SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis

Abstract

Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality. Database URL http://www.tmliang.cn/SLOAD/

Systematic analysis of cancer‐specific synthetic lethal interactions provides insight into personalized anticancer therapy

“…Cancer‐specific synthetic lethal interactions in 31 cancer types were obtained from the SLOAD database (http://www.tmliang.cn/SLOAD/homepage) [27]. To integrate multi‐omics data to evaluate potential molecular interactions, multiple high‐sequencing datasets (mainly including DNA mutation and mRNA expression data in cancers) were obtained from The Cancer Genome Atlas (TCGA) with the “ tcgabiolinks ” r package [48].…”

“…Based on the previous prediction of cancer‐specific synthetic lethal genetic interactions in the SLOAD database using random forest via integration of multi‐omics data [27], this study performed a comprehensive analysis to understand the interactions among involved genes and reveal their potential roles in cancers. Cancer‐associated genes were prone to involvement in synthetic lethality, which implied their potential roles in human carcinogenesis.…”

Multi‐omics characterization of synthetic lethality‐related molecular features: implications for SL‐based therapeutic target screening

“…Using the SL datasets, including 142 602 computationally predicted by DAISY [7] and MiSL [8] algorithms and 5859 experimentally validated pairs from several datasets such as a collection by Lee et al [6] together with 2468 negative SL interactions [9], Guo et al sorted out cancer-specific SL interactions through a machinelearning algorithm. In their computational prediction, multi-omics molecular features were considered, including gene expression, mutation, methylation and copy number variation [10], which were obtained from The Cancer Genome Atlas (TCGA) [11,12]. In another study, to extend experimental SL datasets, Srivas et al predicted candidates by a regression model covering the features of cross-species interaction networks.…”

“…The SL interactions could also be divided into gene level, pathway level, organelle level and conditional SL by another classification system based on biological mechanisms [1]. Several data portals integrating synthetical lethality interactions from various sources have been recently published, such as SLKG [14] and SLOAD [10]. Large numbers of emerging SL interactions and complex interaction mechanisms covered by these databases could further benefit from a systematic analysis for the subsequent validation or clinical application.…”

“…sorted out cancer‐specific SL interactions through a machine‐learning algorithm. In their computational prediction, multi‐omics molecular features were considered, including gene expression, mutation, methylation and copy number variation [10], which were obtained from The Cancer Genome Atlas (TCGA) [11,12]. In another study, to extend experimental SL datasets, Srivas et al.…”