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

Neoadjuvant (NA) therapy, chemotherapy or chemoimmunotherapy given before surgery, has shown promise in improving survival in patients with operable Non-Small Cell Lung Cancer (NSCLC) by shrinking tumors and targeting hidden cancer cells, thereby increasing the chances of complete surgical removal and reducing recurrence. However, responses vary widely: while some patients experience major benefit, others gain little or suffer serious side effects, emphasizing the urgent need for reliable biomarkers to predict treatment outcomes and toxicity. This project aims to identify tumor and blood-based biomarkers linked to response and adverse events using an integrated multi-omics approach that combines genomics, epigenomics, transcriptomics, proteomics, and metabolomics data. Alongside molecular profiling, we will incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non-responders and those prone to toxicity, we aim to create a predictive framework to personalize NA therapy. Ultimately, this work will support more precise patient selection, enhance treatment effectiveness, minimize toxicity, and contribute to biomarker-driven strategies that improve outcomes and advance personalized medicine in NSCLC.

Project Team

• Tenured Professor Egils Stalidzāns
• PhD Rui Afonso Tavares
• MSc Margarita Lušņičenko

This project received funding from the Latvian Council of Science, contract No. ES RTD/2026/08under the frame of the European Partnership for Personalised Medicine, EP PerMed, (GA N° 101137129 of the EU Horizon Europe Research and Innovation Programme.