Literature DB >> 31467181

Expression Levels of Therapeutic Targets as Indicators of Sensitivity to Targeted Therapeutics.

Riti Roy1, Louise N Winteringham1, Timo Lassmann2, Alistair R R Forrest3.   

Abstract

Cancer precision medicine aims to predict the drug likely to yield the best response for a patient. Genomic sequencing of tumors is currently being used to better inform treatment options; however, this approach has had a limited clinical impact due to the paucity of actionable mutations. An alternative to mutation status is the use of gene expression signatures to predict response. Using data from two large-scale studies, The Genomics of Drug Sensitivity of Cancer (GDSC) and The Cancer Therapeutics Response Portal (CTRP), we investigated the relationship between the sensitivity of hundreds of cell lines to hundreds of drugs, and the relative expression levels of the targets these drugs are directed against. For approximately one third of the drugs considered (73/222 in GDSC and 131/360 in CTRP), sensitivity was significantly correlated with the expression of at least one of the known targets. Surprisingly, for 8% of the annotated targets, there was a significant anticorrelation between target expression and sensitivity. For several cases, this corresponded to drugs targeting multiple genes in the same family, with the expression of one target significantly correlated with sensitivity and another significantly anticorrelated suggesting a possible role in resistance. Furthermore, we identified nontarget genes that are significantly correlated or anticorrelated with drug sensitivity, and find literature linking several to sensitization and resistance. Our analyses provide novel and important insights into both potential mechanisms of resistance and relative efficacy of drugs against the same target. ©2019 American Association for Cancer Research.

Entities:  

Mesh:

Year:  2019        PMID: 31467181     DOI: 10.1158/1535-7163.MCT-19-0273

Source DB:  PubMed          Journal:  Mol Cancer Ther        ISSN: 1535-7163            Impact factor:   6.261


  4 in total

1.  Breast cancer stage prediction: a computational approach guided by transcriptome analysis.

Authors:  K Athira; G Gopakumar
Journal:  Mol Genet Genomics       Date:  2022-08-03       Impact factor: 2.980

2.  Systematic identification of biomarker-driven drug combinations to overcome resistance.

Authors:  Matthew G Rees; Lisa Brenan; Mariana do Carmo; Patrick Duggan; Besnik Bajrami; Michael Arciprete; Andrew Boghossian; Emma Vaimberg; Steven J Ferrara; Timothy A Lewis; Danny Rosenberg; Tenzin Sangpo; Jennifer A Roth; Virendar K Kaushik; Federica Piccioni; John G Doench; David E Root; Cory M Johannessen
Journal:  Nat Chem Biol       Date:  2022-03-24       Impact factor: 16.174

3.  Regulatory pattern of abnormal promoter CpG island methylation in the glioblastoma multiforme classification.

Authors:  Rendong Wang; Lei Zhao; Shijia Wang; Xiaoxiao Zhao; Chuanyu Liang; Pei Wang; Dongguo Li
Journal:  Front Genet       Date:  2022-09-19       Impact factor: 4.772

4.  Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.

Authors:  Yi-Ching Tang; Reid T Powell; Assaf Gottlieb
Journal:  Sci Rep       Date:  2022-09-27       Impact factor: 4.996

  4 in total

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