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A precision medicine approach to improve prediction of severe toxicity in fluorouracil chemotherapy

提高氟尿嘧啶化疗严重毒性预测的精准医学方法

基本信息

批准号：
10295231
负责人：
Steven Offer
金额：
$ 36.37万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10295231
关键词：
Address African American Alleles American Applications Grants Biological Markers Breast Cancer Burden Cancer Patient Clinical Clinical Research Clinical Trials Collection Colorectal Cancer Coupled DPYD gene Data Data Collection Dihydropyrimidine Dehydrogenase Dose Enzymes Ethnic Origin European Financial Hardship Fluorouracil Genetic Goals Guidelines Haplotypes Impairment Incidence Individual Life Machine Learning Malignant Neoplasms Masks Measurement Medicine Metabolism Pathway interactions Patients Pharmaceutical Preparations Pharmacogenetics Plasma Population Public Health Publishing Quality of life Race Reaction Research Personnel Risk Safety Severities Signal Transduction Structure Systemic Therapy Testing Therapeutic Therapeutic Uses Toxic effect Treatment Efficacy Underrepresented Populations Variant Western Europe Work adverse drug reaction base candidate marker chemotherapy clinically relevant dose individualization expectation experience genetic variant high risk improved medication safety multidimensional data novel precision medicine predictive modeling predictive test risk variant sample collection side effect volunteer

项目摘要

PROJECT SUMMARY/ABSTRACT My long-term goal is to improve the toxicity profiles for cancer therapeutics. One-third of cancer patients treated with the commonly prescribed chemotherapeutic 5-fluorouracil (5-FU) experience severe and life- threatening toxicity to standard doses of the drug. An appreciable fraction of those patients die—not due to cancer, but because of side-effects related to treatment. Clinical studies indicate that the majority of patients who experience severe toxicity to 5-FU are deficient for an enzyme called dihydropyrimidine dehydrogenase (DPD, DPYD gene); however, only four genetic variants in DPYD have been adequately characterized to be considered predictive of 5-FU toxicity in clinical studies. My preliminary studies demonstrate that these four variants explain only a small fraction of severe 5-FU toxicities and have exceedingly limited clinical value outside of individuals with European ancestry. The primary objective of the studies proposed in this grant application is to identify additional biomarkers of 5-FU toxicity risk that can be used to individualize 5-FU dosing with the goal of improving the safety profile for the drug. My overall hypothesis is that expanded biomarker-based pre-treatment tests will more accurately identify patients with DPD deficiency, as well as the relative degree to which the DPD function is impaired, enabling more accurate dose optimization. My rationale is that improved biomarker-based approaches to dose individualization have strong potential to improve the safety profile for this commonly used therapeutic. Aim #1 will identify risk alleles for severe 5-FU–related toxicity in understudied populations. Aim #2 will characterize multi-marker haplotype contributions to 5-FU toxicity. In Aim #3, I will develop an integrated predictive model of 5-FU toxicity using deep machine learning. It is my expectation that the proposed studies, which will leverage multiple large patient and volunteer data and specimen collections to address various aspects of my primary hypothesis, will answer key questions that have vexed pharmacogenetics researchers for decades. In doing so, the proposed studies are expected to identify clinically relevant biomarkers that can be used to improve the safety profile of 5-FU through dose optimization.

项目总结/文摘