Integration of Genomic and Clinical Data to Enhance Subtyping of Colon Cancer

摘要 结肠癌是一种临床和分子异质性疾病。虽然TCGA的数据表明 许多分子畸变在癌症病因和机制，基因组事件之间的直接联系 病人的结果是缺乏的。虽然TNM（肿瘤、淋巴结、转移）分期系统被广泛使用， 并提供预后信息，CC在结果中显示出相当大的阶段无关的变异性， 这表明需要更稳健的分类器用于预后分层。预测信息至关重要 指导癌症切除术后的患者管理和监测，并为治疗选择提供信息。 仅使用基因表达数据，我们鉴定了CC的四种共有分子亚型（CMS）， 两杯我们假设，包含额外的基因组特征将使更多的颗粒分子， 通过识别额外的分子模式进行分型。为了实现这一目标（目标1），我们将利用多组学 由两个完成的CC中的III期辅助化疗试验产生的数据集（NCCTGN 0147， NSAPB C-08）。我们还将开发一个监督预后模型， 临床病理变量和结果数据（目标2）。我们独特的监督学习资源 是来自临床试验队列的高质量生存数据。我们假设基因组整合 临床相关基因和基因表达水平的改变与临床病理变量， 与单独的传统TNM分期相比，改善了复发/生存的预测。我们将在一个 在我们的训练模型中，逐步选择基因和miRNA表达，体细胞突变，微小突变， 等位基因频率，体细胞拷贝数改变以及CMS和临床特征，以优化预测 性能考虑到免疫和基质浸润细胞被公认为是 在CC的预后，我们建议在不同的CC分子中表征肿瘤免疫和间质标志物， 亚型，并确定其对预后的贡献（目的3）。具体来说，我们将描述这些 转录组学标记计算，并确定他们是否可以细化分子亚型， 改进预测建模。我们的建议代表了CC患者的第一个全面预测 使用来自基因组和转录组学改变的特征的存活， 和基质标记物。这项工作的影响是 实质性在于它将在分子途径水平或肿瘤中鉴定复发的决定因素 微环境，这将有助于优先考虑治疗干预的目标。此外， 预计这项拨款将改变实践，进一步推动精确度领域的发展 肿瘤学

ABSTRACT Colon cancer (CC) is a clinically and molecularly heterogeneous disease. While the TCGA data has implicated numerous molecular aberrations in cancer etiology and mechanisms, a direct link between genomic events and patient outcomes is lacking. While the TNM (tumor, node, metastasis) staging system is widely utilized and provides prognostic information, CCs show considerable stage-independent variability in outcome indicating that more robust classifiers are needed for prognostic stratification. Prognostic information is critical to guide patient management and surveillance after cancer resection and can inform treatment selection. Using only gene expression data, we identified four consensus molecular subtypes (CMS) of CC with distinct prognoses. We hypothesize that inclusion of additional genomic features will enable more granular molecular subtyping by identifying additional molecular patterns. Toward this objective (Aim 1), we will utilize multi-omics data sets generated from two completed phase III adjuvant chemotherapy trials in CC (NCCTG N0147, NSAPB C-08). We will also develop a supervised prognostic model by integrating comprehensive molecular data with clinicopathological variables and outcome data (Aim 2). Our unique resource for supervised learning is the high-quality survival data from the clinical trial cohorts. We hypothesize that integration of genomic alterations within clinically relevant genes and gene expression levels with clinicopathological variables can improve the prediction of recurrence/survival compared to traditional TNM staging alone. We will include in a step-wise fashion in our training models selected genes and miRNA expression, somatic mutations, minor allele frequencies, somatic copy number alterations as well as CMS and clinical features, to optimize predictive performance. Given that immune and stromal infiltrating cells are well recognized as determinants of prognosis in CC, we propose to characterize tumor immune and stromal markers among distinct CC molecular subtypes and determine their contribution to prognosis (Aim 3). Specifically, we will characterize these transcriptomic markers computationally, and determine whether they can refine molecular subtypes and improve prognostic modeling. Our proposal represents the first comprehensive prediction of CC patient survival using features from both genomic and transcriptomic alterations that will be integrated with immune and stromal markers using state-of-the-art supervised learning approaches. The impact of this work is substantial in that it will identify determinants of recurrence at the molecular pathway level or in the tumor microenvironment, which will help prioritize targets for therapeutic intervention. Furthermore, the outcome of this grant is expected to have practice-changing implications that can further advance the field of precision oncology.