Literature DB >> 36173247

Discovery of druggable cancer-specific pathways with application in acute myeloid leukemia.

Quang Thinh Trac1, Tingyou Zhou2, Yudi Pawitan1, Trung Nghia Vu1.   

Abstract

An individualized cancer therapy is ideally chosen to target the cancer's driving biological pathways, but identifying such pathways is challenging because of their underlying heterogeneity and there is no guarantee that they are druggable. We hypothesize that a cancer with an activated druggable cancer-specific pathway (DCSP) is more likely to respond to the relevant drug. Here we develop and validate a systematic method to search for such DCSPs, by (i) introducing a pathway activation score (PAS) that integrates cancer-specific driver mutations and gene expression profile and drug-specific gene targets, (ii) applying the method to identify DCSPs from pan-cancer datasets, and (iii) analyzing the correlation between PAS and the response to relevant drugs. In total, 4,794 DCSPs from 23 different cancers have been discovered in the Genomics of Drug Sensitivity in Cancer database and validated in The Cancer Genome Atlas database. Supporting the hypothesis, for the DCSPs in acute myeloid leukemia, cancers with higher PASs are shown to have stronger drug response, and this is validated in the BeatAML cohort. All DCSPs are publicly available at https://www.meb.ki.se/shiny/truvu/DCSP/.
© The Author(s) 2022. Published by Oxford University Press GigaScience.

Entities:  

Keywords:  AML; cancer-specific pathways; pathway activation score

Mesh:

Year:  2022        PMID: 36173247      PMCID: PMC9520771          DOI: 10.1093/gigascience/giac091

Source DB:  PubMed          Journal:  Gigascience        ISSN: 2047-217X            Impact factor:   7.658


  37 in total

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3.  The Cancer Genome Atlas Pan-Cancer analysis project.

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4.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

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Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

5.  BRCA1 pathway function in basal-like breast cancer cells.

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6.  The Molecular Signatures Database (MSigDB) hallmark gene set collection.

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7.  A community effort to assess and improve drug sensitivity prediction algorithms.

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Journal:  Nat Genet       Date:  2013-01-20       Impact factor: 38.330

10.  Functional genomic landscape of acute myeloid leukaemia.

Authors:  Jeffrey W Tyner; Cristina E Tognon; Daniel Bottomly; Beth Wilmot; Stephen E Kurtz; Samantha L Savage; Nicola Long; Anna Reister Schultz; Elie Traer; Melissa Abel; Anupriya Agarwal; Aurora Blucher; Uma Borate; Jade Bryant; Russell Burke; Amy Carlos; Richie Carpenter; Joseph Carroll; Bill H Chang; Cody Coblentz; Amanda d'Almeida; Rachel Cook; Alexey Danilov; Kim-Hien T Dao; Michie Degnin; Deirdre Devine; James Dibb; David K Edwards; Christopher A Eide; Isabel English; Jason Glover; Rachel Henson; Hibery Ho; Abdusebur Jemal; Kara Johnson; Ryan Johnson; Brian Junio; Andy Kaempf; Jessica Leonard; Chenwei Lin; Selina Qiuying Liu; Pierrette Lo; Marc M Loriaux; Samuel Luty; Tara Macey; Jason MacManiman; Jacqueline Martinez; Motomi Mori; Dylan Nelson; Ceilidh Nichols; Jill Peters; Justin Ramsdill; Angela Rofelty; Robert Schuff; Robert Searles; Erik Segerdell; Rebecca L Smith; Stephen E Spurgeon; Tyler Sweeney; Aashis Thapa; Corinne Visser; Jake Wagner; Kevin Watanabe-Smith; Kristen Werth; Joelle Wolf; Libbey White; Amy Yates; Haijiao Zhang; Christopher R Cogle; Robert H Collins; Denise C Connolly; Michael W Deininger; Leylah Drusbosky; Christopher S Hourigan; Craig T Jordan; Patricia Kropf; Tara L Lin; Micaela E Martinez; Bruno C Medeiros; Rachel R Pallapati; Daniel A Pollyea; Ronan T Swords; Justin M Watts; Scott J Weir; David L Wiest; Ryan M Winters; Shannon K McWeeney; Brian J Druker
Journal:  Nature       Date:  2018-10-17       Impact factor: 49.962

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