Our goal is to establish a novel risk stratification biomarker for patients with head-and-neck squamous cell carcinoma (HNSCC). Our collaboration focuses on the integrated analysis of molecular data (transcriptomics and proteomics) extracted from HNSCC pathological specimens and matched whole slide images of histopathology slides using machine learning.

HNSCC is a cancer type with characteristic histology which principally occurs in the oral cavity, pharynx and larynx. Although the incidence of HNSCC is high in South and Southeast Asia, the incidence in other areas tends to increase by association with HPV infection. From a molecular point of view, the majority of HNSCC cases are characterized by mutations of the TP53 gene (a well-known tumor-suppressor gene). Another molecular characteristic of HNSCC is its diversity of Alternative Splicing (AS) of mRNA: a crucial biological process involved in protein synthesis, which is also known to produce cancer-associated "neoantigens" proteins in cancerous tissues. However, a deeper understanding of how AS impacts HNSCC pathogenesis and prognosis is currently lacking.

To address this problem, our joint project aims at identifying molecular and morphological cancer-associated signatures of AS to develop a new risk stratification biomarker. In parallel, we expect our analysis to lead to the identification of AS-induced neoantigens which are targetable morpho-molecular candidates that could be used in the future to enhance the anti-tumor immune reaction.

Dr. Maxime Lafarge is co-lead in this collaboration and contributes to the study design, software development, proteomic data acquisition and image/data analysis. More about Maxime and his work can be found here.
Dr. Tatsuya Abé, Niigata Unversity, Japan, is co-lead in this collaboration and certified oral pathologist and contributes to study design, cohort building, data analysis and biomedical interpretation.