Examination Date

Spring 3-11-2026

Degree

Thesis

Degree Program

Bioinformatics

Examination Committee

Dr. Fern Tsien, Dr. Fokhrul Hossain, Dr. Chindo Hicks

Abstract

Liver cancer is a leading cause of cancer deaths globally, with limited treatment options and very poor survival rates. A critical unmet need in clinical management of liver cancer pivots around discovery of clinically actionable diagnostic and prognostic biomarkers, therapeutic targets, and development of accurate algorithms to identify individuals at high risk of developing aggressive disease. With the availability of large- scale multi-omics data from Next Generation Sequencing, we are now well-positioned to address this critical unmet need. This Thesis project addressed these critical unmet needs by leveraging integrative multi-omics and machine learning for the discovery of potential clinically actionable immune modulated biomarkers, therapeutic targets, and molecular predictors of survival outcomes in liver cancer. In carrying out this study, we identified unique subsets of somatic mutated, immune-modulated genes whose expression levels differentiated tumor from control and living from deceased samples, as well as a subset that predicted survival in tumor samples.

Additional Files
Hoffman, Lija - Thesis Report Form.pdf (103 kB)
Thesis Report Form
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