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Identification of mutation-specific targeted therapies is a critical challenge in precision medicine. Synthetic lethality provides the basis for an approach to identify new therapeutic targets for mutations that are difficult to target directly, since in synthetic lethal (SL) interactions, an alteration in one gene leads to dependency on a second gene. Neither alteration by itself is essential for survival, but together these alterations lead to cancer cell death. We previously developed a new computational method, Mining Synthetic Lethals (MiSL), that can infer relationships from primary human tumor genomic and transcriptomic data and enable the identification of SL interactions in the context of native human tumors. The underlying assumption of MiSL is that SL partners of a mutation will be selectively amplified or never deleted in primary tumor samples harboring the mutation. Here, we used MiSL to identify novel SL partners of KRAS mutations in lung cancer. KRAS activating mutations are highly prevalent in lung cancer and aside from early clinical data on a novel covalent inhibitor for KRAS G12C mutations, there is no reported treatment to date for patients with KRAS mutations. We applied MiSL to identify SL interactions with KRAS G12 mutations in non-small cell lung cancer (NSCLC) and identified 220 SL partner genes. Some of these genes were foreseeable dependencies, such as MAPK, growth factor and inflammatory signaling associated genes; while others were novel SL candidates and included genes associated with splicing. Since we have access to a splicing-modulating drug in preclinical development, sudemycin D6 (SD6) which targets SF3B1, a spliceosome subunit, we focused on testing the SF3B1 interactors identified by MiSL, SLU7 and SRRM2. To enable validation studies, we first verified KRAS growth dependency in a panel of NSCLC cell lines using siRNA knock-down (KD). Next, we tested siRNA KD of predicted MiSL candidates on growth of KRAS-dependent (H358 and H441 – KRAS mutant) and KRAS-independent (H2228 – KRAS WT) cell lines. We confirmed that the KD of SLU7 expression reduced viability of KRAS mutant (75 to 85% reduced) but not KRAS wild-type (WT) cells lines. Similar differential viability was also observed with siRNA KD of SF3B1. Pharmacological inhibition of SF3B1 using SD6 also demonstrated that KRAS mutant cell lines were more sensitive to SD6 compared to a KRAS WT line (>5-30x). Finally, we validated these findings in an in vivo tumor xenograft model by treating KRAS WT and KRAS mutant tumors with SD6 and showed that the KRAS mutant tumors were significantly more inhibited by SD6 than KRAS WT tumors. Studies are ongoing to further confirm the SL interaction in lung cancer cell lines that are isogenic except for the KRAS mutation. In conclusion, SL predictions based on primary KRAS G12 mutant tumors indicated that splicing modulation is a vulnerability of KRAS tumors. Finally, synthetic lethal targets identified by primary tumor data mining can provide new therapeutic targets for KRAS specific mutant cancers, which can lead to personalized treatment options for NSCLC cancer patients. Citation Format: Claire E Repellin, Puja Patel, Yihui Shi, Helena Gong, Leena Shewade, Thomas Webb, Lidia Sambucetti, Subarna Sinha. Primary tumor data analysis reveals novel synthetic lethal dependencies between KRAS mutation and the spliceosome [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C018. doi:10.1158/1535-7163.TARG-19-C018

Identification of predictive biomarkers for targeted drugs is an active research area in precision oncology. Usually, a genetic alteration in the drug target is used to identify responders, which can limit the use of the drug in other responsive cancers. Alternatively, cell line-based drug sensitivity data are used to build machine-learning models to identify predictive biomarkers. These methods require large amounts of data and the black-box models developed are often hard to interpret. Synthetic lethality provides an alternate approach for predictive biomarker identification of targeted drugs. In synthetic lethal (SL) interactions, a defect in one gene leads to dependency on a second gene. Neither defect by itself is essential for survival, but together they lead to cell death. Thus, the inhibition of the drug target in the presence of a cancer-specific genetic alteration would result in cancer cell death. The genetic alterations can be used as biomarkers for the drug. We developed a novel computational pipeline for predictive biomarker identification based on our tool, Mining Synthetic Lethals (MiSL), that mines SL interactions from pan-cancer primary tumor data (such as TCGA). Since MiSL utilizes primary human tumor data, it enables the identification of SL interactions in the native context of human tumors and is more likely to find relationships relevant to in vivo tumor biology than shRNA/CRISPR screens. To discover biomarkers of a targeted drug, we used MiSL to identify genetic alterations that are SL with targets of the drug in a specific cancer type. As proof of principle, we applied our method to identify biomarkers of sudemycin-D6 (SD6), a potent splicing modulator, in non-small cell lung cancer (NSCLC). First, we identified putative drug targets of SD6. Since SD6 is known to inhibit SF3B1, any gene belonging to the same protein complex as SF3B1 was termed a SD6 drug target. Using these SD6 drug targets, our MiSL-based pipeline identified KRAS mutations, present in ~30% of NSCLC patients, as a SD6 biomarker. To confirm our predictions, we treated a panel of KRAS-mutant-and-dependent NSCLC lines (H358, H23 and H441) and a KRAS-WT NSCLC line (H2228) with SD6 and measured cell viability. All KRAS-mutant lines tested were significantly more sensitive to SD6 (> 5-30X lower IC50) than the KRAS-WT line. Furthermore, the KRAS-mutant lines were also more sensitive (5-20X lower IC50), compared to the KRAS-WT line, to a splicing modulator of a different class – a kinase inhibitor that phosphorylates components of the spliceosome. In vivo studies are underway to further validate our in vitro findings. This study identifies KRAS mutation as a novel predictive biomarker for splicing modulators in NSCLC, which could lead to new therapeutic options for KRAS-mutated NSCLC. Furthermore, our work demonstrates the feasibility of a synthetic lethality-based pipeline to predict biomarkers for targeted drugs. Citation Format: Claire E. Repellin, Puja Patel, Yihui Shi, Helena Gong, Thomas R. Webb, Lidia Sambucetti, Subarna Sinha. Synthetic lethality-based predictive biomarker identification of splicing modulators in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4015.