Overview
Our project is a large-scale characterisation of cHCC-CCA will allow us to determine which subsets harbor actionable gene alterations. We will also aim to improve diagnosis of this tumor type by the use of immunohistochemical biomarkers and the development of deep-learning based models able to help cHCC-CCA diagnosis. This will represent an important step towards precision medicine for the patients with this highly aggressive malignancy.
Description
- Scientific background Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare
liver cancer characterized by a dual hepatocytic and biliary differentiation. It is
resistant to conventional anti-cancer treatments and there is currently no effective
systemic therapy available. Inter-observer agreement for diagnosis of cHCC-CCA is low,
even among expert pathologists, and the development of clinical trials remain thus
challenging. The molecular mechanisms that drive its progression also remain
under-investigated.
- Project objectives and brief description of the methods which will be used to achieve them We aim to perform an integrative molecular, immune and phenotypical study of cHCC-CCA that will allow the distinction of different tumor subgroups linked to particular actionable genetic/immune alterations. The development of immunohistochemical markers and artificial intelligence-based approaches is also likely to improve the diagnosis of cHCC-CCA.
A overall multicentric series of 357 cHCC-CCA samples, already available in our biobanks, will be investigated by means of gene and RNA sequencing, digital pathology and immunohistochemistry in order to build a morphomolecular classification of this tumor. Spatial transcriptomics and in situ proteomics will be performed to decipher the intra-tumor heterogeneity and identify biomarkers of the different subclasses. Finally, deep-learning based models will be developed in order to 1) improve the diagnosis of cHCC-CCA and 2) identify the morphological features linked to prognosis.
• Expected results This large-scale characterisation of cHCC-CCA will allow us to determine which subsets harbor actionable gene alterations. We will also aim to improve diagnosis of this tumor type by the use of immunohistochemical biomarkers and the development of deep-learning based models able to help cHCC-CCA diagnosis. This will represent an important step towards precision medicine for the patients with this highly aggressive malignancy.
Eligibility
Inclusion Criteria:
histological diagnosis of combined tumor biological sample available
Exclusion Criteria:
unequivocal histological features