The prognostic effects of somatic mutations in ER-positive breast cancer.
Malachi GriffithNicholas C SpiesMeenakshi AnuragMalachi GriffithJingqin LuoDongsheng TuBelinda YeoJason KunisakiChristopher A MillerKilannin KrysiakJasreet HundalBenjamin J AinscoughZachary L SkidmoreKatie CampbellRunjun KumarCatrina FronickLisa CookJacqueline E SniderSherri R DaviesShyam M KavuriEric C ChangVincent MagriniDavid E LarsonRobert S FultonShuzhen LiuSamuel LeungDavid VoducRon BoseMitch DowsettRichard K WilsonTorsten O NielsenElaine R MardisMatthew J EllisPublished in: Nature communications (2018)
Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction (q = 0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer.
Keyphrases
- poor prognosis
- long non coding rna
- positive breast cancer
- copy number
- signaling pathway
- ejection fraction
- randomized controlled trial
- clinical trial
- oxidative stress
- immune response
- dna methylation
- newly diagnosed
- zika virus
- gene expression
- postmenopausal women
- prognostic factors
- breast cancer cells
- single molecule
- transcription factor
- insulin resistance
- electronic health record
- adipose tissue
- skeletal muscle
- big data
- pi k akt
- breast cancer risk
- protein kinase
- high density