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MUSTARD—a comprehensive resource of mutation-specific therapies in cancer.

Title: MUSTARD—a comprehensive resource of mutation-specific therapies in cancer.
Authors: Mittal, Gauri; I, Anu R; Vatsyayan, Aastha; Pandhare, Kavita; Scaria, Vinod
Source: Database: The Journal of Biological Databases & Curation; 2021, Vol. 2021, p1-9, 9p
Subject Terms: CANCER treatment; MUSTARD; GENE fusion; INFORMATION resources; GOVERNMENT agencies
Abstract: The steady increase in global cancer burden has fuelled the development of several modes of treatment for the disease. In the presence of an actionable mutation, targeted therapies offer a method to selectively attack cancer cells, increasing overall efficacy and reducing harmful side effects. However, different drug molecules are in different stages of development, with new molecules obtaining approvals from regulatory agencies each year. To augment clinical impact, it is important that this information reaches clinicians, patients and researchers swiftly and in a structured, well-annotated manner. To this end, we have developed Mutation-Specific Therapies Resource and Database in Cancer (MUSTARD), a database that is designed to be a centralized resource with diverse information such as cancer subtype, associated mutations, therapy offered and its effect observed, along with links to external resources for a more comprehensive annotation. In its current version, MUSTARD comprises over 2105 unique entries, including associations between 418 unique drug therapies, 189 cancer subtypes and 167 genes curated and annotated from over 862 different publications. To the best of our knowledge, it is the only resource that offers comprehensive information on mutation-specific, gene fusions and overexpressed gene-targeted therapies for cancer. Database URL : http://clingen.igib.res.in/mustard/ [ABSTRACT FROM AUTHOR]
: Copyright of Database: The Journal of Biological Databases & Curation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index