SL-BioDP: Multi-Cancer Interactive Tool for Prediction of Synthetic Lethality and Response to Cancer Treatment.

Xiang Deng,Kevin Camphausen,Uma Shankavaram,Shaoli Das,Kristin Valdez

doi:10.3390/cancers11111682

Abstract

Synthetic lethality exploits the phenomenon that a mutation in a cancer gene is often associated with new vulnerability which can be uniquely targeted therapeutically, leading to a significant increase in favorable outcome. DNA damage and survival pathways are among the most commonly mutated networks in human cancers. Recent data suggest that synthetic lethal interactions between a tumor defect and a DNA repair pathway can be used to preferentially kill tumor cells. We recently published a method, DiscoverSL, using multi-omic cancer data, that can predict synthetic lethal interactions of potential clinical relevance. Here, we apply the generality of our models in a comprehensive web tool called Synthetic Lethality Bio Discovery Portal (SL-BioDP) and extend the cancer types to 18 cancer genome atlas cohorts. SL-BioDP enables a data-driven computational approach to predict synthetic lethal interactions from hallmark cancer pathways by mining cancer’s genomic and chemical interactions. Our tool provides queries and visualizations for exploring potentially targetable synthetic lethal interactions, shows Kaplan–Meier plots of clinical relevance, and provides in silico validation using short hairpin RNA (shRNA) and drug efficacy data. Our method would thus shed light on mechanisms of synthetic lethal interactions and lead to the discovery of novel anticancer drugs.

Highlights

Drug treatment of cancer depends on the notion that mutations that give rise to the development of cancer bring about a weakness that can be exploited therapeutically
Cancers are mainly caused by mutations/alterations of specific genes or pathways that contribute to the initiation and progression of the disease
For cancer-specific synthetic lethal interactions of each of the 623 primary genes reported in synthetic lethality (SL)-BioDP, the DiscoverSL

Summary

Introduction

Drug treatment of cancer depends on the notion that mutations that give rise to the development of cancer bring about a weakness that can be exploited therapeutically. Large-scale cancer genome sequencing efforts have catalogued mutations in various cancer types that can be explored as tumor-specific vulnerabilities [1] These genetic alterations consist of gain-of-function mutations in which genes are amplified, translocated, or mutated and loss-of-function mutations in which gene function is compromised by missense mutations or deletions. The former group of mutations have been the subject of intense focus by the pharmaceutical industry for the development of targeted cancer drugs. These efforts have resulted in several cancer drugs that target activated driver oncogenes, such as HER2, BCR-ABL, EGFR, and BRAF [2] These drugs target signaling proteins that are aberrantly activated as a direct consequence of an oncogenic mutation, and their inhibition is detrimental to the cancers. This dependence on oncogenic driver pathways is commonly referred to as oncogene

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