Molecular Markers for CRC Recurrence

确定结直肠癌（CRC）患者在手术切除后将受益于辅助化疗，为他们提供最安全和最有效的治疗是一个主要的未满足的需求。目前的做法导致高风险II期患者治疗不足，低风险III期患者治疗过度。核心障碍是缺乏明确的诊断性生物标志物来识别转移概率高和相应临床预后差的癌症。由于其复杂性以及后勤、成本和监管障碍，将基于微阵列的基因图谱转化为临床诊断是复杂的。实体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）组织切片中的蛋白质表达。因此，在新兴的基因组信息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学和蛋白质组学技术来填补这一空白，利用FFPE组织样本识别和验证结直肠癌复发的新分子标记。我们假设CRC易复发表型的分子编码通过转录组学和蛋白质组学特征反映出来。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靶向分析平台，开发和测试档案FFPE标本中的新的生物标志物。我们将检验这一假设，并根据以下具体目标开发这些方法：

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.