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Published on: December 9, 2015
dbMCS: A Database for Exploring the Mutation Markers of Anti-Cancer Drug Sensitivity
Abstract:
The identification of mutation markers and the selection of appropriate treatment for patients with specific genome mutations are important steps in the development of targeted therapies and the realization of precision medicine for human cancers. To investigate the baseline characteristics of drug sensitivity markers and develop computational methods of mutation effect prediction, we presented a manually curated online-based database of mutation Markers for anti-Cancer drug Sensitivity (dbMCS). Currently, dbMCS contains 1271 mutations and 4427 mutation-disease-drug associations (3151 and 1276 for sensitivity and resistance, respectively) with their PubMed indexed articles. By comparing the mutations in dbMCS with the putative neutral polymorphisms, we investigated the characteristics of drug sensitivity markers. We found that the mutation markers tend to significantly impact on high-conservative regions both in DNA sequences and protein domains. And some of them presented pleiotropic effects depending on the tumor context, appearing concurrently in the sensitivity and resistance categories. In addition, we preliminarily explored the machine learning-based methods for identifying mutation markers of anti-cancer drug sensitivity and produced optimistic results, which suggests that a reliable dataset may provide new insights and essential clues for future cancer pharmacogenomics studies. dbMCS is available at http://bioinfo.aielab.cc/dbMCS/.
Insights
A new database, dbMCS, catalogs cancer mutation markers and their effects on anti-cancer drug sensitivity. This resource aids in developing targeted therapies and advancing precision medicine for cancer patients.
Area of Science:
- Genomics
- Pharmacology
- Bioinformatics
Background:
- Precision medicine in cancer relies on identifying mutation markers for targeted therapies.
- Understanding mutation effects on drug sensitivity is crucial for effective cancer treatment.
Purpose of the Study:
- To create a manually curated database (dbMCS) of mutation markers for anti-cancer drug sensitivity.
- To investigate characteristics of drug sensitivity markers and explore computational prediction methods.
Main Methods:
- Developed dbMCS, a manually curated online database.
- Collected mutation data and mutation-disease-drug associations from PubMed.
- Analyzed mutation characteristics and explored machine learning for prediction.
Main Results:
- dbMCS contains 1271 mutations and 4427 associations (sensitivity/resistance).
- Mutation markers significantly impact conservative DNA/protein regions.
- Some markers show context-dependent pleiotropic effects (sensitivity and resistance).
Conclusions:
- dbMCS provides valuable data for cancer pharmacogenomics.
- Mutation markers' characteristics offer insights into drug sensitivity.
- Machine learning approaches show promise for predicting mutation effects on drug sensitivity.
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