Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data.

ABSTRACT:

Background

We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.

Methods

We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate <

SUBMITTER: Lee S 

PROVIDER: S-EPMC7583150 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature