An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach

Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments. Moderate to severe CRF has a negative impact on patients' ability to tolerate treatments as well as on their quality of life. In some patients, CRF is so severe, that they discontinue cancer treatment. Given its high occurrence and significant negative impact, it is imperative that effective treatments be developed for this devastating symptom. Two of the major knowledge gaps for CRF are a lack of a risk prediction model and a lack of knowledge of its underlying mechanisms. A sensitive and specific risk prediction model would assist clinicians to determine which patients are most likely to experience high levels of CRF and provide recommendations regarding activity modifying interventions (e.g., exercise). Increased knowledge of the mechanisms for CRF could identify potential targets for therapeutic interventions. Both of these knowledge gaps will be addressed in this application. Prior studies suggest that patients will experience an increase in the severity of CRF in the weeks following concurrent chemotherapy and radiation therapy (CCRT) that can persist after completion of treatment. However, no models exist to predict the magnitude of this increase. This inability to predict the severity of CRF during and following CCRT limits the ability of clinicians to identify high-risk patients and provide them with recommendations to manage CRF. To address this knowledge gap, demographic, clinical, and molecular data that are collected prior to the initiation of CCRT will be used to evaluate the utility of these features to predict the severity of CRF midway through, at the completion of, and six months following the completion of CCRT. In terms of mechanisms of CRF, while previous studies provide some insights into potential mechanisms that underlie CRF, several limitations warrant consideration, including: poorly defined CRF phenotype; relatively small sample sizes; evaluation of patients receiving only CTX or only RT; evaluation of a single type of molecular data; and evaluation of only cross-sectional molecular data. To address these limitations, we propose to evaluate for associations between changes in CRF and changes in gene expression and circulating cytokines in patients receiving CCRT over two time points (i.e., prior to and at the completion of treatment). This study will provide new insights to be able to identify high-risk patients as well as identify potential therapeutic targets. This project will guide the development of clinical studies to investigate additional mechanisms and therapeutic interventions for CRF and other types of fatigue associated with cancer and its treatment (e.g., immunotherapy, surgery).