Morphophenotypic classification of tumor organoids as an indicator of drug exposure and penetration potential

The dynamics of tumor progression is driven by multiple factors, which can be exogenous to the tumor (microenvironment) or intrinsic (genetic, epigenetic or due to intercellular interactions). While tumor heterogeneity has been extensively studied on the level of cell genetic profiles or cellular composition, tumor morphological diversity has not been given as much attention. The limited analysis of tumor morphophenotypes may be attributed to the lack of accurate models, both experimental and computational, capable of capturing changes in tumor morphology with fine levels of spatial detail. Using a three-dimensional, agent-based, lattice-free computational model, we generated a library of multicellular tumor organoids, the experimental analogues of in vivo tumors. By varying three biologically relevant parameters—cell radius, cell division age and cell sensitivity to contact inhibition, we showed that tumor organoids with similar growth dynamics can express distinct morphologies and possess diverse cellular compositions. Taking advantage of the high-resolution of computational modeling, we applied the quantitative measures of compactness and accessible surface area, concepts that originated from the structural biology of proteins. Based on these analyses, we demonstrated that tumor organoids with similar sizes may differ in features associated with drug effectiveness, such as potential exposure to the drug or the extent of drug penetration. Both these characteristics might lead to major differences in tumor organoid’s response to therapy. This indicates that therapeutic protocols should not be based solely on tumor size, but take into account additional tumor features, such as their morphology or cellular packing density.

[1]  C. Curtis,et al.  Organoids reveal cancer dynamics , 2018, Nature.

[2]  Gordon B Mills,et al.  Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours , 2015, Nature Communications.

[3]  Sylvain V Costes,et al.  Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[4]  Aleksandra Karolak,et al.  Mathematical Modeling of Tumor Organoids: Toward Personalized Medicine , 2018 .

[5]  Qiulian Wu,et al.  A Three-Dimensional Organoid Culture System Derived from Human Glioblastomas Recapitulates the Hypoxic Gradients and Cancer Stem Cell Heterogeneity of Tumors Found In Vivo. , 2016, Cancer research.

[6]  J. Lowengrub,et al.  Three-Dimensional Spatiotemporal Modeling of Colon Cancer Organoids Reveals that Multimodal Control of Stem Cell Self-Renewal is a Critical Determinant of Size and Shape in Early Stages of Tumor Growth , 2018, Bulletin of mathematical biology.

[7]  Ruth E. Falconer,et al.  Characterising the tumour morphological response to therapeutic intervention: an ex vivo model , 2012, Disease Models & Mechanisms.

[8]  Nick Jagiella,et al.  Inferring Growth Control Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image Data , 2016, PLoS Comput. Biol..

[9]  N. Aikawa,et al.  Size-Based Differentiation of Cancer and Normal Cells by a Particle Size Analyzer Assisted by a Cell-Recognition PC Software. , 2018, Biological & pharmaceutical bulletin.

[10]  Alessandro Bevilacqua,et al.  3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained , 2016, Scientific Reports.

[11]  Abbas Shirinifard,et al.  Ovarian Tumor Attachment, Invasion, and Vascularization Reflect Unique Microenvironments in the Peritoneum: Insights from Xenograft and Mathematical Models , 2013, Front. Oncol..

[12]  Nick Barker,et al.  Organoids as an in vitro model of human development and disease , 2016, Nature Cell Biology.

[13]  A G Sorensen,et al.  Comparison of diameter and perimeter methods for tumor volume calculation. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  Eva Forssell-Aronsson,et al.  Specific growth rate versus doubling time for quantitative characterization of tumor growth rate. , 2007, Cancer research.

[15]  Mark A. J. Chaplain,et al.  Integrating Intracellular Dynamics Using CompuCell3D and Bionetsolver: Applications to Multiscale Modelling of Cancer Cell Growth and Invasion , 2012, PloS one.

[16]  Hans Clevers,et al.  Modeling pancreatic cancer with organoids. , 2016, Trends in cancer.

[17]  Roland Eils,et al.  Resolving drug effects in patient-derived cancer cells links organoid responses to genome alterations , 2017, bioRxiv.

[18]  N. Cherdyntseva,et al.  Different morphological structures of breast tumors demonstrate individual drug resistance gene expression profiles. , 2018, Experimental oncology.

[19]  V. Quaranta,et al.  Computational investigation of intrinsic and extrinsic mechanisms underlying the formation of carcinoma. , 2012, Mathematical medicine and biology : a journal of the IMA.

[20]  Andrea Sottoriva,et al.  Patient-derived organoids model treatment response of metastatic gastrointestinal cancers , 2018, Science.

[21]  Andrew J Ewald,et al.  Collective epithelial migration and cell rearrangements drive mammary branching morphogenesis. , 2008, Developmental cell.

[22]  Katarzyna A. Rejniak,et al.  IBCell Morphocharts: A Computational Model for Linking Cell Molecular Activity with Emerging Tissue Morphology , 2014, Discrete and Topological Models in Molecular Biology.

[23]  Aleksandra Karolak,et al.  Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues , 2018, Journal of The Royal Society Interface.

[24]  Akira Ono,et al.  Size-Based Isolation of Circulating Tumor Cells in Lung Cancer Patients Using a Microcavity Array System , 2013, PloS one.

[25]  A. Tripathi,et al.  Multilayer Spheroids To Quantify Drug Uptake and Diffusion in 3D , 2014, Molecular pharmaceutics.

[26]  Randy Heiland,et al.  PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems , 2017, bioRxiv.

[27]  Hans Clevers,et al.  A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity , 2018, Cell.

[28]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[29]  Hongjun Song,et al.  Generation of human brain region–specific organoids using a miniaturized spinning bioreactor , 2018, Nature Protocols.

[30]  E. Milotti,et al.  Emergent Properties of Tumor Microenvironment in a Real-Life Model of Multicell Tumor Spheroids , 2010, PloS one.

[31]  O. V. Galzitskaya,et al.  Radius of gyration as an indicator of protein structure compactness , 2008, Molecular Biology.

[32]  Dmitry A Markov,et al.  Thick-tissue bioreactor as a platform for long-term organotypic culture and drug delivery. , 2012, Lab on a chip.

[33]  J. Glazier,et al.  Front Instabilities and Invasiveness of Simulated 3D Avascular Tumors , 2009, PloS one.

[34]  Jyrki Lötjönen,et al.  Quantification of Dynamic Morphological Drug Responses in 3D Organotypic Cell Cultures by Automated Image Analysis , 2014, PloS one.

[35]  Bahram Parvin,et al.  Linking Changes in Epithelial Morphogenesis to Cancer Mutations Using Computational Modeling , 2010, PLoS Comput. Biol..

[36]  Wei Zhao,et al.  Drug Discovery via Human-Derived Stem Cell Organoids , 2016, Front. Pharmacol..

[37]  M. Bissell,et al.  Organoids: A historical perspective of thinking in three dimensions , 2017, The Journal of cell biology.

[38]  O. Petersen,et al.  Hypoxic Conditions Induce a Cancer-Like Phenotype in Human Breast Epithelial Cells , 2012, PloS one.

[39]  Aleksandra Karolak,et al.  Single-Cell-Based In Silico Models: A Tool for Dissecting Tumor Heterogeneity , 2019, Encyclopedia of Biomedical Engineering.

[40]  Aleksandra Karolak,et al.  Importance of local interactions for the stability of inhibitory helix 1 in apo Ets-1. , 2012, Biophysical chemistry.

[41]  Vladimir P Torchilin,et al.  Barriers to drug delivery in solid tumors , 2014, Tissue barriers.

[42]  C. Larabell,et al.  Reversion of the Malignant Phenotype of Human Breast Cells in Three-Dimensional Culture and In Vivo by Integrin Blocking Antibodies , 1997, The Journal of cell biology.

[43]  D. Rhee,et al.  Molecular signatures associated with transformation and progression to breast cancer in the isogenic MCF10 model. , 2008, Genomics.

[44]  M. Tomayko,et al.  Determination of subcutaneous tumor size in athymic (nude) mice , 2004, Cancer Chemotherapy and Pharmacology.

[45]  Dong Gao,et al.  Patient derived organoids to model rare prostate cancer phenotypes , 2018, Nature Communications.

[46]  Kenneth M. Yamada,et al.  Self-organization and branching morphogenesis of primary salivary epithelial cells. , 2007, Tissue engineering.

[47]  Bon-Kyoung Koo,et al.  Human Primary Liver Cancer -derived Organoid Cultures for disease modelling and drug screening , 2017, Nature Medicine.

[48]  Hans Clevers,et al.  Organoids in cancer research , 2018, Nature Reviews Cancer.

[49]  Alissa M. Weaver,et al.  Microenvironmental independence associated with tumor progression. , 2009, Cancer research.

[50]  Nicole S. Bryce,et al.  Getting to the core of platinum drug bio-distributions: the penetration of anti-cancer platinum complexes into spheroid tumour models. , 2012, Metallomics : integrated biometal science.

[51]  Hossein Baharvand,et al.  Personalized Cancer Medicine: An Organoid Approach. , 2018, Trends in biotechnology.

[52]  D. Ornstein,et al.  Culture requirements of prostatic epithelial cell lines for acinar morphogenesis and lumen formation in vitro: Role of extracellular calcium , 2007, The Prostate.

[53]  Michael R. Boyd,et al.  The NCI Human Tumor Cell Line (60-Cell) Screen , 2004 .

[54]  A. Imbalzano,et al.  Increasingly transformed MCF-10A cells have a progressively tumor-like phenotype in three-dimensional basement membrane culture , 2009, Cancer Cell International.

[55]  K. Rejniak An immersed boundary framework for modelling the growth of individual cells: an application to the early tumour development. , 2007, Journal of theoretical biology.

[56]  Robert J. Gillies,et al.  Current Advances in Mathematical Modeling of Anti-Cancer Drug Penetration into Tumor Tissues , 2013, Front. Oncol..

[57]  Jayanta Debnath,et al.  Modelling glandular epithelial cancers in three-dimensional cultures , 2005, Nature Reviews Cancer.

[58]  Jens Meiler,et al.  Solvent accessible surface area approximations for rapid and accurate protein structure prediction , 2009, Journal of molecular modeling.

[59]  Jyrki Lötjönen,et al.  A Comprehensive Panel of Three-Dimensional Models for Studies of Prostate Cancer Growth, Invasion and Drug Responses , 2010, PloS one.

[60]  Jan Poleszczuk,et al.  Evolution and Phenotypic Selection of Cancer Stem Cells , 2015, PLoS Comput. Biol..

[61]  Katarzyna A Rejniak,et al.  The formation of tight tumor clusters affects the efficacy of cell cycle inhibitors: a hybrid model study. , 2014, Journal of theoretical biology.

[62]  John J. Tyson,et al.  Temporal Organization of the Cell Cycle , 2008, Current Biology.

[63]  Genee Y. Lee,et al.  The morphologies of breast cancer cell lines in three‐dimensional assays correlate with their profiles of gene expression , 2007, Molecular oncology.

[64]  E. Voest,et al.  Tumor Organoids as a Pre-clinical Cancer Model for Drug Discovery. , 2017, Cell chemical biology.

[65]  Olga Kovbasnjuk,et al.  Human mini-guts: new insights into intestinal physiology and host–pathogen interactions , 2016, Nature Reviews Gastroenterology &Hepatology.

[66]  Sunwoo Park,et al.  A computational approach to resolve cell level contributions to early glandular epithelial cancer progression , 2009, BMC Systems Biology.

[67]  Bin Chen,et al.  Predictive Models of Diffusive Nanoparticle Transport in 3-Dimensional Tumor Cell Spheroids , 2013, The AAPS Journal.