Characterizing antigen presentation associated immune escape mechanisms in pancreatic adenocarcinoma using integrative computational approaches

Pancreatic cancer is a lethal disease affecting 367,000 people worldwide with a five year survival rate below 7%. While recent advances in cancer treatments that leverage the immune system against cancers (so called immunotherapies) have been tremendously successful in some cancer types, they have not improved outcomes in pancreatic cancer. However, why these therapies have not worked as well in pancreatic cancer is an open question that I hope to address.

The immune system can help combat cancer by killing cells that look foreign. To do this, immune cells known as killer T cells inspect protein fragments displayed on the outside of cells by a complex known as the major histocompatibility complex (MHC). If these fragments are mutated (as they frequently are in cancer), a T cell can kill the cancer cell. However, cancer cells can adapt to these attacks and lose the genes that encode the MHC allowing them to ‘escape’ T cell attacks. This mode of escaping the immune system is one possible reason why immunotherapies have not worked as well in pancreatic cancer. However, there has been no systematic evaluation of how frequently the genes encoding the MHC are deleted in this cancer type.

My project aims to characterize patients whose cancers have lost the MHC genes and identify possible treatment options for patients who have retained these genes and those who have lost them. To accomplish this I will develop a computational pipeline to analyze existing Marathon of Hope datasets and create custom machine learning tools to further understand how pancreatic cancer evades the immune system. Overall, this will be the most comprehensive analysis of immune escape in pancreatic cancer to date. 

Quotes 

“Pancreatic cancer is predicted to become the second leading cause of cancer related death making the identification of new treatments an urgent priority. My project is focussed on understanding why therapies leveraging the immune system have failed to improve treatments, despite their efficacy in other cancers. I am excited to be using Marathon of Hope datasets to train novel machine learning methods to characterize the cancer-immune interaction to eventually make immunotherapies viable in pancreatic cancer.” – Michael Geuenich, HI&DS Award recipient 

“Pancreatic cancer is a lethal disease with a low survival rate. Therapies that leverage the immune system have thus far not improved treatment, despite being successful in several other cancer types. This project aims to understand possible reasons for their lack of efficacy with the ultimate goal of finding better ways to treat this disease. Overall, this will be the most in depth characterization of the tumour-immune interaction in pancreatic cancer to date.” – Dr. Steven Gallinger, mentor