10.3389/fphar.2019.00001.s002
Marianna A. Zolotovskaia
Marianna A.
Zolotovskaia
Maxim I. Sorokin
Maxim I.
Sorokin
Anna A. Emelianova
Anna A.
Emelianova
Nikolay M. Borisov
Nikolay
M. Borisov
Denis V. Kuzmin
Denis
V. Kuzmin
Pieter Borger
Pieter
Borger
Andrew V. Garazha
Andrew V.
Garazha
Anton A. Buzdin
Anton
A. Buzdin
Data_Sheet_2_Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs.csv
Frontiers
2019
cancer
DNA mutation
molecular pathways
biomarker
target drugs
tyrosine kinase inhibitors
nibs
mabs
2019-01-23 04:23:53
Dataset
https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Pathway_Based_Analysis_of_Mutation_Data_Is_Efficient_for_Scoring_Target_Cancer_Drugs_csv/7616753
<p>Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.</p>