Multi-Omics Analysis for Predicting Response and Adverse Events to Neoadjuvant Treatment in Re-sectable NSCLC

Neoadjuvant (NA) treatment, including chemotherapy, immunotherapy, or their combination, has emerged as a promising strategy to improve longterm survival in patients with resectable Non-Small-Cell Lung Cancer (NSCLC). By reducing tumor burden and eliminating micrometastatic disease before surgical resection, NA therapy enhances the likelihood of complete tumor removal and re-duces recurrence rates. However, response rates remain highly variable, and a significant subset of patients experience limited therapeutic benefit or develop severe adverse events, underscoring the urgent need for reliable predictive biomarkers. Currently, there is no robust strategy to accurately identify patients who are most likely to respond favorably to NA therapy or those at higher risk of treatment-related toxicity, posing a challenge for personalized treatment approaches. This project aims to identify tumor tissue and circulating biomarkers associated with response and adverse events to NA treatment using a comprehensive multiomics approach. By integrating genomics, epi-genomics, transcriptomics, proteomics, and metabolomics data, we seek to uncover molecular signatures that distinguish responders from non-responders, as well as those predisposed to toxicity. In parallel, we will incorporate clinical parameters, patient lifestyle factors, and nutritional status to de-velop predictive models that enhance patient stratification. By leveraging machine learning and advanced bioinformatics tools, we aim to establish a predictive framework that refines patient selection for NA therapy. The identification of reliable biomarkers will facilitate the development of precision medicine strategies tailored to individual patients, optimizing treatment efficacy while minimizing toxicity. Our findings have the potential to refine clinical decision-making, improve patient outcomes, and contribute to the development of biomarkerdriven therapeutic approaches in NSCLC.