SUMMARY Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on-from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six standard deviations from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression-based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

A consortium of investigators is engaged in a functional genomics project centered on the filamentous fungus Neurospora, with an eye to opening up the functional genomic analysis of all the filamentous fungi. The overall goal of the four interdependent projects in this effort is to acccomplish functional genomics, annotation, and expression analyses of Neurospora crassa, a filamentous fungus that is an established model for the assemblage of over 250

Highlights d Rhabdoid cell lines and tumors have few mutations yet highly express a range of RTKs d RTKs and SHP2 are vulnerabilities in small-molecule and CRISPR-knockout screens d RTK inhibitors are effective against a xenografted rhabdoid mouse model in vivo d Perturbational screens may identify vulnerabilities not detectable in genomic analyses

Many children with metastatic or recurrent pediatric solid tumors continue to have poor survival, and there is an immense need to identify novel therapeutic approaches. Moreover, these cancers typically have simple genomes with limited known druggable molecular events. In order to discover new vulnerabilities in pediatric solid tumors, we have performed genome-scale CRISPR-Cas9 loss-of-function screening and deep “omic” characterization in over 60 pediatric cancer cell lines to date, including neuroblastoma, medulloblastoma, Ewing sarcoma, malignant rhabdoid tumor and rhabdomyosarcoma lines, to begin defining a pediatric cancer dependency map. Global analyses of the pediatric dependency landscape have identified emerging classes of pediatric cancers, including epigenetic-driven, aberrant transcription factor-driven and receptor tyrosine kinase-driven malignancies. For example, the preferential dependencies identified in a subset of neuroblastoma, which has aberrantly high expression of the transcription factor MYCN, are highly enriched for an interconnected network of genes annotated to have transcription factor activity. In addition to the global evaluation, we have developed methods and tools for prioritizing targets for further validation within a cancer type. These tools computationally integrate the pediatric dependency data across multiple datasets to identify categories of genetic dependencies that are especially strong hits or enriched hits in a specific pediatric malignancy. As an example, the intersection of MYCN-amplified neuroblastoma specific dependencies and H3-lysine 27 acetylation (H3K27ac) profiling across MYCN-amplified neuroblastoma allowed us to identify a transcriptional core regulatory circuit (CRC) that may drive the malignant state. Furthermore, targeting transcription with the BRD4 inhibitor JQ1 and CDK7 inhibitor THZ1 caused synergistic killing of neuroblastoma cells suggesting a novel therapeutic approach to treating this disease. Thus, defining a comprehensive pediatric cancer dependency map and developing the methods and tools to prioritize vulnerabilities in different cancer types will allow us to discover both novel biology and new therapeutic opportunities in childhood malignancies. Citation Format: Neekesh V. Dharia, Clare Malone, Amanda Balboni Iniguez, Lillian Guenther, Liying Chen, Gabriela Alexe, Adam D. Durbin, Mark W. Zimmerman, Andrew Hong, Pratiti Bandopadhayay, Mariella G. Filbin, Thomas Howard, Brenton Paolella, Iris Fung, Josephine Lee, Phil Montgomery, John M. Krill-Burger, Brian J. Abraham, Jennifer Roth, David E. Root, Richard A. Young, A. Thomas Look, Rameen Beroukhim, Jesse S. Boehm, William C. Hahn, Todd R. Golub, Aviad Tsherniak, Francisca Vazquez, Kimberly Stegmaier. Defining a pediatric cancer dependency map [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2352.

Precision Cancer Medicine requires the identification of vulnerabilities linked to genetic features of tumors. Recent studies utilizing highly annotated small molecule collections to assess dependencies across hundreds of genomically annotated cell lines have demonstrated the potential for such large-scale preclinical “Dependency Map” projects. We have undertaken a complementary approach using genetic perturbation tools (RNAi and CRISPR-Cas9 based loss-of-function viability screens), to systematically catalog preferential genetic dependencies and markers that predict response. These efforts are providing a foundation for the discovery of novel targets poised for early therapeutic discovery projects together with patient populations that may be enriched for responders to such therapies. Here, we present results from our initial Cancer Dependency Map consisting of RNAi loss-of-function screens across 503 cell lines, including both solid and hematopoietic tumors. We discovered 43 genes whose mutation or copy number creates a cancer dependency (oncogene addiction) including a novel dependency on the small GTPase, GNAI2 in Diffuse large B-cell Lymphoma. We discovered 142 genes in which elevated levels of expression create a dependency (gene addiction), a group of genes highly enriched for master regulator transcription factors such as SOX10, SPDEF, PAX8 and HNF1B. We discovered 474 genes for which hemizygous copy number creates a dependency (CYCLOPS genes), a group of genes highly enriched for members of macromolecular protein complexes including the spliceosome and proteasome. Finally, we discovered 171 genes that become a dependency when a redundant functional paralog is lost in cancer cells (redundant essentials). We demonstrate the mechanistic basis behind one such redundant essential dependency relationship in which promoter methylation of the UBB ubiquitin gene eliminates a compensatory mechanism leading to a novel vulnerability on the suppression of the UBC ubiquitin gene. These observations begin to provide an initial census, categorization and prioritization of robust cancer dependencies and support the potential impact for expanding early efforts to develop dependency maps of cancer. Citation Format: Francisca Vazquez, Aviad Tsherniak, Barbara Weir, Phil Montgomery, Glenn Cowley, Stanley Gill, Gregory Kryukov, Sasha Pantel, Will Harrington, Mike Burger, Robin Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Levi Garraway, Jesse Boehm, David Root, Todd Golub, William Hahn. Emerging targets from Cancer Dependency Map v0.1. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr B44.