Patient-Derived Xenografts and Matched Cell Lines Identify Pharmacogenomic Vulnerabilities in Colorectal Cancer

Purpose: Patient-derived xenograft (PDX) models accurately recapitulate the tumor of origin in terms of histopathology, genomic landscape, and therapeutic response, but some limitations due to costs associated with their maintenance and restricted amenability for large-scale screenings still exist. To overcome these issues, we established a platform of 2D cell lines (xeno-cell lines, XL), derived from PDXs of colorectal cancer with matched patient germline gDNA available. Experimental Design: Whole-exome and transcriptome sequencing analyses were performed. Biomarkers of response and resistance to anti-HER therapy were annotated. Dependency on the WRN helicase gene was assessed in MSS, MSI-H, and MSI-like XLs using a reverse genetics functional approach. Results: XLs recapitulated the entire spectrum of colorectal cancer transcriptional subtypes. Exome and RNA-seq analyses delineated several molecular biomarkers of response and resistance to EGFR and HER2 blockade. Genotype-driven responses observed in vitro in XLs were confirmed in vivo in the matched PDXs. MSI-H models were dependent upon WRN gene expression, while loss of WRN did not affect MSS XLs growth. Interestingly, one MSS XL with transcriptional MSI-like traits was sensitive to WRN depletion. Conclusions: The XL platform represents a preclinical tool for functional gene validation and proof-of-concept studies to identify novel druggable vulnerabilities in colorectal cancer.

[1]  A. Bardelli,et al.  Evolving neoantigen profiles in colorectal cancers with DNA repair defects , 2019, Genome Medicine.

[2]  A. Bardelli,et al.  A Genomic Analysis Workflow for Colorectal Cancer Precision Oncology. , 2019, Clinical colorectal cancer.

[3]  Emanuel J. V. Gonçalves,et al.  Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens , 2019, Nature.

[4]  L. Kategaya,et al.  Werner Syndrome Helicase Is Required for the Survival of Cancer Cells with Microsatellite Instability , 2019, iScience.

[5]  Jesse J. Lipp,et al.  Werner syndrome helicase is a selective vulnerability of microsatellite instability-high tumor cells , 2019, bioRxiv.

[6]  Joshua M. Korn,et al.  Next-generation characterization of the Cancer Cell Line Encyclopedia , 2019, Nature.

[7]  James M. McFarland,et al.  WRN Helicase is a Synthetic Lethal Target in Microsatellite Unstable Cancers , 2019, Nature.

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

[9]  Kyoung-Mee Kim,et al.  Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy , 2018, Nature Genetics.

[10]  Joshua M. Dempster,et al.  Genetic and transcriptional evolution alters cancer cell line drug response , 2018, Nature.

[11]  A. Bardelli,et al.  Radiologic and Genomic Evolution of Individual Metastases during HER2 Blockade in Colorectal Cancer. , 2018, Cancer cell.

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

[13]  Cyriac Kandoth,et al.  Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer , 2018, Cell.

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

[15]  R. Lothe,et al.  Colorectal Cancer Consensus Molecular Subtypes Translated to Preclinical Models Uncover Potentially Targetable Cancer Cell Dependencies , 2017, Clinical Cancer Research.

[16]  Claudio Isella,et al.  A Molecularly Annotated Model of Patient-Derived Colon Cancer Stem–Like Cells to Assess Genetic and Nongenetic Mechanisms of Resistance to Anti-EGFR Therapy , 2017, Clinical Cancer Research.

[17]  C. Swanton,et al.  Tumor Evolution as a Therapeutic Target. , 2017, Cancer discovery.

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

[19]  Elisa Ficarra,et al.  Selective analysis of cancer-cell intrinsic transcriptional traits defines novel clinically relevant subtypes of colorectal cancer , 2017, Nature Communications.

[20]  Davide Prandi,et al.  Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine. , 2017, Cancer discovery.

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

[22]  Henrik Edgren,et al.  Drug-screening and genomic analyses of HER2-positive breast cancer cell lines reveal predictors for treatment response , 2017, Breast cancer.

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

[24]  John W. Cassidy,et al.  A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds , 2016, Cell.

[25]  V. Torri,et al.  Dual-targeted therapy with trastuzumab and lapatinib in treatment-refractory, KRAS codon 12/13 wild-type, HER2-positive metastatic colorectal cancer (HERACLES): a proof-of-concept, multicentre, open-label, phase 2 trial. , 2016, The Lancet. Oncology.

[26]  J. Swensen,et al.  High microsatellite instability (MSI-H) colorectal carcinoma: a brief review of predictive biomarkers in the era of personalized medicine , 2016, Familial Cancer.

[27]  A. Bardelli,et al.  MM-151 overcomes acquired resistance to cetuximab and panitumumab in colorectal cancers harboring EGFR extracellular domain mutations , 2016, Science Translational Medicine.

[28]  Mari Mino-Kenudson,et al.  Tumor Heterogeneity and Lesion-Specific Response to Targeted Therapy in Colorectal Cancer. , 2016, Cancer discovery.

[29]  R. Bosotti,et al.  Sensitivity to Entrectinib Associated With a Novel LMNA-NTRK1 Gene Fusion in Metastatic Colorectal Cancer , 2015, Journal of the National Cancer Institute.

[30]  Gang Li,et al.  Acquired Resistance to the TRK Inhibitor Entrectinib in Colorectal Cancer. , 2015, Cancer discovery.

[31]  David E. Fisher,et al.  Precision medicine for cancer with next-generation functional diagnostics , 2015, Nature Reviews Cancer.

[32]  Joshua A. Bittker,et al.  Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. , 2015, Cancer discovery.

[33]  R. Scharpf,et al.  The Genomic Landscape of Response to EGFR Blockade in Colorectal Cancer , 2015, Nature.

[34]  Jeffrey S. Morris,et al.  The Consensus Molecular Subtypes of Colorectal Cancer , 2015, Nature Medicine.

[35]  V. Torri,et al.  Sustained Inhibition of HER3 and EGFR Is Necessary to Induce Regression of HER2-Amplified Gastrointestinal Carcinomas , 2015, Clinical Cancer Research.

[36]  Ron Bose,et al.  HER2 activating mutations are targets for colorectal cancer treatment. , 2015, Cancer discovery.

[37]  Beatriz Bellosillo,et al.  Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients , 2015, Nature Medicine.

[38]  Marco Beccuti,et al.  The molecular landscape of colorectal cancer cell lines unveils clinically actionable kinase targets , 2015, Nature Communications.

[39]  M. J. van de Vijver,et al.  Establishment of patient-derived xenograft models and cell lines for malignancies of the upper gastrointestinal tract , 2015, Journal of Translational Medicine.

[40]  G. Inghirami,et al.  Stromal contribution to the colorectal cancer transcriptome , 2015, Nature Genetics.

[41]  Sabine Tejpar,et al.  IGF2 is an actionable target that identifies a distinct subpopulation of colorectal cancer patients with marginal response to anti-EGFR therapies , 2015, Science Translational Medicine.

[42]  M. Salido,et al.  Emergence of Multiple EGFR Extracellular Mutations during Cetuximab Treatment in Colorectal Cancer , 2015, Clinical Cancer Research.

[43]  A. Bardelli,et al.  Resistance to anti-EGFR therapy in colorectal cancer: from heterogeneity to convergent evolution. , 2014, Cancer discovery.

[44]  Kristian Cibulskis,et al.  RNF43 is frequently mutated in colorectal and endometrial cancers , 2014, Nature Genetics.

[45]  Liam O'Connor,et al.  Colorectal cancer cell lines are representative models of the main molecular subtypes of primary cancer. , 2014, Cancer research.

[46]  Kai Ye,et al.  MSIsensor: microsatellite instability detection using paired tumor-normal sequence data , 2014, Bioinform..

[47]  S. Gabriel,et al.  Discovery and saturation analysis of cancer genes across 21 tumor types , 2014, Nature.

[48]  David T. W. Jones,et al.  Signatures of mutational processes in human cancer , 2013, Nature.

[49]  Jean-Pierre Gillet,et al.  The clinical relevance of cancer cell lines. , 2013, Journal of the National Cancer Institute.

[50]  Heng Li Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM , 2013, 1303.3997.

[51]  Sridhar Ramaswamy,et al.  Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells , 2012, Nucleic Acids Res..

[52]  C. Ostwald,et al.  Establishment, Characterization and Chemosensitivity of Three Mismatch Repair Deficient Cell Lines from Sporadic and Inherited Colorectal Carcinomas , 2012, PloS one.

[53]  Sabine Tejpar,et al.  A robust genomic signature for the detection of colorectal cancer patients with microsatellite instability phenotype and high mutation frequency# , 2012, The Journal of pathology.

[54]  Philippe Dessen,et al.  Characterization of a Large Panel of Patient-Derived Tumor Xenografts Representing the Clinical Heterogeneity of Human Colorectal Cancer , 2012, Clinical Cancer Research.

[55]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[56]  Jeremy Wazny,et al.  Xenome—a tool for classifying reads from xenograft samples , 2012, Bioinform..

[57]  S. Ramaswamy,et al.  Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.

[58]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[59]  C. Sigman,et al.  Cancer biomarkers: selecting the right drug for the right patient , 2012, Nature Reviews Drug Discovery.

[60]  R. Bernards,et al.  Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR , 2012, Nature.

[61]  J. Christensen,et al.  MET Activation Mediates Resistance to Lapatinib Inhibition of HER2-Amplified Gastric Cancer Cells , 2012, Molecular Cancer Therapeutics.

[62]  Davide Corà,et al.  A molecularly annotated platform of patient-derived xenografts ("xenopatients") identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. , 2011, Cancer discovery.

[63]  Víctor J Cid,et al.  A comprehensive functional analysis of PTEN mutations: implications in tumor- and autism-related syndromes. , 2011, Human molecular genetics.

[64]  Colin N. Dewey,et al.  RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.

[65]  C. Isella,et al.  Genetic and Expression Analysis of MET, MACC1, and HGF in Metastatic Colorectal Cancer: Response to Met Inhibition in Patient Xenografts and Pathologic Correlations , 2011, Clinical Cancer Research.

[66]  Derek Y. Chiang,et al.  MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery , 2010, Nucleic acids research.

[67]  Sabine Tejpar,et al.  Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. , 2010, The Lancet. Oncology.

[68]  D. Zecchin,et al.  Replacement of normal with mutant alleles in the genome of normal human cells unveils mutation-specific drug responses , 2008, Proceedings of the National Academy of Sciences.

[69]  A. Joe,et al.  Oncogene addiction. , 2008, Cancer research.

[70]  B. Dutrillaux,et al.  Establishment of human colon cancer cell lines from fresh tumors versus xenografts: comparison of success rate and cell line features. , 2007, Cancer research.

[71]  Limin Fu,et al.  FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data , 2007, BMC Bioinformatics.

[72]  M. Gaub,et al.  Primary tumour genetic alterations and intra‐tumoral heterogeneity are maintained in xenografts of human colon cancers showing chromosome instability , 2006, The Journal of pathology.

[73]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[74]  S Matsuno,et al.  Functional evaluation of PTEN missense mutations using in vitro phosphoinositide phosphatase assay. , 2000, Cancer research.

[75]  K. Kinzler,et al.  Inactivation of the type II TGF-beta receptor in colon cancer cells with microsatellite instability. , 1995, Science.