Development of an Integrated Risk Prediction Model of Taxane-induced Peripheral Neuropathy

紫杉烷诱发的周围神经病变综合风险预测模型的开发

基本信息

批准号：
10566077
负责人：
Daniel Louis Hertz
金额：
$ 60.12万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2027-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10566077
关键词：
Activities of Daily Living Age Amino Acids Antineoplastic Agents Biological Markers Breast Carcinogens Clinical Clinical Practice Guideline Clinical Research Clinical Trials DNA Data Development Diet Dose Funding Generations Genes Genetic Genetic Markers Genotype Goals Histidine Intervention Knowledge Life Link Lipids Lung Machine Learning Malignant Neoplasms Manuals Measures Mission Neuropathy Nutrient Outcome Outcomes Research Ovarian Paclitaxel Patients Peripheral Nervous System Diseases Pharmaceutical Preparations Phospholipids Physiological Plasma Predictive Value Prevention Prevention strategy Process Quality of life Race Recommendation Research Risk Sampling Southwest Oncology Group Sphingomyelins Testing Translating Treatment Effectiveness Treatment outcome United States Variant Vitamin D Vitamins Work candidate marker chemotherapy clinical practice cohort docetaxel experience genome-wide genomic data hereditary neuropathy improved individual patient insight lifestyle factors lipidomics novel predictive marker predictive modeling premature prevent preventive intervention prospective risk prediction risk prediction model side effect taxane treatment strategy tumor

项目摘要

Abstract Several anticancer agents cause a side effect known as peripheral neuropathy that can permanently diminish patient’s functional ability and quality of life. Since there are no effective strategies to prevent or treat peripheral neuropathy, clinical practice guidelines recommend withholding or dose-reducing chemotherapy, which reduces patient survival. Clinical and lifestyle factors do not adequately predict risk of taxane-induced peripheral neuropathy (TIPN). Therefore, there is a critical need to validate clinically useful and mechanistically informative predictive TIPN biomarkers in order to develop effective TIPN prevention strategies that improve taxane treatment outcomes. Dysregulation of nutrients including vitamin D, histidine, and sphingomyelin, and variants in genes linked to hereditary neuropathy conditions, have been identified as candidate biomarkers of TIPN. Confirming these biomarker candidates as predictors of TIPN risk requires a large cohort of taxane- treated patients with biospecimens and detailed TIPN data. The overarching objective of this proposal is to identify clinically-useful and mechanistically-informative TIPN biomarkers and develop an Integrated TIPN Risk Prediction Model that can predict an individual patient’s TIPN risk. We will extend the value of SWOG 1714 (S1714), which is a large (n=1,336) NCI-funded prospective clinical study that collected biospecimens and detailed TIPN data to create a Clinical TIPN Prediction Model. Our central hypothesis is that our physiologic (e.g., vitamin D, histidine, sphingomyelin) and genetic (e.g., EPHA5) biomarker candidates are predictive TIPN biomarkers and will enrich the Clinical TIPN Prediction Model. We will conduct targeted and comprehensive “omics” analyses of S1714 biospecimens to validate clinically useful and mechanistically informative predictive TIPN biomarkers and develop an Integrated TIPN Risk Prediction Model to complete the following specific aims: Aim 1: Assess the predictive value of key nutrients and lipids on TIPN risk. Aim 2: Determine genetic features that contribute to TIPN risk. Aim 3: Develop an Integrated TIPN Prediction Model. The primary expected outcomes of this research are the: 1) confirmation of clinically useful predictive TIPN biomarkers, 2) generation of new knowledge of the mechanistic processes that contribute to TIPN, and 3) creation of an Integrated TIPN Prediction Model. These outcomes will provide critical insight to develop novel agents and biomarker-informed treatment strategies that the study team can test in prospective clinical trials to prevent TIPN. If successful, these strategies could be translated into clinical practice to prevent TIPN and improve long-term clinical outcomes in the nearly one million patients with cancer who receive taxane treatment each year in the United States.

抽象的几种抗癌药物会引起称为周围神经病变的副作用，这种副作用可以永久减少由于没有有效的预防或治疗策略，患者的功能能力和生活质量。周围神经病变，临床实践指南建议停止化疗或减少化疗剂量，临床和生活方式因素不能充分预测紫杉烷引起的风险。因此，迫切需要验证其临床用途和机制。信息预测 TIPN 生物标志物，以制定有效的 TIPN 预防策略，从而改善紫杉烷类治疗结果的营养失调，包括维生素 D、组氨酸和鞘磷脂，以及与遗传性神经病相关的基因变异已被确定为候选生物标志物确认这些候选生物标志物作为 TIPN 风险的预测因子需要大量紫杉烷类药物。接受治疗的患者生物样本和详细的 TIPN 数据该提案的首要目标是识别临床有用且具有机械信息的 TIPN 生物标志物并制定综合 TIPN 风险可以预测个体患者 TIPN 风险的预测模型我们将扩展 SWOG 1714 的价值。 (S1714)，这是一项由 NCI 资助的大型前瞻性临床研究（n=1,336），收集了生物样本并详细的 TIPN 数据来创建临床 TIPN 预测模型我们的中心假设是我们的生理学。候选生物标志物（例如维生素 D、组氨酸、鞘磷脂）和遗传（例如 EPHA5）生物标志物可预测 TIPN 生物标志物，将丰富临床TIPN预测模型，我们将进行有针对性的、全面的。对 S1714 生物样本进行“组学”分析，以验证临床有用性和机械信息预测 TIPN 生物标志物并开发综合 TIPN 风险预测模型来完成以下具体任务目标：目标 1：评估关键营养素和脂质对 TIPN 风险的预测价值。目标 3：开发综合 TIPN 预测模型。本研究的主要预期结果是：1) 确认临床上有用的预测性 TIPN 生物标志物，2) 生成有助于 TIPN 的机械过程新知识，以及 3) 综合 TIPN 预测模型的创建将为开发新颖的模型提供重要的见解。研究团队可以在前瞻性临床试验中测试药物和生物标志物知情的治疗策略如果成功，这些策略可以转化为预防 TIPN 的临床实践。改善接受紫杉烷治疗的近百万癌症患者的长期临床结果每年在美国接受治疗。