Leveraging TCGA gene expression data to build predictive models for cancer drug response.

ABSTRACT:

Background

Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine.

Results

We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple

SUBMITTER: Clayton EA 

PROVIDER: S-EPMC7526215 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature