Early-stage non small cell lung cancer (ES-NSCLC) represents 20-30% of all NSCLC and is characterized by a high survival rate after surgery. However, there is variability in clinical outcomes among patients sharing the same disease stage, suggesting that other factors could determine the risk of relapse. Accurate and validated tools to stratify patients according to their risk of relapse are still lacking. Hypothesis: We hypothesize that multiple factors could influence the prognosis of resected ES-NSCLC patients. In particular, tumor tissue and microenvironment (TME) characteristics, liquid biopsy, radiomics features and clinical-pathological factors could all be involved. Aims: Primary: Development of a machine learning (ML) algorithm acting as a clinical decision support tool for disease free survival (DFS) prediction and patient stratification based on joint analysis of biological, clinical and radiologic features on a training cohort of resected ESNSCLC. Secondary: Validation of the developed algorithm on an independent cohort. Methods: A previously prospectively collected cohort of 220 ES-NSCLC patients will be considered as a training set. Tumor tissue and TME characteristics will be analysed using DNA and RNA sequencing approaches; liquid biopsy will be used to assess free circulating DNA and extracellular vesicles; radiomics parameters will be retrieved from computed tomography images. All these features, together with clinico-pathological factors, will be integrated in a model that will enable personalized patient treatment. The developed algorithm will be validated in a prospective cohort enrolled during MIRACLE. Expected results and potential impact: We expect to develop and validate a practical solution for an algorithm for DFS prediction to identify resected ES-NSCLC patients with different risk of relapse. This algorithm could be useful to improve patient management and establish more efficient and ethical therapeutic strategies.

Diffuse large B-cell lymphoma is a heterogeneous disease, 30-40% of all patients failing induction therapy, with limited success of next-line regimens. Among several pathological and genetic features, deregulation of the MYC and BCL2 oncogenes is a strong predictor of negative prognosis: indeed, overexpression of their protein products - routinely assayed by immunohistochemistry - allows detection of “double-expresser” lymphomas (DEL), representing a sizeable patient population with an acute unmet medical need. Classically, personalized medicine approaches would either aim at identifying actionable lesions at relapse, or utilize information available at diagnosis to expand the induction regimen with targeted therapy. Here, we propose to “re-think” personalized medicine with an innovative phase I/II trial, aimed at eradicating minimal residual disease (MRD) in DEL patients following a clinical response to induction immune-chemotherapy. Toward this aim, we will use a targeted drug combination (Venetoclax and Tigecycline, or V/T) with demonstrated efficacy against BCL2/MYC DEL cells. At the various stages, liquid biopsies will be used to assess the presence of MRD, as well as to establish full mutational and expression profiles, thus allowing us to decipher a posteriori the molecular underpinnings that characterize responders and non-responders. This will be complemented with a systemetic effort to optimize health care by defining conceptually novel stratification criteria for the V/T consolidation regimen, exemplifying a fundamentally novel route into personalized medicine in the clinic. Finally, our clinical activities will be complemented by a focused pre-clinical research plan, in which mouse models that recapitulate high-risk DLBCL subsets will be subject to similar sequential regimens, thereby addressing the mechanisms of action of the relevant drugs, as well as the functional implications of defined mutational and transcriptional profiles.

Breast Cancer (BC) is the most common cancer in Europe. In most cases, BC will present at a locally advanced or distant stage, which requiresanticancer drugs. However, there is no guarantee that the favorable drug benefit/risk balance will align with expectations at an individual level or be maintained over time. Therefore, monitoring tumor response to treatment is critical for timely adjusting the therapeutic strategy to improve patients’ outcomes. To fulfill this unmet medical need,SWEATPATCH proposesan innovative non- invasive approach to monitor in real-time BC response to treatment, based on emerging odorous biomarkers, namely volatile organic compounds (VOCs) from sweat emanated from the breast skin. Due to their connection to breast tumor metabolism, sweat VOCs are promising candidate biomarkers for monitoring tumor response to treatment.SWEATPATCH breakthrough relies on a radically-new technology with cutting-edge straintronic microwave based on phononic metamaterials and nanomaterials chemtronics technologies to solve scientific bottlenecks for sweat VOCs analysis in the near field of cancer cells.SWEATPATCH will gather an interdisciplinary consortium of leading experts in materials, sensors, data processing, and translational/clinical oncology, from 6 countries. The consortium will develop a groundbreaking wireless Lab-on-a-Patch to (1) monitor BC therapeutic response directly from the breast surface using a novel biocompatible flexible patch combined with specific data analysis that (2) will also be adaptable to in vitro 3D culture systems of patients’ derived organoids, a tumor model avatar created from patients’ BC, which will be used for drug screening to modelize tumor response to treatment.SWEATPATCH will convey a considerable conceptual leap that will shift the current healthcare paradigm from «one-size-fit-all» monitoring to «a smart real-time personalized monitoring»synchronized to tumor biology to reach precision oncology.