title: Adverse Drug Event-based Stratification of Tumor Mutations: A Case Study of Breast Cancer Patients Receiving Aromatase Inhibitors.
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.
authors.affiliation: Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Chen
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Michael T
authors.initials: MT
authors.lastname: Zimmermann
authors.affiliation: Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Naresh
authors.initials: N
authors.lastname: Prodduturi
authors.affiliation: Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
authors.affiliation: Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Guoqian
authors.initials: G
authors.lastname: Jiang
mesh_terms: D000970:Antineoplastic Agents / Q000009:adverse effects* / Q000627:therapeutic use; D047072:Aromatase Inhibitors / Q000009:adverse effects* / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics*; D064420:Drug-Related Side Effects and Adverse Reactions; D005260:Female; D006801:Humans; D009154:Mutation; D057216:Propensity Score; D017432:Unified Medical Language System
chemical_list: D000970:Antineoplastic Agents; D047072:Aromatase Inhibitors