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Inoculated cell density as a determinant factor of the growth dynamics and metastatic efficiency of a breast cancer murine model

Gregório, Ana C.,Fonseca, Nuno A.,Moura, Vera,Lacerda, Manuela,Figueiredo, Paulo,Simões, Sérgio,Dias, Sérgio,Moreira, João Nuno

Abstract

4T1 metastatic breast cancer model have been widely used to study stage IV human breast cancer. However, the frequent inoculation of a large number of cells, gives rise to fast growing tumors, as well as to a surprisingly low metastatic take rate. The present work aimed at establishing the conditions enabling high metastatic take rate of the triple-negative murine 4T1 syngeneic breast cancer model. An 87% 4T1 tumor incidence was observed when as few as 500 cancer cells were implanted. 4T1 cancer cells colonized primarily the lungs with 100% efficiency, and distant lesions were also commonly identified in the mesentery and pancreas. The drastic reduction of the number of inoculated cells resulted in increased tumor doubling times and decreased specific growth rates, following a Gompertzian tumor expansion. The established conditions for the 4T1 mouse model were further validated in a therapeutic study with peguilated liposomal doxorubicin, in clinical used in the setting of metastatic breast cancer. Inoculated cell density was proven to be a key methodological aspect towards the reproducible development of macrometastases in the 4T1 mouse model and a more reliable pre-clinical assessment of antimetastatic therapies.

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RESEARCH ARTICLE Inoculated Cell Density as a Determinant Factor of the Growth Dynamics and Metastatic Efficiency of a Breast Cancer Murine Model Ana C. Grego ´rio 1,2 , Nuno A. Fonseca 1,3 , Vera Moura 1,4 , Manuela Lacerda 5 , Paulo Figueiredo 6 , Se ´rgio Simões 1,3 , Se ´rgio Dias 7 , João Nuno Moreira 1,3 * 1CNC—Center for Neurosciences and Cell Biology, University of Coimbra, Coimbra, Portugal, 2IIIUC– Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal, 3FFUC—Faculty of Pharmacy, Po ´lo das Ciências da Sau ´de, University of Coimbra, Coimbra, Portugal, 4TREAT U, SA, Coimbra, Portugal, 5IPATIMUP–Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal, 6IPOFG-EPE–Portuguese Institute of Oncology Francisco Gentil, Coimbra, Portugal, 7IMM–Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal *[email protected] Abstract 4T1 metastatic breast cancer model have been widely used to study stage IV human breast cancer. However, the frequent inoculation of a large number of cells, gives rise to fast growing tumors, as well as to a surprisingly low metastatic take rate. The present work aimed at establishing the conditions enabling high metastatic take rate of the triple-negative murine 4T1 syngeneic breast cancer model. An 87% 4T1 tumor incidence was observed when as few as 500 cancer cells were implanted. 4T1 cancer cells colonized primarily the lungs with 100% efficiency, and distant lesions were also commonly identified in the mesentery and pancreas. The drastic reduction of the number of inoculated cells resulted in increased tumor doubling times and decreased specific growth rates, following a Gompertzian tumor expansion. The established conditions for the 4T1 mouse model were further validated in a therapeutic study with peguilated liposomal doxorubicin, in clinical used in the setting of metastatic breast cancer. Inoculated cell density was proven to be a key methodological aspect towards the reproducible development of macrometastases in the 4T1 mouse model and a more reliable pre-clinical assessment of antimetastatic therapies. Introduction The manifestation of metastasesis predictive of poorclinical outcome [1–4], and prevails one of the most challengingissues faced by cancer treatment today. A continuous effort in dissecting the biologicalprocesses behindcancer cell dissemination has beenpushing forward our understanding of the disease and uncovering vulnerabilitiesthat may be exploited for the development of novel agents to treat metastatic cancer. PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 1 / 19 a11111 OPEN ACCESS Citation: Grego ´rio AC, Fonseca NA, Moura V, Lacerda M, Figueiredo P, Simões S, et al. (2016) Inoculated Cell Density as a Determinant Factor of the Growth Dynamics and Metastatic Efficiency of a Breast Cancer Murine Model. PLoS ONE 11(11): e0165817. doi:10.1371/journal.pone.0165817 Editor: Lu-Zhe Sun, University of Texas Health Science Center, UNITED STATES Received: June 27, 2016 Accepted: October 18, 2016 Published: November 7, 2016 Copyright: ©2016 Grego ´rio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data are presented in the paper. Funding: Ana Cristina Grego ´rio is a student of the international PhD program in Experimental Biology and Biomedicine (PDBEB) from the Institute for Interdisciplinary Research, University of Coimbra and recipient of the fellowship SFRH/BD/51190/ 2010 from the Portuguese Foundation for Science and Technology (FCT). The work was supported by the grants PTDC/SAU-BMA/121028/2010 (FCT) and UID/NEU/04539/2013 (FEDER/COMPETE 2020/FCT). TREAT U provided support in the form Mouse modelsare crucialto our comprehensive knowledgeon the molecularbasis and pathogenesis of cancer disease[5]. Nevertheless, a major impediment for the study of metastases has beenthe unavailability of suitable mouse modelsthat accurately recapitulate the complexity of human tumor progression [6,7]. To bettermimicthe development of metastasesin humans, several parameters need to be considered in a mouse model, namely locationand implantation method of the primary tumor, interaction of cancer cells with the microenvironment at the primaryand secondarysites, dissemination routes and time-to-progressionof the disease.Subcutaneoustransplantation of human (xenograft)and murine (allograft)cell lines into mice, and genetic engineeredmice, are widely used for the establishment of pre-clinical models[6,8]. In the subcutaneousmodel, ectopic location of cancer cells usually fails to produce metastases,owing to the limited tumor microenvironment generated [9]. Furthermore, surgicalresectionof primary tumors is often imperative in order to prolong mice survivaland enable the development of spontaneous metastases[6]. Geneticengineeredmouse models surpass some of these constrains, offering the possibility of orthotopic neoplastic generation in immune competent hosts [6,8]. Nevertheless,metastatic lesions may appear only upon long latency periodsand generally their incidenceis low [6,8]. Even though the existing pre-clinical models still offer valuable information about the biology, molecularbasis and therapeutic opportunities,the setting up of spontaneous metastasesfaces severalchallenges,and improvement of its modelingremains of major importance[6,7,10]. The murine4T1 breast carcinoma cell line has remarkable tumorigenicand invasive characteristics.Upon injectionin the mammary gland of BALB/c mice, 4T1 cells spontaneously generate tumors and are describedto metastasizeto thelungs, liver, lymph nodes, brain and bones,in a way that closely resembles human breast cancer [11]. Owingto its characteristics, 4T1 cells have beenwidely used to study stage IV human breast cancer [12–15]. Moreover, 4T1 murine tumors represent a clinically relevant triple-negative breast cancer model[16–18], which, alongside the cancer cell invasion and metastization,is an important challenge due to its lack of responsiveness to endocrinetherapy. However, 4T1 metastatic breast cancer model suffersfrom the liability of fast growingtumors enhancedby the frequent inoculationof a large number of cells, rendering a tumor microenvironment that does not recapitulate human breast tumors, early mice euthanasia [15,19–25], along with a surprisinglylow metastatic take rate. 2Notwithstanding the widespread use of the 4T1 animal model,some of the aforementioned issues truly limit its usefulnessto understand the biology of metastatic breast cancer and therefore the identificationof novel therapeutic opportunities and the corresponding proof of concept. The need of translatable and predictivetumor modelsis a recognizedneed for successfuldrug development. The present work aimed at establishing the conditions enabling high metastatic take rate of the widespread triple-negative murine 4T1 syngeneic breast cancer model,towards a more reliable pre-clinicalscreeningof anticancer drugs.It was demonstrated that the significantreductionof 4T1 cancer cell density implanted orthotopically, is a key methodologicalaspect underlyingthe reproducible development of macrometastasesin this mouse model. Materials and Methods Ethics statement All animal experimentswere conductedaccordingto human standards of animal care (2010/ 63/EU directiveand Portuguese Act 113/2013, for the use of experimentalanimals), and approved by the correspondingnational authority (DireçãoGeral de Alimentação e Veterinária). Animals were euthanizedby cervicaldislocation. Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 2 / 19 of salaries for author VM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ’author contributions’ section. Competing Interests: Author Vera Moura (VM) received funding from TREAT U, SA, a commercial company, in the form of salaries. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. Materials Ethylenediaminetetraaceticacid disodiumsalt dihydrate, potassium phosphate monobasic, disodiumphosphate anhydrous, potassiumchlorideand sodium chloridewere purchase from Sigma-Aldrich(USA).Caelyx1was kindly provided by the Pharmacy of the University Hospital of Coimbra (Portugal). Cell culture 4T1 [19,26] (ATCC 1 CRL-2539™, USA) mycoplasma-free cells were cultured in RPMI-1640 (Sigma-Aldrich,USA) supplemented with 10% (v/v) heat-inactivated Fetal BovineSerum (Invitrogen, USA), 100 U/ml penicilin,100 μg/ml streptomycin (Lonza, Switzerland) and maintained at 37°C in a 5% CO 2 atmosphere. In vivo experiments Cell suspensions were prepared at 5 x 10 3 , 2 x 10 4 , 5 x 10 5 and 10 x 10 6 cells/mLin phosphatebufferedsolution,and maintained at room temperature. Five to six weeks old Balb/cfemale mice (Balb/cAnNCrl)were orthotopically inoculatedwithin 40 min after preparation of cell suspension,in the fourth inguinal mammary fat pad (100 μL/mouse). For the cell titration, mice were assigned to different groups according to the number of 4T1 cells to be injected:500, 2000, 5 x 10 4 and 1 x 10 6 . Tumor volume was measuredwith a caliper everyother day, and determinedbased on the equation π/6(axb 2 ), where ais the largest tumor diameter and bis the smallest [27]. Tumors were allowedto grow between100–200 mm 3 or >250 mm 3 , after which animals were euthanizedfor necropsy and organs harvested for histologicalanalysis. For the therapeutic study, mice were orthotopically inoculatedwith 500 4T1 cancer cells per mouse. Mice bearing 100–150 mm 3 tumors were intravenously treated with Caelyx1at 5 mg doxorubicin/kgbody weight/week,for five weeks. An additional group was injected with saline. The development of clinical signs of distress caused by the metastatic disease and body weight losses higherthan 20% were not consented and were reason for animal euthanasia. Upon necropsy, the organs were removed, weighedand processedfor histologicalanalysis. All animal experimentswere conductedaccording to human standards of animal care (2010/63/EU directiveand Portuguese Act 113/2013, for the use of experimentalanimals). Relative tumor volume, metastatic incidence and metastatic burden Mean relative tumor volume was expressed as the percentage of the ratio betweenthe tumor volume in each time point and at the beginningof the treatment. Metastatic incidencewas determinedby the ratio between the number of mice that developedmetastases, upon histological confirmation,and the total number of mice assessed.The weight of the lungs was usedas a measure of the metastatic burden in this organ, as extensively reported by others [28–31] and confirmedby us, upon comparing non-tumoror tumor-bearing mice, with or without treatment with Caelyx1(S1 Fig). Relative weight of the organs was expressed as percentage of body weight at the time of death. Tumor growth curves Exponentialand Gompertzmathematical models[32] were fit to the mean tumor volume data (overtime)of all mice using non-linear regression, and goodness-of-fitof the models was compared through the Akaike’s Information Criteria(AIC) values and the extra sum-of-squares F Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 3 / 19 test. Specificgrowth rate (SGR) and doubling time(DT) were determinedand given in the output of the fitting analysis. Histological analysis of primary tumors and metastases Primary tumors and organs were kept on fixative solution (Tissue-Tek1Xpress1Molecular Fixative, Sakura) for 24 h, after which tissues were paraffin embedded,sectionedonto slides (4 μm) and stained with hematoxylin/eosin (H&E) or mouse anti-CD31 monoclonal antibody (clone JC70, pre-diluted ref. n° 760–4378, Ventana Medical Systems, Inc., USA) using the BenchMark ULTRA IHC/ISH staining module(Ventana Medical Systems, Inc., USA). Mean vasculardensity was the mean of CD31 stained blood vessels counted in 4 different fields (400x) of a primarytumor section. H&E stained sections were visualizedin a Axioskop 2 Plus microscope(Zeiss,Germany) for the histologicalevaluationof metastasesand primarytumors invasion of surroundingtissues. Viable rim area was determinedin the entire sectionby excluding necrotic areas, and represented as a ratio of the total area of that section,analyzed with the Fiji software (Life-Lineversion, 2014 November 25, NIH, USA). Statistical analysis Data were analyzed using unpaired nonparametric one-way ANOVA, followed by Dunn’s multiple comparisons test, except when only two groups were compared, in which case unpaired nonparametric Mann-Whitney test was applied. Viable rimarea and vessel density for different tumor sizes were analyzed usingunmatched two-way ANOVA with Tukey’s multiple comparisons test. Log-ranktest was applied for the survivalcurvesand metastatic incidencein the therapeutic study was analyzed with a two-tailedchi-square test. All the analyses were performed with a 95% confidenceinterval. Results Metastatic pattern and efficiency The 4T1 metastatic breast carcinoma model is amply used. However, a large number of cells are often implanted in mice [15,19–24] and require primarytumor removal to extend the disease time course, besidespresenting a low metastatic efficiency. Herein, we assessedthe effect of inoculated4T1 cell density on themetastaticefficiency, without removal of the primary tumor. Immunocompetent Balb/c female mice were orthotopically injectedwith four cell densities, ranging from 500 to 1 x 10 6 cells, and tumor incidence,time for tumor onset, and metastatic efficiencywere evaluated. No obvious correlation was detectedbetweentumor incidenceand the inoculated4T1 cell density (Table 1). The percentage of mice that developed breast carcinomas varied from 85% to 92%, in animals implanted with 5 x 10 4 and 1x10 6 cells, respectively. However, the mean time for tumor onset was significantlydifferent betweenthe groups inoculated.Palpable tumors were detectedat 17.5 and 16.5 d post injectionof 500 and 2000 cancer cells, respectively, whereas this latency time drastically decreasedwhen 5 x 10 4 (7.6 d, p<0.01) or 1 x 10 6 (3.6 d, p<0.0001) 4T1 cells were implanted (Table 1). The time course of primary tumor growth might have implications on its metastatic efficiency. In fact, the number of mice with detectablelung metastasessignificantly increasedin those groups with the longest tumor onset, achieving100% efficacyon the group where only 500 cells were inoculated(Table 1). In contrast, only 45% of mice developed lung metastases (or metastatic nodules in other tissues), for cell densities superiorto 5 x 10 4 . As the endpoint of Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 4 / 19 this experiment was a similar primary tumor burden across the four groups (p= 0.1938), the time elapsed from cell inoculationto animals sacrificewas significantlyreducedin the groups injectedwith 5 x 10 4 and 1 x 10 6 4T1 cells (Table 1). In these experimentalconditions, the metastatic process was more efficient when lower numbers of 4T1 cells were inoculated.Nonetheless, the observeddifferencesin the metastatic efficiencywill likely be attenuated if the animals inoculatedwith highercell densities were allowed to live longer (a condition that would be associatedwith highertumor burdens). Fig 1 shows representative images of 4T1 breast tumors and metastatic lesions in several tissues.The highly invasive nature of these tumors was confirmedby their capacity to invade neighboringmammaryparenchyma (Fig 1A–1C), muscle fibers(Fig 1D) and adjacent skin (Fig 1E and 1F). Macrometastatic lesions in several organs and tissues were also observed(Fig 1G–1K). In these experiments,4T1 cells have colonizedmainly the lungs (Fig 1G), as previously described[19,33], while macrometastases in the liver were rarely observed.As important, macrometastasesin the brain were not identified.Metastases in tissues such as the mesentery(Fig 1H) and pancreas (Fig 1I) were often observed,and occasionallyin lymphatic nodes(Fig 1J). Less frequently, other tissues, like the salivary gland (Fig 1K), were affected. However, the extent of pulmonary and viscerallesions was considerably variable amongst individuals. Different metastatic patterns were observedin mice, particularlyon the lowest cell densities:either small lesions extensively spread, or scarce but larger metastatic nodules.Therefore, it was difficultto establish the metastatic burden basedon the number or volume of the lesions. Moreover, it was difficultto performweight estimates in certaintissues,such as the mesentery. Dynamics of 4T1 tumor growth Dynamicsof tumor growth were analyzed by two mathematical models commonly used to describetumor growth: the Exponentialand Gompertzmodels [34]. The first is a simplistic model that assumes that the number of cancer cells doubles during cell cycle,resulting in exponential growth of solid tumors. However, tumor growth involves other biologicalprocesses, such as regulation of proliferation, stromal recruitment,escape from immunesurveillanceand angiogenesis,thereby being usually explained by the Gompertz model,which considers growth rate decay as tumors becomelarger [35,36]. Tumor growth curvesfitted to the experimental mean tumor volumes over time are presented in Fig 2A. The model providing the best fit was chosen based on Akaike’s information criteriaand extra sum-of-squares F test (Fig 2B) analysis [37,38]. Table 1. Tumor growth and metastases in 4T1 breast carcinoma-bearing mice. No. 4T1 cells inoculated No. of mice with tumors/total mice (%) Mean tumor onset (days until palpable tumors ±SEM) Time from inoculation to euthanasia (days ±SEM) Mean tumor volume at euthanasia (mm 3 ± SEM) No. of mice with detectable lung metastases/no. of mice evaluated (%) 1x10 6 11/12 (92%) 3.6 ±2.54 a 18.4 ±1.26 a 229.5 ±45.82 5/11 (45%) 5x10 4 11/13 (85%) 7.6 ±0.84 b 24.9 ±2.28 a 210.6 ±31.44 5/11 (45%) 2000 13/15 (87%) 16.5 ±1.05 35.4 ±1.65 285.9 ±44.35 7/9 (78%) 500 13/15 (87%) 17.5 ±0.91 37.0 ±2.08 318.1 ±34.05 7/7 (100%) c a p<0.0001. b p<0.01 relative to 500 and 2000 cell densities (nonparametric one-way ANOVA with Dunn’s multiple comparisons test). c p= 0.0167, relative to 5x10 4 and 1x10 6 cell densities (two-tailed chi-square test). doi:10.1371/journal.pone.0165817.t001 Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 5 / 19 Tumor growth showed a Gompertzbehavior for all groups, except for thosegenerated from inoculationof 1x10 6 cells, where exponentialgrowth was prevalent (Fig 2). Notwithstanding, Gompertziantumor growth was diverse among the different groups. It was evident that tumors generated from 5 x 10 4 cells attained the decay phase much earlier than the tumors resulting from the inoculationof lower cell densities (Fig 2A). Doubling time and specificgrowth rate, two parameters usually usedto quantify and characterizeneoplastic growth, were also determined.One million and 5 x 10 4 cell density groups averaged growth rates of 14.9% and 16.1% per day, with doubling times of 4.6 and 4.3 days, respectively(Fig 2B). Fig 1. Representative sections from orthotopic 4T1 tumors and nodular metastatic deposits. Tumor cells invading the surrounding mammary parenchyma (A—C), muscle fibers (D) and adjoining skin (E–F) show the highly invasive capacity of 4T1 breast tumors. Examples of metastatic lesions were observed in the lungs (G), mesentery (H), pancreas (I) lymph nodes (J), or salivary gland (K). MP, mammary parenchyma; MF, muscle fibers; S, skin; LP, lung parenchyma; LN, lymph node; MES, mesentery; PC, pancreas; SG, salivary gland; *indicates tumor areas. All images present original magnification x200, except upper left and inset images, x50. doi:10.1371/journal.pone.0165817.g001 Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 6 / 19 4T1 tumors viable rim area and vasculature Tumor necrosisis thought to result from rapidly proliferating cancercells outpacing their blood supply in certaintumor regions [39,40]. As necrotic cells release pro-inflammatoryfactors into the tumor microenvironment, which are known to promote tumor growth and dissemination[39,41], it was furtherquestionedwhetherthe altered dynamics would affect the viable rim area and vasculardensity of primary tumors, and whether it would relate to their metastatic efficiency. Sectionsfrom tumors of all groups were stained either with H&E, to Fig 2. Fitting mathematical growth models to tumor experimental data as a function of inoculated cell density. Exponential and Gompertz models were fitted to the population’s tumor growth curves (A) and compared using the Akaike’s information criteria (AIC) and the extra sum-of-squares F test (B). Specific growth rates (SGR) and doubling times of each group were determined from the mathematical equations of the best fit (B). Dark symbols represent experimental mean tumor volumes. The solid line represents the best fit for each group with a 95% confidence interval, light lines. doi:10.1371/journal.pone.0165817.g002 Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 7 / 19 determinethe viable rim area, or with CD31, to assess vasculardensity. Additionally, the analysis accounted for differenceson tumor volumes, distinguishingbetweensmaller (100–200 mm 3 ) and larger (>250 mm 3 ) tumors, within each group of mice. Both the viable rimarea and vascular density were independent from tumor volume and cell density inoculated(Fig 3A and 3B, respectively). Representative images of tumor sectionsstainedwith CD31 (Fig 3C) confirmedthe high vascularizednature of thesetumors, regardless the numberof inoculatedcells theywere generated from.These tumors already entailed a goodvascularnetwork at volumes between100–150 mm 3 . Overall,neither the viable rim area nor the vasculardensity of tumors originatingfrom the different cell densities correlated with their respectivemetastatic efficiency. Nevertheless, the inoculationof 500 4T1 cancer cells provided the best metastatic efficiency, possibly due to lower specificgrowth rates (or extendeddoubling times), yieldingan optimal model to study metastatic breast cancer. Validation of the established conditions for the 4T1 metastatic breast cancer mouse model In order to validate the previously characterized4T1 metastatic breast carcinoma mouse model,a therapeuticstudy was conducted with pegylatedliposomaldoxorubicin(Caelyx1),a cytotoxic agent used in the clinical setting of metastatic breast cancer. Fig 3. Effect of cell density and tumor mean volume on viable rim area and vascular density. Quantitative analysis of viable rim area (A) and vascular density (B) was assessed in tumors with mean volumes of 100–200 mm 3 and >250 mm 3 , based on H&E or CD31 immunostaining in carcinoma sections derived from 500, 2000, 5 x 10 4 and 1 x 10 6 cancer cells, original magnification x400 (C). Data represent the mean ±SEM of 3–6 independent sections. ns, p>0.05 two-way ANOVA with Tukey’s multiple comparisons test. doi:10.1371/journal.pone.0165817.g003 Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 8 / 19 Upon inoculationof 500 4T1 cancer cells in the mammary fat pad, Balb/cmice were monitored for bodyweight and symptoms of distress caused by metastatic disease[42,43]. In view of the precedingresults, therapeuticprotocol was initiated when tumors presented an established vascular network (100–150 mm 3 ). Mice were weekly treated via the lateral tail vein with Caelyx1, at 5 mg doxorubicin/kgbody, for 5 weeks, and a control group was injected with saline. Due to widespread of metastatic disease,not all animals completed this therapeuticregimen.The majority of mice in the control group survivedup to the 3 rd dose, and only those treated with Caelyx1managed to achieve the 5 th administration.A significant reductionof the primarytumor was observedin three Caelyx1-treated mice, although followed by regrowth, as well as two complete remissions (Fig 4A). The latter remained tumor-free for more than 60 days after the last doseand did not present metastatic nodules upon necropsy. Nevertheless, three animals responded poorly to the Caelyx1therapy and died from the disease(Fig 4B). Non-treated mice presented quite a different response, namely in terms of primarytumor growth (p<0.0001 relative to Caelyx-treated mice, Fig 4A) and overall survival(median16 days versus 46 days in the Caelyx1-treated group, Fig 4B; Log-rankp= 0.0064. Histological analysis of the neoplastictissues confirmedthe vast capacity of 4T1 breast cancer cells to invade muscle, skin and surroundingmammaryparenchyma (Fig 4C). Caelyx1did not limit tumoral invasiveness (Fig 4C), nor induced a significant reduction of the viable rim area as compared to non-treated mice (Fig 4D). This effectwas consistent with the comparable mean vasculardensity (Fig 4E and 4F) observedbetweentreatment with Caelyx1(287 ± 17.95 counts/mm 2 ) and non-treated mice (332 ± 32.91 counts/mm 2 ). Secondarymetastatic lesions in severalorgans/tissues were also observedin all animals, with the exception of the two mice that presented a complete response to Caelyx1 (Table 2). One hundred percent of non-treated animals presented lung metastases.The incidence decreasedin the Caelyx1cohort (75%) owing to diseaseremission in two mice. Noteworthy, the results also pointed to a reductionof mesenteric nodules in mice underCaelyx1therapy compared to non-treated mice (38% versus 77%, p= 0.0708), with the contribution of the two disease-freemice at the end of the experiment (Table 2). Despite lung metastatic burden of non-treated animals (1.37 ± 0.12) was comparable to the one of Caelyx1group (1.65 ± 0.31), Fig 5, the latter presented the longest survivalrate (Fig 4B). Notwithstanding the significant effectof Caelyx1on primarytumor growth inhibition,the extended survivaltime of these animals enabled a sufficient time frame for metastatic development, already in site by the time of treatment initiation. Splenomegaly was confirmedby visual examination and relative organ weight quantification,in all mice bearing4T1 tumors compared to naïve animals (Fig 6A), in agreement with data from other studies [33,44]. Interestingly, treatments with Caelyx1resulted in a slight decreaseof relative spleen weight, in comparison with non-treated mice (Fig 6A). Histological examination of the spleens revealedhyperplasia of the red pulp with a concomitant reduction of the white pulp, with particularemphasis in non-treated mice (Fig 6B), and consistent with extramedullaryhematopoiesis. Nonetheless, some degreeof toxicity might have occurredgiventhe increaseof mean relative kidneysweight in Caelyx1-treatedanimals (pvalues = 0.0048), relative to non-treated mice (Fig 6C). Symptoms of palmar-plantar erythrodysesthesiawere not registered in mice treated with Caelyx1, in contrast with previous reports [45], possibly due to a lower dose of doxorubicinusedherein. Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 9 / 19 3. Wallace TJ, Torre T, Grob M, Yu J, Avital I, Bru¨cher B, et al. Current Approaches, Challenges and Future Directions for Monitoring Treatment Response in Prostate Cancer. Journal of Cancer. 2014; 5 (1):3–24. doi: 10.7150/jca.7709 PMID: 24396494 4. Tevaarwerk AJ, GR J., SB P., SM L., WL I., FJ H., et al. Survival in patients with metastatic recurrent breast cancer after adjuvant chemotherapy. Cancer. 2013; 119(6):1140–8. doi: 10.1002/cncr.27819 PMID: 23065954 5. Haddad TC, Yee D. Of Mice and (Wo)Men: Is This Any Way to Test a New Drug? 2008. doi: 10.1200/ JCO.2007.14.9062 6. Francia G, Cruz-Munoz W, Man S, Xu P, Kerbel RS. Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nat Rev Cancer. 2011; 11(2):135–41. doi: 10.1038/nrc3001 PMID: 21258397. 7. Bos PD, Nguyen DX, Massague ´J. Modeling metastasis in the mouse. Curr Opin Pharmacol. 2010; 10 (5):571–7. doi: 10.1016/j.coph.2010.06.003 PMID: 20598638. 8. Gould SE, Junttila MR, de Sauvage FJ. Translational value of mouse models in oncology drug development. Nature Medicine. 2015; 21:431–9. doi: 10.1038/nm.3853 PMID: 25951530 9. Fidler IJ. The pathogenesis of cancer metastasis: the ’seed and soil’ hypothesis revisited. Nat Rev Cancer. 2003; 3(6):453–8. doi: 10.1038/nrc1098 PMID: 12778135 10. Weber GF. Why does cancer therapy lack effective anti-metastasis drugs? Cancer Letters. 2013; 328 (2):207–11. doi: 10.1016/j.canlet.2012.09.025 PMID: 23059758. 11. Miller FR, Miller BE, Heppner GH. Characterization of metastatic heterogeneity among subpopulations of a single mouse mammary tumor: heterogeneity in phenotypic stability. Invasion & metastasis. 1983; 3(1):22–31. Epub 01/01. PMID: 6677618. 12. Abu N, Mohamed NE, Yeap SK, Lim KL, Akhtar MN, Zulfadli AJ, et al. In vivo antitumor and antimetastatic effects of flavokawain B in 4T1 breast cancer cell-challenged mice. Drug design, development and therapy. 2015; 9:1401–17. Epub 04/04. doi: 10.2147/dddt.s6797610.2147/DDDT.S67976. eCollection 2015 PMID: 25834398. 13. Gao ZG, Tian L, Hu J, Park IS, Bae YH. Prevention of metastasis in a 4T1 murine breast cancer model by doxorubicin carried by folate conjugated pH sensitive polymeric micelles. J Control Release. 2011. S0168-3659(11)00027-7 [pii] doi: 10.1016/j.jconrel.2011.01.021 PMID: 21295088. 14. HIRANO T, HIROSE K, SAKURAI K, MAKISHIMA M, SASAKI K, AMANO S. Inhibition of Tumor Growth by Antibody to ADAMTS1 in Mouse Xenografts of Breast Cancer. 2011. PMID: 22110207 15. Wenzel J, Zeisig R, Fichtner I. Inhibition of metastasis in a murine 4T1 breast cancer model by liposomes preventing tumor cell-platelet interactions. Clin Exp Metastasis. 2010; 27(1):25–34. Epub 2009/ 11/17. doi: 10.1007/s10585-009-9299-y PMID: 19916050. 16. Ferrari-Amorotti G, Chiodoni C, Shen F, Cattelani S, Soliera AR, Manzotti G, et al. Suppression of invasion and metastasis of triple-negative breast cancer lines by pharmacological or genetic inhibition of slug activity. Neoplasia. 2014; 16(12):1047–58. Epub 12/17. doi: 10.1016/j.neo.2014.10.006 PMID: 25499218. 17. Sato M, Matsubara T, Adachi J, Hashimoto Y, Fukamizu K, Kishida M, et al. Differential Proteome Analysis Identifies TGF-beta-Related Pro-Metastatic Proteins in a 4T1 Murine Breast Cancer Model. PLoS One. 2015; 10(5):e0126483. Epub 05/21. doi: 10.1371/journal.pone.0126483 eCollection 2015. PMID: 25993439. 18. Peiris PM, Deb P, Doolittle E, Doron G, Goldberg A, Govender P, et al. Vascular Targeting of a Gold Nanoparticle to Breast Cancer Metastasis. Journal of Pharmaceutical Sciences. 2015:n/a—n/a. doi: 10.1002/jps.24518 PMID: 26036431 19. Aslakson CJ, Miller FR. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res. 1992; 52(6):1399–405. PMID: 1540948. 20. Ma L, Reinhardt F, Pan E, Soutschek J, Bhat B, Marcusson EG, et al. Therapeutic silencing of miR10b inhibits metastasis in a mouse mammary tumor model. Nat Biotech. 2010; 28(4):341–7. http:// www.nature.com/nbt/journal/v28/n4/abs/nbt.1618.html#supplementary-information. 21. Kim EJ, Choi M-R, Park H, Kim M, Hong JE, Lee J-Y, et al. Dietary fat increases solid tumor growth and metastasis of 4T1 murine mammary carcinoma cells and mortality in obesity-resistant BALB/c mice. Breast Cancer Research: BCR. 2011; 13(4):R78–R. doi: 10.1186/bcr2927 PMID: 21834963 22. Heimburg J, Yan J, Morey S, Glinskii OV, Huxley VH, Wild L, et al. Inhibition of Spontaneous Breast Cancer Metastasis by Anti—Thomsen-Friedenreich Antigen Monoclonal Antibody JAA-F11. 2006; 8 (11):939–48. doi: 10.1593/neo.06493 PMID: 17132226 Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 16 / 19 23. Lirdprapamongkol K, Sakurai H, Kawasaki N, Choo MK, Saitoh Y, Aozuka Y, et al. Vanillin suppresses in vitro invasion and in vivo metastasis of mouse breast cancer cells. Eur J Pharm Sci. 2005; 25(1):57– 65. S0928-0987(05)00054-0 [pii] doi: 10.1016/j.ejps.2005.01.015 PMID: 15854801. 24. Nasti TH, Bullard DC, Yusuf N. P-selectin enhances growth and metastasis of mouse mammary tumors by promoting regulatory T cell infiltration into the tumors. 2015; 131:11–8. doi: 10.1016/j.lfs. 2015.02.025 PMID: 25865803 25. Samant RS, Debies MT, Hurst DR, Moore BP, Shevde LA, Welch DR. Suppression of murine mammary carcinoma metastasis by the murine ortholog of breast cancer metastasis suppressor 1 (Brms1). 2006; 235(2):260–5. doi: 10.1016/j.canlet.2005.04.032 PMID: 15978719 26. Dexter DL, Kowalski HM, Blazar BA, Fligiel Z, Vogel R, Heppner GH. Heterogeneity of tumor cells from a single mouse mammary tumor. Cancer research. 1978; 38(10):3174–81. PMID: 210930 27. Euhus DM, Hudd C, Laregina MC, Johnson FE. Tumor measurement in the nude mouse. Journal of surgical oncology. 1986; 31(4):229–34. PMID: 3724177 28. Welch DR. Technical considerations for studying cancer metastasis in vivo. Clinical & Experimental Metastasis. 1997; 15(3):272–306. 29. Quang CT, Leboucher S, Passaro D, Fuhrmann L, Nourieh M, Vincent-Salomon A, et al. The calcineurin/NFAT pathway is activated in diagnostic breast cancer cases and is essential to survival and metastasis of mammary cancer cells. Cell death & disease. 2015; 6(2):e1658. 30. Poeschinger T, Renner A, Weber T, Scheuer W. Bioluminescence imaging correlates with tumor serum marker, organ weights, histology, and human DNA levels during treatment of orthotopic tumor xenografts with antibodies. Molecular Imaging and Biology. 2013; 15(1):28–39. doi: 10.1007/s11307012-0559-x PMID: 22528864 31. Jechlinger M, Sommer A, Moriggl R, Seither P, Kraut N, Capodiecci P, et al. Autocrine PDGFR signaling promotes mammary cancer metastasis. The Journal of clinical investigation. 2006; 116(6):1561– 70. doi: 10.1172/JCI24652 PMID: 16741576 32. Mehrara E, Forssell-Aronsson E, Johanson V, Ko ¨lby L, Hultborn R, Bernhardt P. A new method to estimate parameters of the growth model for metastatic tumours. Theoretical Biology and Medical Modelling C7–31. 2013; 10(1):1–12. 33. Tao K, Fang M, Alroy J, Sahagian GG. Imagable 4T1 model for the study of late stage breast cancer. BMC Cancer. 2008; 8(1):228. doi: 10.1186/1471-2407-8-228 PMID: 18691423 34. Benzekry S, Lamont C, Beheshti A, Tracz A, Ebos JM, Hlatky L, et al. Classical mathematical models for description and prediction of experimental tumor growth. PLoS computational biology. 2014; 10(8): e1003800. Epub 08/29. doi: 10.1371/journal.pcbi.1003800 eCollection 2014 Aug. PMID: 25167199. 35. Norton L. A Gompertzian model of human breast cancer growth. Cancer research. 1988; 48(24 Part 1):7067–71. 36. Comen E, Norton L, Massague J. Clinical implications of cancer self-seeding. Nat Rev Clin Oncol. 2011; 8(6):369–77. doi: 10.1038/nrclinonc.2011.64 PMID: 21522121 37. Parham F, Portier C. Benchmark Dose Approach. Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment: John Wiley & Sons, Ltd; 2005. p. 239–54. 38. Ludden TM, Beal SL, Sheiner LB. Comparison of the Akaike Information Criterion, the Schwarz criterion and the F test as guides to model selection. Journal of Pharmacokinetics and Biopharmaceutics. 1994; 22(5):431–45. PMID: 7791040 39. Proskuryakov SY, Gabai VL. Mechanisms of tumor cell necrosis. Current pharmaceutical design. 2010; 16(1):56–68. PMID: 20214618 40. Leek RD, Landers RJ, Harris AL, Lewis CE. Necrosis correlates with high vascular density and focal macrophage infiltration in invasive carcinoma of the breast. British journal of cancer. 1999; 79(5– 6):991. doi: 10.1038/sj.bjc.6690158 PMID: 10070902 41. Vakkila J, Lotze MT. Inflammation and necrosis promote tumour growth. Nature Reviews Immunology. 2004; 4(8):641–8. doi: 10.1038/nri1415 PMID: 15286730 42. Carstens E, Moberg GP. Recognizing Pain and Distress in Laboratory Animals. ILAR Journal. 2000; 41(2):62–71. doi: 10.1093/ilar.41.2.62 PMID: 11304586 43. Workman P, Aboagye EO, Balkwill F, Balmain A, Bruder G, Chaplin DJ, et al. Guidelines for the welfare and use of animals in cancer research. Br J Cancer. 2010; 102(11):1555–77. doi: 10.1038/sj.bjc. 6605642 PMID: 20502460 44. DuPre ´SA, Redelman D, Hunter KW. The mouse mammary carcinoma 4T1: characterization of the cellular landscape of primary tumours and metastatic tumour foci. Int J Exp Pathol. 2007; 88(5):351–60. doi: 10.1111/j.1365-2613.2007.00539.x PMID: 17877537. Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 17 / 19 45. Charrois GJR, Allen TM. Multiple Injections of Pegylated Liposomal Doxorubicin: Pharmacokinetics and Therapeutic Activity. Journal of Pharmacology and Experimental Therapeutics. 2003; 306 (3):1058–67. doi: 10.1124/jpet.103.053413 PMID: 12808004 46. Polyak K. Breast cancer: origins and evolution. The Journal of clinical investigation. 2007; 117(117 (11)):3155–63. doi: 10.1172/JCI33295 PMID: 17975657 47. Singh M, Ferrara N. Modeling and predicting clinical efficacy for drugs targeting the tumor milieu. Nature biotechnology. 2012; 30(7):648–57. doi: 10.1038/nbt.2286 PMID: 22781694 48. Bailey-Downs LC, Thorpe JE, Disch BC, Bastian A, Hauser PJ, Farasyn T, et al. Development and characterization of a preclinical model of breast cancer lung micrometastatic to macrometastatic progression. PLoS One. 2014; 9(5):e98624. Epub 06/01. doi: 10.1371/journal.pone.0098624 eCollection 2014. PMID: 24878664. 49. Weng D, Penzner JH, Song B, Koido S, Calderwood SK, Gong J. Metastasis is an early event in mouse mammary carcinomas and is associated with cells bearing stem cell markers. Breast Cancer Research C7—R18. 2012; 14(1):1–13. 50. Psaila B, Lyden D. The Metastatic Niche: Adapting the Foreign Soil. Nature reviews Cancer. 2009; 9 (4):285–93. doi: 10.1038/nrc2621 PMID: 19308068 51. Burton AC. Rate of growth of solid tumours as a problem of diffusion. Growth. 1966; 30(2):157–76. PMID: 5963695 52. Folkman J. What Is the Evidence That Tumors Are Angiogenesis Dependent? Journal of the National Cancer Institute. 1990; 82(1):4–7. PMID: 1688381 53. Kim M-Y, Oskarsson T, Acharyya S, Nguyen DX, Zhang XHF, Norton L, et al. Tumor Self-Seeding by Circulating Cancer Cells. 2009; 139(7):1315–26. doi: 10.1016/j.cell.2009.11.025 PMID: 20064377 54. Adler EP, Lemken CA, Katchen NS, Kurt RA. A dual role for tumor-derived chemokine RANTES (CCL5). Immunology letters. 2003; 90(2):187–94. 55. Kurt RA, Baher A, Wisner KP, Tackitt S, Urba WJ. Chemokine receptor desensitization in tumor-bearing mice. Cellular immunology. 2001; 207(2):81–8. doi: 10.1006/cimm.2000.1754 PMID: 11243697 56. duPre ´SA, Hunter KW Jr. Murine mammary carcinoma 4T1 induces a leukemoid reaction with splenomegaly: association with tumor-derived growth factors. Experimental and molecular pathology. 2007; 82(1):12–24. doi: 10.1016/j.yexmp.2006.06.007 PMID: 16919266 57. Vitiello PF, Shainheit MG, Allison EM, Adler EP, Kurt RA. Impact of tumor-derived CCL2 on T cell effector function. Immunology letters. 2004; 91(2):239–45. 58. Huang Y, Ma C, Zhang Q, Ye J, Wang F, Zhang Y, et al. CD4+ and CD8+ T cells have opposing roles in breast cancer progression and outcome. Oncotarget. 2015. 59. Liao D, Luo Y, Markowitz D, Xiang R, Reisfeld RA. Cancer Associated Fibroblasts Promote Tumor Growth and Metastasis by Modulating the Tumor Immune Microenvironment in a 4T1 Murine Breast Cancer Model. PLoS ONE. 2009; 4(11):e7965. doi: 10.1371/journal.pone.0007965 PMID: 19956757 60. duPre SA, Redelman D, Hunter KW Jr. Microenvironment of the murine mammary carcinoma 4T1: Endogenous IFN-γaffects tumor phenotype, growth, and metastasis. 2008; 85(3):174–88. doi: 10. 1016/j.yexmp.2008.05.002 PMID: 18929358 61. Waight JD, Hu Q, Miller A, Liu S, Abrams SI. Tumor-derived G-CSF facilitates neoplastic growth through a granulocytic myeloid-derived suppressor cell-dependent mechanism. PloS one. 2011; 6(11): e27690. doi: 10.1371/journal.pone.0027690 PMID: 22110722 62. Hanahan D, Coussens LM. Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment. 2012; 21(3):309–22. doi: 10.1016/j.ccr.2012.02.022 PMID: 22439926 63. Chen F, Zhuang X, Lin L, Yu P, Wang Y, Shi Y, et al. New horizons in tumor microenvironment biology: challenges and opportunities. BMC Medicine C7–45. 2015; 13(1):1–14. 64. Maeda H. The enhanced permeability and retention (EPR) effect in tumor vasculature: the key role of tumor-selective macromolecular drug targeting. Advances in Enzyme Regulation. 2001; 41(1):189– 207. http://dx.doi.org/10.1016/S0065-2571(00)00013-3. PMID: Maeda2001189. 65. Gabizon A, Martin F. Polyethylene glycol-coated (pegylated) liposomal doxorubicin. Rationale for use in solid tumors. Drugs. 1997; 54(4):15–21. 66. Gabizon A, Goren D, Cohen R, Barenholz Y. Development of liposomal anthracyclines: from basics to clinical applications. Journal of controlled release. 1998; 53(1):275–9. 67. Yokoi K, Tanei T, Kai M, Saito Y, Liu YT, Ferrari M. Abstract P1-07-13: Extramedullary hematopoiesis aids initiation of cancer metastasis. Cancer Research. 2015; 75(9 Supplement):P1-07-13-P1-07-13. doi: 10.1158/1538-7445.SABCS14-P1-07-13 Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 18 / 19 68. Yan HH, Pickup M, Pang Y, Gorska AE, Li Z, Chytil A, et al. Gr-1+ CD11b+ myeloid cells tip the balance of immune protection to tumor promotion in the premetastatic lung. Cancer research. 2010; 70 (15):6139–49. doi: 10.1158/0008-5472.CAN-10-0706 PMID: 20631080 69. Zhou R, Mazurchuk R, Straubinger RM. Antivasculature Effects of Doxorubicin-containing Liposomes in an Intracranial Rat Brain Tumor Model. Cancer Research. 2002; 62(9):2561–6. PMID: 11980650 70. Pastorino F, Di Paolo D, Piccardi F, Nico B, Ribatti D, Daga A, et al. Enhanced Antitumor Efficacy of Clinical-Grade Vasculature-Targeted Liposomal Doxorubicin. Clinical Cancer Research. 2008; 14 (22):7320–9. doi: 10.1158/1078-0432.CCR-08-0804 PMID: 19010847 71. Nguyen L, Fifis T, Malcontenti-Wilson C, Chan L, Costa PNL, Nikfarjam M, et al. Spatial morphological and molecular differences within solid tumors may contribute to the failure of vascular disruptive agent treatments. BMC Cancer C7–522. 2012; 12(1):1–13. 72. Hori K, Akita H, Nonaka H, Sumiyoshi A, Taki Y. Prevention of cancer recurrence in tumor margins by stopping microcirculation in the tumor and tumor–host interface. Cancer Science. 2014; 105(9):1196– 204. doi: 10.1111/cas.12477 PMID: 24981848 Inoculated Cell Density Is a Determinant Factor of Metastatic Efficiency PLOS ONE | DOI:10.1371/journal.pone.0165817 November 7, 2016 19 / 19