Paclitaxel Neuropathy: A Glycoproteomic Approach to Predictive Biomarkers in Breast Cancer.

Abstract

Paclitaxel, a potent chemotherapeutic agent, is extensively used in the treatment of breast cancer, one of the most prevalent forms of cancer worldwide. Despite its efficacy, a significant challenge in the clinical use of paclitaxel is its association with peripheral neuropathy, a severe and often debilitating side effect that can dramatically affect patients' quality of life. This neuropathy is characterized by numbness, tingling, or pain in the patient's hands and feet, limiting their daily activities and often necessitating dose reduction or treatment discontinuation. Currently, the capacity to predict which patients are more likely to develop Paclitaxel-induced PN is lacking. This inability to forecast this adverse effect hampers the clinicians' ability to personalize treatment plans, potentially compromising treatment efficacy and patient quality of life. To address this critical gap, our project aims to explore the blood serum glycoproteome of patients prior to paclitaxel treatment who had acquired paclitaxel neuropathy or whose treatment had not caused these issues. Glycoproteomics, the study of changes in the glycosylation status of proteins, holds promise in uncovering potential biomarkers for disease states and responses to treatment. We hypothesize that distinct patterns of protein glycosylation could signal an elevated risk of developing peripheral neuropathy in response to paclitaxel treatment. Following our in-depth glycoproteomic investigation, our plan is to use machine learning methods to determine whether there is a particular combination of these proteoforms that could serve as a predictive model for paclitaxel-induced peripheral neuropathy