Abstract A27: Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer primary tumor data

Abstract

Abstract Synthetic lethality, in which a single gene defect leads to dependency on a second gene that is otherwise not essential, is an attractive paradigm to identify targeted therapies for somatic mutations. Current methods to detect synthetic lethal (SL) partners for somatic mutations use large-scale shRNA screens in cell lines, combine shRNA data with tumor genomic data or use human orthologs of yeast SL interactions. These approaches are limited as they rely on cell line or yeast data, which are not representative of primary tumors. We have developed MiSL, a novel computational algorithm that utilizes large pan-cancer patient datasets (mutation, copy number and gene expression) to identify SL partners for specific mutations in specific cancer types. The underlying assumption of our approach is that, across multiple cancers, SL partners of a mutation will be amplified more frequently or deleted less frequently, with concordant changes in expression, in primary tumor samples harboring the mutation. Application of MiSL produced candidate SL partners for 30-80% of recurrent mutations in 12 cancers. Importantly, MiSL identified candidate SL partners for mutations (mut) in genes such as IDH1 that are not well-represented in existing cell lines. This is a distinct advantage over recent computational methods that combine shRNA data along with genomic data to make their predictions. Since MiSL uses only genomic and gene expression data, it allows assessment of a wide range of primary human tumors and mutations found in large primary tumor data sets such as TCGA. We validated MiSL using existing data and large-scale shRNA experiments we performed in doxycycline-inducible expression systems. We found that IDH1mut MiSL candidates in acute myeloid leukemia (AML) were enriched (p=0.004) for essential genes specific to IDH1mut but not IDH1 wildtype cells determined by a DECIPHER shRNA screen covering 9,965 human genes performed in doxycycline-inducible IDH1 (R132) THP-1 cells. Importantly, 1 out of 5 MiSL candidates was a SL partner of IDH1mut in AML cells as per the shRNA screen, indicating MiSL's strong predictive power. Also, for multiple mutations in colorectal cancer, MiSL candidates were enriched (p<0.05) with genes that were selectively essential in the mutated colorectal cell-lines in Achilles data. Next, we used MiSL to identify novel and druggable SL partners in (i) AML and (ii) breast cancer. MiSL predicted a novel SL interaction in AML between IDH1mut and ACACA, the rate-limiting enzyme of fatty acid synthesis. Consistent with our prediction, pharmacologic or genetic blockade of ACACA prevented cell proliferation in the presence of IDH1mut, but not with IDH1 wildtype, in AML cell lines. Furthermore, when transduced with lentivirus encoding RFP-marked shRNA to ACACA, primary IDH1mut AML cells exhibited markedly reduced engraftment of RFP-positive human CD45+CD33+ leukemic cells compared to scrambled non-targeting shRNA (p<0.05) at 12 weeks post-engraftment, validating the SL interaction between mutant IDH1 and ACACA. This vulnerability indicates a novel role for IDH1mut in reprogramming lipid metabolism. MiSL also predicted that AKT1 is a SL partner of PIK3CAmut in breast cancer which we experimentally confirmed using 8 breast cancer lines. All four PIK3CAmut (but not wildtype) breast cancers were sensitive to AKT1 inhibition in viability and colony assays. In summary, MiSL is a general computational solution that finds novel SL interactions. Specifically, IDH1mut-ACACA is the first in vivo validated synthetic lethal in human tumor cells discovered purely by computational analysis of tumor genomic data. MiSL can greatly accelerate identification of pharmacologic targets associated with specific somatic mutations in specific tumor types for all kinds of mutations, thereby making it directly translatable to clinical applications. MiSL can also pinpoint predictive genetic biomarkers that can identify/extend indications for targeted therapies. Citation Format: Subarna Sinha, Daniel Thomas, Steven Chan, Yang Gao, Diede Brunen, Damoun Torabi, Andreas Reinisch, Rene Bernards, Ravindra Majeti, David L. Dill. Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer primary tumor data. [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 A27.