Introduction

Immunotherapies have been a breakthrough in cancer treatment, allowing us to cure and extend the lives of tens of thousands of patients. Immunotherapies train a patient’s immune system to attack their tumor, but oftentimes the immune system attacks healthy cells too. These toxicities are called Immune related Adverse Events (IrAEs). Right now, doctors have to take a blind guess as to weather a patient is going to suffer from an IrAE or not. We have no idea what the patient who gets an IrAE looks like: if we can find out the sex, age, cancer type and genomic profiles of a patient who gets an IrAE when on an immunotherapy, we can model patient risk and save thousands of lives.

We don’t know the characteristics (clinical phenotypes and genomics) of patients who get IrAEs because noone has ever performed a study on them at a large scale. If we want to perform a large scale study of IrAEs, we need data on which patients get IrAEs and which patients dont. My project focuses on creating the first ever integrated IrAE dataset, and using it to discover the type of patient who gets IrAEs.

Prior to my senior project, I had run a computational analysis that discovered HLA DRB1 associations with ICI induced Addison’s disease. I want to investigate the biophysical mechanisms driving this interaction and hopefully work towards a biomarker or blood test for adverse event risk.