Cancer cells accumulate mutations in protein-coding genes, leading to altered protein products called neo- antigens. These neo-antigens can be recognized by elements of the adaptive immune system and lead to killing of cancer cells. Moreover, these neo-antigen coding mutations are theorized to be the targets of immune cells that are activated by a novel class of immune checkpoint inhibitor drugs. Thus, describing the neo- antigens found in cancer is an important goal with important applications to human health. Our long-term goal is to understand the extent to which cancers gain or lose certain mutations in response to the selective pressure of the adaptive immune system. The first objective in this proposal is to qualitatively and quantitatively describe the neo-antigens that accrue in human tumors. We will accomplish this objective using DNA and RNA sequence data collected from thousands of cancer patients across 10 tumor types by consortia such as The Cancer Genome Atlas (TCGA). We will compare the properties of tumor mutations that can be recognized by the immune system against those that cannot be recognized by the immune system and identify cancer-type specific differences in these mutations. In some cancers, there are thousands of mutations that lead to neo-antigens, yet these cancers are not eliminated by the immune system. Our second objective is to identify if mutations that code for neo-antigens have been depleted in human cancers. To accomplish this objective, we will leverage the genetic differences between patients that affect their ability to recognize neo-antigens as foreign. Thus, we will identify if cancer patients have selectively lost mutations that code for neo-antigens that their immune system are capable of recognizing.
It is known that the genetic differences between patients can affect cancer onset, prognosis and response to therapy. We are identifying how genetic differences in patient immune systems can affect tumor evolution and escape from immune surveillance. With this information, we can: (1) identify biomarkers to help us decide which patients will benefit from treatment with immune checkpoint blockade inhibitors (which can have serious adverse effects), (2) help identify novel cancer vaccine targets and (3) discover more about the fundamental processes that govern cancer evolution.
