Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

Abstract

Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival

“…Our method discovered 12,447 SDL interactions (SI Appendix, 2, and Dataset S1). Reassuringly, the ranked list of SDLs significantly matches the top-ranked metabolic SDLs identified by the data mining synthetic lethality identification pipeline (DAISY), an approach for data-driven inference of genetic interactions in cancer that is based on the discovery of underrepresented gene pairs in cancer genomic data (20) (Wilcoxon rank-sum P < 0.0038).…”

“…We hence performed a survival analysis analogous to the tumor size analysis described earlier. The significance of the results obtained for SDL were compared with the single effects and random pairs by a modified signed Kaplan-Meier test introduced in a previous publication (20) (SI Appendix, 6).…”

“…Finally, we created random enzyme pairs (n = 5,000) to serve as control for testing the specific effects of the SDLs. Statistical significance for all comparisons was computed with a signed Wilcoxon rank-sum test, analogous to the signed Kaplan-Meier test as previously defined (20) (SI Appendix, 6).…”

“…Moreover, it has been shown that the overexpression of specific genes can be detrimental to cancer cell growth (19). Recently, a data-mining approach was used that identifies SLs and SDLs by analyzing large volumes of cancer genomic data (20). Here we aim to complement datadriven computational efforts with a biological network model approach to identify SDLs.…”

Treatment of Head and Neck Squamous Cell Carcinoma According on the Specific Molecular Features of the Tumor (A Literature Review)

“…Синтетическая летальность обозначает тот факт, что ингибирование 2 генов летально, а 1 гена -нет. Интерес представляет установление паттерна синтети-ческой летальности неактивных генов как супрессоров опухолевого роста в целях идентификации новых те-рапевтических мишеней [21]. Недавно разработано направление для идентификации широкого спектра возможных участников процесса синтетической ле-тальности с возможностью выявления паттернов син-тетической летальности онкогенов и супрессоров опухолевого роста.…”