Colorectal Cancer Molecular Subtype Assay Development and Validation

结直肠癌分子亚型检测的开发和验证

• 批准号: 9789655
• 负责人: Scott Kopetz
• 金额: $ 16.37万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-21 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789655
• 关键词: Address; Advisory Committees; Age; Behavior; Benefits and Risks; Biological Assay; Biological Markers; Biology; CLIA certified; Cancer and Leukemia Group B; Cessation of life; Cetuximab; Characteristics; Classification; Clinic; Clinical; Clinical Chemistry; Clinical Trials; Collaborations; Colon Adenocarcinoma; Colon Carcinoma; Colorectal Cancer; Conflict (Psychology); Consensus; Custom; DNA; Data; Development; Discrimination; Disease; Enrollment; Ensure; Formalin; Freezing; Future; Gene Expression; Gene Expression Profile; Generations; Genetic Transcription; Goals; Grant; Immune; Individual; Infiltration; Institution; International; Knowledge; Laboratories; Machine Learning; Measures; Mesenchymal; Metabolic; Methods; Modeling; Molecular; Mutation; National Surgical Adjuvant Breast and Bowel Project; Outcome; Paraffin Embedding; Pathologic; Patients; Pattern; Performance; Prevalence; Primary Neoplasm; Procedures; Prognostic Marker; Prospective Studies; Publishing; Quality Control; RNA; Randomized; Randomized Controlled Clinical Trials; Reproducibility; Research; Research Personnel; Risk; Risk Factors; Sampling; Series; Southwest Oncology Group; Specimen; Subgroup; System; Testing; Therapeutic; Tissue Embedding; Transforming Growth Factor beta; Tumor Biology; Validation; assay development; base; bevacizumab; chemotherapy; clinical biomarkers; cohort; colon cancer patients; drug development; genetic signature; hazard; high risk; improved; innovation; insight; irinotecan; molecular subtypes; nano-string; novel; novel therapeutics; outcome forecast; oxaliplatin; phase 3 study; predicting response; prognostic; prognostic assays; prognostic signature; prognostic value; standard of care; tool; tumor; tumor behavior

ABSTRACT Stage III colorectal cancer (CRC) demonstrates substantial variability in tumor biology and clinical outcomes and there is a need to understand prognosis for patients in order to gauge risk benefit for chemotherapy and intensity of chemotherapy administration. These features are not well recapitulated by the current biomarkers in use in the clinic, majority of them are DNA based mutation assays. RNA expression patterns have been described by various investigators and may more fully recapitulate tumor biology. The clinical utility of these findings have been limited by the apparent conflicting subgrouping efforts and lack of a validated gene expression signature as a clinical grade assay applicable on formalin fixed paraffin embedded (FFPE) tissue. In our international collaboration with several academic leaders who have previously published in this field, we have identified a robust consensus subgroup classification based on clustering approaches independent of clinical outcomes. Remarkably, this classification system, termed consensus molecular subtypes (CMS), identified 4 subgroups that provide novel insights into the classification of CRC. One subgroup with mesenchymal, TGF-β, and angiogenic features (CMS4) is associated with a hazard ratio for death of 2.26 (95% CI of 1.41 to 3.61, P=.001), significantly higher than other subgroups, in a multivariate model inclusive of current clinical and pathologic risk factors and genetic signature (Oncotype Dx). We hypothesize that a gene expression signature classifier can be developed and validated for determining the CMS in FFPE tissues, and that this classifier can be implemented to improve prognostication of stage III CRC by classifying them in CMS 4 vs. other subtypes. We have developed a support-vector-machine classifier with very high accuracy for classification based on an Affymetrix array from fresh frozen specimens. We have demonstrated good classification accuracy (>90%) using customized Nanostring codesets on FFPE tumor samples of 85 patients with stage III CRC. We have also demonstrated good technical reproducibility in six of those 85 samples. In this application, we will transfer the assay using the Nanostring Codeset to fresh frozen (FF) and FFPE using a set of paired samples, while maintaining classifier performance. We will then pursue technical and analytic validation of the assay, including precision in repeatability, reproducibility between sample types, inter- lab reproducibility, and impact of RNA quality/quantity. In the UH3 portion of the grant, we will clinically validate the prognostic utility of the gene expression signature assay in single-institution cohort, and then in a completed prospective study of FOLFOX chemotherapy (NRG/NSABPC-08), in a CLIA certified laboratory. Additional data will be used in predicting response to various standard of care therapeutics, which represents a series of future potential applications of the assay. By utilizing an assay developed to classify CRC by its tumor biology, we anticipate development of an enduring tool that will be of greater use than traditional fit-for-purpose tests.

抽象的 第三阶段结直肠癌（CRC）表现出肿瘤生物学和临床结局的显着差异， 有必要了解患者的提示，以评估化学疗法和强度的风险益处 化学疗法给药。当前使用的生物标志物在 诊所，大多数是基于DNA的突变测定法。 RNA表达模式已通过 各种研究者，可能会更充分地概括肿瘤生物学。这些发现的临床实用性具有 受到明显相互冲突的子组工作和缺乏经过验证的基因表达签名的限制 作为适用于福尔马林固定石蜡（FFPE）组织的临床级断言。在我们的国际 与以前在该领域发表过的几位学术领导者合作，我们已经确定了 基于聚类方法的强大共识亚组分类，而与临床无关 结果。值得注意的是，该分类系统称为分子亚型（CMS），确定了4 为CRC分类提供新颖见解的亚组。一个具有间质，TGF-β的亚组 血管生成特征（CMS4）与死亡的危害比率为2.26（95％CI为1.41至3.61， p = .001），在包括当前临床和 病理风险因素和遗传特征（ONCOTYPE DX）。我们假设基因表达 可以开发和验证签名分类器，以确定FFPE组织中的CMS，并且 可以实现此分类器，以通过将其分类为III期CRC提示 CMS 4与其他子类型。我们已经开发了一个支持矢量机分类器，其精度非常高 基于来自新鲜冷冻标本的Affymetrix阵列的分类。我们表现​​不错 使用FFPE肿瘤样品上的自定义纳米串代码的分类精度（> 90％）85 III期CRC患者。我们还证明了其中六个85中的良好技术再生产 样品。在此应用程序中，我们将使用纳米弦法转移测定到新鲜冷冻（FF）和 FFPE使用一组配对样品，同时保持分类器性​​能。然后，我们将纯化技术和 分析测定的分析验证，包括可重复性的精度，样本类型之间的繁殖，间隔 实验室可重复性和RNA质量/数量的影响。在赠款的UH3部分中，我们将在临床上验证 基因表达签名测定在单机构队列中的预后效用，然后在完整的 FOLFOX化学疗法（NRG/NSABPC-08）的前瞻性研究，在CLIA认证的实验室中。其他数据 将用于预测对各种护理疗法的反应，这代表了一系列未来 测定的潜在应用。通过使用开发的测定法来对CRC进行肿瘤生物学分类， 我们预计开发一种持久的工具，该工具将比传统的拟合方式更有用 测试。