Prediction of RNA splicing changes from genetic variations is high-risk cancers

Pancreatic cancer is the third leading cause of cancer death in North America. Because pancreatic cancer is hard to treat and is often diagnosed at a late stage, patient survival is often measured in months. Current treatments consisting in chemotherapy and surgery are not effective. However, new therapies referred to as “immunotherapies” that leverage the patient’s own immune system against the cancer offer hope. Therapeutic vaccines are a form of immunotherapy that showed promising results in a clinical trial for pancreatic cancer. Much like vaccines against viruses and bacteria can trigger an immune response that protect from infections, vaccines against cancer can trigger an immune response that will eliminate cancer cells. The key to the development of an effective vaccine against pancreatic cancer is the identification of antigens present in tumors. We propose to use a computational approach to engineer personalized pancreatic cancer vaccines. We will sequence the DNA (“genome”) and the RNA (“transcriptome”) from tumor samples to identify antigens that are present in cancer cells, but not in normal cells. With this approach, we can design vaccines that will be optimized for each individual patient.

Faculty Supervisor:

Mathieu Quesnel-Vallières

Student:

Partner:

Universidad de Ingeniería y Tecnología

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology

University:

Université de Sherbrooke

Program: