Molecular Markers for CRC Recurrence

在提供最安全和最有效的治疗方面，确定将从手术切除后的辅助化疗中受益的结直肠癌(CRC)患者是一个主要的未得到满足的需求。目前的做法导致高危II期患者治疗不足，而低危III期患者过度治疗。核心障碍是缺乏一个明确的诊断生物标记物(S)来识别转移概率高且相应的临床结果较差的癌症。由于其复杂性，以及后勤、成本和监管方面的障碍，将基于微阵列的图谱转化为临床诊断是复杂的。实体肿瘤的病理评估通常包括免疫组织化学和其他免疫分析，以检测福尔马林固定、石蜡包埋(FFPE)组织切片中的蛋白表达。因此，在新兴的基因组信息体和诊断应用之间存在差距。我们建议通过结合新兴的基因组和蛋白质组技术来填补这一空白，以使用FFPE组织样本来识别和验证结直肠癌复发的新分子标记物。我们假设，结直肠癌易复发表型的分子编码同时反映在转录和蛋白质组特征上。我们将结合高维网络分析和针对特定转录本和蛋白质的新的、有针对性的分析平台，开发和测试FFPE档案样本中的新生物标记物。我们将检验这一假设，并根据以下具体目标开发这些方法： 目的1：开发我们的34基因核酸结肠癌预后分类器，用于FFPE组织样本，并通过对选定的和已发表的签名元件进行竞争性评估，使用新的nCounter多重表达分析方法进行提炼。我们将使用高维网络模型来预测蛋白质生物标记物候选，这将通过有针对性的蛋白质组学分析进行系统评估。 目的2：通过靶向蛋白质组学分析寻找候选蛋白质生物标记物。 目的3：在一组独立的存档结肠癌组织样本中测试基于蛋白质和基于核酸的标志性生物标记物，并注明患者的结果。

The identification of colorectal cancer (CRC) patients that will benefit from adjuvant chemotherapy after surgical resection poses a major unmet need in providing their safest and most effective care. The current practice results in under-treatment of high-risk stage II patients and overtreatment of low-risk stage III patients. The core obstacle is the lack of a definitive diagnostic biomarker(s) to identify cancers with a high probability of metastasis and corresponding poor clinical outcome. Translation of microarray-based profiles into clinical diagnostics is complicated by their complexity, as well as by logistical, cost and regulatory barriers. Pathological assessment of solid tumors typically Involves immunohistochemistry and other immunoassays to detect protein expression in formalin-fixed, paraffin-embedded (FFPE) tissue sections. Thus, there Is a gap between an emerging body of genomic information and diagnostic application. We propose to fill this gap by combining emerging genomic and proteomic technologies to identify and validate new molecular markers of colorectal cancer recurrence using FFPE tissue samples. We hypothesize that molecular encoding of a recurrence-prone phenotype in CRC is reflected by both transcriptomic and proteomic features. We will combine high-dimensional network analysis and new, targeted analysis platforms for specific transcripts and proteins to develop and test new biomarkers in archival FFPE specimens. We will test this hypothesis and develop these approaches according to the following specific aims: Aim 1: Develop our 34-gene nucleic acid-based colon cancer prognostic classifier for use in FFPE tissue samples and refine through a competitive evaluation of selected and published signature elements, using the novel nCounter multiplex expression analysis approach. We will use high-dimensional network models to predict protein biomarker candidates, which will be systematically evaluated with targeted proteomic analyses. Aim 2: Identify candidate protein biomarkers by targeted proteomics analysis. Aim 3: Test the protein-based and nucleic acid-based signature biomarkers in an independent set of archived colon cancer tissue samples annotated with patient outcomes.