Thursday, April 25, 2024
4/25/2024
Summary/Abstract The public health impact of breast cancer (BC), its treatments, and resultant symptom burden is an ongoing problem given the increasing incidence and prevalence of BC and its significant health-related economic and social consequences. One of the most distressing issues for breast cancer patients and survivors is the burden of symptoms, specifically cognitive dysfunction, fatigue, pain, and depressive/anxiety symptoms, termed “psychoneurologic symptoms.” The biological underpinnings of these psychoneurologic symptoms are not yet clear, thus limiting the development of risk profiles and targeted therapeutic modalities. Guided by the National Institutes of Health Symptom Science Model (NIH-SSM), this application directly addresses the relationship of metabolomics, hundreds of key small molecules/end products (e.g., low molecular weight biochemicals including lipids, hormones, saccharides, organic acids, and amino acids) that serve as substrates of metabolic pathway, and their relationship to PN symptoms over the first year treatment trajectory. Leveraging our banked samples and highly phenotyped sample, we will use robust analytical techniques, including state-of-the-art high resolution mass spectrometry coupled with ultra-high performance liquid chromatography (UHPLC), advanced bioinformatics and multivariate statistical analyses to perform a longitudinal evaluation of the global metabolomics profile of N=75 women with early-stage breast cancer at 4 time-points, prior to chemotherapy (T1), at mid-cycle chemotherapy (T2), six months after the inception of chemotherapy (T3) and one year post chemotherapy inception (T4). We will address the following specific aims using multivariate statistical modeling: Specific Aim 1: To examine associations between surgical status, breast cancer characteristics, patient-related characteristics, PN symptoms and global metabolomics measures prior to the start of chemotherapy. Specific Aim 2: To identify the global metabolites that arise and are retained following BC and its treatment and determine if these metabolic alterations predict the severity of PN symptoms (individually and clusters) across time. Specific Aim 3: To test for the presence of metabolomic signatures differentiating women with high versus low symptom levels across time. Summary: This R21 poised to make a novel, and significant impact by illuminating potential metabolomics measures that are associated with PN symptoms in women with early-stage BC. Our team continues to develop the requisite scientific background for refining phenotypes, novel biological factors (metabolomics), and integrated signatures that may form a basis for future biomarker risk assays as well as targeted interventions to prevent and/or mitigate the severity of PN symptoms in women with BC.
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