Dr. Vincent Quoc-Huy Trinh and his team utilize imaging and characterization techniques of cancerous tissue to develop new diagnostic and therapeutic tools. These techniques are centered around his training as a medical pathologist and medical knowledge associated with various visual manifestations identified in the tumors of both patients and animals. His team employs, among other methods, multiplex imaging, secretome analysis, artificial intelligence applied to images, transgenic murine models, and spatial transcriptomics.
Research Theme
Hepatic and pancreatic cancers have very low survival rates, among the lowest of all cancers affecting patients. The low survival is attributed to the absence of early diagnostic tools, leading to an advanced stage of the disease upon diagnosis. Their approach is to develop diagnostic and therapeutic tools to identify the disease early and treat it at this stage. Indeed, if the disease is caught and treated at a less advanced phase, survival approaches 100%.
Unfortunately, there is no treatment for these early-stage diseases other than morbid and sometimes lethal surgeries. Therefore, the approach involves 1) modeling the disease at this early phase, 2) identifying signaling pathways that sustain early tumor cells, and 3) developing therapies specific to this stage of the disease.
Instead of directly targeting tumor cells, the focus is on the numerous cells surrounding them, particularly fibroblasts. It has been demonstrated that fibroblasts release many molecules that enhance cancer progression. The goal is to identify these molecules and block their pro-tumoral action.
Research Objectives
The first objective is the analysis of molecules released by fibroblasts and their function on cancer cells, the “secretome”. Previously, they included extracellular vesicles, exosomes, and exomeres. Recently, we contributed to the discovery of a new category called “supermere” and our lab’s goal is to study its crucial importance specifically in the signaling between fibroblasts and tumor cells.
The second objective is creating an early pancreatic tumor model with fibroblast depletion through the adoptive transfer of cytotoxic T lymphocytes. They have previously developed the most effective fibroblast depletion model published in the literature (99.6%) and are now testing it in pancreatic tumors. Once fibroblasts are removed from the tumor, they will examine the transcriptomic pathways and proteins deregulated by the absence of fibroblasts. Thus, through negation, they can understand how fibroblasts maintain cancer cells.
The third objective involves the creation of imaging tools for hepatic and pancreatic cancer tissue using multiplex imaging methods, training deep learning-based cell classification algorithms (artificial intelligence), and deploying multi-spectral and hyper-spectral methods such as spatial transcriptomics. These methods are used to identify central tumor cells in the initiation and progression of the disease, complementing the other study objectives.