity of lung fibroblast cells was measured using
cell viability assay (XTT), and data for radiation doses of 4 Gy and 8 Gy were plotted. The control group,
except for not being irradiated, was kept under identical conditions to the two experimental groups. Data
from three independent experiments are presented as mean ± standard deviation. (b) Following
radiotherapy at 24 hours and 48 hours, metabolic activity of A549 cells was assessed using cell viability
assay (XTT), and data for radiation doses of 4 Gy and 8 Gy were plotted. The control group, except for
not being irradiated, was kept under identical conditions to the two experimental groups. Data from three
independent experiments are presented as mean ± standard deviation, **** p < 0.0001. (c-e) Cell
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
60
cytotoxicity analysis was performed on the non-irradiated control (c), 4Gy irradiation group (d) and 8Gy
irradiation group (e), at 24 hours post-radiotherapy using calcein-AM and ethidium bromide-1 staining.
Under fluorescence microscopy, viable cells appeared green, while dead cells appeared red. Scale bar:
200 µm. (f) Following radiotherapy at 24 hours, 48 hours and 5 days metabolic activity of lung cancer
models were measured using cell viability assay (XTT), and data for radiation doses of 4 Gy and 8 Gy
were plotted. The control group, except for not being irradiated, was kept under identical conditions to
the two experimental groups. Data from three independent experiments are presented as mean ± standard
deviation, **** p < 0.0001. (g) A cyto-toxicity analysis of the lung cancer models was conducted at 24
hours post-radiotherapy using calcein-AM and ethidium bromide-1 staining. Under fluorescence
microscopy, viable cells appeared green, while dead cells appeared red. Scale bar: 500 µm. This figure
is adapted from Paper Ⅱ, Mei, et al., 2024 (https://doi.org/10.3390/ijms251910268) used under a
Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/.
Figure 15. Standardized counting of dead cells in the cell viability staining figure 24 and 48
hours post-radiotherapy for 2D cells. Quantitative analysis of red fluorescent cells was
performed using ImageJ 1.53e to compare the number of dead cells in the field of view.
Standardized to the number of dead cells in the control group 24 hours post-radiotherapy. Data
from three independent experiments are presented as mean ± standard deviation, **** p <
0.0001. This figure is adapted from Paper Ⅱ, Mei, et al., 2024 (https://doi.org/10.3390/ijms251910268)
used under a Creative Commons Attribution 4.0 International License:
http://creativecommons.org/licenses/by/4.0/.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
61
4.2.4 Radiotherapy induces DNA double-strand breaks in 2D cells and 3D models
In this study, the DNA damage marker γH2AX was employed to identify DNA double-
strand breaks. At the chromatin level, the localization and amplification of the damage
signal are characterized by the phosphorylation of histone H2AX. This process is
enhanced by the three main kinases involved in DNA double-strand breaks, which can
be marked with phospho-specific antibodies, forming nuclear substructures known as
γH2AX foci (96).
First, A549 cells and FB cells were seeded in 48-well plates and irradiated with doses
of 4 Gy and 8 Gy, respectively. The non-irradiated group served as a negative control.
Twenty-four hours following radiotherapy, the cells were fixed and stained. As
illustrated in Figure 16a, no γH2AX foci signals were observed in either cell type in the
control group. In contrast, the 4 Gy group exhibited approximately two γH2AX foci per
nucleus (Figure 16b). In the 8 Gy group, a greater number of γH2AX foci were ob-
served in the nuclei of both cell types, with the entire nuclear area displaying a dispersed
distribution of γH2AX foci. Although the number of γH2AX foci varied between cells,
statistical analysis of multiple randomly selected cells indicated that the number of
γH2AX foci in the 8 Gy group was approximately twice that of the 4 Gy group, with
statistically significant differences (Figure 16g).
For the 3D models, 24 hours post-radiotherapy, the models were fixed, dehydrated,
paraffin-embedded, and subsequently sectioned into 16 mm paraffin sections. Subse-
quently, the sections were subjected to immunohistochemical analysis for γH2AX. To
distinguish between the two cell types in different regions (A549 tumor cells in the
central part and FB cells in the peripheral part), red fluorescence representing
cytokeratin was employed to label A549 cells. As illustrated in Figure 16d, neither the
peripheral lung fibroblasts nor the central A549 cells exhibited γH2AX signals in the
control group. Following exposure to a 4 Gy dose of radiation, A549 cells exhibited
γH2AX foci. However, in FB cells, although the γH2AX signal completely overlapped
with DAPI, multiple γH2AX foci did not form (Figure 16e). Following irradiation with
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
62
8 Gy, multiple γH2AX foci were observed in the nuclei of A549 cells (Figure 16f), a
result consistent with those obtained from 2D cell cultures. However, in FB cells, the
number of γH2AX foci was relatively low, and the proportion of cells expressing the
γH2AX antibody was minimal. In conclusion, the expression of γH2AX in the tumor
portion of the 3D model was found to be largely consistent with that observed in 2D
A549 cells. However, in the 3D model, FB cells exhibited reduced sensitivity to X-rays
and were not directly exposed to X-rays, resulting in a less pronounced DNA double-
strand break response compared to 2D cells.
Figure 16. γH2AX immunofluorescent staining of irradiated 2D cells and 3D models (a-c)
Immunostaining images of A549 cells and lung fibroblasts from non-irradiated control (a), 4Gy
irradiation group (b) and 8Gy irradiation group (c). These cells were fixed 24 hours after irradiation, as
shown. γH2AX staining (green channel) indicates DNA double-strand breaks. DAPI was used for nuclear
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
63
counterstaining (blue channel). Scale bar: 20 µm. (d-f) Immunohistochemical staining images of 3D lung
cancer models from the non-irradiated control group (d), 4Gy irradiation group (e) and 8Gy irradiation
group (f). The models were fixed, dehydrated, and paraffin-embedded 24 h after irradiation, followed by
sectioning at a thickness of 16 mm. The sections were then subjected to immunostaining, as shown.
γH2AX staining (green channel) indicates DNA double-strand breaks. The samples were also stained
with antibodies against pan-cytokeratin to confirm their identity as epithelial cells (red channel), Pan-ck
stands for pan-cytokeratin. DAPI was used for nuclear counterstaining (blue channel). Scale bar: 20 µm.
(g) After treatment with different radiation doses, the average number of γH2AX foci was determined in
the nuclei of randomly selected cells in 2D A549 and FB cells, as well as in regions of A549 and FB cells
within 3D model sections. Data are presented as mean ± SD; n = 3. ** p < 0.01, **** p < 0.0001. This
figure is adapted from Paper Ⅱ, Mei, et al., 2024 (https://doi.org/10.3390/ijms251910268) used under
a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/.
4.2.5 Radiotherapy exerts its effects by inducing apoptosis in 2D cells and 3D
models
Ionizing radiation induces DNA damage in cancer cells, which, when unrepaired, leads
to cell death through initiation of apoptosis and cell necrosis. Following the
confirmation of DNA damage induced by radiotherapy, the examination of the
apoptotic marker Cleaved caspase-3 was conducted. Immunostaining results in 2D cell
cultures demonstrated that lung fibroblasts exhibited minimal Cleaved caspase-3
signals at 24 hours post 4 Gy and 8 Gy irradiation (Figure 17b, c). A549 cells also
exhibited minimal levels of Cleaved caspase-3 signal at 4 Gy and 8 Gy irradiation doses
(Figure 17b, c).
In contrast, the impact of radiotherapy on apoptosis in 3D cell models was investigated
using histological section staining. Cleaved caspase-3 staining revealed that at the 4 Gy
dose, some cytokeratin-marked A549 cells exhibited apoptotic signals within the field
of view (Figure 17e), while an increase in apoptotic signals was observed at the 8 Gy
dose (Figure 17f). Lung fibroblasts did not exhibit any apoptotic signals under non-
irradiated and 4 Gy irradiation conditions. Nevertheless, a minimal amount of Cleaved
caspase-3 signal was observed at the 8 Gy dose. In general, the degree of apoptosis
induced by X-rays in the 3D lung cancer model was significantly less than that observed
in 2D cells, with lung fibroblasts exhibiting the most notable difference.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
64
Figure 17. Cleaved caspase-3 immunofluorescent staining of irradiated 2D cells and 3D models (a-
c) Immunostaining images of A549 cells and lung fibroblasts from non-irradiated control (a), 4Gy
irradiation group (b) and 8Gy irradiation group (c). These cells were fixed 24 hours after irradiation, as
shown. Cleaved caspase-3 staining (green channel) indicates apoptosis effect. DAPI was used for nuclear
counterstaining (blue channel). Scale bar: 20 µm. (d-f) Immunohistochemical staining images of 3D lung
cancer models from the non-irradiated control group (d), 4Gy irradiation group (e) and 8Gy irradiation
group (f). The models were fixed, dehydrated, and paraffin-embedded 24 h after irradiation, followed by
sectioning at a thickness of 16 mm. The sections were then subjected to immunostaining, as shown.
Cleaved caspase-3 staining (green channel) indicates apoptosis effect. The samples were also stained
with antibodies against pan-cytokeratin to confirm their identity as epithelial cells (red channel), Pan-ck
stands for pan-cytokeratin. DAPI was used for nuclear counterstaining (blue channel). Scale bar: 20 µm.
This figure is adapted from Paper Ⅱ, Mei, et al., 2024 (https://doi.org/10.3390/ijms251910268) used
under a Creative Commons Attribution 4.0 International License:
http://creativecommons.org/licenses/by/4.0/.
4.2.6 Radiotherapy-induced LDH release in 2D cells and 3D models
Radiotherapy rays affect cells in different ways, including the induction of apoptosis,
pyroptosis, necrosis, and senescence. When a cell dies, its contents are released,
resulting in an increase in lactate dehydrogenase (LDH) levels. LDH release was
observed in both 2D and 3D cells, as shown in Figure 18a. For A549 cells, there was
no discernible difference in LDH release between the 4 Gy and control groups. The 8
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
65
Gy group exhibited a slight increase in LDH release compared to the control group, but
this was less than 50% of the positive control. The 3D model demonstrated no
difference in LDH release between the 4 Gy and control groups. However, the 8 Gy
group released more LDH than the control and 4 Gy groups (Figure 18b). The release
of LDH from lung fibroblasts in response to radiotherapy was not observed. This
suggests that radiotherapy primarily exerts other effects on tumor cells.
Figure 18. LDH release assay of 2D cells and 3D models 24 hours after radiotherapy (a) Relative
LDH release in 2D cells 24 hours after radiotherapy, with LDH release from lysis buffer-treated cells
used as a positive control. All data are presented as the mean ± standard error of the mean (SEM) of at
least three independent experiments. (b) Relative LDH release in 3D models 24 hours after radiotherapy,
with LDH release from lysis buffer-treated cells used as a positive control. All data are presented as the
mean ± standard error of the mean (SEM) of at least three independent experiments. Data are presented
as mean ± SD; n = 3. ****p < 0.0001. This figure is adapted from Paper Ⅱ, Mei, et al., 2024
(https://doi.org/10.3390/ijms251910268) used under a Creative Commons Attribution 4.0 International
License: http://creativecommons.org/licenses/by/4.0/.
4.2.7 Comparison of radiotherapy with and without phantom
The mouse phantom was selected as a model for the simulation of the process of
radiation traversing the skin, bones, and other organs of an animal to reach the tumor
site. As illustrated in the CT scan above, the intensity of radiation is attenuated as it
traverses the phantom (Figure 13). To ascertain whether the positioning of the 3D lung
models within the phantom had any impact on cellular survival, 3D models that had
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
66
been irradiated within the phantom were compared with 3D models that had been
placed directly in a 12-well plate. The total dose of irradiation was identical in both
cases. As illustrated in Figure 19a, the cell viability in the phantom-free group was
significantly lower than that of the 3D models in the mouse phantom at both 4 Gy and
8 Gy irradiation doses. Even at the 4 Gy dose, the cell viability was reduced to less than
half of the control.
To evaluate the two cell types in different regions of the model (central tumor area and
surrounding normal lung fibroblasts), cell cytotoxicity staining was performed. Figure
19b illustrates the 3D models 24 hours after direct irradiation in a 12-well plate. As
anticipated, the number of dead cell signals observed in both the central area and the
peripheral areas of the control group was minimal. However, in stark contrast to the
observations of the models in the phantom (Figure 14g), in both the 4 Gy and 8 Gy dose
groups, the dead cells in the tumor region and the surrounding healthy region are
indistinguishable, and there is no clear boundary between the two regions. The
discrepancy between the staining results and those observed in Figure 13g is striking.
These findings indicate that by situating the 3D model within the mouse phantom, it is
possible to create a more physiologically representative environment.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
67
Figure 19. Post-radiotherapy cytotoxicity testing of a model without a mouse phantom (a) Metabolic
activity of a lung cancer model placed in a 12-well plate was measured by cell viability assay (XTT) 24
h after radiotherapy and plotted for radiation doses of 4 Gy and 8 Gy. The control group was subjected
to the same conditions as the two experimental groups, with the exception that it did not receive radiation.
The data from three independent experiments are expressed as the mean ± standard deviation, ****p＜
0.0001. (b) Cytotoxicity analysis of a lung cancer model placed in a 12-well plate 24 h after radiotherapy
using calcein-AM and ethidium bromide-1 staining. In fluorescence microscopy, live cells appear green,
while dead cells appear red. Scale bar: 500 µm. This figure is adapted from Paper Ⅱ, Mei, et al., 2024
(https://doi.org/10.3390/ijms251910268) used under a Creative Commons Attribution 4.0 International
License: http://creativecommons.org/licenses/by/4.0/.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
68
5. Discussion
5.1. Perfusable 3D lung cancer model
Additive manufacturing technology has achieved significant breakthroughs in the field
of construction technology and is regarded as the "Third Industrial Revolution" (97).
Additive manufacturing, commonly known as 3D printing, enables the layer-by-layer
construction of parts from 3D computer models, allowing for rapid design optimization
and customization. Due to these unique characteristics, medical applications have
rapidly developed, including 3D-printed prosthetics, implants, and organ models (98).
The ease of use and rapid prototyping capabilities of 3D printing even contributed to
the swift response to medical needs during the COVID-19 pandemic (99,100).
3D organ models have emerged as a viable alternative to traditional animal
experimentation, utilizing human cells to potentially reduce the number of animals
required (95). Bioprinting has been identified as one of the most promising technologies
for generating disease models in oncology research, with applications in developing
anticancer drugs and optimizing radiotherapy protocols (101).
In drug screening research, accurately replicating in vivo tissues in preclinical models
is crucial for evaluating the efficacy of candidate drugs (102). For example, tumor cells
exist in vivo as 3D aggregates, exhibiting unique proliferative characteristics due to
their 3D structure. Additionally, solid tumors are embedded in ECM composed of
proteins, tumor-associated cells, and soluble factors, all of which impact tumor biology
and consequently affect cellular responses to drug treatments (103). Furthermore, in
vitro culture of cells within a 3D matrix more accurately replicates the tumor
microenvironment, enabling interactions between cells and the ECM and thereby
regulating the morphology, cellular behavior, and gene expression observed in vivo
(104). In vitro 2D cancer cell cultures do not provide a controlled extracellular
environment in space and time, which is critical to accurately simulate in vivo
conditions. Monolayer cell cultures lack the tissue architecture and organization
inherent to native tissues, limiting the reliability of 2D cancer cell cultures for studying
complex processes within the tumor microenvironment (105). Notably, cells grown in
a matrix often self-organize into structures that more closely resemble their in vivo
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
69
tissue architecture, which promotes cell-cell contact and communication (106). This in
vivo-like state is further enhanced by the use of primary cells and stem cell-derived cells
in 3D cell cultures, rather than the immortalized cell lines commonly used for
generating 2D cell culture models (107). Consequently, these characteristics of 3D cell
models add another layer of in vivo relevance to drug screening research by attempting
to replicate drug diffusivity (108), the phenotypic and genotypic changes induced by
3D cell arrangement (109), intracellular and extracellular signaling interactions driven
by the 3D environment (110), and the activation or deactivation of cell survival genes
(111).
Increasing evidence suggests that 3D in vitro cell culture models more accurately
predict therapeutic efficacy and drug response compared to 2D cultures, potentially
accelerating the translation of effective treatments from preclinical research to human
clinical trials (112). Notably, some cell types may exhibit greater sensitivity to drug
toxicity in 2D cultures than in 3D cultures (113). For certain tumor cell types, the
hypoxic environments generated within 3D cell models can lead to a significant
reduction in drug sensitivity (114). The biological relevance of 3D cultures relative to
2D cultures has been demonstrated in drug responses for colorectal cancer (112), breast
cancer (115), pancreatic cancer (116), and chondrosarcoma (117), where drug dosages
mimic in vivo responses. Additionally, 3D cell cultures facilitate the co-culture of
cancer cells and immune cells within physiologically relevant in vitro models, thereby
simulating the human immune response observed in vivo (118).
Furthermore, before assessing the efficacy of new drugs in patients, their safety must
first be established. Currently, following the discovery and optimization of new
compounds, the drug development process relies heavily on animal models for
preclinical toxicity evaluation (119). However, recent studies comparing drug toxicity
between animals and humans have raised concerns about the long-held belief that
animal models are reliable predictors of toxicity in new therapeutic approaches
(120,121). Consequently, incorporating 3D human cells and tissues into drug
development programs may enhance the predictive accuracy of toxicity testing (122).
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
70
Unlike other 3D culture techniques, such as organoid formation, bioprinting offers the
distinct advantage of enabling computer-based design of 3D objects with predefined
structures (123). This capability allows for the integration of vascular channel systems
into the models, thereby enhancing their physiological relevance. Various strategies
have been developed to achieve this, including the use of fugitive inks to create specific
vascular patterns during the printing process, which are subsequently removed during
the culture period (124). DLP bioprinting, in contrast to the widely used extrusion-based
bioprinting, combines exceptionally high resolution and stability of the produced
constructs, facilitating the generation of vascular structures without the need for
additional materials.
Although bioprinted models of lung cancer cells have been reported using extrusion-
based technologies (125), this is the first lung cancer model fabricated using DLP
bioprinting. The exceptionally high resolution of DLP bioprinting enables the
incorporation of pre-designed vascular structures. The model developed in this study
was connected to a perfusion system to investigate the mode of action of a cytostatic
drug. A single channel, winding through the entire structure, was implemented to supply
all parts of the model. The resulting model exhibits high toughness, flexibility, and
resistance to damage. Similarly, in human tumors, secretion of extracellular matrix
proteins and cross-linking of the fibrillar collagen matrix often result in relatively high
tissue stiffness (126).
By utilizing a peristaltic pump to circulate the culture medium, 3D models cultured
under static and dynamic conditions were compared, demonstrating that perfusion
enhances cell viability during long-term cultivation.
The current cell density remains relatively low compared to primary tissues. Increasing
cell density would enhance the physiological relevance of the model. However,
increased cell density may compromise the resolution of DLP due to heightened light
scattering. A recent study by You et al. reported that the addition of iodixanol can reduce
scattering and improve the quality and resolution of DLP-based bioprinting processes
(127).
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
71
In these experiments, the anticancer drug gemcitabine was introduced via the perfusion
system. Control models were cultured under static conditions by immersion in a drug-
containing medium. In contrast, the perfused models received the drug only through the
media flow, simulating drug delivery through the bloodstream in human patients.
A challenge for channel systems is the formation of bubbles, which can negatively
affect cell viability and the efficacy of applied agents, and over time may even obstruct
the circulation. The prevention and removal of bubbles is a complex task (128). The
developed perfusion system is designed to reduce the occurrence of bubbles. For
example, the force applied by the peristaltic pump is uniform and stable. In addition to
peristaltic pumps, studies have shown that several novel pumps have been employed
for cell perfusion culture. For instance, Wang et al. developed an innovative
microfluidic chip integrated with an osmotic micropump (129). This micropump
features two sealed chambers: an inner chamber containing the osmotic agent and an
outer chamber filled with water, separated by a semipermeable membrane. Water in the
outer chamber is driven by osmotic pressure to flow across the membrane into the inner
chamber, thereby facilitating continuous fluid flow within the channel. The power-free
micropump has been demonstrated to be pulse-free, providing a stable flow rate over
prolonged operation periods.
For proof-of-principle experiments, gemcitabine was used to test the response of the
perfused organ model to cytostatic treatment. Like many chemotherapeutic drugs,
gemcitabine exerts its effects by inducing apoptosis in tumor cells (130). Caspase-3 is
a key molecule in the apoptotic pathway of cells. The inactive precursor of caspase is
activated by proteolytic cleavage to Cleaved caspase-3. The active form of caspase-3,
as a protease, further activates downstream factors, including PARP-1, which is
involved in DNA damage repair (131). Immunohistochemical analysis of these two
markers, Cleaved caspase-3 and PARP-1, was performed to detect gemcitabine-induced
apoptosis in H358 cells. A dose-response assay revealed a clear concentration-
dependent effect of drug treatment. In 2D culture, the measured IC50 was as low as 2.5
nM, which is consistent with the IC50 reported by (132). However, in 3D culture,
approximately 1,000 times higher concentrations were required to induce cell death.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
72
This phenomenon can be attributed to the dense network formed by the hydrogel and
the cells, which mirrors the situation in dense tumor tissue in patients. This density
often hinders the penetration of anticancer drugs into cancerous tissue. Similar
phenomena have been observed in previous studies (133). For example, Imamura et al.
found that cell lines forming dense multicellular spheroids were more resistant to
paclitaxel and doxorubicin than cells in 2D culture (114). Similarly, certain head and
neck squamous cell carcinomas have been reported to exhibit reduced sensitivity to
cisplatin and cetuximab in 3D culture (134). Prostate cancer cells DU145 and
glioblastoma cells U87 showed significantly higher resistance to dasatinib-induced
toxicity, which was explained by the very limited ability of the drug to penetrate the 3D
models (135). Therefore, cells within 3D constructs are generally less sensitive to drug
treatment than those in monolayer cultures. Based on their thorough analysis, Imamura
et al. concluded that 3D models better mimic the characteristics of in vivo tumors, such
as hypoxia, dormancy, and anti-apoptotic properties, and the resulting drug resistance,
compared to cells in 2D cultures.
Even at the highest concentrations of gemcitabine used in this study, a significant
proportion of cells survived. Although gemcitabine is a well-established anticancer
drug (136), it is typically used in combination with other drugs to enhance its antitumor
activity. Therefore, the partial cytostatic effect observed in the 3D culture is more
representative of the biological conditions in human patients than the absolute efficacy
of gemcitabine observed in the 2D culture.
5.2. 3D bioprinted lung cancer models for radiotherapy research
To reduce reliance on live animals and to better mimic physiological conditions, the use
of rodent phantoms in radiation biology research has increased. In this study, a
previously published rodent phantom designed to simulate the process by which
radiation passes through the skin, bones, and other organs of an animal before reaching
the tumor site, was used. This phantom includes adjustable components for bones, lungs,
and organ cavities with linear attenuation coefficients that closely resemble those of
rodent tissues. In addition, the rodent phantom is open source and has a manufacturing
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
73
cost of approximately 100 Euros, making it accessible for widespread use by different
research teams (137). To maintain sterility during the transfer of the 3D model into the
rodent phantom, a resin box using the same material as the phantom was printed. This
allows for quick and efficient transfer of the 3D model from the cell culture plate to the
phantom for radiation treatment.
The tumor microenvironment (TME) is defined as the intricate network of interactions
between tumor cells, healthy cells, immune cells, cytokines, and other biological
components. The interactions between these components can be classified as either
anti-tumor or pro-tumor, which ultimately determine the trajectory of treatments (87).
Consequently, there has been a gradual shift in interest from single tumor cell models
to more complex models. In the context of lung cancer models used for radiotherapy,
the relationship between normal tissue cells and tumor cells becomes of particular
significance, as radiation not only targets tumor cells but also exerts cytotoxic effects
on normal tissue cells.
In contrast to the DLP printing method used for the lung cancer radiotherapy model in
the first part, an extrusion-based printing approach was selected for the radiotherapy
model. While DLP printing offers several advantages, such as extremely high precision,
high fidelity, and excellent hydrogel biocompatibility, it is limited by its ability to print
only a single material at a time. The radiotherapy model consists of two types of cells,
each located in different regions of the model, making it impractical to incorporate both
cell types into a single hydrogel for printing. Additionally, the design of the
radiotherapy model does not require the exceptionally high precision that DLP printing
provides, making extrusion-based printing a suitable choice. Theoretically, extrusion-
based printing can accommodate the printing of models with more than three different
materials, which paves the way for further complexity in future model designs. The
combination of gelatin and alginate for extrusion-based bioprinting has become a well-
established method for the construction of three-dimensional organ models.
As shown in this study, a method was developed to simulate the actual lesions found in
lung cancer patients by printing tumor cells in the center and surrounding them with
normal primary lung fibroblasts in the outer layers. Both regions were exposed to the
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
74
same X-ray radiation, and the live-dead staining results clearly showed the selective
cytotoxic effects of radiotherapy. It is arguable that the images obtained from the 3D
model provide a more realistic physiological representation compared to irradiating
each type of 2D cell separately. In addition, radiation-induced DNA damage and
apoptosis-mediated effects were more pronounced in 2D cells than in the 3D model.
Specifically, 2D cells exhibited a significant number of γH2AX foci within 24 hours of
irradiation, whereas this effect was less evident in lung fibroblasts within the 3D model.
A similar pattern was observed with regard to apoptotic effects. The reasons for these
phenomena are complex and warrant further investigation. First, radiation must
penetrate the rodent phantom and the hydrogel surrounding the cells in the 3D model,
resulting in a reduction in treatment efficacy. In addition, interactions between different
cell types within the 3D model may also contribute to the observed differences. In
conclusion, this approach better approximates physiological conditions and more
accurately reflects the clinical efficacy of radiotherapy.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
75
5.3. Outlook
Our research has identified several areas for potential improvement. Firstly, the
objective is to make the model more physiologically relevant by introducing a variety
of cells into the model, including not only NSCLC cells, but also healthy cells, immune
cells, and others. However, mixing multiple cell types for printing is challenging
because they should be distributed based on their functional properties. For instance,
Wei et al. demonstrated a technique for on-demand droplet-based 3D bioprinting of a
three-layered human alveolar model in 2020 (138). However, achieving this with the
Lumen X printer is challenging because each print can only include one type of
hydrogel. Extrusion-based printing could potentially use multiple print heads and
bioinks to create structures with layers of different cell types. However, hydrogels
composed of common materials, such as alginate and gelatin or collagen, lack strong
structural rigidity and are unable to meet the high-resolution printing requirements for
fine vasculature. Even when tubular structures are printed, the models tend to collapse
inward, leading to vessel occlusion.
Wu et al. recently developed hydrogels that are suitable for extrusion-based printing of
vascular structures (139). Structures printed with this hydrogel exhibit improved
structural strength, enabling the formation of vascular structures. Furthermore, the
printed models have smaller volumes and require less hydrogel. However, this method
also has drawbacks, such as operational complexity and difficulty in replication.
In addition, the incorporation of endothelial cells onto the vessel walls to mimic
vascular barriers is another area for future improvement. Lewis et al. reported a method
for bioprinting 3D cell-laden, vascularized tissues with a thickness exceeding 1 cm and
the capacity for long-term perfusion (over 6 weeks) on chips (140). They combined
parenchymal tissue, matrix, and endothelium into a single thick tissue by
simultaneously printing different inks, including human mesenchymal stem cells
(hMSCs), human neonatal dermal fibroblasts (hNDFs), and a customized extracellular
matrix embedded with a vascular system, subsequently lined with human umbilical vein
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
76
endothelial cells (HUVECs). These thick vascularized tissues were actively perfused
with media containing growth factors, which induced hMSCs to differentiate toward
the osteogenic lineage (140).
In 2021, Akhilesh K. Gaharwar et al. introduced a more advanced approach utilizing
novel nanoengineered hydrogel-based cell-laden bioinks that can be printed into 3D
anatomically accurate multicellular blood vessels to replicate the physical and chemical
microenvironment of the human vasculature (141). The vascular structures are
composed of living co-cultures of endothelial cells and vascular smooth muscle cells,
providing an opportunity to model vascular function and pathophysiology. Under
cytokine stimulation and blood perfusion, these 3D bioprinted vessels can recapitulate
thrombus inflammatory responses observed only in advanced in vitro preclinical
models or in vivo. In summary, the construction of increasingly complex vascular
structures is the directive for future improvement and development.
Apart from the future development and advancement of the models, there are also many
aspects of the current analytical technology that need improvement. For instance, the
staining of 3D models, particularly when analysing the viability of cells within the
model, can be difficult due to the thickness of the model, which often results in high
background fluorescence levels. These background levels have a significant impact on
the quality of the final image. Even with confocal microscopy, it is challenging to
conduct fluorescence analysis on models thicker than 1 mm. Section staining is a
promising solution, but different hydrogel materials require different sectioning
methods, requiring the development of multiple protocols and demanding a high level
of operator skill given the significant differences between hydrogels and animal tissues.
Additionally, the process of releasing cells from the model becomes necessary for
analysis of cellular contents (such as RNA, proteins), but the digestion of the hydrogel
to release the cells may itself affect the cells. For example, the digestion buffer may
impact protein expression in the cells. All of these experimental techniques need to be
improved in the future.
The use of three-dimensional models in place of two-dimensional cell cultures provides
novel experimental avenues for the advancement of radiotherapy in NSCLC. A defining
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
77
feature of tumor cells is their rapid proliferation, which results in localized hypoxia.
This hypoxic condition, resulting from both physical and chemical processes as well as
biological mechanisms, contributes to the reduced sensitivity of hypoxic tumor cells to
ionizing radiation and their resistance to hypoxia-targeted therapies (142).
Consequently, the study of cellular hypoxia has become a prominent area of interest
within the field of NSCLC radiotherapy. For example, hypoxia and acidity can
modulate immune checkpoint molecules and the release of type I interferons, thereby
enabling cancer cells to evade immune surveillance. It may therefore be posited that the
targeting of hypoxia and acidity could serve to enhance the efficacy of immune
checkpoint inhibitors in NSCLC (143).
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
78
6. Conclusion
6.1. Perfusable 3D lung cancer model
This study outlines the development of a perfused lung cancer model using DLP
bioprinting technology. In particular, the model can be easily adapted by other
researchers in cancer research and 3D culture, as all necessary details for replication
using commercially available equipment and materials are open source. Cells have been
shown to remain viable after printing, and the model can be used to assess the toxicity
of cytostatic agents. In addition, the inclusion of a channel allows connection to a
perfusion system that mimics certain properties of the vasculature in living organisms.
Compared to static culture, perfusion resulted in improved cell viability. In a proof-of-
concept experiment, this setup was used to study the activity and mechanism of action
of an approved anticancer drug. The model is therefore considered suitable for testing
new compounds with potential antitumor activity. Future improvements, such as the
addition of an endothelial lining, are planned to further enhance its physiological
relevance.
6.2. 3D bioprinted lung cancer models for radiotherapy research
This study demonstrates that the use of a micro-extrusion bioprinting technique to
develop a three-dimensional human lung cancer model represents a promising
alternative to conventional animal models in radiotherapy research. By embedding lung
tumor cells within human primary lung fibroblasts and integrating them into a mouse
phantom, the model successfully mimics the physiological conditions and radiation
attenuation properties of real tissues. The selective cytotoxic effects of X-rays on tumor
cells, along with variations in metabolic activity, cell death, apoptosis, and DNA
damage, highlight the model's potential to accurately reflect therapeutic outcomes. This
innovative approach not only addresses ethical concerns related to animal testing, but
also provides a more relevant and accurate tool for studying radiation effects and
advancing new lung cancer treatments.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
79
References
1. Siegel, R.L., Miller, K.D., Fuchs, H.E. and Jemal, A. (2021) Cancer Statistics, 2021.
Ca-
cancer J Clin
, 71, 7-33.
2. Oliver, A.L. (2022) Lung Cancer: Epidemiology and Screening.
Surg Clin N Am
, 102, 335-
344.
3. Sankar, K., Gadgeel, S.M. and Qin, A. (2020) Molecular therapeutic targets in non-small
cell lung cancer.
Expert Rev Anticanc
, 20, 647-661.
4. Chohan, K.L., Siegler, E.L. and Kenderian, S.S. (2023) CAR-T Cell Therapy: the Efficacy
and Toxicity Balance.
Curr Hematol Malig Rep
, 18, 9-18.
5. Siegel, R.L., Miller, K.D., Fuchs, H.E. and Jemal, A. (2022) Cancer statistics, 2022.
Ca-
cancer J Clin
, 72, 7-33.
6. Zhou, F., Qiao, M. and Zhou, C. (2021) The cutting-edge progress of immune-
checkpoint blockade in lung cancer.
Cell Mol Immunol
, 18, 279-293.
7. Gettinger, S.N., Wurtz, A., Goldberg, S.B., Rimm, D., Schalper, K., Kaech, S., Kavathas, P.,
Chiang, A., Lilenbaum, R., Zelterman, D.
et al.
(2018) Clinical Features and Management
of Acquired Resistance to PD-1 Axis Inhibitors in 26 Patients With Advanced Non-Small
Cell Lung Cancer.
J Thorac Oncol
, 13, 831-839.
8. Bade, B.C. and Dela Cruz, C.S. (2020) Lung Cancer 2020: Epidemiology, Etiology, and
Prevention.
Clin Chest Med
, 41, 1-24.
9. Jenkins, R.W., Barbie, D.A. and Flaherty, K.T. (2018) Mechanisms of resistance to immune
checkpoint inhibitors.
Br J Cancer
, 118, 9-16.
10. Schoenfeld, A.J. and Hellmann, M.D. (2020) Acquired Resistance to Immune Checkpoint
Inhibitors.
Cancer Cell
, 37, 443-455.
11. Sharma, P., Hu-Lieskovan, S., Wargo, J.A. and Ribas, A. (2017) Primary, Adaptive, and
Acquired Resistance to Cancer Immunotherapy.
Cell
, 168, 707-723.
12. Lam, E.H.Y., Yu, F., Zhu, S. and Wang, Z. (2023) 3D Bioprinting for Next-Generation
Personalized Medicine.
Int J Mol Sci
, 24, 6357.
13. Dankó, T., Peto vári, G., Raffay, R., Sztankovics, D., Moldvai, D., Vetlényi, E., Krencz, I.,
Rókusz, A., Sipos, K., Visnovitz, T.
et al.
(2022) Characterisation of 3D Bioprinted Human
Breast Cancer Model for
In Vitro
Drug and Metabolic Targeting.
Int J Mol Sci
, 23, 7444.
14. Hong, S. and Song, J.M. (2022) 3D bioprinted drug-resistant breast cancer spheroids for
quantitative in situ evaluation of drug resistance.
Acta Biomater
, 138, 228-239.
15. Mao, S., He, J., Zhao, Y., Liu, T., Xie, F., Yang, H., Mao, Y., Pang, Y. and Sun, W. (2020)
Bioprinting of patient-derived
in vitro
intrahepatic cholangiocarcinoma tumor model:
establishment, evaluation and anti-cancer drug testing.
Biofabrication
, 12, 045014.
16. Zhao, Y., Yao, R., Ouyang, L., Ding, H., Zhang, T., Zhang, K., Cheng, S. and Sun, W. (2014)
Three-dimensional printing of Hela cells for cervical tumor model
in vitro
.
Biofabrication
,
6, 035001.
17. Teli, P., Kale, V. and Vaidya, A. (2023) Beyond animal models: revolutionizing
neurodegenerative disease modeling using 3D
in vitro
organoids, microfluidic chips, and
bioprinting.
Cell Tissue Res
, 394, 75-91.
18. Brown, Z.J., Heinrich, B. and Greten, T.F. (2018) Mouse models of hepatocellular
carcinoma: an overview and highlights for immunotherapy research.
Nat Rev Gastro
Hepat
, 15, 536-554.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
80
19. Passaro, A., Brahmer, J., Antonia, S., Mok, T. and Peters, S. (2022) Managing Resistance
to Immune Checkpoint Inhibitors in Lung Cancer: Treatment and Novel Strategies.
J Clin
Oncol
, 40, 598-610.
20. Lv, D., Hu, Z., Lu, L., Lu, H. and Xu, X. (2017) Three-dimensional cell culture: A powerful
tool in tumor research and drug discovery.
Oncol Lett
, 14, 6999-7010.
21. Butterworth, K.T. (2019) Evolution of the Supermodel: Progress in Modelling
Radiotherapy Response in Mice.
Clin Oncol-uk
, 31, 272-282.
22. Li, Y., Liu, J., Xu, S. and Wang, J. (2023) 3D Bioprinting: An Important Tool for Tumor
Microenvironment Research.
Int J Nanomedicine
, 18, 8039-8057.
23. Gronberg, M.P., Tailor, R.C., Smith, S.A., Kry, S.F., Followill, D.S., Stojadinovic, S.,
Niedzielski, J.S., Lindsay, P.E., Krishnan, S., Aguirre, F.
et al.
(2020) A Mail Audit
Independent Peer Review System for Dosimetry Verification of a Small Animal Irradiator.
Radiat Res
, 193, 341-350.
24. Silvestre Patallo, I., Subiel, A., Westhorpe, A., Gouldstone, C., Tulk, A., Sharma, R.A. and
Schettino, G. (2020) Development and Implementation of an End-To-End Test for
Absolute Dose Verification of Small Animal Preclinical Irradiation Research Platforms.
Int
J Radiat Oncol Biol Phys
, 107, 587-596.
25. Pedersen, K.H., Kunugi, K.A., Hammer, C.G., Culberson, W.S. and DeWerd, L.A. (2016)
Radiation Biology Irradiator Dose Verification Survey.
Radiat Res
, 185, 163-168.
26. Seed, T.M., Xiao, S., Manley, N., Nikolich-Zugich, J., Pugh, J., Van den Brink, M.,
Hirabayashi, Y., Yasutomo, K., Iwama, A., Koyasu, S.
et al.
(2016) An interlaboratory
comparison of dosimetry for a multi-institutional radiobiological research project:
Observations, problems, solutions and lessons learned.
Int J Radiat Biol
, 92, 59-70.
27. Ventola, C.L. (2014) Medical Applications for 3D Printing: Current and Projected Uses.
P
t
, 39, 704-711.
28. Filippou, V. and Tsoumpas, C. (2018) Recent advances on the development of phantoms
using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound.
Med Phys
, 45,
e740-760.
29. Tino, R., Yeo, A., Leary, M., Brandt, M. and Kron, T. (2019) A Systematic Review on 3D-
Printed Imaging and Dosimetry Phantoms in Radiation Therapy.
Technol Cancer Res
Treat
, 18, 1533033819870208.
30. De, S. and Singh, N. (2022) Advancements in Three Dimensional In-Vitro Cell Culture
Models.
Chem Rec
, 22, e202200058.
31. Mendes, N., Dias Carvalho, P., Martins, F., Mendonça, S., Malheiro, A.R., Ribeiro, A.,
Carvalho, J. and Velho, S. (2020) Animal Models to Study Cancer and Its
Microenvironment.
Adv Exp Med Biol
, 1219, 389-401.
32. Lin, M., Tang, M., Duan, W., Xia, S., Liu, W. and Wang, Q. (2023) 3D Bioprinting for
Tumor Metastasis Research.
ACS Biomater Sci Eng
, 9, 3116-3133.
33. Marshall, L.J., Bailey, J., Cassotta, M., Herrmann, K. and Pistollato, F. (2023) Poor
Translatability of Biomedical Research Using Animals - A Narrative Review.
Altern Lab
Anim
, 51, 102-135.
34. Wong, C.H., Siah, K.W. and Lo, A.W. (2019) Estimation of clinical trial success rates and
related parameters.
Biostatistics
, 20, 273-286.
35. Meagan, M. and Nasser, H. (2021) Advances in systemic therapy for non-small cell lung
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
81
cancer.
BMJ
, 375, n2363.
36. Klebe, R.J. (1988) Cytoscribing: a method for micropositioning cells and the construction
of two- and three-dimensional synthetic tissues.
Exp Cell Res
, 179, 362-373.
37. Cui, X., Boland, T., D'Lima, D.D. and Lotz, M.K. (2012) Thermal inkjet printing in tissue
engineering and regenerative medicine.
Recent Pat Drug Deliv Formul
, 6, 149-155.
38. Murphy, S.V. and Atala, A. (2014) 3D bioprinting of tissues and organs.
Nat Biotechnol
,
32, 773-785.
39. Faulkner-Jones, A., Greenhough, S., King, J.A., Gardner, J., Courtney, A. and Shu, W.
(2013) Development of a valve-based cell printer for the formation of human embryonic
stem cell spheroid aggregates.
Biofabrication
, 5, 015013.
40. Murphy, S.V. and Atala, A. (2014) 3D bioprinting of tissues and organs.
Nat Biotechnol
,
32, 773-785.
41. Tasoglu, S. and Demirci, U. (2013) Bioprinting for stem cell research.
Trends Biotechnol
,
31, 10-19.
42. Gao, G., Kim, B.S., Jang, J. and Cho, D.W. (2019) Recent Strategies in Extrusion-Based
Three-Dimensional Cell Printing toward Organ Biofabrication.
ACS Biomater Sci Eng
, 5,
1150-1169.
43. Berg, J. and Kurreck, J. (2021) Clean bioprinting - Fabrication of 3D organ models
devoid of animal components.
Altex-altern Anim Ex
, 38, 269-288.
44. Cui, X., Li, J., Hartanto, Y., Durham, M., Tang, J., Zhang, H., Hooper, G., Lim, K. and
Woodfield, T. (2020) Advances in Extrusion 3D Bioprinting: A Focus on Multicomponent
Hydrogel-Based Bioinks.
Adv Healthc Mater
, 9, 1901648.
45. Koch, F., Tröndle, K., Finkenzeller, G., Zengerle, R., Zimmermann, S. and Koltay, P. (2020)
Generic method of printing window adjustment for extrusion-based 3D-bioprinting to
maintain high viability of mesenchymal stem cells in an alginate-gelatin hydrogel.
Bioprinting
, 20, e00094.
46. Odde, D.J. and Renn, M.J. (1999) Laser-guided direct writing for applications in
biotechnology.
Trends Biotechnol
, 17, 385-389.
47. Li, H., Tan, C. and Li, L. (2018) Review of 3D printable hydrogels and constructs.
Mater
Design
, 159, 20-38.
48. Peng, W., Datta, P., Ayan, B., Ozbolat, V., Sosnoski, D. and Ozbolat, I.T. (2017) 3D
bioprinting for drug discovery and development in pharmaceutics.
Acta Biomater
, 57,
26-46.
49. Donderwinkel, I., van Hest, J.C.M. and Cameron, N.R. (2017) Bio-inks for 3D bioprinting:
recent advances and future prospects.
Polym Chem-uk
, 8, 4451-4471.
50. Yu, J., Park, S.A., Kim, W.D., Ha, T., Xin, Y.Z., Lee, J. and Lee, D. (2020) Current Advances
in 3D Bioprinting Technology and Its Applications for Tissue Engineering.
Polymers
(Basel)
, 12.
51. Ma, X., Qu, X., Zhu, W., Li, Y.-S., Yuan, S., Zhang, H., Liu, J., Wang, P., Lai, C.S.E., Zanella,
F.
et al.
(2016) Deterministically patterned biomimetic human iPSC-derived hepatic
model via rapid 3D bioprinting.
P Natl Acad Sci Usa
, 113, 2206-2211.
52. Magalhães, L.S.S.M., Santos, F.E.P., Elias, C.d.M.V., Afewerki, S., Sousa, G.F., Furtado,
A.S.A., Marciano, F.R. and Lobo, A.O. (2020) Printing 3D Hydrogel Structures Employing
Low-Cost Stereolithography Technology.
J. Funct. Biomater.
, 11, 12.
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
82
53. Bernal, P.N., Delrot, P., Loterie, D., Li, Y., Malda, J., Moser, C. and Levato, R. (2019)
Volumetric Bioprinting of Complex Living-Tissue Constructs within Seconds.
Adv Mater
,
31, 1904209.
54. Mei, Y., Wu, D., Berg, J., Tolksdorf, B., Roehrs, V., Kurreck, A., Hiller, T. and Kurreck, J.
(2023) Generation of a Perfusable 3D Lung Cancer Model by Digital Light Processing.
Int
J Mol Sci
, 24.
55. Regehly, M., Garmshausen, Y., Reuter, M., König, N.F., Israel, E., Kelly, D.P., Chou, C.-Y.,
Koch, K., Asfari, B. and Hecht, S. (2020) Xolography for linear volumetric 3D printing.
Nature
, 588, 620-624.
56. Fan, C., Xu, K., Huang, Y., Liu, S., Wang, T., Wang, W., Hu, W., Liu, L., Xing, M. and Yang,
S. (2021) Viscosity and degradation controlled injectable hydrogel for esophageal
endoscopic submucosal dissection.
Bioact Mater
, 6, 1150-1162.
57. Wang, C., Honiball, J.R., Lin, J., Xia, X., Lau, D.S.A., Chen, B., Deng, L. and Lu, W.W. (2022)
Infiltration from Suspension Systems Enables Effective Modulation of 3D Scaffold
Properties in Suspension Bioprinting.
Acs Appl Mater Inter
, 14, 27575-27588.
58. Xie, M., Su, J., Zhou, S., Li, J. and Zhang, K. (2023) Application of Hydrogels as Three-
Dimensional Bioprinting Ink for Tissue Engineering.
Gels
, 9, 88.
59. Huebsch, N., Arany, P.R., Mao, A.S., Shvartsman, D., Ali, O.A., Bencherif, S.A., Rivera-
Feliciano, J. and Mooney, D.J. (2010) Harnessing traction-mediated manipulation of the
cell/matrix interface to control stem-cell fate.
Nat Mater
, 9, 518-526.
60. Khetan, S., Guvendiren, M., Legant, W.R., Cohen, D.M., Chen, C.S. and Burdick, J.A. (2013)
Degradation-mediated cellular traction directs stem cell fate in covalently crosslinked
three-dimensional hydrogels.
Nat Mater
, 12, 458-465.
61. Patrick, P.S., Bear, J.C., Fitzke, H.E., Zaw-Thin, M., Parkin, I.P., Lythgoe, M.F., Kalber, T.L.
and Stuckey, D.J. (2020) Radio-metal cross-linking of alginate hydrogels for non-
invasive
in vivo
imaging.
Biomaterials
, 243, 119930.
62. Berg, J., Weber, Z., Fechler-Bitteti, M., Hocke, A.C., Hippenstiel, S., Elomaa, L., Weinhart,
M. and Kurreck, J. (2021) Bioprinted Multi-Cell Type Lung Model for the Study of Viral
Inhibitors.
Viruses
, 13.
63. Ojansivu, M., Rashad, A., Ahlinder, A., Massera, J., Mishra, A., Syverud, K., Finne-
Wistrand, A., Miettinen, S. and Mustafa, K. (2019) Wood-based nanocellulose and
bioactive glass modified gelatin-alginate bioinks for 3D bioprinting of bone cells.
Biofabrication
, 11, 035010.
64. Kang, D., Liu, Z., Qian, C., Huang, J., Zhou, Y., Mao, X., Qu, Q., Liu, B., Wang, J., Hu, Z.
et
al.
(2023) 3D bioprinting of a gelatin-alginate hydrogel for tissue-engineered hair follicle
regeneration.
Acta Biomater
, 165, 19-30.
65. Colosi, C., Shin, S.R., Manoharan, V., Massa, S., Costantini, M., Barbetta, A., Dokmeci,
M.R., Dentini, M. and Khademhosseini, A. (2016) Microfluidic Bioprinting of
Heterogeneous 3D Tissue Constructs Using Low-Viscosity Bioink.
Adv Mater
, 28, 677-
684.
66. Stratesteffen, H., Köpf, M., Kreimendahl, F., Blaeser, A., Jockenhoevel, S. and Fischer, H.
(2017) GelMA-collagen blends enable drop-on-demand 3D printablility and promote
angiogenesis.
Biofabrication
, 9, 045002.
67. Seyedmahmoud, R., Çelebi-Saltik, B., Barros, N., Nasiri, R., Banton, E., Shamloo, A.,
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
83
Ashammakhi, N., Dokmeci, M.R. and Ahadian, S. (2019) Three-Dimensional Bioprinting
of Functional Skeletal Muscle Tissue Using Gelatin Methacryloyl-Alginate Bioinks.
Micromachines
, 10, 679.
68. Kim, S.H., Hong, H., Ajiteru, O., Sultan, M.T., Lee, Y.J., Lee, J.S., Lee, O.J., Lee, H., Park,
H.S., Choi, K.Y.
et al.
(2021) 3D bioprinted silk fibroin hydrogels for tissue engineering.
Nat Protoc
, 16, 5484-5532.
69. Sun, W., Gregory, D.A., Tomeh, M.A. and Zhao, X. (2021) Silk fibroin as a functional
biomaterial for tissue engineering.
Int J Mol Sci
, 22, 1499.
70. Diamantides, N., Dugopolski, C., Blahut, E., Kennedy, S. and Bonassar, L.J. (2019) High
density cell seeding affects the rheology and printability of collagen bioinks.
Biofabrication
, 11, 045016.
71. Osidak, E.O., Karalkin, P.A., Osidak, M.S., Parfenov, V.A., Sivogrivov, D.E., Pereira, F.D.A.S.,
Gryadunova, A.A., Koudan, E.V., Khesuani, Y.D., Кasyanov, V.A.
et al.
(2019) Viscoll
collagen solution as a novel bioink for direct 3D bioprinting.
J Mater Sci-mater M
, 30,
31.
72. Sarrigiannidis, S.O., Rey, J.M., Dobre, O., González-García, C., Dalby, M.J. and Salmeron-
Sanchez, M. (2021) A tough act to follow: Collagen hydrogel modifications to improve
mechanical and growth factor loading capabilities.
Materials Today Bio
, 10, 100098.
73. Duarte Campos, D.F., Blaeser, A., Korsten, A., Neuss, S., Jäkel, J., Vogt, M. and Fischer, H.
(2015) The stiffness and structure of three-dimensional printed hydrogels direct the
differentiation of mesenchymal stromal cells toward adipogenic and osteogenic
lineages.
Tissue Eng Part A
, 21, 740-756.
74. Khodadadi Yazdi, M., Taghizadeh, A., Taghizadeh, M., Stadler, F.J., Farokhi, M.,
Mottaghitalab, F., Zarrintaj, P., Ramsey, J.D., Seidi, F., Saeb, M.R.
et al.
(2020) Agarose-
based biomaterials for advanced drug delivery.
J Control Release
, 326, 523-543.
75. Liu, Y., Wong, C.-W., Chang, S.-W. and Hsu, S.-h. (2021) An injectable, self-healing
phenol-functionalized chitosan hydrogel with fast gelling property and visible light-
crosslinking capability for 3D printing.
Acta Biomater
, 122, 211-219.
76. Paget, S. (1989) The distribution of secondary growths in cancer of the breast. 1889.
Cancer Metastasis Rev
, 8, 98-101.
77. Hinshaw, D.C. and Shevde, L.A. (2019) The Tumor Microenvironment Innately Modulates
Cancer Progression.
Cancer Res
, 79, 4557-4566.
78. Pietras, K. and Ostman, A. (2010) Hallmarks of cancer: interactions with the tumor
stroma.
Exp Cell Res
, 316, 1324-1331.
79. Baghban, R., Roshangar, L., Jahanban-Esfahlan, R., Seidi, K., Ebrahimi-Kalan, A.,
Jaymand, M., Kolahian, S., Javaheri, T. and Zare, P. (2020) Tumor microenvironment
complexity and therapeutic implications at a glance.
Cell Commun Signal
, 18, 59.
80. Forster, J.C., Harriss-Phillips, W.M., Douglass, M.J. and Bezak, E. (2017) A review of the
development of tumor vasculature and its effects on the tumor microenvironment.
Hypoxia (Auckl)
, 5, 21-32.
81. Nagy, J.A., Chang, S.H., Shih, S.C., Dvorak, A.M. and Dvorak, H.F. (2010) Heterogeneity of
the tumor vasculature.
Semin Thromb Hemost
, 36, 321-331.
82. Hashizume, H., Baluk, P., Morikawa, S., McLean, J.W., Thurston, G., Roberge, S., Jain, R.K.
and McDonald, D.M. (2000) Openings between defective endothelial cells explain tumor
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
84
vessel leakiness.
Am J Pathol
, 156, 1363-1380.
83. Dudley, A.C. (2012) Tumor endothelial cells.
Cold Spring Harb Perspect Med
, 2, a006536.
84. Chen, Daniel S. and Mellman, I. (2013) Oncology Meets Immunology: The Cancer-
Immunity Cycle.
Immunity
, 39, 1-10.
85. Khong, H.T. and Restifo, N.P. (2002) Natural selection of tumor variants in the
generation of "tumor escape" phenotypes.
Nat Immunol
, 3, 999-1005.
86. Locy, H., de Mey, S., de Mey, W., De Ridder, M., Thielemans, K. and Maenhout, S.K.
(2018) Immunomodulation of the Tumor Microenvironment: Turn Foe Into Friend.
Front
Immunol
, 9, 2909.
87. Lv, B., Wang, Y., Ma, D., Cheng, W., Liu, J., Yong, T., Chen, H. and Wang, C. (2022)
Immunotherapy: Reshape the Tumor Immune Microenvironment.
Front Immunol
, 13,
844142.
88. Tang, M., Tiwari, S.K., Agrawal, K., Tan, M., Dang, J., Tam, T., Tian, J., Wan, X.,
Schimelman, J., You, S.
et al.
(2021) Rapid 3D Bioprinting of Glioblastoma Model
Mimicking Native Biophysical Heterogeneity.
Small
, 17, e2006050.
89. Heinrich, M.A., Bansal, R., Lammers, T., Zhang, Y.S., Michel Schiffelers, R. and Prakash, J.
(2019) 3D-Bioprinted Mini-Brain: A Glioblastoma Model to Study Cellular Interactions
and Therapeutics.
Adv Mater
, 31, 1806590.
90. Dey, M., Ayan, B., Yurieva, M., Unutmaz, D. and Ozbolat, I.T. (2021) Studying Tumor
Angiogenesis and Cancer Invasion in a Three-Dimensional Vascularized Breast Cancer
Micro-Environment.
Adv Biol (Weinh)
, 5, e2100090.
91. Jiang, T., Munguia-Lopez, J., Flores-Torres, S., Grant, J., Vijayakumar, S., Leon-Rodriguez,
A.D. and Kinsella, J.M. (2018) Bioprintable Alginate/Gelatin Hydrogel 3D
In Vitro
Model
Systems Induce Cell Spheroid Formation.
JoVE
, e57826.
92. Hagenbuchner, J., Nothdurfter, D. and Ausserlechner, Michael J. (2021) 3D bioprinting:
novel approaches for engineering complex human tissue equivalents and drug testing.
Essays Biochem
, 65, 417-427.
93. Wegner, M., Frenzel, T., Krause, D. and Gargioni, E. (2023) Development and
characterization of modular mouse phantoms for end-to-end testing and training in
radiobiology experiments.
Phys. Med. Biol.
, 68, 085009.
94. Chen, M., Jiang, Y., Cai, X., Lu, X. and Chao, H. (2021) Combination of Gemcitabine and
Thymosin alpha 1 exhibit a better anti-tumor effect on nasal natural killer/T-cell
lymphoma.
Int Immunopharmacol
, 98, 107829.
95. Al-Zeer, M.A., Prehn, F., Fiedler, S., Lienert, U., Krisch, M., Berg, J., Kurreck, J.,
Hildebrandt, G. and Schültke, E. (2022) Evaluating the Suitability of 3D Bioprinted
Samples for Experimental Radiotherapy: A Pilot Study.
Int J Mol Sci
, 23, 9951.
96. Hauth, F., Toulany, M., Zips, D. and Menegakis, A. (2017) Cell-line dependent effects of
hypoxia prior to irradiation in squamous cell carcinoma lines.
Clin Transl Radiat Oncol
, 5,
12-19.
97. Huang, S.H., Liu, P., Mokasdar, A. and Hou, L. (2013) Additive manufacturing and its
societal impact: a literature review.
J. Adv. Manuf. Technol.
, 67, 1191-1203.
98. Germain, N., Dhayer, M., Dekiouk, S. and Marchetti, P. (2022) Current Advances in 3D
Bioprinting for Cancer Modeling and Personalized Medicine.
Int J Mol Sci
, 23.
99. Belhouideg, S. (2020) Impact of 3D printed medical equipment on the management of
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
85
the Covid19 pandemic.
Int. J. Health Plan. Manag.
35, 1014-1022.
100. Tino, R., Moore, R., Antoline, S., Ravi, P., Wake, N., Ionita, C.N., Morris, J.M., Decker, S.J.,
Sheikh, A., Rybicki, F.J.
et al.
(2020) COVID-19 and the role of 3D printing in medicine.
3D Printing in Medicine
, 6, 11.
101. Weinhart, M., Hocke, A., Hippenstiel, S., Kurreck, J. and Hedtrich, S. (2019) 3D organ
models-Revolution in pharmacological research?
Pharmacol Res
, 139, 446-451.
102. Maltman, D.J. and Przyborski, S.A. (2010) Developments in three-dimensional cell
culture technology aimed at improving the accuracy of
in vitro
analyses.
Biochem Soc
Trans
, 38, 1072-1075.
103. Eble, J.A. and Niland, S. (2019) The extracellular matrix in tumor progression and
metastasis.
Clin Exp Metastasis
, 36, 171-198.
104. Langhans, S.A. (2018) Three-Dimensional
in Vitro
Cell Culture Models in Drug Discovery
and Drug Repositioning.
Front Pharmacol
, 9, 6.
105. Shukla, P., Yeleswarapu, S., Heinrich, M.A., Prakash, J. and Pati, F. (2022) Mimicking
tumor microenvironment by 3D bioprinting: 3D cancer modeling.
Biofabrication
, 14,
032002.
106. Edmondson, R., Broglie, J.J., Adcock, A.F. and Yang, L. (2014) Three-dimensional cell
culture systems and their applications in drug discovery and cell-based biosensors.
Assay Drug Dev Technol
, 12, 207-218.
107. Takeda, M., Miyagawa, S., Fukushima, S., Saito, A., Ito, E., Harada, A., Matsuura, R.,
Iseoka, H., Sougawa, N., Mochizuki-Oda, N.
et al.
(2018) Development of
In Vitro
Drug-
Induced Cardiotoxicity Assay by Using Three-Dimensional Cardiac Tissues Derived from
Human Induced Pluripotent Stem Cells.
Tissue Eng Part C Methods
, 24, 56-67.
108. Liang, J., Susan Sun, X., Yang, Z. and Cao, S. (2017) Anticancer Drug Camptothecin Test
in 3D Hydrogel Networks with HeLa cells.
Sci Rep-uk
, 7, 37626.
109. Tasdemir, N., Bossart, E.A., Li, Z., Zhu, L., Sikora, M.J., Levine, K.M., Jacobsen, B.M., Tseng,
G.C., Davidson, N.E. and Oesterreich, S. (2018) Comprehensive Phenotypic
Characterization of Human Invasive Lobular Carcinoma Cell Lines in 2D and 3D Cultures.
Cancer Res
, 78, 6209-6222.
110. Yamada, K.M., Collins, J.W., Cruz Walma, D.A., Doyle, A.D., Morales, S.G., Lu, J.,
Matsumoto, K., Nazari, S.S., Sekiguchi, R., Shinsato, Y.
et al.
(2019) Extracellular matrix
dynamics in cell migration, invasion and tissue morphogenesis.
Int J Exp Pathol
, 100,
144-152.
111. Rolver, M.G., Elingaard-Larsen, L.O. and Pedersen, S.F. (2019) Assessing Cell Viability and
Death in 3D Spheroid Cultures of Cancer Cells.
J Vis Exp
.
112. Fang, Y. and Eglen, R.M. (2017) Three-Dimensional Cell Cultures in Drug Discovery and
Development.
SLAS Discov
, 22, 456-472.
113. Loessner, D., Stok, K.S., Lutolf, M.P., Hutmacher, D.W., Clements, J.A. and Rizzi, S.C.
(2010) Bioengineered 3D platform to explore cell-ECM interactions and drug resistance
of epithelial ovarian cancer cells.
Biomaterials
, 31, 8494-8506.
114. Imamura, Y., Mukohara, T., Shimono, Y., Funakoshi, Y., Chayahara, N., Toyoda, M., Kiyota,
N., Takao, S., Kono, S., Nakatsura, T.
et al.
(2015) Comparison of 2D- and 3D-culture
models as drug-testing platforms in breast cancer.
Oncol Rep
, 33, 1837-1843.
115. Lovitt, C.J., Shelper, T.B. and Avery, V.M. (2018) Doxorubicin resistance in breast cancer
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
86
cells is mediated by extracellular matrix proteins.
BMC Cancer
, 18, 41.
116. Fang, J.Y., Tan, S.J., Wu, Y.C., Yang, Z., Hoang, B.X. and Han, B. (2016) From competency
to dormancy: a 3D model to study cancer cells and drug responsiveness.
J Transl Med
,
14, 38.
117. Voissiere, A., Jouberton, E., Maubert, E., Degoul, F., Peyrode, C., Chezal, J.M. and Miot-
Noirault, É. (2017) Development and characterization of a human three-dimensional
chondrosarcoma culture for
in vitro
drug testing.
PLoS One
, 12, e0181340.
118. Candini, O., Grisendi, G., Foppiani, E.M., Brogli, M., Aramini, B., Masciale, V., Spano, C.,
Petrachi, T., Veronesi, E., Conte, P.
et al.
(2019) A Novel 3D
In Vitro
Platform for Pre-
Clinical Investigations in Drug Testing, Gene Therapy, and Immuno-oncology.
Sci Rep-
uk
, 9, 7154.
119. Collins, S.D., Yuen, G., Tu, T., Budzinska, M.A., Spring, K., Bryant, K. and Shackel, N.A.
(2019) In Tirnitz-Parker, J. E. E. (ed.),
Hepatocellular Carcinoma
. Codon Publications
Copyright: The Authors., Brisbane (AU).
120. Bailey, J., Thew, M. and Balls, M. (2014) An analysis of the use of animal models in
predicting human toxicology and drug safety.
Altern Lab Anim
, 42, 181-199.
121. Van Norman, G.A. (2019) Limitations of Animal Studies for Predicting Toxicity in Clinical
Trials: Is it Time to Rethink Our Current Approach?
JACC Basic Transl Sci
, 4, 845-854.
122. Moffat, J.G., Vincent, F., Lee, J.A., Eder, J. and Prunotto, M. (2017) Opportunities and
challenges in phenotypic drug discovery: an industry perspective.
Nat Rev Drug Discov
,
16, 531-543.
123. Germain, N., Dhayer, M., Dekiouk, S. and Marchetti, P. (2022) Current Advances in 3D
Bioprinting for Cancer Modeling and Personalized Medicine.
Int J Mol Sci
, 23, 3432.
124. Assad, H., Assad, A. and Kumar, A. (2023) Recent Developments in 3D Bio-Printing and
Its Biomedical Applications.
Pharmaceutics
, 15, 255.
125. Nothdurfter, D., Ploner, C., Coraça-Huber, D.C., Wilflingseder, D., Müller, T., Hermann,
M., Hagenbuchner, J. and Ausserlechner, M.J. (2022) 3D bioprinted, vascularized
neuroblastoma tumor environment in fluidic chip devices for precision medicine drug
testing.
Biofabrication
, 14, 035002.
126. Gebeyehu, A., Surapaneni, S.K., Huang, J., Mondal, A., Wang, V.Z., Haruna, N.F., Bagde,
A., Arthur, P., Kutlehria, S., Patel, N.
et al.
(2021) Polysaccharide hydrogel based 3D
printed tumor models for chemotherapeutic drug screening.
Sci Rep
, 11, 372.
127. You, S., Xiang, Y., Hwang, H.H., Berry, D.B., Kiratitanaporn, W., Guan, J., Yao, E., Tang, M.,
Zhong, Z., Ma, X.
et al.
(2023) High cell density and high-resolution 3D bioprinting for
fabricating vascularized tissues.
Sci Adv
, 9, eade7923.
128. Terutsuki, D., Mitsuno, H. and Kanzaki, R. (2020) 3D-Printed Bubble-Free Perfusion
Cartridge System for Live-Cell Imaging.
Sensors (Basel)
, 20.
129. Xu, Z.R., Yang, C.G., Liu, C.H., Zhou, Z., Fang, J. and Wang, J.H. (2010) An osmotic micro-
pump integrated on a microfluidic chip for perfusion cell culture.
Talanta
, 80, 1088-
1093.
130. Sui, X., Kong, N., Ye, L., Han, W., Zhou, J., Zhang, Q., He, C. and Pan, H. (2014) p38 and
JNK MAPK pathways control the balance of apoptosis and autophagy in response to
chemotherapeutic agents.
Cancer Lett
, 344, 174-179.
131. Asselin, E., Mills, G.B. and Tsang, B.K. (2001) XIAP regulates Akt activity and caspase-3-
3D Bioprinted Lung Cancer Models for Biomedical Research Yikun Mei
87
dependent cleavage during cisplatin-induced apoptosis in human ovarian epithelial
cancer cells.
Cancer Res
, 61, 1862-1868.
132. Nirmalanandhan, V.S., Duren, A., Hendricks, P., Vielhauer, G. and Sittampalam, G.S.
(2010) Activity of anticancer agents in a three-dimensional cell culture model.
Assay
Drug Dev Technol
, 8, 581-590.
133. Wu, D., Berg, J., Arlt, B., Röhrs, V., Al-Zeer, M.A., Deubzer, H.E. and Kurreck, J. (2022)
Bioprinted Cancer Model of Neuroblastoma in a Renal Microenvironment as an
Efficiently Applicable Drug Testing Platform.
Int J Mol Sci
, 23, 122.
134. Melissaridou, S., Wiechec, E., Magan, M., Jain, M.V., Chung, M.K., Farnebo, L. and
Roberg, K. (2019) The effect of 2D and 3D cell cultures on treatment response, EMT
profile and stem cell features in head and neck cancer.
Cancer Cell Int
, 19, 16.
135. Sabetta, S., Vecchiotti, D., Clementi, L., Di Vito Nolfi, M., Zazzeroni, F. and Angelucci, A.
(2023) Comparative Analysis of Dasatinib Effect between 2D and 3D Tumor Cell
Cultures.
Pharmaceutics
, 15, 372.
136. Danesi, R., Altavilla, G., Giovannetti, E. and Rosell, R. (2009) Pharmacogenomics of
gemcitabine in non-small-cell lung cancer and other solid tumors.
Pharmacogenomics
,
10, 69-80.
137. Wegner, M., Frenzel, T., Krause, D. and Gargioni, E. (2023) Development and
characterization of modular mouse phantoms for end-to-end testing and training in
radiobiology experiments.
Phys Med Biol
, 68.
138. Ng, W.L., Ayi, T.C., Liu, Y.C., Sing, S.L., Yeong, W.Y. and Tan, B.H. (2021) Fabrication and
Characterization of 3D Bioprinted Triple-layered Human Alveolar Lung Models.
Int J
Bioprint
, 7, 332.
139. Wu, D., Pang, S., Berg, J., Mei, Y., Ali, A.S.M., Röhrs, V., Tolksdorf, B., Hagenbuchner, J.,
Ausserlechner, M.J., Deubzer, H.E.
et al.
(2024) Bioprinting of Perfusable Vascularized
Organ Models for Drug Development via Sacrificial-Free Direct Ink Writing.
Adv Funct
Mater
, 34, 2314171.
140. Kolesky, D.B., Homan, K.A., Skylar-Scott, M.A. and Lewis, J.A. (2016) Three-dimensional
bioprinting of thick vascularized tissues.
P Natl Acad Sci Usa
, 113, 3179-3184.
141. Gold, K.A., Saha, B., Rajeeva Pandian, N.K., Walther, B.K., Palma, J.A., Jo, J., Cooke, J.P.,
Jain, A. and Gaharwar, A.K. (2021) 3D Bioprinted Multicellular Vascular Models.
Adv
Healthc Mater
, 10, e2101141.
142. Beckers, C., Pruschy, M. and Vetrugno, I. (2024) Tumor hypoxia and radiotherapy: A
major driver of resistance even for novel radiotherapy modalities.
Semin Cancer Biol
, 98,
19-30.
143. Mitrakas, A.G., Giatromanolaki, A. and Koukourakis, M.I. (2023) Hypoxia and acidity
regulate immune checkpoint molecule and IFN-β expression in non-small cell lung
cancer cell lines.
J Recept Signal Transduct Res
, 43, 31-36.
