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.
This research project if successful will surely have an important impact in cancer treatment. The following outcomes are foreseen:
1- A string of variables (tumor and immune parameters) will be determined in immunotherapy treated patients to build a network analysis to design a roadmap for optimal activation of immunity in cancer.
2- The identification of immune and immune-related components whose functions need to be maintained or sustained (ex: T cell activation) and those whose activity must be altered or corrected.
3- Mechanisms underlaying critical resistance/activation immune pathway and/or intersections will be indicated and possibly elucidated
4- Anticipate novel guidelines in the treatment of cancer targeting the immune system proposing an innovative network-thinking logic for oncologists for practice changing and for the benefit of cancer patients.
A network analysis of new and old parameters assessed along the dynamic revolution occurring in a cancer patient undergoing immunotherapy is the only way we have today to envision novel roadmaps for cancer treatment and avoid unnecessary treatment in patients that cannot benefit of immunotherapy or that have an immune system that must be "prepared" to receive the treatment. The network analysis approach applied to novel trials /drugs could also explain the failure of negative trials leading to the rejection of probably important drugs designed to target molecules necessary for anti-tumor immune activation but tested in trials with no information on the link between the occurring immune changes.
The immunological parameters of each patient evaluated in this project will be included in the medical records and the results obtained will be object of scientific publications and will be presented to several national and international scientific meeting. They will be publicized on the web sites of the University and Department of researchers involved in the project. Moreover, the results of network medicine will be also discussed as "case studies" within the program of PhD in Network Oncology and Precision Medicine of Sapienza University, which includes the PI as member and the project results will be presented in the PhD website.