Leveraging Biophysicochemical Motifs in T-Cell Receptor Antigen Binding Regions and Antigen Co-occurrence to Predict Response to Immune Checkpoint Inhibitors
Abstract
Traditionally, patients with metastatic clear cell renal cell carcinoma have been treated with therapies that target the tumor blood vessels. More recently, immune-based therapies have emerged that have significantly changed the treatment and outcomes for our patients. Immune checkpoint inhibitors (ICIs) are drugs that use the body s immune system to fight diseases such as cancer. Today, patients with metastatic renal cell carcinoma are treated with a combination of drugs that involve ICI and/or drugs that target the tumor blood vessels. However, none of these treatments uniformly benefit all patients and many suffer from lot of side effects from these drugs. Different drugs target different molecular pathways and possibly benefit different groups of patients and therefore there is a need for markers that can tell us if the patient will respond to one group of drugs or not (ICI or those that target blood vessels). We also need to understand what induces the anti-tumor immunity, so we can develop new approaches to bring the benefits of ICI to more RCC patients. Past studies have tried to identify patients that may respond to ICIs by correlating levels of a protein called PD-L1 on tumors to how patients respond to treatment. These studies have found only a minimal correlation between clinical outcomes and PD-L1 levels, and patients with no PD-L1 expression may also respond to the ICIs. We and others have shown previously that tumors that have alterations in a gene called BAP1 tend to have more inflammation and respond better to ICI. This contrasts with tumors that have alterations in another frequently altered gene PBRM1. In this proposal, we will obtain sequencing data from immune cells in the tumors and integrate it with the sequencing data from the tumor cells. We have built a state-of-the-art model that we will apply to identify the unique properties of the immune cells present in tumors with BAP1 alterations and compare them with tumors with PBRM1 alterations. These results will enable us to understand the unique features of the immune cells that provide antitumor immunity and build a model to predict response to ICI. If successful, our efforts will lead to the first integrated predictive biomarker in renal cell carcinoma. This will enable appropriate allocation of drugs to the patients who will show treatment benefit and not be given to those patients who will not benefit and thus minimize toxicity.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2024
- Source ID
- HT94252310794
Entities
People
- Payal Kapur
Organizations
- United States Army
- University of Texas Southwestern Medical Center