Literature DB >> 28482068

mTCTScan: a comprehensive platform for annotation and prioritization of mutations affecting drug sensitivity in cancers.

Mulin Jun Li1,2, Hongcheng Yao2,3, Dandan Huang1, Huanhuan Liu1, Zipeng Liu2, Hang Xu2,3, Yiming Qin2,3, Jeanette Prinz4, Weiyi Xia5, Panwen Wang4, Bin Yan2,3, Nhan L Tran6, Jean-Pierre Kocher4, Pak C Sham2,7, Junwen Wang4,8.   

Abstract

Cancer therapies have experienced rapid progress in recent years, with a number of novel small-molecule kinase inhibitors and monoclonal antibodies now being widely used to treat various types of human cancers. During cancer treatments, mutations can have important effects on drug sensitivity. However, the relationship between tumor genomic profiles and the effectiveness of cancer drugs remains elusive. We introduce Mutation To Cancer Therapy Scan (mTCTScan) web server (http://jjwanglab.org/mTCTScan) that can systematically analyze mutations affecting cancer drug sensitivity based on individual genomic profiles. The platform was developed by leveraging the latest knowledge on mutation-cancer drug sensitivity associations and the results from large-scale chemical screening using human cancer cell lines. Using an evidence-based scoring scheme based on current integrative evidences, mTCTScan is able to prioritize mutations according to their associations with cancer drugs and preclinical compounds. It can also show related drugs/compounds with sensitivity classification by considering the context of the entire genomic profile. In addition, mTCTScan incorporates comprehensive filtering functions and cancer-related annotations to better interpret mutation effects and their association with cancer drugs. This platform will greatly benefit both researchers and clinicians for interrogating mechanisms of mutation-dependent drug response, which will have a significant impact on cancer precision medicine.
© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2017        PMID: 28482068      PMCID: PMC5793836          DOI: 10.1093/nar/gkx400

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  42 in total

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10.  DGIdb 2.0: mining clinically relevant drug-gene interactions.

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1.  regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.

Authors:  Shijie Zhang; Yukun He; Huanhuan Liu; Haoyu Zhai; Dandan Huang; Xianfu Yi; Xiaobao Dong; Zhao Wang; Ke Zhao; Yao Zhou; Jianhua Wang; Hongcheng Yao; Hang Xu; Zhenglu Yang; Pak Chung Sham; Kexin Chen; Mulin Jun Li
Journal:  Nucleic Acids Res       Date:  2019-12-02       Impact factor: 16.971

2.  Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes.

Authors:  Xiaobao Dong; Dandan Huang; Xianfu Yi; Shijie Zhang; Zhao Wang; Bin Yan; Pak Chung Sham; Kexin Chen; Mulin Jun Li
Journal:  Commun Biol       Date:  2020-01-07

3.  OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level.

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Review 4.  Bioinformatics Research on Drug Sensitivity Prediction.

Authors:  Yaojia Chen; Liran Juan; Xiao Lv; Lei Shi
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  4 in total

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