Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer.

Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data.

Clinical-Molecular Prospective Cohort Study in Non-Small Cell Lung Cancer (PROMOLE study): A Comprehensive Approach to Identify New Predictive Markers of Pharmacological Response.

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.