A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds

Summary The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

[1]  A. Bouchard-Côté,et al.  PyClone: statistical inference of clonal population structure in cancer , 2014, Nature Methods.

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

[3]  Simon Tavaré,et al.  beadarray: R classes and methods for Illumina bead-based data , 2007, Bioinform..

[4]  M. Adams,et al.  Recent Segmental Duplications in the Human Genome , 2002, Science.

[5]  Carlos Caldas,et al.  A new genome‐driven integrated classification of breast cancer and its implications , 2013, The EMBO journal.

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

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

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

[9]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[11]  Mark T. W. Ebbert,et al.  Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes , 2011, Nature Medicine.

[12]  D. Haber,et al.  Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents , 2010, Nature Reviews Cancer.

[13]  Carlos Caldas,et al.  Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer , 2015, Nature Communications.

[14]  M. Wigler,et al.  Circular binary segmentation for the analysis of array-based DNA copy number data. , 2004, Biostatistics.

[15]  John Quackenbush,et al.  A three-gene model to robustly identify breast cancer molecular subtypes. , 2012, Journal of the National Cancer Institute.

[16]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[17]  Sridhar Ramaswamy,et al.  Patient-derived models of acquired resistance can identify effective drug combinations for cancer , 2014, Science.

[18]  Joshua F. McMichael,et al.  Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft , 2010, Nature.

[19]  Manuel Hidalgo,et al.  Patient-derived xenograft models: an emerging platform for translational cancer research. , 2014, Cancer discovery.

[20]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[21]  Jeremy M. Stark,et al.  53BP1 Inhibits Homologous Recombination in Brca1-Deficient Cells by Blocking Resection of DNA Breaks , 2010, Cell.

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

[23]  M. DePristo,et al.  A framework for variation discovery and genotyping using next-generation DNA sequencing data , 2011, Nature Genetics.

[24]  Ryan D. Morin,et al.  Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution , 2009, Nature.

[25]  Peter Bouwman,et al.  REV7 counteracts DNA double-strand break resection and affects PARP inhibition , 2015, Nature.

[26]  G. Heppner.  Tumor heterogeneity. , 1984, Cancer research.

[27]  W. Greco,et al.  The search for synergy: a critical review from a response surface perspective. , 1995, Pharmacological reviews.

[28]  C. I. Bliss.  THE TOXICITY OF POISONS APPLIED JOINTLY1 , 1939 .

[29]  Carlos Caldas,et al.  Maintaining Tumor Heterogeneity in Patient-Derived Tumor Xenografts. , 2015, Cancer research.

[30]  K. Polyak,et al.  Tumorigenesis: it takes a village , 2015, Nature Reviews Cancer.

[31]  Justin Guinney,et al.  GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.

[32]  Felix Krueger,et al.  Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications , 2011, Bioinform..

[33]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[34]  L. Trusolino,et al.  Oncogene addiction as a foundational rationale for targeted anti-cancer therapy: promises and perils , 2011, EMBO molecular medicine.

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

[36]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Li Ding,et al.  Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. , 2013, Cell reports.

[38]  P. Sorger,et al.  Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs , 2016, Nature Methods.

[39]  J. Mesirov,et al.  Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[40]  S. Wood.  Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models , 2004 .

[41]  W. Kaelin.  The Concept of Synthetic Lethality in the Context of Anticancer Therapy , 2005, Nature Reviews Cancer.

[42]  Andrea Sottoriva,et al.  The shaping and functional consequences of the microRNA landscape in breast cancer , 2013, Nature.

[43]  A. Jimeno,et al.  Efficacy and pharmacodynamic effects of bosutinib (SKI-606), a Src/Abl inhibitor, in freshly generated human pancreas cancer xenografts , 2009, Molecular Cancer Therapeutics.

[44]  B. Taylor,et al.  deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution , 2016, Genome Biology.

[45]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[46]  C. Perou,et al.  Allele-specific copy number analysis of tumors , 2010, Proceedings of the National Academy of Sciences.

[47]  D. Adams,et al.  53BP1 loss rescues BRCA1 deficiency and is associated with triple-negative and BRCA-mutated breast cancers , 2010, Nature Structural &Molecular Biology.

[48]  D. Durocher,et al.  MAD2L2 controls DNA repair at telomeres and DNA breaks by inhibiting 5′ end-resection , 2015, Nature.

[49]  Bin Liu,et al.  Erratum: The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes , 2016, Nature Communications.

[50]  James E. Bradner,et al.  Response and resistance to BET bromodomain inhibitors in triple negative breast cancer , 2015, Nature.

[51]  Neville E. Sanjana,et al.  High-throughput functional genomics using CRISPR–Cas9 , 2015, Nature Reviews Genetics.

[52]  Alison Stopeck,et al.  HSP90 Inhibition Is Effective in Breast Cancer: A Phase II Trial of Tanespimycin (17-AAG) Plus Trastuzumab in Patients with HER2-Positive Metastatic Breast Cancer Progressing on Trastuzumab , 2011, Clinical Cancer Research.

[53]  E. Mroz,et al.  MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma. , 2013, Oral oncology.

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

[55]  A. Vincent-Salomon,et al.  A New Model of Patient Tumor-Derived Breast Cancer Xenografts for Preclinical Assays , 2007, Clinical Cancer Research.

[56]  Scott E. Martin,et al.  Reproducible pharmacogenomic profiling of cancer cell line panels , 2016, Nature.

[57]  Kenny Q. Ye,et al.  An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.

[58]  Jos Jonkers,et al.  CopywriteR: DNA copy number detection from off-target sequence data , 2015, Genome Biology.

[59]  S. Jackson,et al.  BRCA1-associated exclusion of 53BP1 from DNA damage sites underlies temporal control of DNA repair , 2012, Journal of Cell Science.

[60]  I. Adzhubei,et al.  Predicting Functional Effect of Human Missense Mutations Using PolyPhen‐2 , 2013, Current protocols in human genetics.

[61]  H. Hakonarson,et al.  ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data , 2010, Nucleic acids research.

[62]  S. Paik,et al.  Cancer Cell Line Panels Empower Genomics-Based Discovery of Precision Cancer Medicine , 2015, Yonsei medical journal.

[63]  N. Rosenfeld,et al.  The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes , 2016, Nature Communications.

[64]  Jana Marie Schwarz,et al.  MutationTaster evaluates disease-causing potential of sequence alterations , 2010, Nature Methods.

[65]  S. Henikoff,et al.  Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm , 2009, Nature Protocols.

[66]  Paul Shannon,et al.  VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants , 2014, Bioinform..

[67]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[68]  Joshua M. Stuart,et al.  Subtype and pathway specific responses to anticancer compounds in breast cancer , 2011, Proceedings of the National Academy of Sciences.

[69]  Carlos Caldas,et al.  The implications of clonal genome evolution for cancer medicine. , 2013, The New England journal of medicine.

[70]  Hayley E. Francies,et al.  Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients , 2015, Cell.

[71]  M. Poupon,et al.  Patient-derived tumour xenografts as models for breast cancer drug development , 2014, Current opinion in oncology.

[72]  Irmtraud M. Meyer,et al.  The clonal and mutational evolution spectrum of primary triple-negative breast cancers , 2012, Nature.

[73]  Ji Luo,et al.  Principles of Cancer Therapy: Oncogene and Non-oncogene Addiction , 2009, Cell.

[74]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[75]  Chih-Yang Wang,et al.  Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells , 2015, Nature.

[76]  Raphael Gottardo,et al.  Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.

[77]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[78]  Y. Drew,et al.  Therapeutic potential of poly(ADP-ribose) polymerase inhibitor AG014699 in human cancers with mutated or methylated BRCA1 or BRCA2. , 2011, Journal of the National Cancer Institute.

[79]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[80]  Pieter Wesseling,et al.  DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly , 2014, Genome research.

[81]  M. Dunning,et al.  Genome-driven integrated classification of breast cancer validated in over 7,500 samples , 2014, Genome Biology.

[82]  Gary D Bader,et al.  Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance , 2016, Cell.

[83]  K. Kinzler,et al.  Cancer Genome Landscapes , 2013, Science.

[84]  R. Gibbs,et al.  Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. , 2015, Human molecular genetics.