Ricerc@Sapienza

The identification of biomarkers in response to therapeutic treatment is one of the main objectives of personalized oncology. Predictive biomarkers are particularly relevant for oncologists challenged by the busy scenario of possible therapeutic options, including immunotherapy. Currently cancer treatment is based on immunotherapy and the activation of the immune system can determine the outcome and success of this therapeutic strategy. However, not all patients benefit from immunotherapy, and to date, there are still no validated biomarkers that can be included in the therapeutic algorithm. Thus, the identification of predictive biomarkers is necessary to increase the number of responsive patients and to understand the underlying immunity.
In this research project we will investigate mechanisms of resistance that impede or hampere anti-tumor immunoactivation and clinical response in cancer patients (Lung, Kidney, Head and Neck cancer) undergoing immunotherapy treatment (anti-PD1), providing a network analysis of relevant biomarkers related to immune response and activation in order to plan or propose corrective measures. A network based approache that includes patient's characteristics, immunogical parameters (CD137+T cells, soluble immune-checkpoint inhibitors, IDO activity, RF) and gut microbiota composition is proposed.
A string of predictors will be available to design roadmaps of immune interventions in cancer patients. Mechanisms underlying critical resistance/activation immune pathway and or intersections will be indicated and elucidated.
Novel guidelines in the treatment of cancer targeting the immune system can be designed proposing an innovative logic network-thinking for oncologists for practice changing and for the benefit of cancer patients.
The impact on cancer is foreseen since patients will be selected and treated for the right drug avoiding unnecessary, costly and toxic treatments and in particular increasing the rate of cure.