title: Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code.
abstract: Triple-negative (TN) breast cancer (BC) patients have limited treatment options and poor prognosis even after extant treatments and standard chemotherapeutic regimens. Linking TN patients to clinically known phenotypes with appropriate treatments is vital. Location-specific sequence variants are expected to be useful for this purpose by identifying subgroups within a disease population. Single gene mutational signatures have been widely reported, with related phenotypes in literature. We thoroughly survey currently available mutations (and mutated genes), linked to BC phenotypes, to demonstrate their limited performance as sole predictors/biomarkers to assign phenotypes to patients. We then explore mutational combinations, as a pilot study, using The Cancer Genome Atlas Research Network mutational data of BC and three machine learning methods: association rules (limitless arity multiple procedure), decision tree and hierarchical disjoint clustering. The study results in a patient classification scheme through combinatorial mutations in Phosphatidylinositol-4,5-Bisphosphate 3-Kinase and tumor protein 53, being consistent with all three methods, implying its validity from a diverse viewpoint. However, it would warrant further research to select multi-gene signatures to identify phenotypes specifically and be clinically used routinely.
authors.forename: Sohiya
authors.initials: S
authors.lastname: Yotsukura
authors.forename: Masayuki
authors.initials: M
authors.lastname: Karasuyama
authors.forename: Ichigaku
authors.initials: I
authors.lastname: Takigawa
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Mamitsuka
mesh_terms: D001943:Breast Neoplasms; D006801:Humans; D009154:Mutation; D010641:Phenotype; D010865:Pilot Projects
keywords: breast cancer; genotypes; machine learning; mutations; phenotypes