Development of an Integrated Risk Prediction Model of Taxane-induced Peripheral Neuropathy

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Abstract Several anticancer agents cause a side effect known as peripheral neuropathy that can permanently diminish patient’s functional ability and quality of life. Since there are no effective strategies to prevent or treat peripheral neuropathy, clinical practice guidelines recommend withholding or dose-reducing chemotherapy, which reduces patient survival. Clinical and lifestyle factors do not adequately predict risk of taxane-induced peripheral neuropathy (TIPN). Therefore, there is a critical need to validate clinically useful and mechanistically informative predictive TIPN biomarkers in order to develop effective TIPN prevention strategies that improve taxane treatment outcomes. Dysregulation of nutrients including vitamin D, histidine, and sphingomyelin, and variants in genes linked to hereditary neuropathy conditions, have been identified as candidate biomarkers of TIPN. Confirming these biomarker candidates as predictors of TIPN risk requires a large cohort of taxane- treated patients with biospecimens and detailed TIPN data. The overarching objective of this proposal is to identify clinically-useful and mechanistically-informative TIPN biomarkers and develop an Integrated TIPN Risk Prediction Model that can predict an individual patient’s TIPN risk. We will extend the value of SWOG 1714 (S1714), which is a large (n=1,336) NCI-funded prospective clinical study that collected biospecimens and detailed TIPN data to create a Clinical TIPN Prediction Model. Our central hypothesis is that our physiologic (e.g., vitamin D, histidine, sphingomyelin) and genetic (e.g., EPHA5) biomarker candidates are predictive TIPN biomarkers and will enrich the Clinical TIPN Prediction Model. We will conduct targeted and comprehensive “omics” analyses of S1714 biospecimens to validate clinically useful and mechanistically informative predictive TIPN biomarkers and develop an Integrated TIPN Risk Prediction Model to complete the following specific aims: Aim 1: Assess the predictive value of key nutrients and lipids on TIPN risk. Aim 2: Determine genetic features that contribute to TIPN risk. Aim 3: Develop an Integrated TIPN Prediction Model. The primary expected outcomes of this research are the: 1) confirmation of clinically useful predictive TIPN biomarkers, 2) generation of new knowledge of the mechanistic processes that contribute to TIPN, and 3) creation of an Integrated TIPN Prediction Model. These outcomes will provide critical insight to develop novel agents and biomarker-informed treatment strategies that the study team can test in prospective clinical trials to prevent TIPN. If successful, these strategies could be translated into clinical practice to prevent TIPN and improve long-term clinical outcomes in the nearly one million patients with cancer who receive taxane treatment each year in the United States.