New Relevant Evidence in Cholangiocarcinoma Biology and Characterization.

Among solid tumors, cholangiocarcinoma (CCA) emerges as one of the most difficult to eradicate. The silent and asymptomatic nature of this tumor, particularly in its early stages, as well as the high heterogeneity at genomic, epigenetic, and molecular levels delay the diagnosis, significantly compromising the efficacy of current therapeutic options and thus contributing to a dismal prognosis. Extensive research has been conducted on the molecular pathobiology of CCA, and recent advances have been made in the classification and characterization of new molecular targets.

CT-radiomics and pathological tumor response to systemic therapy: A predictive analysis for colorectal liver metastases. Development and internal validation of a clinical-radiomic model.

Introduction: The standard treatment of colorectal liver metastases (CRLM) is surgery with perioperative chemotherapy. A tumor response to systemic therapy confirmed at pathology examination is the strongest predictor of survival, but it cannot be adequately predicted in the preoperative setting. This bi-institutional retrospective study investigates whether CT-based radiomics of CRLM and peritumoral tissue provides a reliable non-invasive estimation of the pathological tumor response to chemotherapy.

Ganglioside GD2 Contributes to a Stem-Like Phenotype in Intrahepatic Cholangiocarcinoma.

Background & Aims: GD2, a member of the ganglioside (GS) family (sialic acid-containing glycosphingolipids), is a potential biomarker of cancer stem cells (CSC) in several tumours. However, the possible role of GD2 and its biosynthetic enzyme, GD3 synthase (GD3S), in intrahepatic cholangiocarcinoma (iCCA) has not been explored.

Methods: The stem-like subset of two iCCA cell lines was enriched by sphere culture (SPH) and compared to monolayer parental cells (MON).

Hepatocellular Carcinoma Immune Microenvironment Analysis: A Comprehensive Assessment with Computational and Classical Pathology.

Purpose: The spatial variability and clinical relevance of the tumor immune microenvironment (TIME) are still poorly understood for hepatocellular carcinoma (HCC). In this study, we aim to develop a deep learning (DL)-based image analysis model for the spatial analysis of immune cell biomarkers and microscopically evaluate the distribution of immune infiltration.

Experimental Design: Ninety-two HCC surgical liver resections and 51 matched needle biopsies were histologically classified according to their immunophenotypes: inflamed, immune-excluded, and immune-desert.