Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research

Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular on the bioinformatics and state-of-the art techniques that accompany preclinical model development. We discuss the strength and limitations of currently used technologies, particularly ‘omics profiling and bioinformatics analyses, in addressing the ‘efficacy’ of preclinical models, both for tumour characterization as well as their use in identifying potential therapeutics. We select pancreatic ductal adenocarcinoma (PDAC) as a case study to highlight the state of the art of the field, and address new avenues for improved bioinformatics characterization of preclinical models.

[1]  T. Golub,et al.  Genomic evolution of cancer models: perils and opportunities , 2018, Nature Reviews Cancer.

[2]  Deena M A Gendoo,et al.  Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer , 2017, bioRxiv.

[3]  D. Tuveson,et al.  Organoid Models for Cancer Research , 2019, Annual Review of Cancer Biology.

[4]  Hans Clevers,et al.  Intra-tumour diversification in colorectal cancer at the single-cell level , 2018, Nature.

[5]  Guillem Pratx,et al.  Imaging metabolic heterogeneity in cancer , 2016, Molecular Cancer.

[6]  Agnieszka K Witkiewicz,et al.  Pancreatic cancer cell lines as patient-derived avatars: genetic characterisation and functional utility , 2017, Gut.

[7]  S. Muthuswamy Organoid Models of Cancer Explode with Possibilities. , 2018, Cell stem cell.

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

[9]  D. Magouliotis,et al.  Patient Derived Xenografts (PDX) for personalized treatment of pancreatic cancer: emerging allies in the war on a devastating cancer? , 2018, Journal of proteomics.

[10]  Hans Clevers,et al.  Personalized Proteome Profiles of Healthy and Tumor Human Colon Organoids Reveal Both Individual Diversity and Basic Features of Colorectal Cancer. , 2017, Cell reports.

[11]  R. Gibbs,et al.  Genomic analyses identify molecular subtypes of pancreatic cancer , 2016, Nature.

[12]  L. Wessels,et al.  Mouse models in the era of large human tumour sequencing studies , 2018, Open Biology.

[13]  Cassandra Willyard Copy number variations' effect on drug response still overlooked , 2015, Nature Medicine.

[14]  Robert E Denroche,et al.  Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. , 2018, Cancer discovery.

[15]  Aik Choon Tan,et al.  Patient-derived tumour xenografts as models for oncology drug development , 2012, Nature Reviews Clinical Oncology.

[16]  Alexander Muir,et al.  Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies , 2018, Disease Models & Mechanisms.

[17]  Gordon Keller,et al.  Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell– and patient-derived tumor organoids , 2015, Nature Medicine.

[18]  P. Spellman,et al.  Subtypes of Pancreatic Ductal Adenocarcinoma and Their Differing Responses to Therapy , 2011, Nature Medicine.

[19]  R. Lindeboom,et al.  Integrative multi‐omics analysis of intestinal organoid differentiation , 2018, Molecular systems biology.

[20]  Alex J Walsh,et al.  Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer. , 2014, Cancer research.

[21]  Andrew L. Kung,et al.  Examining the utility of patient-derived xenograft mouse models , 2015, Nature Reviews Cancer.

[22]  L. Hutchinson,et al.  High drug attrition rates—where are we going wrong? , 2011, Nature Reviews Clinical Oncology.

[23]  M Eileen Dolan,et al.  Clinically relevant genetic variations in drug metabolizing enzymes. , 2011, Current drug metabolism.

[24]  T. Litman,et al.  A Serial Analysis of Gene Expression (SAGE) database analysis of chemosensitivity: comparing solid tumors with cell lines and comparing solid tumors from different tissue origins. , 2004, Cancer research.

[25]  R. Deberardinis,et al.  Applications of metabolomics to study cancer metabolism. , 2018, Biochimica et biophysica acta. Reviews on cancer.

[26]  Sohrab P. Shah,et al.  Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution , 2014, Nature.

[27]  Tao Xie,et al.  Whole Exome Sequencing of Rapid Autopsy Tumors and Xenograft Models Reveals Possible Driver Mutations Underlying Tumor Progression , 2015, PloS one.

[28]  H. Clevers,et al.  Xenograft and organoid model systems in cancer research , 2019, The EMBO journal.

[29]  I. Kola,et al.  Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[30]  Hans Clevers,et al.  Interrogating open issues in cancer precision medicine with patient-derived xenografts , 2017, Nature Reviews Cancer.

[31]  C. Sander,et al.  Evaluating cell lines as tumour models by comparison of genomic profiles , 2013, Nature Communications.

[32]  A. Ranga,et al.  Artificial three-dimensional niches deconstruct pancreas development in vitro , 2013, Development.

[33]  M. Dewhirst,et al.  Exercise inhibits tumor growth and central carbon metabolism in patient-derived xenograft models of colorectal cancer , 2018, Cancer & Metabolism.

[34]  Joshua M. Korn,et al.  High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response , 2015, Nature Medicine.

[35]  Yun-Gui Yang,et al.  Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma , 2019, Cell Research.

[36]  W. Bodmer,et al.  Cancer cell lines for drug discovery and development. , 2014, Cancer research.

[37]  Gordon B Mills,et al.  Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer. , 2016, Cell reports.

[38]  T. Sørlie,et al.  Subtype‐specific response to bevacizumab is reflected in the metabolome and transcriptome of breast cancer xenografts , 2013, Molecular oncology.

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

[40]  R. Weiss,et al.  New opportunities from the cancer metabolome. , 2013, Clinical chemistry.

[41]  Hayley E. Francies,et al.  Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics , 2018, Nature Communications.

[42]  M. Spector,et al.  Organoid Models of Human and Mouse Ductal Pancreatic Cancer , 2015, Cell.

[43]  C. Begley,et al.  Drug development: Raise standards for preclinical cancer research , 2012, Nature.

[44]  J. Beekman,et al.  Novel opportunities for CFTR-targeting drug development using organoids , 2013, Rare diseases.

[45]  Yong-shu He,et al.  [Structural variation in the human genome]. , 2009, Yi chuan = Hereditas.

[46]  Lincoln D. Stein,et al.  Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes , 2012, Nature.

[47]  J. Iovanna,et al.  Pancreatic Adenocarcinoma Therapeutic Targets Revealed by Tumor-Stroma Cross-Talk Analyses in Patient-Derived Xenografts , 2017, Cell reports.

[48]  Erica K. Barnell,et al.  Oral Cavity Squamous Cell Carcinoma Xenografts Retain Complex Genotypes and Intertumor Molecular Heterogeneity , 2018, Cell reports.

[49]  Hans Clevers,et al.  A functional CFTR assay using primary cystic fibrosis intestinal organoids , 2013, Nature Medicine.

[50]  Hans Clevers,et al.  Disease Modeling in Stem Cell-Derived 3D Organoid Systems. , 2017, Trends in molecular medicine.

[51]  Kenneth H. Yu,et al.  Preclinical models of pancreatic ductal adenocarcinoma. , 2017, Chinese clinical oncology.

[52]  D. Tuveson,et al.  Organoid models for translational pancreatic cancer research. , 2019, Current opinion in genetics & development.

[53]  Do-Hyun Nam,et al.  Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells , 2015, Genome Biology.

[54]  Rameen Beroukhim,et al.  Patient-derived xenografts undergo murine-specific tumor evolution , 2017, Nature Genetics.

[55]  Gun Ho Jang,et al.  A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns , 2016, Nature.

[56]  Yun Pyo Kang,et al.  Recent advances in cancer metabolism: a technological perspective , 2018, Experimental & Molecular Medicine.

[57]  Hans Clevers,et al.  A Comprehensive Human Gastric Cancer Organoid Biobank Captures Tumor Subtype Heterogeneity and Enables Therapeutic Screening. , 2018, Cell stem cell.

[58]  J. Kench,et al.  Whole genomes redefine the mutational landscape of pancreatic cancer , 2015, Nature.

[59]  Daniel R. Berger,et al.  Cell diversity and network dynamics in photosensitive human brain organoids , 2017, Nature.

[60]  S. Chakradhar Put to the test: Organoid-based testing becomes a clinical tool , 2017, Nature Medicine.

[61]  J. Takagi,et al.  Human Pancreatic Tumor Organoids Reveal Loss of Stem Cell Niche Factor Dependence during Disease Progression. , 2018, Cell stem cell.