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

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 1year 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 < 0.05) and genomic (P<.001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided. Results: Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P<.001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P<5.00 x 10(-8)) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P=.01) and marginally improved with clinical variables (area under the curve=0.60, P=.005). Single nucleotide polymorphisms found to be associated (P<.001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P=.03), cognitive disorders (P=1.51 x 10(-12)), and synaptic transmission (P=6.28 x 10(-8)). Conclusions: Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.