Objective
Cholangiocarcinoma (CCA) is one of the deadliest cancers comprising a heterogeneous cluster of malignancies that are distinct to hepatocellular carcinoma (HCC) and that can emerge at any point in the biliary tree. While not very common, CCA is responsible for 2% of cancer-related deaths worldwide annually and rising. The incidence in European countries is increasing rapidly ranging from 1 to more than 4 cases/100,000. CCA is mostly diagnosed very late resulting in aggressive disease progression, poor treatment response, and dismal prognosis with a median survival of less than 2 years. Curative hepatic resection is an option in 10-30% cases and is mostly linked to recurrence within 12 months in over 50% of patients. For most patients, diagnosis will occur when the disease is already too advanced, and the only options are chemotherapies or palliative care. Current standard-of-care for CCA is unsatisfactory with very limited efficacy and adverse effects. Recently approved second-line therapeutics slightly improve outcome but are only suited to a small subset of CCA patients. Thus, there is a major unmet medical need for novel therapeutic strategies to improve patient outcome. Based on a robust data package obtained in a panel of CCA models within ERC HEPCIR, here we will explore the use of a novel class of compounds exploiting a unique and differentiated first-in-class mechanism-of-action and validate their efficacy in vivo as a first-line therapeutic for CCA. Proof-of-concept studies will be performed in state-of-the-art patient-derived models combined with scRNASeq and proteomics. The data obtained will serve as a pre-IND data package for this indication. An established collaboration with a pharma partner will ensure fast-track completion of the preclinical development towards IND. Collectively, ERC PoC CANDY will deliver completed proof-of-concept for a novel therapeutic approach for CCA which has the potential to transform patient care.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-AG-LS - HORIZON Lump Sum GrantHost institution
75654 Paris
France