Evaluation of Patient-Matched Primary and Metastatic Samples to Identify and Vali
Evaluation of Patient-Matched Primary and Metastatic Samples to Identify and Vali
批准号:
8486586
负责人:
JEANETTE E ECKEL PASSOW
金额:
$25.08万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-04-01 至 2015-03-31
关键词:
Basic ScienceBioinformaticsBiologyBiometryCandidate Disease GeneClear CellClinicClinicalDataData AnalysesDevelopmentDiseaseEpidemiologyEvaluationEventFrequenciesGene ExpressionGene Expression ProfileGene FusionGenesGoalsInvestigationLungMalignant NeoplasmsMetastatic Neoplasm to the LungMetastatic Renal Cell CancerMetastatic toMolecularMolecular TargetMutationNeoplasm MetastasisNephrectomyOperative Surgical ProceduresOrganPathogenesisPathologyPatientsPharmaceutical PreparationsPositioning AttributePrimary NeoplasmProbabilityRNA SplicingRegistriesRenal Cell CarcinomaReportingResearchResearch PersonnelResourcesSamplingSiteTestingTherapeuticTranslatingValidationVariantWorkbasecohortdesigngenetic varianthigh riskimprovedmortalitymultidisciplinarynew therapeutic targetnoveloutcome forecastprognosticpublic health relevanceresponsetertiary preventiontherapeutic targettissue resourcetranscriptome sequencingtreatment responsetumor
中文摘要
描述(由申请人提供):导致ccRCC转移的分子事件仍然知之甚少,这是导致ccRCC死亡率三十多年来缓慢上升的主要因素。事实上,目前批准用于转移性ccRCC的药物被设计为靶向已报道在原发性ccRCC肿瘤中常见的分子改变,但不一定特异性地在转移性ccRCC肿瘤中。因此,尽管在过去十年中转移性ccRCC的治疗方法稳步增加,但五年生存率仍低于10%,这并不奇怪。因此,一个关键的临床问题是需要询问ccRCC转移性肿瘤,以鉴定转移性肿瘤特有的分子改变。正是这些改变将最有可能增强预后预测,预测对当前疗法的反应,并为新型靶向疗法的开发提供信息。为了直接满足这一需求,我们的多学科研究人员团队使用Affytron基因阵列分析了14例患者匹配的原发性和转移性ccRCC肿瘤(所有转移均发生在肺),并确定了7个在转移性与原发性ccRCC中差异表达的新候选基因:DCN,SLIT 2,LUM,LAMA 2,ADAMTS12,CEACAM6和LMO 3。在本文中,我们建议通过以下方式扩展我们的试点工作:(1)在大量独立队列的良好注释的患者匹配的原发性和转移性ccRCC肿瘤中验证这些候选者,所述肿瘤包括除肺以外的其他转移部位,(2)评估转移性肿瘤中这些基因的表达与存活和治疗反应的关联,和(3)扩大我们现有的努力,包括探索与转移后预后相关的其他转移性遗传改变。总之,我们的总体目标是更好地了解RCC转移的发病机制,以帮助集中三级预防和治疗工作。通过鉴定与ccRCC从原发性肿瘤进展为致死性转移性疾病相关的遗传变异,该项目最终将有可能为ccRCC进展的生物学提供信息,改善预后和对治疗努力的反应,并为新疗法提供合理的靶点。
英文摘要
DESCRIPTION (provided by applicant): The molecular events that cause ccRCC metastasis remain poorly understood and this is a major contributing factor to why ccRCC mortality rates have been slowly rising for more than three decades. Indeed, current drugs approved for metastatic ccRCC are designed to target molecular alterations that have been reported to be common in primary ccRCC tumors, but not necessarily in metastatic ccRCC tumors specifically. As such, it is not surprising that despite a steady increase over the past decade in therapeutics for metastatic ccRCC, the five year survival is still less than 10%. Thus, a key clinical issue is the need to interrogate ccRCC metastatic tumors in order to identify molecular alterations that are unique to metastatic tumors. It is these alterations that will have the highest probability to enhance prognostic forecasting, predict response to current therapies, and inform the development of novel, targeted therapeutics. In direct response to this need, our multidisciplinary team of investigators has used Affymetrix gene arrays to analyze 14 patient-matched primary and metastatic ccRCC tumors (all metastases are to lung) and identified seven novel candidate genes that are differentially expressed in metastatic versus primary ccRCC: DCN, SLIT2, LUM, LAMA2, ADAMTS12, CEACAM6 and LMO3. Herein, we propose to expand on our pilot work by (1) validating these candidates in a large independent cohort of well annotated patient-matched primary and metastatic ccRCC tumors that include other metastatic sites in addition to lung, (2) evaluating the association of expression of these genes in the metastatic tumors with survival and treatment response, and (3) expanding our existing efforts to include the exploration of additional metastatic genetic alterations that are associated with prognosis after metastasis. In summary, our overall goal is to better understand the pathogenesis of RCC metastasis in order to help focus tertiary prevention and treatment efforts. By identifying genetic variants that are associated with ccRCC progression from primary tumor to lethal metastatic disease, this project will ultimately have the potential to inform the biologyof ccRCC progression, improve prognostic and response to treatment efforts as well as provide rationale targets for novel therapeutics.
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会议论文
Diagnosis of indeterminate brain lesions using MRI-based machine learning and polygenic risk models
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批准号:10406296
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项目类别:
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资助金额:$62.31万
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财政年份:2020
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负责人:JEANETTE E ECKEL PASSOW
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依托单位:
Diagnosis of indeterminate brain lesions using MRI-based machine learning and polygenic risk models
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批准号:10224946
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项目类别:
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资助金额:$62.31万
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财政年份:2020
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负责人:JEANETTE E ECKEL PASSOW
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依托单位:
Diagnosis of indeterminate brain lesions using MRI-based machine learning and polygenic risk models
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批准号:10654009
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项目类别:
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资助金额:$62.31万
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财政年份:2020
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负责人:JEANETTE E ECKEL PASSOW
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依托单位:
海外基金