Abstract 4257: Predicting fatigue levels of head and neck cancer patients with gene expression using machine learning
Authors: Eldridge Ronald C. , Miller Andrew H. , Bruner Deborah W. , Beitler Jonathan J. , Higgins Kristin A. , Wommack Evanthia C. , Huynh Linh Kha , Saba Nabil F. , Shin Dong M. , Xiao Canhua 
Published: August 2018

Introduction: Cancer-related fatigue is physical, emotional, or cognitive exhaustion which can affect treatment adherence and quality of life, and predict survival. Head and neck cancer (HNC) patients experience high levels of fatigue due to the degree of radiotherapy (RT), but that alone does not fully explain it. Recent studies suggest that fatigue may be related to a patient's personalized metabolic and inflammatory response, suggesting a patient's gene expression (GE) may be used to predict and monitor fatigue - a precursor to managing it. In this analysis, we examined whether GE is predictive of pre-RT patient reported fatigue using cross-validated penalized Lasso regression - a machine learning approach.

Study population: From Emory University Clinics, 44 HNC patients donated blood samples before undergoing RT. GE was assessed using an Affymetrix Clariom S Human microarray which measures gene transcripts for roughly 24,000 genes; each probe was log-transformed, normalized, and standardized. The validated 20-item self-report multidimensional fatigue inventory questionnaire measured each patient's continuous fatigue score.

Methods: To predict fatigue, we used leave-one-out cross validation (CV). This means we built a Lasso regression model using GE probes from 43 of 44 subjects and used that model to predict fatigue for the remaining subject; this process was repeated 44 times until all subjects had a predicted fatigue score. We chose penalized Lasso regression because the ‘penalty' performs variable selection and mitigates collinearity between the GE probes; the penalty was chosen by an extra layer of CV not described. We compared the predicted fatigue scores to the corresponding patient reported fatigue scores using R2 (higher values mean stronger correlation and better prediction).

Results: To test the approach, we allowed the Lasso regression to build a model based on all subjects and predict fatigue on all subjects. This prediction was expected to be high, and it was (R2=0.98). However, the approach was less successful predicting fatigue during the leave-one-out CV prediction (R2=0.15). The probes most influential predicting fatigue are linked to genes involved in tryptophan metabolism (precursor to the neurotransmitter serotonin), double strand break DNA repair, and transforming growth factor beta receptor signaling (inflammation cytokine).

Conclusions: Gene expression may predict fatigue in head and neck cancer patients, but there is room for improvement. The model suggests that a patient's personalized DNA repair process, metabolic and inflammatory response may play key roles in patient fatigue. Integrating more data focused on these biological processes, for instance patient metabolomics, may improve the model performance. Future efforts include collecting a larger sample, trying alternative machine learning methods, and testing the robustness of the model over time.

Citation Format: Ronald C. Eldridge, Andrew H. Miller, Deborah W. Bruner, Jonathan J. Beitler, Kristin A. Higgins, Evanthia C. Wommack, Linh Kha Huynh, Nabil F. Saba, Dong M. Shin, Canhua Xiao. Predicting fatigue levels of head and neck cancer patients with gene expression using machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4257.