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

Author:

Deng,Xiang

Das, Shaoli

Valdez,Kristin

Camphausen, Kevin

Shankavaram, Uma
Author Address

Bioinformatics core facility, Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. dengx@mail.nih.gov., Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA. dengx@mail.nih.gov., Bioinformatics core facility, Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. shaoli.das@nih.gov., Bioinformatics core facility, Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. kristin.valdez@nih.gov., Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA. kristin.valdez@nih.gov., Bioinformatics core facility, Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. camphauk@mail.nih.gov., Bioinformatics core facility, Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. uma@mail.nih.gov.,

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 39;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.

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