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Development of Genomic Risk Prediction Models for T-cell Malignancies

NCI - National Cancer Institute

PROJECT SUMMARY/ABSTRACT Relapsed acute lymphoblastic leukemia (ALL) and lymphoblastic lymphoma remain leading causes of cancer- related death in children. For many pediatric cancers, risk classification using clinical and genomic features has reduced risk of relapse by identifying high-risk patients and allocating intensified therapy, while simultaneously avoiding treatment related mortality and toxicity for children with low-risk disease. However, no risk classifiers exist in clinical practice for T-cell ALL (T-ALL) and T-cell lymphoblastic lymphoma (T-LBL). Clinically useful risk prediction models are urgently needed to prevent more children from relapsing and dying of T-ALL and T-LBL. The overall objective of the proposed research is to improve outcomes in T-ALL/T-LBL by 1) identifying high risk genomic features by genetic ancestry, and b) developing and validating risk prediction models integrating clinical features, early disease response, ancestry informative markers, and genomic features to identify high- and low- risk patients. In Specific Aim 1, the candidate will identify recurrent, prognostic genomic features associated with relapse and death in T-LBL by genetic ancestry and compare this to ancestry-informed prognostic genomic alterations in T-ALL. In Specific Aim 2, the candidate will develop and validate risk prediction models in T-ALL and T-LBL, integrating clinical features, disease response, multi-omics (whole genome sequencing, RNA sequencing), and ancestry informative markers. Notably, DNA-based genetic ancestry is not included in risk allocation for any cancer, despite growing data demonstrating its impact on the prognostic value of somatic alterations. This work will promote a novel approach to improve outcomes by incorporating germline markers of genetic ancestry a priori into risk prediction modeling. The long-term goal of this work is to better understand differences in the biology of T-ALL and T-LBL and develop and validate risk prediction models which can be implemented into phase 3 clinical trials to allocate high-risk patients for intensified/alternative therapies, and low- risk patients for de-intensified treatment. The candidate is a pediatric oncologist committed to harnessing multi-omics and predictive modeling to improve outcomes and reduce toxicity for individuals with leukemia and lymphoma. The proposed training plan and research project will be conducted at the Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania (Penn). CHOP and Penn are leaders in multi-omic research will provide an outstanding environment in which to conduct the proposed work. The candidate will be mentored by Dr. David Teachey along with a very strong multidisciplinary advisory committee. The long-term goal of the candidate is to develop a research program devoted to advancing translational multi-omic epidemiologic research and provide the base for her future R01 proposal implementing the risk prediction model in a prospective phase 3 clinical trial. Completion of the proposed research project and the complementary training plan will provide a robust foundation to achieve this goal.

Up to $277K

2031-05-31

health research

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Development of Genomic Risk Prediction Models for T-cell Malignancies

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Grants

Development of Genomic Risk Prediction Models for T-cell Malignancies

Development of Guidance for Alternative Fuel Vehicles and Fueling Infrastructure Deployment Under the Clean School Bus Funding Programs

Development of GW Cincinnati Mill Creek

Development of High-Speed Atomic Force Microscope-Infrared Microscopy in the Aqueous Phase

Development of HTS for discovery of IL-25 inhibitors

Development of inhibitors to punish carbapenem-resistant Acinetobacter baumannii

Development of innovative tools, processes, equipment and technologies through responses to disasters and emergencies for search and rescue in hazardous conditions

Development of Interventions to Prevent and Treat Substance Use Disorders and Overdose (UG3/UH3 - Clinical Trial Optional)

Development of Interventions to Prevent and Treat Substance Use Disorders and Overdose (UG3/UH3 - Clinical Trial Optional)

Development of Long-Acting Antiretroviral Therapy for Sustained SIV Suppression in Rhesus Macaques

Development of methods for robust gene integration into native gut E. coli

Development of mini-USH2A gene therapy to treat Usher syndrome type 2A.

Development of miRNA-based approach for pediatric high-grade gliomas

Development of Models of Comprehensive Blood and Lymphatic Systems for Radiation Dosimetry

Development of molecular tools and mouse models to study neglected Sindbis virus genotypes and their epidemic potential

Development of Muscle Optimized Non-viral Gene Editors

Development of Nav channel targeting antisense oligonucleotides as chronic pain therapeutics using an integrated platform based on machine learning and optical electrophysiology

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