Profound Tissue Specificity in Proliferation Control Underlies Cancer Drivers and Aneuploidy Patterns

Genomics has provided a detailed structural description of the cancer genome. Identifying oncogenic drivers that work primarily through dosage changes is a current challenge. Unrestrained proliferation is a critical hallmark of cancer. We constructed modular, barcoded libraries of human open reading frames (ORFs) and performed screens for proliferation regulators in multiple cell types. Approximately 10% of genes regulate proliferation, with most performing in an unexpectedly highly tissue-specific manner. Proliferation drivers in a given cell type showed specific enrichment in somatic copy number changes (SCNAs) from cognate tumors and helped predict aneuploidy patterns in those tumors, implying that tissue-type-specific genetic network architectures underlie SCNA and driver selection in different cancers. In vivo screening confirmed these results. We report a substantial contribution to the catalog of SCNA-associated cancer drivers, identifying 147 amplified and 107 deleted genes as potential drivers, and derive insights about the genetic network architecture of aneuploidy in tumors.

[1]  Joshua M. Korn,et al.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2008, Nature.

[2]  Steven L Salzberg,et al.  HISAT: a fast spliced aligner with low memory requirements , 2015, Nature Methods.

[3]  D Saur,et al.  Oncogenic KRAS signalling in pancreatic cancer , 2014, British Journal of Cancer.

[4]  S. Elledge,et al.  Identification of the FANCI Protein, a Monoubiquitinated FANCD2 Paralog Required for DNA Repair , 2007, Cell.

[5]  M. Badr,et al.  Peroxisome proliferator‐activated receptors and cancer: challenges and opportunities , 2011, British journal of pharmacology.

[6]  D. Pe’er,et al.  Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers , 2014, Cell.

[7]  Qikai Xu,et al.  Sources of Error in Mammalian Genetic Screens , 2016, G3: Genes, Genomes, Genetics.

[8]  S. Elledge,et al.  A Role for Mitochondrial Translation in Promotion of Viability in K-Ras Mutant Cells. , 2017, Cell reports.

[9]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

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

[11]  Jun S. Liu,et al.  MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens , 2014, Genome Biology.

[12]  M. Meyerson,et al.  Recurrent Hemizygous Deletions in Cancers May Optimize Proliferative Potential , 2012, Science.

[13]  M. Daly,et al.  PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.

[14]  Jason I. Herschkowitz,et al.  The pINDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo , 2011, Proceedings of the National Academy of Sciences.

[15]  Antoine de Weck,et al.  Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening , 2017, Cell.

[16]  M. Stratton,et al.  A census of amplified and overexpressed human cancer genes , 2010, Nature Reviews Cancer.

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

[18]  Le Cong,et al.  Multiplex Genome Engineering Using CRISPR/Cas Systems , 2013, Science.

[19]  Joo Hoon Kim,et al.  Rho GTPase RhoJ is Associated with Gastric Cancer Progression and Metastasis , 2016, Journal of Cancer.

[20]  G. Getz,et al.  GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers , 2011, Genome Biology.

[21]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[22]  Derek Y. Chiang,et al.  The landscape of somatic copy-number alteration across human cancers , 2010, Nature.

[23]  A. Ganesan,et al.  RhoJ modulates melanoma invasion by altering actin cytoskeletal dynamics , 2013, Pigment cell & melanoma research.

[24]  L. Stein,et al.  Annotating Cancer Variants and Anti-Cancer Therapeutics in Reactome , 2012, Cancers.

[25]  Matthias Meyer,et al.  Illumina sequencing library preparation for highly multiplexed target capture and sequencing. , 2010, Cold Spring Harbor protocols.

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

[27]  D. Durocher,et al.  High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities , 2015, Cell.

[28]  Julie M Sheridan,et al.  edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens , 2014, F1000Research.

[29]  Robert L. Sutherland,et al.  Cyclin D as a therapeutic target in cancer , 2011, Nature Reviews Cancer.

[30]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[31]  Marie-Liesse Asselin-Labat,et al.  Gata-3 is an essential regulator of mammary-gland morphogenesis and luminal-cell differentiation , 2007, Nature Cell Biology.

[32]  Dong-Dong Wu,et al.  Molecular evolution of the keratin associated protein gene family in mammals, role in the evolution of mammalian hair , 2008, BMC Evolutionary Biology.

[33]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[34]  S. Elledge,et al.  Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome , 2013, Cell.

[35]  S. Elledge,et al.  A gain-of-function senescence bypass screen identifies the homeobox transcription factor DLX2 as a regulator of ATM–p53 signaling , 2016, Genes & development.

[36]  S. Gabriel,et al.  Pan-cancer patterns of somatic copy-number alteration , 2013, Nature Genetics.

[37]  Martin A. Nowak,et al.  A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity , 2015, Nature.

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

[39]  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.

[40]  H. Kipen,et al.  Questions and Answers 1 , 1994 .

[41]  A. Fulton,et al.  Prostaglandin E2 EP receptors as therapeutic targets in breast cancer , 2011, Cancer and Metastasis Reviews.

[42]  Hidetoshi Shimodaira,et al.  Pvclust: an R package for assessing the uncertainty in hierarchical clustering , 2006, Bioinform..

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

[44]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[45]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[46]  S. Elledge,et al.  Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy , 2017, Science.

[47]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.