Characterizing the Invasive Tumor Front of Aggressive Uterine Adenocarcinoma and Leiomyosarcoma

fcell-09-670185 June 2, 2021 Time: 13:49 # 1 ORIGINAL RESEARCH published: 03 June 2021 doi: 10.3389/fcell.2021.670185 Edited by: Dong Han, National Center for Nanoscience and Technology (CAS), China Reviewed by: Hisham F. Bahmad, Mount Sinai Medical Center, United States Orest William Blaschuk, McGill University, Canada *Correspondence: Xavier Matias-Guiu [email protected] Rosa Noguera [email protected] †These authors have contributed equally to this work Specialty section: This article was submitted to Cell Adhesion and Migration, a section of the journal Frontiers in Cell and Developmental Biology Received: 20 February 2021 Accepted: 12 April 2021 Published: 03 June 2021 Citation: Sanegre S, Eritja N, de Andrea C, Diaz-Martin J, Diaz-Lagares Á, Jácome MA, Salguero-Aranda C, García Ros D, Davidson B, Lopez R, Melero I, Navarro S, Ramon y Cajal S, de Alava E, Matias-Guiu X and Noguera R (2021) Characterizing the Invasive Tumor Front of Aggressive Uterine Adenocarcinoma and Leiomyosarcoma. Front. Cell Dev. Biol. 9:670185. doi: 10.3389/fcell.2021.670185 Characterizing the Invasive Tumor Front of Aggressive Uterine Adenocarcinoma and Leiomyosarcoma Sabina Sanegre1,2, Núria Eritja1,3, Carlos de Andrea1,4, Juan Diaz-Martin1,5, Ángel Diaz-Lagares1,6, María Amalia Jácome7, Carmen Salguero-Aranda1,5, David García Ros4, Ben Davidson8,9, Rafel Lopez1,10,11, Ignacio Melero1,4, Samuel Navarro1,2, Santiago Ramon y Cajal1,12, Enrique de Alava1,5, Xavier Matias-Guiu1,3*†and Rosa Noguera1,2*† 1Cancer CIBER (CIBERONC), Madrid, Spain, 2Department of Pathology, School of Medical, University of Valencia-INCLIVA, Valencia, Spain, 3Institut de Recerca Biomèdica de LLeida (IRBLLEIDA), Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Department of Pathology, Hospital U Arnau de Vilanova and Hospital U de Bellvitge, University of Lleida - University of Barcelona, Barcelona, Spain, 4Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain, 5Institute of Biomedicine of Sevilla, Virgen del Rocio University Hospital/CSIC/University of Sevilla/CIBERONC, Seville, Spain, 6Cancer Epigenomics, Translational Medical Oncology Group (Oncomet), Health Research Institute of Santiago (IDIS), University Clinical Hospital of Santiago (CHUS/SERGAS), Santiago de Compostela, Spain, 7Department of Mathematics, MODES Group, CITIC, Faculty of Science, Universidade da Coruña, A Coruña, Spain, 8Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 9Department of Pathology, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway, 10 Translational Medical Oncology Group (Oncomet), Health Research Institute of Santiago (IDIS), University Clinical Hospital of Santiago (CHUS/SERGAS), Santiago de Compostela, Spain, 11 Roche-Chus Joint Unit, Translational Medical Oncology Group (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain, 12 Department of Pathology, Vall d’Hebron University Hospital, Autonomous University of Barcelona, Barcelona, Spain The invasive tumor front (the tumor–host interface) is vitally important in malignant cell progression and metastasis. Tumor cell interactions with resident and infiltrating host cells and with the surrounding extracellular matrix and secreted factors ultimately determine the fate of the tumor. Herein we focus on the invasive tumor front, making an in-depth characterization of reticular fiber scaffolding, infiltrating immune cells, gene expression, and epigenetic profiles of classified aggressive primary uterine adenocarcinomas (24 patients) and leiomyosarcomas (11 patients). Sections of formalinfixed samples before and after microdissection were scanned and studied. Reticular fiber architecture and immune cell infiltration were analyzed by automatized algorithms in colocalized regions of interest. Despite morphometric resemblance between reticular fibers and high presence of macrophages, we found some variance in other immune cell populations and distinctive gene expression and cell adhesion-related methylation signatures. Although no evident overall differences in immune response were detected at the gene expression and methylation level, impaired antimicrobial humoral response might be involved in uterine leiomyosarcoma spread. Similarities found at the invasive tumor front of uterine adenocarcinomas and leiomyosarcomas could facilitate the use of common biomarkers and therapies. Furthermore, molecular and architectural characterization of the invasive front of uterine malignancies may provide additional prognostic information beyond established prognostic factors. Keywords: tumor-host interface, tumor microenvironment, extracellular matrix, reticular fibers, immune cells, gene expression, epigenetic profiles Frontiers in Cell and Developmental Biology | www.frontiersin.org 1June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 2 Sanegre et al. Uterine-Myometrial Tumor Interface INTRODUCTION Uterine endometrial adenocarcinoma (uADC) is the fourth most common cancer among women in the Western world, with an estimated incidence of 10–20 per 100,000 women. Although prognosis is favorable for patients identified with low-grade tumors and early-stage disease, outcomes for patients with highgrade and metastatic/recurrent tumors remain poor (Ferlay et al., 2010) and represent a therapeutic challenge. The World Health Organization (WHO) tumor classification distinguishes several histopathological types of uADC, particularly based on microscopic appearance (Cree et al., 2020): (1) endometrioid carcinoma, low grade (grades I–II) or high grade (grade III), (2) serous carcinoma, (3) clear cell carcinoma, (4) mixed carcinoma, (5) undifferentiated carcinoma, (6) carcinosarcoma, (7) neuroendocrine carcinomas, and (8) other unusual types. These types have different histological and molecular features, precursor lesions, and natural history (Matias-guiu et al., 2001; Yeramian et al., 2013). They are also stratified by Tumor Cancer Genome Atlas (TCGA)-based molecular classification into four risk groups combining gene encoding DNA polymerase ε(POLE) mutational analysis with IHC analysis of p53 and mismatch repair (MMR) proteins (PMS-2 and MSH-6) (Getz et al., 2013). This provides additional prognostic information to complement the microscopic features. Uterine leiomyosarcoma (uLMS) accounts for 1% of all uterine malignancies, with an annual incidence rate of 0.4–0.9 per 100,000 women. Like other forms of sarcoma, uLMS spreads to other parts of the body via the bloodstream rather than the lymphatic system. Although rare, this cancer can be extremely aggressive and is known to be generally unresponsive to radiation or chemotherapy. Patient survival is highly dependent on speed of diagnosis and treatment, falling to 14% at 5 years post-diagnosis for metastatic uLMS. From a molecular viewpoint, uLMS contains complex karyotypes, with numerous chromosomal aberrations and frequent deletions affecting chromosomal arms 2p, 2q, 10q, and 13q, as well as amplifications on 1p, 5q, and 8q, and precise alterations in TP53, RB1, PTEN, MED12, YWHAE, and VIPR2 (Cuppens et al., 2018). There is growing interest in understanding the molecular features involved in myometrial invasion, a highly valuable parameter due to its direct association with poor prognosis and limited therapeutic response in uADC and uLMS. From a histopathological perspective, a variety of invasion patterns frequently coexist in different areas of the same tumor (Euscher et al., 2013). Moreover, several tumor types show specific invasion patterns not seen in other types of cancer. These differences can be explained by the fact that cancer cell invasion is currently viewed as an adaptive and heterogeneous process (Friedl and Alexander, 2011) involving key processes such as cytoskeleton dynamics, cell adhesion plasticity, and mechanotransduction of external stimuli. Type-specific new stroma generated at the site of active tumor invasion, the invasive tumor front (ITF), is crucial in tumor growth and invasion processes (Provenzano et al., 2006; Giatromanolaki et al., 2007). In fact, high proliferation rates and cell cycle derailment have been shown at the ITF in uADC (Horrée et al., 2007). Structures surrounding tumors such as mature collagen and smooth or striated muscle can act as a barrier to tumor invasion, as shown by collagen organization at the ITF of oral squamous cell carcinomas (Devendra et al., 2018). However, tumor cells can disrupt the continuity of such structures by remodeling the immediate stroma of the tumor to carve out paths for invasion. Indeed, invasive tumor growth in pancreatic ductal adenocarcinoma is mediated via matrix remodeling metalloproteinases (Croft et al., 2004;Rath et al., 2017). Changes in the fibrillar pattern of adjacent stroma of tumor tissues have been also observed in skin tumor (Stenbäck et al., 1999), oral squamous cell carcinoma, and lymph nodes with metastasis, among others (Yinti et al., 2015;Kardam et al., 2016). In fact, the tumor-derived extracellular matrix (ECM) is biochemically distinct in its composition and is stiffer than normal ECM. This new associated ECM compartment, rich in cross-linked collagen III (reticular fibers), has been proposed as a prospective marker of early stromal invasion in incipient tumors such as breast cancer (Sivridis et al., 2004;Acerbi et al., 2015). In addition, not only the ECM collagen composition but also the orientation of the fibers have been proposed as a prognostic signature for survival in breast cancer (Conklin et al., 2011; Xi et al., 2021). The complex interaction between heterogenic immune cell subpopulations and tumor cells may drive tumor progression, metastasis, and resistance to therapy (Galli et al., 2020). The intensifying development of immunotherapeutic strategies calls for a better understanding of tumor-immune subpopulation interactions and spatial distribution at the ITF (Blomberg et al., 2018;König et al., 2019). In this context, the use of a multiplex immunolabeling panel is essential as it enables different cell subpopulations to be identified in one tissue section (Gorris et al., 2018). Multiplexed analysis can also simultaneously measure the expression of distinct markers in a single cell as well as spatial associations between immune cell subpopulations. We applied this technology to evaluate the complex immune environment of the ITF of uADC and uLMS. A previously developed and validated multiplex immunolabeling panel was used to simultaneously assess the phagocytic cell marker CD68 of macrophages, CD3 +and CD8 +T cells, and CD20 +B lymphocytes in a single FFPE tissue (Abengozar-Muela et al., 2020;Salas-Benito et al., 2021). Changes in activated-leukocyte cell adhesion molecule (ALCAM) expression at the endometrial tumoral cell surface (Devis et al., 2018) and increased expression of cytoplasmic Cyclin D1 (Fusté et al., 2016) have been reported to allow dissemination and invasion of endometrial neoplastic cells. Epigenetic mechanisms play an important role in regulating gene expression during many biological processes (Sharma et al., 2009). DNA methylation is the most widely studied epigenetic modification, produced by adding a methyl group (CH3) to the 5’ carbon of cytosines in cytosine–phosphate–guanine (CpG) dinucleotides to generate 5-methylcytosine (5mC) (Bao-Caamano et al., 2020). Deregulation of this epigenetic mechanism has major implications for cancer development and progression (Diaz-Lagares et al., 2016). In this context, recent genome-wide analyses have revealed striking alterations in the Frontiers in Cell and Developmental Biology | www.frontiersin.org 2June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 3 Sanegre et al. Uterine-Myometrial Tumor Interface methylation profile of uterine uADC and uLMS (Zhang et al., 2014;Kommoss et al., 2020;Vargas et al., 2021). Cancers show a clear coevolution between tumor cells and the tumor microenvironment. Specific differences in the tumor microenvironment at different locations may play a role in tumor growth, metastatic progression, and therapy responses (Oliver et al., 2018;Zhang and Yu, 2019). In fact, tumor invasion is a dynamic process facilitated by bidirectional interactions between tumor cells and the microenvironment, being particularly intense at the ITF. Unlike tumor cells, tumor microenvironment elements are genetically stable and thus represent an attractive therapeutic target. Uterine uLMS and uADC are different tumor types occurring in the same organ, both of which infiltrate the myometrium during local progression. The main objective of the present study is to analyze several aspects of microenvironment response at the ITF in these two tumor types, looking for similarities and differences that could provide insight into potential new common therapeutic approaches, an aim that to our best knowledge has not previously been addressed. MATERIALS AND METHODS Patients, Sample Description, and Case Selection A search for uADC and uLMS cases was conducted in the study institutions. Selection criteria included available pathology reports, representative sections of the ITF, histologically proven distant metastasis, and acceptable pre-analytical conditions. Cases were reviewed by a panel of expert gynecological pathologists from the institutions involved. In total, 24 uADC and 11 uLMS fulfilled all criteria and were included in the study. The study used formalin-fixed and paraffin-embedded (FFPE) tissue samples from 24 uADC and 11 uLMS obtained from five Spanish hospitals (Hospital Clínico de Valencia; Hospital Virgen del Rocio, Seville; Hospital Universitari Vall d’Hebron and Hospital Universitari de Bellvitge, Barcelona; Hospital Universitari Arnau de Vilanova, Lleida) and the Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway. Tumors were classified following the most recent WHO criteria and were surgically staged and graded according to the FIGO (International Federation of Gynecology and Obstetrics) staging and grading systems. The study was approved by the local research ethics committee, and specific informed consent was obtained. Whole slide FFPE tissue sections of 5 µm of selected uADC and uLMS were stained with H&E (hematoxylin and eosin) and examined by the centralized expert group of pathologists to select the representative areas to include in the study. The interface between tumor tissue and adjacent myometrium was microdissected under the microscope. Microscopic images were obtained using a digital slide scanner [Pannoramic 250 FLASH II 2.0 (3D Histec)]. All uADC were of endometrioid type and were classified according to the Cancer Genome Atlas (TCGA) surrogate (0% POLE mutated, 58.5% non-specific molecular profile, 29% mismatch repair-deficient tumors, and 12.5% p53 abnormal). All uLMS were conventional-type high-grade tumors. Selection of Regions of Interest Sections of FFPE tissue samples were scanned before and after microdissection. Whole sections including the interface between tumor tissue and adjacent myometrium areas (the ITF) were used for morphometric analysis. ITF microdissected tissue was employed for transcriptomic and epigenomic studies (Supplementary Figure 1A). The amount of ITF microdissected tissue varied from case to case, but the median width was 5 mm (±1.66 mm) and median length 15 mm (±4.91 mm). The amount of tumor vs. myometrium is shown in Supplementary Table 1. Serial uADC and uLMS whole slides were used for histomorphometric analysis of reticular fibers and multiplex immunofluorescence-based immune profiling. ITF regions were identified in stained tissue (Gomori and multiplex immunofluorescence) by extrapolation of previous H&Eselected regions. A region of interest (ROI) of 5 ×4 mm for each sample was identified within the ITF-stained area. These 5×4-mm regions were used to correlate the results between genomic, epigenetic, and morphometric studies (Supplementary Figure 1A). To achieve in-depth characterization of the reticular fibers and immune infiltrate of the ITF, we further broke down the 5 ×4-mm ROIs into 1-mm2ROIs representing the following categories: (a) tumor (uADC or uLMS with absence of myometrium), (b) myometrium (excluding infiltrated myometrium from the analysis), and (c) balanced representation of the invasion front (containing 50% tumor and 50% myometrium). Supplementary Figure 1 shows a schematic representation of the procedure. Cases and ROIs where the algorithm failed because of poor or excessive staining were excluded from the analysis, as were samples with unsatisfactory segmentation. The number of 5 ×4-mm and 1-mm2ROIs of each case in Gomori and multiplex immunofluorescence stained tissue are shown in Supplementary Table 1. Histomorphometric Analysis of Reticular Fibers The architecture of uADC and uLMS reticular fibers stained using Gomori’s method was uncovered. All samples were digitized with the whole-slide scanner Ventana iScan HT (Roche) at 20 ×with a resolution of 0.46 µm/pixel. We used the opensource digital pathology software QuPath for sample visualization and identification of ROIs (Bankhead et al., 2017). 5 ×4-mm and 1-mm2ROIs were exported to ImageJ (Schneider et al., 2012), and these were saved as TIFF for image analysis. In this study, we employed an advanced morphometric methodology based on a probabilistic method for the automatic segmentation of reticular fibers. We used Gomoripath, a morphometric tool for easy segmentation of reticular fibers developed by researchers from Incliva Biomedical Research Institute/University of Valencia, University of Castilla La Mancha, and the Andalucía Public Health System (EspinosaAranda et al., 2016). Briefly, the algorithm is based on nominal logistic regression applied to the optical density of Frontiers in Cell and Developmental Biology | www.frontiersin.org 3June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 4 Sanegre et al. Uterine-Myometrial Tumor Interface the histopathological images. The optical density is calculated iteratively, allowing reticular fibers to be enhanced. Subsequently, a logit model is used to calculate the probability of each pixel belonging to the structure of reticular fibers depending on their topology, thus creating a probability map for the entire image. The ROC (Receiver Operating Characteristic) curve generated by the model finally obtained an AUC (area under the ROC curve) of 0.9563 in reticular fiber detection. Fifteen morphometric parameters defining the histological organization of reticular fibers were calculated for each fiber detected and the mean for each sample calculated. Morphometric parameters were extracted to characterize the size and shape of the morphometric variables at the ITF. In addition, the algorithm measured the stained area of the tissue analyzed (excluding holes and damaged tissue), allowing us to determine the number of fibers per mm2(density) and the percentage of fiber-stained area (%SA) (taking into account the sum of the areas of all fibers). Morphometric parameters defining the histological organization of the reticular fiber networks have been explained elsewhere (Tadeo et al., 2016). The mean of each parameter of similar uADC and uLMS ROIs was calculated for comparison (Figures 1–3). Multiplex Immunofluorescence Cell Phenotype We next sought to investigate the uADC and uLMS myeloid and lymphocytic contexture in the FFPE tissue samples. A multiplex immunofluorescence panel was used to enable simultaneous examination of several cellular markers, including the phagocytic cell marker CD68 of macrophages, CD3 +and CD8 +T cells, and CD20 +B lymphocytes. Multiplex immunofluorescence development and validation workflow and protocols were implemented as previously described (Schalper et al., 2019;Abengozar-Muela et al., 2020;Salas-Benito et al., 2021). Briefly, 5-µm sections of FFPE tissue were deparaffinized and antigen retrieval was performed using DAKO PT-Link heat-induced antigen retrieval with low pH (pH 6) or high pH (pH 9) target retrieval solution (DAKO). Each tissue section was subjected to five successive rounds of antibody staining, each round consisting of protein blocking with antibody diluent/block (Akoya Biosciences ARD1001EA) and incubation with a primary antibody, Opal Polymer anti-mouse/rabbit HRP (Akoya Biosciences ARH1001EA), followed by tyramide signal amplification (TSA) with Opal fluorophores (Akoya Biosciences) diluted 1:100 in 1 ×plus amplification diluent (Akoya Biosciences FP1498). The myeloid and lymphoid cell panel included CD68 (Mouse monoclonal, clone PG-M1, ready-touse, Agilent IR613), CD3 (Rabbit polyclonal, IgG, ready-to-use, Agilent IR503), CD8 (Mouse monoclonal, clone C8/144B, readyto-use, Agilent IR623), CD20 (Mouse monoclonal, IgG2α, clone L26, ready-to-use, Roche 760-2531), and cytokeratin (Mouse monoclonal, clone AE1/AE3, diluted 1:100, Agilent M3515). Finally, in the last round, nuclei were counterstained with spectral DAPI (Akoya Biosciences FP1490) and sections mounted with Faramount Aqueous Mounting Medium (Dako S3025). Each whole-tissue section was scanned on a VectraPolaris Automated Quantitative Pathology Imaging System (Akoya Biosciences). Tissue imaging and spectral unmixing were performed using InForm software (version 2.4.8, Akoya Biosciences), as previously described (Abengozar-Muela et al., 2020;Salas-Benito et al., 2021). Image analysis was performed on 5 ×4-mm and 1-mm2ROIs using the open-source digital pathology software QuPath version 0.2.3, as previously described (Abengozar-Muela et al., 2020). In short, cell segmentation based on nuclear detection was performed on QuPath using the StarDist 2D algorithm, a method that localizes nuclei via star-convex polygons, incorporated into QuPath software by scripting. A random-tree algorithm classifier was trained separately for each cell marker by an experienced pathologist annotating the tumor regions. Interactive feedback on cell classification performance is provided during training in the form of mark-up image, significantly improving the accuracy of machine learning-based phenotyping (Bankhead et al., 2017; Abengozar-Muela et al., 2020). All phenotyping and subsequent quantifications were performed blinded to the sample identity. Cells close to the border of the images were removed to reduce the risk of artifacts. Based on the fluorescence panels, cells were further subclassified as CD68+, CD3+, CD8+, and CD20+. Cells negative for these markers were defined as other cell types. The mean of the frequency of each cell marker of similar uADC and uLMS ROIs was calculated for comparison (Figures 3,4). Transcriptomic Profiling Total RNA for gene expression assays was prepared from 5-µm FFPE tissue sections of microdissected ITF using the Agencourt FormaPure kit (A33341; Beckman Coulter, Indianapolis, IN, United States) and following the manufacturer’s instructions. The RNA concentration was determined with Qubit 4 Fluorometer and QubitR RNA HS Reagent (Thermo Fisher Scientific, Waltham, MA, United States). RNA samples passing the quality control evaluation (uADC n= 19, uLMS n= 11) were selected. Transcriptomic profiling was performed with HTG EdgeSeq Precision Immuno-Oncology Panel, which interrogates 1,392 genes involved in tumor/immune interaction1. HTG EdgeSeq Chemistry was employed to synthetize the RNA-Seq library. Briefly, target capture was performed by hybridizing the mRNA with Nuclease Protection Probes (NPPs). The S1 nuclease was added to the mix, producing a stoichiometric amount of target mRNA/NPP duplexes. This reaction was blocked by enzyme heat denaturation of S1. The samples were randomized before inclusion in the HTG EdgeSeq system to reduce potential biases in the run. Each hybridized sample was used as template to set up PCR reactions with specially designed tags, sharing common sequences that are complementary to both 50-end and 30-sequences of the probes, and common adaptors required for cluster generation on an Illumina sequencing platform. In addition, each tag contains a unique barcode used for sample identification and multiplexing. After PCR amplification, a cleanup procedure was performed using Agencourt AMPure XP (Beckman Coulter). The library was quantified by quantitative PCR, using KAPA Library Quantification (Roche), according to the manufacturer’s instructions. All samples and controls were quantified in triplicate, and no template control was 1https://www.htgmolecular.com/assets/htg/publications/GL-HTG-EdgeSeqPrecision-Immuno-Oncology-Panel-GeneListAnnotated_01.pdf Frontiers in Cell and Developmental Biology | www.frontiersin.org 4June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 5 Sanegre et al. Uterine-Myometrial Tumor Interface A BC FIGURE 1 | Histomorphometric features of reticular fibers in uterine adenocarcinomas (uADC) and leiomyosarcomas (uLMS) in the 5 ×4-mm region of interest (ROI) are very similar at the invasive tumor front (ITF). (A) Representative image of fiber segmentation in 5 ×4-mm ITF ROIs in uADC and uLMS. Reticular fibers are highlighted in red. Scale bar represents 1 mm. (B) Comparison of morphometric parameters obtained after reticular fiber segmentation between uADC and uLMS 5×4-mm ITF ROIs. Fisher–Snedecor test and Student T-test were applied for statistical comparison. Mean, standard deviation (SD), p-Values, and significance (∗<0.05) are shown. Total fibers, area of the core, and sum of the total area of the fibers were measured to calculate the density (number of fibers/mm2) and percentage of the stained area (% SA). Area in µm2. Length and width in µm. (C) The same comparison as in B with more restrictive criteria for case selection. included in any run. Library denaturation was performed by first adding 2N NaOH, followed by addition of 2N HCl. The PhiX was spiked in at 5% (concentration of 12.5 pM). Normalized libraries were sequenced by NGS. Four demultiplexed FASTQ file per sample was retrieved from the sequencer for data processing. HTG EdgeSeq host software Frontiers in Cell and Developmental Biology | www.frontiersin.org 5June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 6 Sanegre et al. Uterine-Myometrial Tumor Interface FIGURE 2 | In-depth invasive tumor front (ITF) histomorphometric characterization of reticular fibers in uterine adenocarcinomas (uADC) and leiomyosarcomas (uLMS) reveals a high degree of architectural similarity. Representative image of fiber segmentation in 1 ×1-mm tumor ROIs in uADC and uLMS. Comparison of morphometric parameters obtained after reticular fiber segmentation between uADC and uLMS in panels (A) Intratumor 1 ×1-mm ROIs. (B) 1×1-mm myometrium ROIs, and (C) 1×1-mm invasive tumor front (ITF) ROIs in uADC and uLMS. Reticular fibers are highlighted in red. Fisher–Snedecor test and Student T-test were applied for statistical comparison. Mean, standard deviation (SD), p-Values, and significance (∗<0.05, ∗∗ <0.01, ∗∗∗ <0.001) are shown. Total fibers, area of the core, and sum of the total area of fibers were measured to calculate the density (number of fibers/mm2) and percentage of stained area (% SA). Area in µm2. Length and width in µm. Scale bars represent 250 µm. FIGURE 3 | Distinct immune infiltrate distribution at the invasive tumor front (ITF) of uterine adenocarcinomas (uADC) and leiomyosarcomas (uLMS). (A) Representative multiplex immunofluorescence images of approximately 5 ×4-mm regions of interest (ROIs) of an adenocarcinoma (uADC) (4 ×4) and a leiomyosarcoma (uLMS) (5 ×3 mm). Color code as follows: DAPI (blue), CD20 (yellow), CD68 (green), CD8 (red), CD3 (orange), cytokeratin (cyan). (B) The mean of the densities (number of cells/mm2) for each cell marker is shown for uADC and uLMS as well as the mean for the total cells and the total immune infiltrate. Fisher–Snedecor test and Student T-test were applied for statistical comparison. P-values and significance (∗<0.05, ∗∗ <0.01) for statistical comparison between the means of uADC and uLMS are displayed. Scale bars represent 1 mm. performed the alignment of the FASTQ files to the probe list, then results were parsed, and the output obtained as a read count matrix. Raw count normalization and differential expression analysis were calculated using DESeq2 R package (1.30.0). Sample outliers were identified through variance stabilizing transformation. Frontiers in Cell and Developmental Biology | www.frontiersin.org 6June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 7 Sanegre et al. Uterine-Myometrial Tumor Interface FIGURE 4 | Immune infiltrate characterization in 1 ×1-mm regions of interest (ROIs) in uterine adenocarcinomas (uADC) and leiomyosarcomas (uLMS). (A) Representative multiplex images of 1 ×1-mm ROIs of intratumor, myometrium, and invasive tumor front (ITF) in uADC and uLMS. Color code as follows: DAPI (blue), CD20 (yellow), CD68 (green), CD8 (red), CD3 (orange), cytokeratin (cyan). (B) The mean of the densities (number of cells/mm2) for each cell marker in each region is shown for uADC and uLMS as well as the mean for the total cells and the total immune infiltrate. Fisher–Snedecor test and Student T-Test were applied for statistical comparison. P-values and significance (∗<0.05, ∗∗ <0.01, ∗∗∗ <0.001) for statistical comparison between the means of intratumor, myometrium, and ITF in uADC and uLMS are displayed. Scale bars represent 250 µm. iDEP v0.922was used for pathway analysis of normalized expression values from RNA-Seq data (Ge et al., 2018). Genome-Wide DNA Methylation Analysis Total genomic DNA from 10-µm FFPE tissue sections of microdissected ITF (uADC n= 24, uLMS n= 11) was isolated using the AllPrep DNA/RNA FFPE Kit (Qiagen) and following the manufacturer’s instructions. All DNA samples were quantified by the fluorometric method using the Qubit 1×dsDNA HS (High-Sensitivity) Assay Kit (Thermo Fisher) and were also checked for suitability for FFPE restoration following the Infinium HD FFPE QC Assay (Illumina). DNA samples (100–250 ng) that passed this quality control evaluation (uADC n= 22, uLMS n= 9) were selected for bisulfite conversion 2http://ge-lab.org/idep/ using the EZ DNA Methylation kit (Zymo Research) and were moved on to the FFPE Restore protocol (Illumina). The restoring step was followed by Infinium HD FFPE methylation assay for hybridization with Infinium MethylationEPIC BeadChips, which cover over 850,000 CpG sites along the human genome (Moran et al., 2016). Whole-genome amplification and hybridization were performed on the BeadChips followed by single-base extension and analysis on a HiScan (Illumina) to assess the cytosine methylation states. Image intensities were extracted using GenomeStudio (V2011.1) Methylation module (1.9.0) software from Illumina. Data quality control was assessed with GenomeStudio and BeadArray Controls Reporter, based on the internal control probes present on the array. The methylation score of each CpG from samples that passed this quality control (uADC n= 21, uLMS n= 9) was represented as β-value and previously normalized for color bias adjustment, background Frontiers in Cell and Developmental Biology | www.frontiersin.org 7June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 8 Sanegre et al. Uterine-Myometrial Tumor Interface level adjustment, and quantile normalization across arrays. Probes and sample filtering involved a two-step process for removing SNPs and unreliable β-values with a high detection P-value >0.01. After this filtering step, the remaining CpGs were considered valid for the study. Non-parametric Wilcoxon tests were applied to determine differentially methylated CpGs (DMCpGs), which were considered significant with a false discovery rate (FDR) below 5%. All statistical analyses were performed in the R statistical environment (v.3.6.1). The enrichment analysis of biological pathways for the methylation profiles were evaluated by gene ontology (GO) using GENECODIS (Tabas-Madrid et al., 2012). RESULTS Reticulin Fiber Scaffolding in uLMS and uADC Is Similar Several parameters of reticular fibers were assessed in 83.3% of uADC and 81.8% of uLMS (20 of 24 uADC and 9 of 11 uLMS) (Supplementary Table 1). Interestingly, we found a high degree of similarity between the 5 ×4 mm ROIs of uADC and uLMS ITFs (Figures 1A,B); in fact, the only significant differences observed in the cases studied were in area size and deformity of the individual or meshwork fibers. However, we next performed a more restrictive analysis, excluding one case of each group based not only on algorithm quality control but on subjective assessment by two independent scientists (Figure 1C). In this case, we analyzed a more homogeneous sample group and observed no significant size or shape-related differences between the two tumor types. Large uADC ROIs with reticular fibers occupying a higher proportion of stained area (higher %SA) than uLMS were detected. To test the robustness of the algorithm and assess the representativeness of the 1-mm2ROIs, we compared four ROIs of 1 mm2inside the 5 ×4 mm against four ROIs of 1 mm2 along the ITF for both uADC and uLMS. We found no significant differences between ROIs (data not shown) and therefore accepted the 1 ×1-mm areas as representative of the tumor, myometrium, and selected field of ITF. Only regions meeting the quality control parameters were included in the analysis. In total, we compared n= 21 vs. n= 34 tumor ROIs, n= 39 vs. n= 24 myometrium ROIs, and n= 79 vs. n= 35 selected ITF ROIs of uADC and uLMS, respectively (Supplementary Table 1). We employed the same morphometric feature extraction procedure as above, also performing the statistical Student test to compare the mean of each acquired parameter by patient sample. In the 1-mm2ITF ROIs, the two tumor types presented a high number of significantly different parameters (7/15) (Figure 2A). uADC fibers appeared larger (area = 363.2 µm2vs. 231.9 µm2) and thicker (width = 16.21 µm vs. 13.6 µm) than in uLMS. However, because uADC has significantly lower density of fibers per µm2(83.2 vs. 257.48 fibers/µm2) when compared to uLMS, a smaller%SA of the tissue (2.73% vs. 5.84%) was shown. Furthermore, uADC reticular fibers appeared smoother than the wavy ones in uLMS, as indicated by the perimeter ratio (0.62 vs. 0.68). The higher values for vertices in uADC compared to uLMS (6.04 vs. 5.56) suggest that reticular fibers have greater branching in uADC. Finally, the fractal dimension revealed that reticular fibers are more haphazardly arranged in uADC than in uLMS (1.44 vs. 1.40). On the other hand, the myometrium of uADC and uLMS displayed no significant differences in any of the parameters studied (Figure 2B). Interestingly, only one shape parameter (deformity) was significantly different between uADC vs. uLMS (1932.4 vs. 1345.7) (Figure 2C). This observation is a more reliable indication than the 5 ×4-mm ROI results of a high degree of similarity between the reticular fiber scaffolding at the ITF of uADC and uLMS. To further characterize uLMS and uADC, the tumor, myometrium, and selected ITF were compared against each other for each tumor type. In contrast to the great similarity between 1-mm2ROIs of different tissue areas in uLMS, we found that uADC exhibited multiple differences when ITF was compared against the tumor or myometrium (Table 1). There was higher deposition of fibers, indicated by increased fiber density, in ITF compared to tumor (307.10 vs. 83.28), leading to an increased %SA (7.44 vs. 2.73). Reticular fibers at the uADC also appeared thinner at ITF than in tumor (width = 13.03 vs. 16.22), were linearized (aspect = 4.20 vs. 3.42), and presented less branching (vertices = 5.68 vs. 6.04). Finally, when comparing 1-mm2ROIs of uADC ITF vs. myometrium, the fibers appeared bigger at the ITF (area = 300.56 vs. 270.29), were thicker (width = 13.03 vs. 11.71) and longer (height = 48.36 vs. 43.02), and therefore had a larger perimeter (88.25 vs. 72.83). In addition, the fibers at the uADC ITF appeared less linearized (aspect = 4.20 vs. 4.15) and branched (vertices = 5.68 vs. 5.54) (Table 1A). Interestingly, in uLMS the only significant difference when comparing both ITF and tumor vs. myometrium was fiber shape (shape ITF = 16.76 and tumor 21.05 vs. myometrium 11.04) (Table 1B). Heterogeneous Immune Environment at the ITF In order to characterize immune infiltrate at the ITF, we used six-color multiplex immunostaining (CD20, CD3, CD8, CD68, CK, and DAPI) to estimate four different immune infiltrate subpopulations in the same 5 ×4 mm ROIs of uADCs and uLMS, used for reticular fiber analysis in sequential cuts in large areas of uADCs and uLMS. The immune infiltrate was assessed in 95.8% of uADC and in 81% of uLMS (23 out of 24 and 9 out of 11, respectively) (Supplementary Table 1). Overall, approximately 1,595,232 cells were counted and evaluated by digital pathology. Although the uLMS had a lower number of total cells, the two tumor types displayed the same amount of total immune infiltrate (956 for uADC and 991 for uLMS) (Figure 3). However, we observed diverse immune cellular compositions at the ITF, finding a clearly heterogeneous distribution of B and T lymphocytes and macrophages in the different tumors analyzed. Comparing the two tumors, B lymphocytes (CD20+) appeared in lowest numbers out of the total immune population, being statistically smaller in uLMS, while there was a significant increase in macrophages, which represented the highest immune population in uLMS. Conversely, T lymphocytes (CD3+) emerged as the predominant immune infiltrate in uADC. The relative frequency of CD20, CD3, CD8, and CD68-positive cells within each tumor reflected that uADCs and uLMS exhibited Frontiers in Cell and Developmental Biology | www.frontiersin.org 8June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 9 Sanegre et al. Uterine-Myometrial Tumor Interface TABLE 1 | Heterogeneous architecture of reticular fibers between 1 ×1-mm regions of interest (ROIs) of tumor, myometrium, and invasive tumor front (ITF) within uterine adenocarcinomas (uADC). A B Myometrium ITF Myometrium ITF Mean p value Mean p value Fibers 263,94 246,26 0,397 Fibers 245,38 257,15 0,809 Area core (mm2) 0,97 0,96 0,020 * Area core (mm2) 0,92 0,96 0,531 Density 270,29 307,10 0,526 Density 277,25 268,95 0,888 Fiber total area (mm2) 0,05 0,07 0,060 Fiber total area (mm2) 0,05 0,06 0,180 % SA 5,97 7,44 0,042 * % SA 5,94 6,47 0,714 Area 227,58 300,55 0,021 * Area 215,28 248,33 0,175 Width 11,71 13,02 0,030 * Width 11,69 12,34 0,640 Height 43,02 48,35 0,013 * Height 42,57 46,73 0,072 Angle −50,86 −50,84 0,994 Angle −55,72 −48,86 0,094 Roundness 0,13 0,13 0,010 * Roundness 0,14 0,13 0,064 Aspect 4,15 4,20 0,858 Aspect 4,63 4,44 0,798 Perimeter ratio 0,67 0,66 0,336 Perimeter ratio 0,68 0,68 0,979 Perimeter 72,83 88,24 0,022 * Perimeter 67,34 83,88 0,056 Deformity 1193,94 1932,43 0,009 ** Deformity 1018,52 1345,71 0,096 Shape 24,14 21,02 0,645 Shape 11,04 16,76 0,027 * Vertices 5,54 5,67 0,042 * Vertices 5,60 5,63 0,839 Fractal dimension 1,39 1,39 0,838 Fractal dimension 1,40 1,39 0,742 Tumor ITF Tumor ITF Mean p value Mean p value Fibers 79,07 246,27 0,000 *** Fibers 248,74 257,15 0,750 Area core (mm2) 0,93 0,96 0,468 Area core (mm2) 0,97 0,96 0,396 Density 83,28 307,1 0,007 * Density 257,48 268,95 0,690 Fiber total area (mm2) 0,02 0,07 0,000 *** Fiber total area (mm2) 0,05 0,06 0,357 % SA 2,73 7,44 0,000 *** % SA 5,84 6,47 0,339 Area 363,23 300,56 0,109 Area 231,91 248,33 0,530 Width 16,21 13,03 0,000 *** Width 13,65 12,34 0,283 Height 49,54 48,36 0,718 Height 44,49 46,73 0,426 Angle −50,50 −50,84 0,867 Angle −49,74 −48,86 0,647 Roundness 0,13 0,13 0,228 Roundness 0,14 0,13 0,343 Aspect 3,42 4,2 0,011 * Aspect 3,82 4,44 0,072 Perimeter ratio 0,63 0,66 0,078 Perimeter ratio 0,68 0,68 0,651 Perimeter 106,88 88,25 0,241 Perimeter 78,03 83,88 0,524 Deformity 2156,74 1932,44 0,554 Deformity 1579,96 1345,71 0,425 Shape 13,10 21,02 0,140 Shape 21,05 16,76 0,285 Vertices 6,041 5,68 0,000 *** Vertices 5,57 5,63 0,482 Fractal dimension 1,45 1,39 0,000 *** Fractal dimension 1,40 1,39 0,306 Myometrium Tumor Myometrium Tumor Mean p value Mean p value Fibers 263,94 79,07 0,000 *** Fibers 245,38 248,74 0,941 Area core (mm2) 0,97 0,93 0,258 Area core (mm2) 0,92 0,97 0,414 Density 270,28 83,28 0,000 *** Density 277,25 257,48 0,714 Fiber total area (mm2) 0,05 0,02 0,000 *** Fiber total area (mm2) 0,05 0,05 0,541 % SA 5,97 2,73 0,001 ** % SA 5,94 5,84 0,944 Area 227,58 363,23 0,001 ** Area 215,28 231,91 0,566 Width 11,71 16,21 0,000 *** Width 11,69 13,65 0,182 Height 43,02 49,53 0,066 Height 42,57 44,49 0,574 (Continued) Frontiers in Cell and Developmental Biology | www.frontiersin.org 9June 2021 | Volume 9 | Article 670185 fcell-09-670185 June 2, 2021 Time: 13:49 # 16 Sanegre et al. Uterine-Myometrial Tumor Interface ETHICS STATEMENT The studies involving human participants were reviewed and approved by the Comité Coordinador de Ética de la Investigación Biomédica de Andalucía: Deciphering the site-specific tumor microenvironment of advanced uterine tumors (Biobank code: S1800086) and Comité de Ética de la Investigación with the codes CEIC-1892, CEIC-2083, and CEIC-1858. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. AUTHOR CONTRIBUTIONS RN and XM-G contributed to conception and design of the study. SS wrote the first draft of the manuscript. SS, NE, CA, JD-M, ÁD-L, EA, XM-G, and RN wrote sections of the manuscript. BD, RL, SR, EA, and XM-G provided the clinicopathological data. NE provided the collaborative consortium with microdissected samples. SS and RN designed and performed the morphometric characterization of the reticulin fibers. CA and DG performed the multiplex staining and analysis. JD-M, CS-A, and EA evaluated the transcriptomic profile. ÁD-L and MJ performed the genomewide methylation analysis. All authors contributed to manuscript revision, read, and approved the submitted version. FUNDING This research was supported by grants from the ISCIII and ERDF (PI17/01558 and PI20/01107), by the CIBERONC (contracts CB16/12/00484, CB16/12/0328, CB16/12/00363, CB16/12/00364, CB16/12/00481, and CB16/12/00231) and Grupos Coordinados Estables from the Asociación Española Contra el Cáncer (AECC). ÁD-L was funded by a contract “Juan Rodés” from the ISCIII (JR17/00016). The funders had no involvement in the research process nor in the preparation and submission of the article. ACKNOWLEDGMENTS The authors are grateful to Aitor Carretero (research collaborator of the INCLIVA-Instituto de Investigación Sanitaria) for the initial analysis of reticulin fibers and Nuria Santonja (medical specialist in anatomic pathology at the Hospital General Universitario of Valencia) for providing leiomyosarcoma samples, and to Aida Bao-Caamano (Epigenomics Unit, IDIS) for her technical support with genome-wide DNA methylation analysis. The authors also thank Kathryn Davies for English corrections. SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2021. 670185/full#supplementary-material Supplementary Figure 1 | In-depth characterization of the invasive tumor front (ITF) in uterine adenocarcinomas (uADC) and leiomyosarcomas (uLMS). A tumor biopsy from an adenocarcinoma (uADC) is shown. (A) Schematic pipeline for tumor ITF area selection for histomorphometric analysis, immune infiltrate, transcriptomics and methylation analysis. Scale bars represent 250 µm. (B) Selection from a 5 ×4 mm region of interest (ROI) of 1 ×1 mm ROIs of invasive tumor front (ITF), myometrium and tumor. The same criteria were applied for uADC and uLMS. For 5 ×4 mm image, scale bar represents 1 mm. For 1 ×1 mm images, scale bars represent 250 µm. Supplementary Table 1 | Case selection and regions of interest (ROIs) used in each characterization of uterine adenocarcinomas (uADC) and leiomyosarcomas (uLMS). Case identification is shown by tumor ID. The percentage of tumor/myometrium tissues for genomic and epigenetic profiling was evaluated in H&E stained samples. The total numbers of ROIs and tumors used for all the studies are shown. Invasive tumor front (ITF). 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