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Principal Investigator: William Hall
Organization: MEDICAL COLLEGE OF WISCONSIN
Fiscal Year: 2024
Award: $589,425
Funding agency: National Cancer Institute
PROJECT SUMMARY
Radiotherapy is a cornerstone of treatment for prostate cancer, but radiation-related genitourinary (GU)
and gastrointestinal (GI) toxicities can negatively impact quality of life among survivors. Radiotherapy can
damage the bladder and rectum leading to gross bleeding, inflammation, pain, fibrosis, and when severe, life-
threatening complications. Up to 20% of men treated with radiotherapy for prostate cancer develop mild to
moderate late GU and/or GI toxicities that are often permanent and negatively impact quality of life; up to 5%
develop severe or life-threatening effects requiring medical or surgical intervention. Radiation exposure drives
risk of late toxicity, but genetic predisposition is a significant contributor and can explain why some patients
develop toxicity while others no not despite identical treatment plans. Our prior work shows that late GU and GI
toxicities are polygenic in etiology, with risk modified by the combined effects of many low-penetrance single
nucleotide polymorphisms (SNPs), raising the attractive possibility of using a polygenic risk score to identify
susceptible patients prior to starting radiotherapy. Towards this goal, we developed a novel machine learning
approach to combine information from many risk SNPs and dose-volume parameters into a radiogenomic (ML-
RGx) risk score. Our preliminary data shows that this modelling approach out-performs existing methods and
shows promise for use in the clinic. The proposed project will apply this method to a large training dataset from
the NCI-supported International Radiogenomics Consortium to build ML-RGx models of GU and GI toxicity that
will then be externally validated using data and biospecimens from two large phase III radiotherapy trials
completed through the NRG Oncology cooperative group. Bioinformatic approaches will be applied to prioritize
SNPs for inclusion in the modelling and to uncover biologic pathways underlying genetic predisposition to normal
tissue injury. The study has three aims: (1) to train ML-RGx models for each of radiation-induced GU and GI
toxicity and define a threshold for low and high risk; (2) to validate ML-RGx models in two independent datasets
from NRG Oncology cooperative group trials; and (3) to assess feasibility and impact of ML-RGx models on
treatment planning workflow in a Radiation Oncology clinic. This work will bring personalized medicine to the
field of radiation oncology and improve prostate cancer care. Our innovative modelling approaches will also
uncover important molecular pathways that could be targeted with interventions to prevent and/or mitigate
toxicities.
Terms: <3-D><3-D CRT><3-Dimensional><3-Dimensional Conformal Radiation Therapy><3-dimensional conformal ablation><3D><3D-CRT><ACE Inhibitors><Active Follow-up><Angiotensin I-Converting Enzyme Inhibitors><Angiotensin-Converting Enzyme Antagonists><Angiotensin-Converting Enzyme Inhibitors><Angiotensinogen><Bio-Informatics><Bioinformatics><Biological><Bladder><Bladder Urinary System><Bleeding><Calibration><Cancer Patient><Cancer Radiotherapy><Causality><Clinic><Clinical><Clinical Oncology><Clinical Research><Clinical Study><Collaborations><Computer software><Computerized Medical Record><Conformal Radiotherapy><Conformal Therapy><DNA><Data><Data Set><Deoxyribonucleic Acid><Diagnosis><Dose><Dose Limiting><Electronic Medical Record><Etiology><Fibrosis><Fractionated radiotherapy><Future><GWA study><GWAS><Genetic><Genetic Predisposition><Genetic Predisposition to Disease><Genetic Risk><Genetic Susceptibility><Genetic Toxicity Tests><Genetic propensity><Genitourinary><Genitourinary system><Genotoxicity Tests><Goals><Health Care Systems><Healthcare Systems><Hemorrhage><Hereditary><Heritability><Hypertensinogen><Incidence><Individual><Inflammation><Inherited><Inherited Predisposition><Inherited Susceptibility><Injury><International><Interpretable ML><Interpretable machine learning><Intervention><Intervention Strategies><Kininase II Antagonists><Kininase II Inhibitors><Life><Linkage Disequilibrium><Machine Learning><Malignant Tumor of the Prostate><Malignant neoplasm of prostate><Malignant prostatic tumor><Medical><Methods><Modeling><Molecular><Mutagen Screening><Mutagenicity Tests><Normal Tissue><Normal tissue morphology><Oncology><Oncology Cancer><Operative Procedures><Operative Surgical Procedures><Outcome><Pain><Painful><Pathway interactions><Patient Selection><Patients><Penetrance><Phase><Physicians><Predisposition><Prevention><Proangiotensin><Prostate CA><Prostate CA therapy><Prostate Cancer><Prostate Cancer therapy><Prostate malignancy><Prostatic Cancer><Publications><QOL><Quality of life><RTOG><Radiation><Radiation Conformal Therapy><Radiation Oncologist><Radiation Oncology><Radiation Therapy Oncology Group><Radiation exposure><Radiation therapy><Radiogenomics><Radiotherapeutics><Radiotherapy><Randomization trial><Rectum><Renin-Substrate><Risk><Saliva><Sampling><Scientific Publication><Single Base Polymorphism><Single Nucleotide Polymorphism><Software><Subgroup><Surgical><Surgical Interventions><Surgical Procedure><Survey Instrument><Surveys><Survival Analyses><Survival Analysis><Survival Rate><Survivors><Susceptibility><Testing><Therapy trial><Time><Toxic effect><Toxicities><Training><Treatment-related toxicity><United States><Urogenital><Urogenital System><Validation><Work><active followup><biologic><blood loss><cancer care><cancer radiation therapy><causation><clinical decision-making><clinical practice><cohort><conformal radiation><conformal radiation therapy><cost><data to train><dataset to train><design><designing><disease causation><efficacy trial><experimental arm><explainable ML><explainable machine learning><feasibility testing><follow up><follow-up><followed up><followup><gastrointestinal><genetic etiology><genetic mechanism of disease><genetic vulnerability><genetically predisposed><genome wide association><genome wide association scan><genome wide association studies><genome wide association study><genomewide association scan><genomewide association studies><genomewide association study><high risk><improved><improved outcome><individual patient><injuries><injury to tissue><innovate><innovation><innovative><interest><interventional strategy><machine based learning><machine learning based method><machine learning method><machine learning methodologies><member><men><mutagen testing><novel><participant enrollment><pathway><patient enrollment><personalization of treatment><personalized medicine><personalized therapy><personalized treatment><polygenic risk score><prevent><preventing><prospective><prospective test><prostate cancer treatment><prostate radiotherapy><prostatectomy radiotherapy><radiation treatment><randomized trial><simulation><single nucleotide variant><surgery><therapeutic toxicity><therapy associated toxicity><therapy related toxicity><therapy toxicity><three dimensional><tissue injury><tool><training data><treatment planning><treatment toxicity><treatment with radiation><treatment-associated toxicity><trial design><tumor><urinary bladder><validations><whole genome association analysis><whole genome association studies><whole genome association study>