Project Funding Details
- Title
- (PQ8) Patient- and tumor-specific biomarkers and mechanisms that predict irAEs resulting from checkpoint inhibition
- Alt. Award Code
- 5R01CA227481-04
- Funding Organization
- National Cancer Institute
- Budget Dates
- 2022-04-01 to 2023-03-31
- Principal Investigator
- Balko, Justin M
- Institution
- Vanderbilt University Medical Center
- Region
- North America
- Location
- Nashville, TN, US
Collaborators
View People MapThis project funding has either no collaborators or the information is not available.
Technical Abstract
PROJECT SUMMARY/ABSTRACT
In this proposal, we will identify clinically-translatable predictive and early-response biomarkers for the development of
immune-related adverse events (irAEs) caused by immune checkpoint inhibitor (ICI) therapy in cancer patients. Using
both focused and unbiased screening approaches, we will leverage a large inter-institutional and multi-disciplinary team
of investigators, as well as a large (>350 patients) retrospective and prospectively growing tissue and peripheral blood
bank of specimens from ICI treated patients, many of whom developed severe irAEs. Using this tissue bank, as well as
additional specimens prospectively collected at our institution and through collaborating institutions, we will identify
TCRs and autoantibodies that are expanded or upregulated in HLA-matched patients experiencing severe irAEs. Using
wide-net technologies (whole-proteome peptide microarray, 1 billion yeast pMHC display libraries, digital spatial
profiling), we will identify pathogenic T and B cell antigens in peripheral blood and tissue before and after ICI therapy.
In longitudinal studies, changes in TCR clonality, changes in autoantibody screening, and CyTOF for T cell compartments
will be performed in patients experiencing irAE and in clinically/HLA-matched controls. Findings will be compared to
treatment outcomes (clinical response and organ-specific irAEs) and we will test whether these biomarkers can be
detected prior to ICI therapy initiation. Translatable autoantibody biomarkers will be validated with a novel point-of-care
custom array technology for clinical utility. Finally we will profile the TCR repertoire in matched tumor and site-of-irAE
specimens using single-cell RNA sequencing of T cells, coupled with antigen identification through a highly novel ~1
billion yeast pMHC display library approach to identify the pathogenic mechanism behind irAEs.
Using these data, we will address three specific aims in this proposal: 1) we will prospectively characterize on-treatment
cell-mediated mechanisms of irAEs; 2) we will determine whether irAE-associated autoantibodies or TCRs can be
identified prior to treatment with ICIs; and 3) we will identify the antigen targets of pathogenic TCRs and profile their
expression across tumor and diseased tissue.
Due to the overwhelming success of ICIs, these treatments will be used in increasing numbers of patients and moved to
earlier lines of therapy. Thus, the numbers of patients at risk for irAEs will continue to rise; this proposal will address the
growing unmet need of how to identify and manage patients at risk for severe adverse sequelae from ICIs, while making
new discoveries that identify the pathogenic mechanism of irAEs.
Public Abstract
PROJECT NARRATIVE Immune checkpoint inhibitors (ICIs) have transformed cancer treatment and provide benefit in a large number of cancer patients. However, due to the mechanism of action of these agents, severe immune-related adverse events (irAEs) can occur and limit therapeutic use. This proposal seeks to identify predictive biomarkers through wide-net technologies and elucidate novel mechanisms underlying these irAEs that can be leveraged for clinical utility and appropriate risk management of cancer patients.
Cancer Types
- Not Site-Specific Cancer
Common Scientific Outline (CSO) Research Areas
- 6.1 Cancer Control, Survivorship and Outcomes Research Patient Care and Survivorship Issues
- 4.1 Early Detection, Diagnosis, and Prognosis Technology Development and/or Marker Discovery