An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach
使用多阶段综合组学方法研究癌症化疗相关疲劳的分子机制并预测其严重程度
基本信息
- 批准号:10204963
- 负责人:
- 金额:$ 65.39万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-07-03 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:AbbreviationsAddressAftercareBiological MarkersCancer EtiologyCandidate Disease GeneCaringCharacteristicsChemotherapy-Oncologic ProcedureClinicalClinical MarkersClinical ResearchCyclophosphamideDNADataData AnalysesDevelopmentDistressDiurnal RhythmEnvironmentEpigenetic ProcessExerciseFamily memberFatigueFundingGene ExpressionGene Expression ProfilingGenesGeneticGenetic VariationGrantInflammatoryInheritedInterventionInvestigationKnowledgeMalignant NeoplasmsMeasurementMethylationModelingMolecularMutationNatureOncologyOperative Surgical ProceduresPathway interactionsPatientsPhenotypePreparationProcessQuality of lifeRNARadiation therapyRecommendationReportingResearchSample SizeSamplingSerumSeveritiesSourceSymptomsTherapeutic InterventionWorkassociated symptombasecancer therapychemotherapycohortcommon symptomcytokinedepressive symptomsdifferential expressioneffective therapyepigenetic variationexperiencegene productgenomic datahigh riskinnovationinsightmolecular markerpredictive modelingresponserisk prediction modeltargeted treatmenttherapeutic target
项目摘要
Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments.
Moderate to severe CRF has a negative impact on patients’ ability to tolerate treatments as well as on their
quality of life. In some patients, CRF is so severe, that they discontinue cancer treatment. Given its high
occurrence and significant negative impact, it is imperative that effective treatments be developed for this
devastating symptom. Two of the major knowledge gaps for CRF are a lack of a risk prediction model and a
lack of knowledge of its underlying mechanisms. A sensitive and specific risk prediction model would assist
clinicians to determine which patients are most likely to experience high levels of CRF and provide
recommendations regarding activity modifying interventions (e.g., exercise). Increased knowledge of the
mechanisms for CRF could identify potential targets for therapeutic interventions. Both of these knowledge
gaps will be addressed in this application. This study will use multiple sources of “omics” data to investigate
the molecular mechanisms associated with the severity of CRF in a well characterized sample of oncology
patients (n=1343) who are experiencing low versus high levels of morning and evening CRF. Because these
patients are undergoing chemotherapy (CTX), our study will investigate CTX-related fatigue (CTXRF). We will
use a multi-staged analysis to integrate the gene expression, genetic, and epigenetic data. We will take
advantage of the functional candidate genes identified in a gene expression profiling analyses to provide loci
for analysis in subsequent genetic and epigenetic analyses. Candidate genes and pathways identified in this
study will provide new and needed information on CTXRF mechanisms, as well as potential therapeutic
targets. Prior studies suggest that patients will experience an increase in the severity of CTXRF in the week
following CTX. However, no models exist to predict the magnitude of this increase. This inability to predict the
severity of CTXRF during subsequent cycles of CTX limits the ability of clinicians to identify high-risk patients
and provide them with recommendations to manage CTXRF. To address this knowledge gap, we propose to
use demographic, clinical, and omics data to develop a model to predict the severity of morning and evening
CTXRF experienced by a patient one week following CTX based on their profile for CTXRF in the week prior to
the receipt of this cycle of CTX. This study will provide new insights to be able to identify high-risk patients as
well as identify potential therapeutic targets. This project will guide the development and clinical studies to
investigate additional mechanisms and therapeutic interventions for CTXRF and other types of fatigue
associated with cancer and its treatment (e.g., radiation therapy, surgery).
癌症相关性疲劳(CRF)是与癌症及其治疗相关的最常见症状。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Kord Michael Kober其他文献
Kord Michael Kober的其他文献
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{{ truncateString('Kord Michael Kober', 18)}}的其他基金
An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach
使用多阶段综合组学方法研究癌症化疗相关疲劳的分子机制并预测其严重程度
- 批准号:
10657397 - 财政年份:2019
- 资助金额:
$ 65.39万 - 项目类别:
An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach
使用多阶段综合组学方法研究癌症化疗相关疲劳的分子机制并预测其严重程度
- 批准号:
9762396 - 财政年份:2019
- 资助金额:
$ 65.39万 - 项目类别:
An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach
使用多阶段综合组学方法研究癌症化疗相关疲劳的分子机制并预测其严重程度
- 批准号:
10516695 - 财政年份:2019
- 资助金额:
$ 65.39万 - 项目类别:
An Evaluation of Cloud Computing for Symptom Science Research: Moving Genomics and Machine Learning Analyses of Cancer Chemotherapy-Related Fatigue to the Cloud
云计算对症状科学研究的评估:将癌症化疗相关疲劳的基因组学和机器学习分析转移到云端
- 批准号:
10827722 - 财政年份:2019
- 资助金额:
$ 65.39万 - 项目类别:
An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach
使用多阶段综合组学方法研究癌症化疗相关疲劳的分子机制并预测其严重程度
- 批准号:
10430171 - 财政年份:2019
- 资助金额:
$ 65.39万 - 项目类别:
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