TIPIT: Towards an Integrative approach for Precision ImmunoTherapy in Lung Cancer

Non-small cell lung cancer (NSCLC) is the first cause of cancer-related death in France. NSCLC is diagnosed at a metastatic stage in about 70% of patients.. In this situation chemotherapy combined with pembrolizumab is becoming the preferred option even in patients with a tumor with PD-L1 expression ≥50%. The survival of patients with metastatic NSCLC has been increasing with such strategies, but only for 45-50% of those patients a response can be demonstrated. Therapeutic decision is thus suboptimal, and there is a critical need for biomarkers for response prediction.

Our goal is to optimize our current standard-of-care of immunotherapy combined with chemotherapy in NSCLC through the integration of comprehensive, multimodal assessment of factors predicting the efficacy, including clinical data, pathological, radiological and metabolic imaging data, multi-omics profiling, and immunomonitoring. Our strategy will be based on machine learning techniques for extracting relevant features from each data modality : molecular, cellular and tissular level. Our assumption is that this multi-level approach will combine the advantages and information of each level in an optimal predictor.

Our objectives are then 1) to identify signatures predicting response and long-term survival to combination of immunotherapy (based on pembrolizumab as per current approval in France) plus chemotherapy; 2) to decipher biological pathways and mechanisms modulating immune responses, so as to develop prediction models of patient responses treatment.