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

• Published in: Cell 2018-04, Vol.173 (2), p.499-514.e23
• Main Authors: Sack, Laura Magill, Davoli, Teresa, Li, Mamie Z., Li, Yuyang, Xu, Qikai, Naxerova, Kamila, Wooten, Eric C., Bernardi, Ronald J., Martin, Timothy D., Chen, Ting, Leng, Yumei, Liang, Anthony C., Scorsone, Kathleen A., Westbrook, Thomas F., Wong, Kwok-Kin, Elledge, Stephen J.
• Format: Article
• Language: English
• Subjects: Aneuploidy; Animals; cancer drivers; Cell Line, Tumor; Cell Proliferation; Chromosome Mapping; Chromosomes - genetics; E2F1 Transcription Factor - antagonists & inhibitors; E2F1 Transcription Factor - genetics; E2F1 Transcription Factor - metabolism; Female; gain of function screens; Gene Library; genetic screens; Genomics; Humans; Keratins - metabolism; KRTAP; Mice; Mice, Inbred NOD; Mice, SCID; Neoplasms - pathology; Oncogenes; Open Reading Frames - genetics; ORF screens; proliferation; RNA Interference; RNA, Small Interfering - metabolism; SCNA; tissue specificity
• ISSN: 0092-8674; 1097-4172
• DOI: 10.1016/j.cell.2018.02.037

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. [Display omitted] •Barcoded genome-scale ORF expression libraries allow gain-of-function screens•Regulators of proliferation exhibit a striking degree of tissue-specificity•Proliferation driver genes help predict focal SCNAs and cancer aneuploidy patterns•Approximately 250 candidate cancer drivers identified in recurring SCNAs The highly tissue-specific epigenetic landscape of a given cell type establishes its responsiveness to oncogenic proliferation signals and determines which drivers, somatic copy number changes, and anueploidies are selected during tumorigenesis.