Integrated Analysis of Somatic Genomic Mutations and Antigen Presentation as Predictive Biomarkers for Combination Immunotherapy in Kidney Cancer
Abstract
Scientific Objective and Rationale: Immuno-oncology (IO) drugs increase the ability of a patient s immune system to recognize cancer, increasing the body s ability to fight the cancer. In clinical trials, new IO drugs have improved survival outcomes for kidney cancer patients and have become a standard part of treatment for metastatic kidney cancer. An IO drug can be used either by itself, or in combination with another IO drug (IO/IO combination), or in combination with a tyrosine kinase inhibitor (TKI) drug that works on the space around the kidney tumor in order to kill tumor cells (TKI/IO combination). Combining two drugs can make the treatment more effective, but also increases the side-effects. Some patients will respond better to one drug or combination than another drug or combination, so it is vitally important to be able to predict which treatment will be best for a given patient, to decrease side-effects and increase the chance that the treatment will work. Our proposed research hopes to find out what type of information about a patient s tumor cells can be used to personalize care when choosing an IO drug or drug combination, to improve effectiveness and have fewer side effects; this information is a "biomarker." How the immune system recognizes tumor cells is a multi-step system. IO treatment approaches try to specifically combat the multiple ways that tumor cells evade detection, to help the immune system kill the tumor. To be effective, the immune system must recognize differences between tumor cells and normal cells. Previous attempts to predict the effectiveness of IO drugs compared differences in genetic mutations between tumor and normal cells. In some other cancers, simply having more difference is associated with having better outcomes with IO treatment. However, this is not true in kidney cancer, where there are fewer genetic mutations overall than other types of cancer, so there are fewer mutations for the immune system to recognize. Also, kidney cancer patients often have similar numbers of mutations as each other but very different responses to the same treatment, so the number of mutations alone is not the biomarker we need. Genetic mutations can cause differences in the substances presented on the surface of a cell, therefore changing the immune system s ability to recognize a cancer cell. Each patient has a different genetic makeup to start with, and then the common mutations in kidney cancer change the choices that tumor cells make about which genes to express and which substances to present on the cell surface. Our theory is that when patients have about the same number of possible mutations, then some of the downstream factors become more important, such as what percentage of the mutations are expressed and how efficiently patients are able to present "antigen" (the substance marking the cell as foreign so the immune system will destroy it). We want to build an information model that predicts the amount of tumor antigen that can be presented by a particular patient, to predict that patient s response and resistance to IO drugs. Aim 1: Determine how common mutations in kidney cancer can have effects on the total number of mutations and how those mutations are expressed. Aim 2: See whether a patient s ability to present antigen is sufficient to predict response to IO drugs, or if it works better to combine that information with the number of potential different antigens. Areas of Emphasis: Mechanism of Response and Resistance; Prognosis; and Biomarker Development. Innovation: We will define a new concept for understanding tumor antigen presentation in kidney cancer, because the presentation of unique antigens is vital for recognition by the immune system. Our proposal shifts the focus from looking just at the potential genetic mutations to looking at the potential antigens, which may be more relevant for predicting the response to immunotherapy drugs. We wil
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010917
Entities
People
- Chung-Han Lee
Organizations
- Sloan-Kettering Institute
- United States Army