Immune Classification of Hepatocellular Carcinoma

Presenter

Eren Gulbas

Campus

UMass Amherst

Sponsor

Leili Shahriyari, Department of Mathematics and Statistics, UMass Amherst

Schedule

Session 2, 11:30 AM - 12:15 PM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A46, Campus Center Auditorium, Row 3 (A41-A60) [Poster Location Map]

Abstract

The tumor microenvironment consists of many different immune cell types and immune checkpoint molecules essential to cancer promotion, progression, and migration in the body. Therefore, when considering treatment options for patients with hepatocellular carcinoma (HCC), it is critical to understand the unique characteristics of immune infiltration in each patient sample. Cellular deconvolution, a mathematical method involving RNA expression data to predict the immune cell type proportions, can reveal any existing relationships between these cell abundances and phenotypical outcomes. This study identifies different immune cell patterns and clinical characteristics among patients with HCC. The deconvolution method CIBERSORTX along with HCC RNA sequencing data is used to obtain tumor immune microenvironment. The individuals are assigned to clusters using a K-means clustering method, where their tumor microenvironment characteristics can then be compared to phenotypical outcomes, such as the time to death, the presence of hepatitis B or C, or alcohol consumption status. Identifying different patterns between an individual tumor immune microenvironment and their phenotypical outcome provides the insight necessary for these personalized cancer treatments to be constructed or improved.

Keywords

Tumor Immune Microenvironment , Cancer Statistical Analysis, Cancer Mathematics

Research Area

Mathematics and Statistics

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