Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data

Subarna Sinha,Yang Gao,Rene Bernards,Daniel Thomas,David Hernandez,Damoun Torabi,Steven Chan,Ravindra Majeti,David L Dill,Andy Chan,Erinn B Rankin,Andreas Reinisch,Diede Brunen

doi:10.1038/ncomms15580

Abstract

Two genes are synthetically lethal (SL) when defects in both are lethal to a cell but a single defect is non-lethal. SL partners of cancer mutations are of great interest as pharmacological targets; however, identifying them by cell line-based methods is challenging. Here we develop MiSL (Mining Synthetic Lethals), an algorithm that mines pan-cancer human primary tumour data to identify mutation-specific SL partners for specific cancers. We apply MiSL to 12 different cancers and predict 145,891 SL partners for 3,120 mutations, including known mutation-specific SL partners. Comparisons with functional screens show that MiSL predictions are enriched for SLs in multiple cancers. We extensively validate a SL interaction identified by MiSL between the IDH1 mutation and ACACA in leukaemia using gene targeting and patient-derived xenografts. Furthermore, we apply MiSL to pinpoint genetic biomarkers for drug sensitivity. These results demonstrate that MiSL can accelerate precision oncology by identifying mutation-specific targets and biomarkers.

Highlights

Two genes are synthetically lethal (SL) when defects in both are lethal to a cell but a single defect is non-lethal
Since experimental screens are usually performed in cell lines, they can be negatively impacted by: (1) the limited representation of newly discovered mutations in existing cell lines: for example, the Cancer Cell Line Encyclopedia (CCLE) collection of 1,000 cell lines contains no acute myeloid leukaemia (AML) cell line with an oncogenic IDH1 mutation, even though the mutation is present in 10% of AML patients[10], and (2) the artificiality of in vitro screening conditions[11,12], which cannot fully capture in vivo tumour evolution in the tumour microenvironment
We demonstrate that MiSL solves two problems that are directly translatable to clinical applications: identifying novel mutation-specific SL interactions, in particular IDH1 mutation and acetyl-CoA carboxylase 1 (ACACA) in AML, and pinpointing predictive genetic biomarkers that can guide precise targeting of existing therapies

Summary

Introduction

Two genes are synthetically lethal (SL) when defects in both are lethal to a cell but a single defect is non-lethal. DAISY primarily utilizes a small number of inactivating (nonsense and frameshift) mutations and uses shRNA data from existing cell lines as part of its inference strategy, which means it will miss SL interactions that are false negatives in shRNA screens caused either by incomplete genetic knockdown or by inadequate representation of mutations in existing cell lines To address these limitations, we have developed MiSL (Mining Synthetic Lethals), a novel algorithm based on Boolean implications mined from large pan-cancer patient data sets to identify SL partners for specific cancer mutations in specific cancer types. We demonstrate that MiSL solves two problems that are directly translatable to clinical applications: identifying novel mutation-specific SL interactions, in particular IDH1 mutation and ACACA in AML, and pinpointing predictive genetic biomarkers that can guide precise targeting of existing therapies

Methods

Results

Conclusion