Grant Number:	1R01CA279749-01A1 Interpret this number
Primary Investigator:	Naing, Aung
Organization:	University Of Tx Md Anderson Can Ctr
Project Title:	Integrating Patient-Reported Outcomes and T-Cell Receptor Sequencing to Predict Immune-Related Adverse Events
Fiscal Year:	2024

Abstract. This proposal is disease agnostic and patient centered, focusing on symptoms associated with side effects of immune checkpoint inhibitors (ICPis). Although ICPis are effective in the treatment of several can- cers, the risk of immune-related adverse events (irAEs) due to unrestrained activation of the immune system presents a significant challenge. Severe irAEs (grade 3 or higher) can be devastating in many ways. The symptoms associated with severe irAEs can be debilitating, resulting in frequent hospitalizations. Their impact on quality of life can be significant enough to mandate discontinuation of an ICPi that is having beneficial anti- tumor activity, resulting in progression of disease and decreased overall survival. In the absence of prompt in- tervention, severe irAEs may cause life-threatening decline in organ function and are potentially fatal. Our insti- tution (MD Anderson Cancer Center) has shown that early detection and treatment of irAEs improves treat- ment outcomes. However, biomarkers to predict a patient’s risk for irAEs that could help in developing surveil- lance strategies and early diagnosis and management of irAEs are lacking, representing a gap in knowledge. The overall objective of this application is to build and validate a risk-prediction model and develop a clinical tool to predict the risk for severe irAEs in patients treated with ICPis. In preliminary studies, we found that poly- morphism in the germline-encoded T-cell receptor beta (TCRB) variable (TRBV) gene serves as a predictive biomarker of severe irAEs. We also observed that changes in symptoms reported using the MD Anderson Symptom Inventory (MDASI)-Immunotherapy measure within the first 3 weeks of immunotherapy initiation are predictive of subsequent development of severe irAEs. Guided by our preliminary findings, we hypothesize that integration of TRBV polymorphism and early changes in MDASI-Immunotherapy symptom items are pre- dictive of severe irAEs. To test our hypothesis, we will evaluate 500 patients on treatment with ICPis at MD Anderson or the National Cancer Institute. In Aim 1, we will perform long-amplicon TCRB repertoire sequenc- ing of RNA extracted from baseline peripheral blood samples to predict risk of severe irAEs based on TRBV polymorphism. In Aim 2, we will longitudinally administer patient-reported outcome (PRO) measures to deter- mine symptom, function, and health status changes that predict the risk of severe irAEs. Finally, in Aim 3, com- bining TRBV polymorphism and PRO changes with relevant demographic, socioeconomic, treatment, and clini- cal factors, we will build a predictive model and validate the model in an independent cohort of patients. To translate our results into clinical practice, we will also develop a web-based application to predict the risk of severe irAEs during treatment with ICPis. Our contribution is significant since risk assessment using the novel, simple, web-based tool will enable patients and their treating physicians to formulate personalized irAE- monitoring care plans to mitigate irAEs. This multidisciplinary approach will facilitate early detection and prompt management of irAEs, which will reduce the chance of progression to life-threatening or fatal events.





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