ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Adverse Drug Event-based Stratification of Tumor Mutations: A Case Study of Breast Cancer Patients Receiving Aromatase Inhibitors.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- An ontology for representing hematologic malignancies: the cancer cell ontology.BMC bioinformatics · 2019Article
- Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Adverse Drug Events-based Tumor Stratification for Ovarian Cancer Patients Receiving Platinum Therapy.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
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Authors and funding
5 authors.
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Abstract
Adverse drug events (ADEs) are a critical factor for selecting cancer therapy options. The underlying molecular mechanisms of ADEs associated with cancer therapy drugs may overlap with their antineoplastic mechanisms; an aspect of toxicity. In the present study, we develop a novel knowledge-driven approach that provides an ADE-based stratification (ADEStrata) of tumor mutations. We demonstrate clinical utility of the ADEStrata approach through performing a case study of breast invasive carcinoma (BRCA) patients receiving aromatase inhibitors (AI) from The Cancer Genome Atlas (TCGA) (n=212), focusing on the musculoskeletal adverse events (MS-AEs). We prioritized somatic variants in a manner that is guided by MS-AEs codified as 6 Human Phenotype Ontology (HPO) terms. Pathway enrichment and hierarchical clustering of prioritized variants reveals clusters associated with overall survival. We demonstrated that the prediction of per-patient ADE propensity simultaneously identifies high-risk patients experiencing poor outcomes. In conclusion, the ADEStrata approach could produce clinically and biologically meaningful tumor subtypes that are potentially predictive of the drug response to the cancer therapy drugs.
Indexed as
Identifiers
25954427PMC4419882What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.