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

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.

Precision Oncology: The Road Ahead

“…The pair was also used to predict in vitro drug responses in order to identify novel drug repurposing indications for potentially treating renal cancer [136]. Unlike the approach of using expression and copy number data ([136]), an algorithm was recently developed to mine pan-cancer human tumor data and define mutation-specific SL interactions for specific cancers [139]. Its SL predictions were validated against published SL screens and one specific SL gene pair interaction between mutated IDH1 and acetyl-CoA carboxylase 1 ( ACACA ) in leukemia was experimentally validated; this interaction attenuating tumor growth in patient-derived xenografts (PDX) [139].…”

“…Unlike the approach of using expression and copy number data ([136]), an algorithm was recently developed to mine pan-cancer human tumor data and define mutation-specific SL interactions for specific cancers [139]. Its SL predictions were validated against published SL screens and one specific SL gene pair interaction between mutated IDH1 and acetyl-CoA carboxylase 1 ( ACACA ) in leukemia was experimentally validated; this interaction attenuating tumor growth in patient-derived xenografts (PDX) [139]. Finally, certain predicted SL interactions where shown to successfully predict drug sensitivity, thus serving as biologically interpretable biomarkers of the latter [139].…”

“…Its SL predictions were validated against published SL screens and one specific SL gene pair interaction between mutated IDH1 and acetyl-CoA carboxylase 1 ( ACACA ) in leukemia was experimentally validated; this interaction attenuating tumor growth in patient-derived xenografts (PDX) [139]. Finally, certain predicted SL interactions where shown to successfully predict drug sensitivity, thus serving as biologically interpretable biomarkers of the latter [139]. Overall, while these studies have laid a solid basis for some of these genome-wide approaches, extensive research is warranted to further elucidate the potential of SLi based approaches in precision oncology.…”

Mining Pathway Associations from Networks of Mutual Exclusivity Interactions

“…Such interactions across pathways can result in cross-talk between pathways [29,45] and methods have been developed to identify such between-pathway motifs [6,23]. Many other computational approaches have also been developed to infer ME [11,3,31,26,12,8] and SL interactions [27,34,44,42,30,32].…”

Predicting Synthetic Lethal Interactions using Heterogeneous Data Sources

“…Computational methods have been developed to identify potential SL pairs, reducing the number of candidates that can be functionally analyzed through genome-wide screens. These include machine learning based methods to predict genetic interactions in different species (Costanzo et al, 2010;Lu et al, 2013), in cancer (using yeast SL pairs) (Conde-Pueyo et al, 2009;Srivas et al, 2016), using metabolic modeling (Folger et al, 2011;Frezza et al, 2011), using evolutionary characteristics (Lu et al, 2013;Srivas et al, 2016), using transcriptomic profiles (Kim et al, 2016) and by mining cancer patient data Sinha et al, 2017;Lee et al, 2018). All of these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels.…”