Literature DB >> 30059974

DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers.

Shaoli Das1, Xiang Deng1, Kevin Camphausen1, Uma Shankavaram1.   

Abstract

SUMMARY: Synthetic lethality is a state when simultaneous loss of two genes is lethal to a cancer cell, while the loss of the individual genes is not. We developed an R package DiscoverSL to predict and visualize synthetic lethality in cancers using multi-omic cancer data. Mutation, copy number alteration and gene expression data from The Cancer Genome Atlas project were combined to develop a multi-parametric Random Forest classifier. The effects of selectively targeting the predicted synthetic lethal genes is tested in silico using shRNA and drug screening data from cancer cell line databases. The clinical outcome in patients with mutation in primary gene and over/under-expression in the synthetic lethal gene is evaluated using Kaplan-Meier analysis. The method helps to identify new therapeutic approaches by exploiting the concept of synthetic lethality.
AVAILABILITY AND IMPLEMENTATION: DiscoverSL package with user manual and sample workflow is available for download from github url: https://github.com/shaoli86/DiscoverSL/releases/tag/V1.0 under GNU GPL-3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2018.

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Year:  2019        PMID: 30059974      PMCID: PMC6378931          DOI: 10.1093/bioinformatics/bty673

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

1.  SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.

Authors:  Jie Wang; Min Wu; Xuhui Huang; Li Wang; Sophia Zhang; Hui Liu; Jie Zheng
Journal:  Database (Oxford)       Date:  2022-05-13       Impact factor: 4.462

Review 2.  Computational methods, databases and tools for synthetic lethality prediction.

Authors:  Jing Wang; Qinglong Zhang; Junshan Han; Yanpeng Zhao; Caiyun Zhao; Bowei Yan; Chong Dai; Lianlian Wu; Yuqi Wen; Yixin Zhang; Dongjin Leng; Zhongming Wang; Xiaoxi Yang; Song He; Xiaochen Bo
Journal:  Brief Bioinform       Date:  2022-05-13       Impact factor: 13.994

3.  Pan-Cancer Analysis of Potential Synthetic Lethal Drug Targets Specific to Alterations in DNA Damage Response.

Authors:  Shaoli Das; Kevin Camphausen; Uma Shankavaram
Journal:  Front Oncol       Date:  2019-10-25       Impact factor: 6.244

4.  Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.

Authors:  Salvatore Benfatto; Özdemirhan Serçin; Francesca R Dejure; Amir Abdollahi; Frank T Zenke; Balca R Mardin
Journal:  Mol Cancer       Date:  2021-08-28       Impact factor: 27.401

5.  Overcoming Selection Bias In Synthetic Lethality Prediction.

Authors:  Colm Seale; Yasin Tepeli; Joana P Gonçalves
Journal:  Bioinformatics       Date:  2022-07-25       Impact factor: 6.931

6.  Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer.

Authors:  Hsih-Te Yang; Ming-Yu Chien; Jung-Hsien Chiang; Peng-Chan Lin
Journal:  Comput Struct Biotechnol J       Date:  2022-09-21       Impact factor: 6.155

7.  G2G: A web-server for the prediction of human synthetic lethal interactions.

Authors:  Yom Tov Almozlino; Iftah Peretz; Martin Kupiec; Roded Sharan
Journal:  Comput Struct Biotechnol J       Date:  2020-04-27       Impact factor: 7.271

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

Authors:  Xiang Deng; Shaoli Das; Kristin Valdez; Kevin Camphausen; Uma Shankavaram
Journal:  Cancers (Basel)       Date:  2019-10-29       Impact factor: 6.639

9.  Synthetic Lethal Drug Combinations Targeting Proteasome and Histone Deacetylase Inhibitors in TP53-Mutated Cancers.

Authors:  Shaoli Das; Xiang Deng; Kevin Camphausen; Uma Shankavaram
Journal:  Arch Cancer Biol Ther       Date:  2020

10.  Mapping the landscape of synthetic lethal interactions in liver cancer.

Authors:  Chen Yang; Yuchen Guo; Ruolan Qian; Yiwen Huang; Linmeng Zhang; Jun Wang; Xiaowen Huang; Zhicheng Liu; Wenxin Qin; Cun Wang; Huimin Chen; Xuhui Ma; Dayong Zhang
Journal:  Theranostics       Date:  2021-08-26       Impact factor: 11.556

  10 in total

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