Integrative prediction models for metastasis risk in colon cancer

结肠癌转移风险的综合预测模型

• 批准号: 8517624
• 负责人: Robert Daniel Beauchamp
• 金额: $ 43.7万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8517624
• 关键词: Adjuvant; Adjuvant Chemotherapy; Biological Assay; Biological Preservation; Biology; Blinded; Cancer Patient; Clinical; Clinical Trials; Collaborations; Colon Carcinoma; Colorectal Cancer; Data; Development; Elements; Evaluation; Formalin; Gene Expression; Gene Expression Profile; Genes; Genome; Goals; Human Resources; Individual; Investments; Lead; Medicine; Methods; Modeling; Molecular; Morbidity - disease rate; Mus; Mutation; Neoplasm Metastasis; Operative Surgical Procedures; Outcome; Pathologic; Patient Selection; Patients; Phase; RNA; Recurrence; Reproducibility; Research; Risk; Sensitivity and Specificity; Somatic Mutation; Specimen; Staging; Standardization; Systemic Therapy; Testing; Time; Tissue Banks; Tissues; Translating; base; cancer care; cancer cell; cancer prevention; cancer therapy; clinical application; clinical risk; cohort; comparative effectiveness; cost; design; disorder later incidence prevention; evidence base; head-to-head comparison; high risk; improved; mortality; novel; nuclease; outcome forecast; prospective; research clinical testing; response; sample fixation; standard care; tool; Adjuvant; Adjuvant Chemotherapy; Biological Assay; Biological Preservation; Biology; Blinded; Cancer Patient; Clinical; Clinical Trials; Collaborations; Colon Carcinoma; Colorectal Cancer; Data; Development; Elements; Evaluation; Formalin; Gene Expression; Gene Expression Profile; Genes; Genome; Goals; Human Resources; Individual; Investments; Lead; Medicine; Methods; Modeling; Molecular; Morbidity - disease rate; Mus; Mutation; Neoplasm Metastasis; Operative Surgical Procedures; Outcome; Pathologic; Patient Selection; Patients; Phase; RNA; Recurrence; Reproducibility; Research; Risk; Sensitivity and Specificity; Somatic Mutation; Specimen; Staging; Standardization; Systemic Therapy; Testing; Time; Tissue Banks; Tissues; Translating; base; cancer care; cancer cell; cancer prevention; cancer therapy; clinical application; clinical risk; cohort; comparative effectiveness; cost; design; disorder later incidence prevention; evidence base; head-to-head comparison; high risk; improved; mortality; novel; nuclease; outcome forecast; prospective; research clinical testing; response; sample fixation; standard care; tool

DESCRIPTION (provided by applicant): While adjuvant chemotherapy for stage III colon cancer results in an overall survival benefit, 42 to 44% of stage III patients will not recur in five years even without adjuvant treatment. Conversely, clinical trials have failed to demonstrate an overall benefit of adjuvant chemotherapy for stage II colon cancer; however, a subset of high-risk patients may benefit from adjuvant treatment. Thus, an accurate and reliable method of determining risk of recurrence, and corresponding likelihood of benefit of systemic therapy, is greatly needed. We have recently developed a novel gene expression signature ("34-gene signature") based on the metastatic biology of mouse colon cancer cells, capable of segregating colon cancer patient groups into low and high risk of recurrence. A gene signature such as this may allow for more appropriate selection of patients to receive or not receive adjuvant chemotherapy, thus enabling those phase III patients at low risk to avoid the potential morbidity, occasional mortality, and definite financial expense of systemic therapy, while improving the survival of phase II patients. To accurately identify personal risk, clinical, demographic, pathologic, and somatic mutation data all must be incorporated with gene expression; to incorporate these various data, we propose to conduct research to develop an integrative metastatic risk prediction model that allows for integration of diverse types of data. We will validate the predictive power of this model and, over the next five years, explore platforms to apply this tool prospectively as an approach to guide treatment decisions in colon cancer patients. This focused study will translate our molecular findings to clinical application in a relatively short time, in advance of clinical trials. The long-term goal for this proposal is to develop a clinically useful metastasis score from diverse type of data that can be applied to stage II and III colon cancer patients for the purpose of reducing mortality, morbidity, and the cost associated with colon cancer and colon cancer treatment. To this end, our specific aims are as follows: 1) develop an integrative metastasis risk prediction model for colon cancer; 2) determine the optimum platform for the 34-gene metastasis score clinical test; and 3) test the optimized prognosis signature in a blinded fashion on archival tissue, to determine the robustness of the test and whether the metastasis risk score should be advanced to a prospective clinical trial to predict outcomes in stage II and III patients.

描述（由申请人提供）：虽然第三阶段结肠癌的辅助化学疗法可带来总体生存益处，但即使没有辅助治疗，也有42％至44％的III期患者在五年内也不会复发。相反，临床试验未能证明辅助化疗对II期结肠癌的总体好处。但是，高危患者的一部分可能受益于辅助治疗。因此，非常需要一种确定复发风险的准确而可靠的方法，以及系统治疗的相应可能性。 我们最近基于小鼠结肠癌细胞的转移生物学开发了一种新型的基因表达特征（“ 34基因签名”），能够将结肠癌患者群体隔离为低和高复发风险。诸如此类的基因特征可能会使患者选择接受或不接受辅助化疗，从而使那些具有低风险的III期患者能够避免潜在的发病率，偶尔死亡率和全身治疗的明确财务费用，同时改善II期患者的存活率。 要准确地确定个人风险，临床，人口统计学，病理和体细胞突变数据必须与基因表达一起纳入；为了合并这些各种数据，我们建议进行研究，以开发一个综合转移性风险预测模型，该模型允许整合各种类型的数据。我们将验证该模型的预测能力，并在接下来的五年中探索平台以前瞻性地应用该工具作为指导结肠癌患者治疗决策的方法。这项重点研究将在相对较短的时间内将我们的分子发现转化为临床应用。 该提案的长期目标是从各种数据类型的数据中建立一个有用的转移评分，该数据可用于降低死亡率，发病率以及与结肠癌和结肠癌治疗相关的成本，可应用于II和III期结肠癌患者。为此，我们的具体目标如下：1）为结肠癌开发一个综合转移风险预测模型； 2）确定34基因转移评分临床测试的最佳平台； 3）在档案组织上以盲目的方式测试优化的预后签名，以确定测试的鲁棒性以及是否应提高转移风险评分，以预测一项前瞻性临床试验，以预测II期和III期患者的结果。