Molecular Markers for CRC Recurrence

确定手术切除后将受益于辅助化疗的结直肠癌（CRC）患者在提供最安全和最有效的护理方面存在重大未满足的需求。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。核心障碍是缺乏确定的诊断生物标志物来识别具有高转移概率和相应的不良临床结果的癌症。将基于微阵列的图谱转化为临床诊断是复杂的，因为它们的复杂性以及物流、成本和监管障碍。实体瘤的病理学评估通常涉及免疫组织化学和其他免疫测定，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学和蛋白质组学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志物。我们假设，在CRC中的复发倾向表型的分子编码反映了转录组和蛋白质组学特征。我们将结合联合收割机高维网络分析和针对特定转录物和蛋白质的新的、有针对性的分析平台，在档案FFPE标本中开发和测试新的生物标志物。我们将测试这一假设，并根据以下具体目标开发这些方法： 目标1：开发我们的基于34基因核酸的结肠癌预后分类器，用于FFPE组织样本，并使用新的nCounter多重表达分析方法，通过对选定和已发表的签名元素的竞争性评估进行优化。我们将使用高维网络模型来预测蛋白质生物标志物候选物，并将通过靶向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确定候选蛋白质生物标志物。 目标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特征生物标志物，这些样本注释有患者结局。

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.