Study for the Multidimensional Analyses of Resistance and Toxicity to Immune- and Targeted-therapies.

Biomarkers to predict response and toxicity to Targeted Therapies. Targeted therapies such as kinase inhibitors are usually associated with extremely elevated response rates, precisely because their use requires the prior detection of a companion biomarker. However, secondary resistance almost invariably develops, resulting in often moderate improvements in overall survival despite major delays in disease progression. Resistance mechanisms can be broadly classified in two groups: i) "cis"-mutations that directly impinge on the binding of the drug to its target and ii) "trans" alterations that activate additional pathways able to override drug-induced inhibition or transcriptional upregulation of parallel pathways . In both cases these mutations are mostly acquired as an evolutionary response to the selective environment generated by the drug itself, and their identification is crucial to identify potential subsequent treatments able to circumvent the resistance mechanism. As more targeted therapies enter clinical practice, including novel classes like Antibody-drug conjugates (which maintain high target specificity but have completely different mechanisms of action), extensive investigation of the molecular mechanisms associated with secondary resistance becomes more and more relevant. Furthermore, many targeted therapies are associated with specific toxicities such as interstitial lung disease that are themselves poorly characterized from a mechanistic point of view, and this lack of knowledge prevents effective diagnosis and treatment. Biomarkers to predict response and toxicity to Immune Checkpoint Inhibitors. Several biomarkers with predictive power in Immune checkpoint inhibitorsI-treated patients have been reported (e.g. tumor mutational burden, extent of tumour T-cell infiltration at baseline, expression by tumor cells of the respective Immune checkpoint inhibitors targets). However each of these, individually, bears very little accuracy for outcome . A recent meta-analysis of tumor-intrinsic data across \>1,000 patients and multiple tumor types elaborated a multivariable predictive model for each cancer type using 11 features derived from genomic (whole exome sequencing ) and transcriptomic (total ribonucleic acid sequencing ) data of primary tumors (see below for the list of markers). The multivariable predictor attained an Afea Under the Receiver-Operating Characteristic value of 0.86, thus strongly indicating that an integrated assessment of multiple and novel biomarkers achieves an accuracy that can significantly impact on decision making. Moreover, recent findings suggest a critical role for the gut microbiome. Notably, specific species in the gut microbiota promote anti-cancer immunity during Immune checkpoint inhibitors treatment, which can be transferred by faecal microbiome transplantation to rescues Immune checkpoint inhibitors sensitivity in model systems . Underlying molecular mechanisms, however, are unknown, and may involve immunological mimicry of tumour neoantigens by microbial peptides from the gut or tumor microbiota . The issue of drug-related toxicities (Immune-related Adverse events) is possibly even more crucial in Immune checkpoint inhibitors-treated patients. Immune-related Adverse events commonly develop after a long latency, are associated with significant morbidity and mortality and often represent a reason for treatment discontinuation and disease relapse. Immune-related Adverse events are often difficult to diagnose, since their pathophysiology is different from that of clinically similar idiopathic autoimmune disorders. Despite the definition of consensus guidelines on the diagnosis and treatment of Immune-related Adverse events, therapeutic options are limited and invariably include generalized immune suppression, to the detriment of the anticancer response. Biomarkers strongly predictive or diagnostic of Immune-related Adverse events have not been identified to date. Some studies have identified specific cytokine combinations but these studies remain correlative and require validation in larger cohorts and different clinical contexts.