AAII Seminar: May 22, 2019
When/where: E2-215 at 12pm
Presenter: David Haan (PBSE)
LURE (Learning UnRealized Events): Finding New(or Equivalent) Driver Mutation Events using Supervised Machine Learning
Cancer is a genetic disease typically resulting from an accumulation of mutations. Mutations in normal cells generally result in repair or cell suicide. In cancer cells, the mutations accumulate leading to an uncontrolled growth otherwise known as a tumor. There are two broadly defined types of mutations, driver and passenger mutations. Tumors contain around 2-5 driver mutations which cause and accelerate cancer, and about 10-200 passenger mutations which are accidental by products and result of thwarted DNA repair mechanism. The driver mutations are what defines the tumor, subtype and are therapeutic targets.
The Cancer Genome Atlas (TCGA) is a publicly accessible atlas of cancer related data from the National Cancer Institute (NCI). This atlas of data is a comprehensive analysis of 9000 patients and 33 cancer subtypes cataloging mutation data, DNA, mRNA, methylation, and protein expression. In particular, the TCGA study of Papillary Thyroid Carcinoma identified two subtypes, one harboring mutations in BRAF and the other were more RAS-like with mutations in KRAS, NRAS, HRAS. The study identified driver mutations, whether BRAF or H/K/NRAS, in about 95% of the samples, leaving about 5% with no known driver mutations. Here we present a tool, “Learning UnRealized Events” (LURE) designed to identify driver mutations in those samples without known driver mutations.