Thursday, November 21, 2024
11/21/2024
This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-24-015. Radiation-induced lymphopenia (RIL) is a common immunotoxicity associated with radiotherapy (RT) or chemoradiotherapy. Severe RIL (sRIL) has been shown to significantly impact patient survival and other outcomes. Most recently, it has become evident that sRIL can also diminish the effectiveness of immunotherapy significantly, when used in conjunction with RT. We have demonstrated that proton therapy, because of its smaller dose bath, can lead to significant sparing of the immune system. Project 2 of the parent NCI P01 is exploring the dosimetric and clinical determinants of RIL and correlation of RIL with patient outcomes, developing personalized machine learning RIL prediction models, and investigating strategies to mitigate RIL for cancers of the esophagus, lung, brain, liver, and pancreas. Our preliminary studies have suggested that the most effective RIL mitigation approach is the use of intensity- modulated proton therapy (IMPT) optimized based on personalized RIL models. The purpose of the research proposed in this supplement application is to develop a DTRO framework with the following objectives: (1) Incorporate the response deduced from lymphocyte depletion rate after initial treatment fractions (up to 5) to dynamically boost the RIL prediction accuracy for a given patient and adapt (reoptimize) the IMPT plan to minimize immune toxicity. (2) Validate and dynamically update the personalized RIL prediction models based on continuous feedback from actual responses. (3) Quantify uncertainties in model predictions and consider them when making treatment decisions. Specific Aims include the following: (1) Deploy and evaluate a bi-directional feedback DTRO system between the physical and virtual components of the Phase I clinical trial planned in Project 2 of the parent grant. (2) Amend the Phase I trial schema to form a feedback loop to dynamically update the personalized models. (3) Conduct uncertainty quantification analyses to establish confidence intervals around the model predictions and incorporate uncertainty estimates in decisions before treatment start and again after adaptation and reoptimization based on the response to initial fractions. The proposed trial will be implemented for a cohort of esophagus patients to investigate the potential of DTROs in general and the clinical effectiveness of the RIL mitigation approach. Our research will utilize multiscale data, including patient clinical characteristics, dosimetric parameters, adjuvant and concurrent chemotherapy details, and immune system biomarkers for a large cohort of esophagus patients previously treated at our institution. Similar data will be collected for each patient enrolled and treated prospectively in the DTRO-based trial. This research will be a collaborative effort among radiation oncologists, radiation physicists and data scientists. Our ultimate objective is to gain a comprehensive understanding of factors contributing to RIL incidence and severity. We will incorporate this knowledge to enhance the accuracy of prediction models and apply them to optimize treatment plans and clinical decision-making. We anticipate that this DTRO will lead to further improvements in outcomes beyond what is possible with the research proposed in the parent P01. Concepts and models developed as part of this research will be made available for broader use.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
