RESUMEN
Lung cancers account for over 90% of thoracic malignancies and the rapid development of specific cytotoxic drugs and molecular therapies requires a detailed identification of the different histologies, gene drivers or immune microenvironment biomarkers. Nevertheless, the heterogeneous clonal evolution, the emergency of drug-induced resistance and the limited occurrence of genetic alterations claim the need of a deep integration of the tumor's and the patient's biological features. The aim of the present study is to generate a tecnological platform for precision medicine in order to set predictive personalized algorithms for patient diagnosis and therapy. All resectable patients having histologically confirmed stage IB-IIIA non-small cell lung cancer will be enrolled for tissue sampling. A large biobank of lung cancer samples and the corresponding healthy tissues and biological components (ie, blood, stools, etc.) with complete clinical, pathological and molecular information will be collected. The platform will include: a) digital patient data collection; b) whole NGS molecular analyses (exome, transcriptome, methylome) for tumor characterization; c) exploitation and collection of organoids from tissue patients; d) Surface Amplified Raman Spectroscopy; e) microfluidic-based technological drug screening; f) preclinical in vivo models based on patient-derived xenografts; g) generation of specific predictive algorithms taking into account all collected multiparameters. The project will lay the basis of a knowledge hub and qualified technology aimed not only at answering the medical and scientific community's questions, but also meant to be useful to individual patients by predicting the response to adjuvant and second-line drugs in case of relapse of the disease.