Cholangiocarcinomas (CCA) are a rare tumors with a dismal prognosis and limited treatment options. Only 1 out 3 patients are eligible for curative-intent surgery, and recurrence rates are high. The majority of patients are diagnosed in the advanced stage, in which chemotherapy yields a median survival of less than one year. Thus, CCA epitomizes the notion of hard-to-treat cancer. Recent advances in high-throughput genomic sequencing have led to a detailed understanding of CCA genetic alterations, yielding the picture of a disease fragmented in several genomically distinct subtypes. About 40% of CCA genomic alterations (namely FGFR2 fusions, ERBB2 amplification, IDH1 and BRAF gain of function mutations) predict patients’ assignment to oncogene-targeted drugs (OTDs). While results from precision oncology trials were encouraging, enthusiasm was mitigated by the observation that rate and duration of responses to OTDs in CCA are limited by resistance. Our leading hypothesis is that exploitation of OTDs to their full potential in CCA requires that resistance mechanisms are understood and counteracted pharmacologically in combination with OTDs. Because the inhibition of oncogenic drivers in other tumor types has been shown to cause also changes in the cellular composition of the tumor microenvironment (TME) – consequently affecting tumor-host interactions – we further hypothesize that understanding OTD-induced changes in the immune CCA TME will be key to design rationale-based combinations of immune-oncology (IO) drugs with OTDs. Our experimental approach entails the multi-omics (DNA and RNA sequencing, functional kinomics, unbiased gain/loss of function genetic screenings) interrogation of molecular determinants of OTD resistance in CCA clinical samples and genetically defined pre-clinical models (PDX, patient-derived organoids and 2D cell lines, genetically defined murine models) carrying the above cited BRAF, ERBB2, FGFR2 and IDH1 genetic alterations. The multidimensional data will be analyzed at increasing depth, i.e. from single-level approach (e.g. transcriptome analysis of a patient cohort selected for a specific driver mutation) to AI-driven network-level analyses that integrate multi-omics data from several models (e.g. patients’ data, PDX and mouse models). These analyses are expected to discover determinants of resistance/sensitivity of CCA cells to OTDs and guide the design, experimental validation and clinical translation of biomarker-driven approaches and combination therapies capable of increasing OTD efficacy in CCA. In addition, we will study driver oncogene-specific TME composition in human CCA and mouse models and identify OTD-induced TME changes that could aid in the rational design of combinations between OTDs and IO drugs. We expect that the above studies will produce a significant step forward of personalized cancer medicine approaches to CCA.
