Multimodal Biomarkers For Oropharyngeal Cancer

摘要 头颈癌是美国第五种最常见的癌症类型，总体存活率高 税率低于50%。尽管头颈癌的其他亚部位的发病率有所下降 在过去的几十年里，口咽鳞状细胞癌(OPSCC)的病例数量稳步增加 显著增加。大多数OPSCC患者接受标准的癌症治疗。 结果差异很大，很难预测。在治疗早期预测肿瘤是否有可能 对治疗的反应是提供个性化癌症护理的最困难但也是最重要的任务之一。 人乳头瘤病毒(HPV)是口咽癌的已知致癌因素，也是一种 患者生存的重要预后生物标记物。国际负责人进行的回溯性研究 和宫颈癌流行病学联合会(INHANCE)已经证明，临床生物标记物具有 帮助将OPSCC患者分成不同死亡或疾病风险组的预后价值 进步。然而，HPV阳性的口咽癌患者的转移率与 HPV阴性患者。对于与其他临床生物标记物分层的患者组也是如此。更强健 需要预后生物标志物来准确地对患者进行分层，以获得最佳有效的治疗。 MicroRNAs(MiRNAs)是一类小的非编码RNA分子，它们共同控制着 数千个蛋白质编码基因的表达。多项研究表明miRNAs很有希望成为癌症 生物标记物在口咽癌中起着关键的调节作用。从医学图像中提取图像特征 图像是一类令人兴奋的新的癌症生物标记物，用于表征肿瘤栖息地。对于几种肿瘤 部位、成像生物标记物在准确区分预后良好和不利方面显示出希望 病人。然而，目前利用高维多模式生物标记物评价治疗结果的努力 相对于特征空间维度，预测因患者数量较少而受到影响； 特征冗余、异质性和不确定性；以及结果不平衡的患者队列。这个 多模式生物标志物的相关性、独立性和互补性(成像、miRNA、HPV、 临床和组织病理学生物标记物)也仍未被发现。 这项研究的主要目标是开发一种基于多模式生物标志物的模型，该模型可以可靠地预测 治疗失败风险低和高风险的OPSCC患者亚群。该模型将作为临床应用 决策工具。具体地说，我们提出了一种新颖的原理和系统的机器学习方法 有效识别和无缝组合多模式携带的预测信息的策略 生物标志物。目的1：在给定OPSCC患者数据的情况下，确定预后的多模式生物标志物。目标2：发展和 测试基于多模式生物标记物的预测OPSCC治疗结果的综合模型。目标3： 评估该模型对OPSCC患者分层和个体化治疗的临床效益。

Abstract Head and neck cancers are the fifth most common cancer type in the United States, with an overall survival rate lower than 50%. Although the incidence of other sub-sites of head and neck cancer has decreased steadily in past decades, the number of oropharyngeal squamous cell carcinoma (OPSCC) cases has increased significantly. Most OPSCC patients receive standard cancer therapy.4 However, the clinical outcomes vary significantly and are difficult to predict. Predicting early in treatment whether a tumor is likely to respond to treatment is one of the most difficult yet important tasks in providing individualized cancer care. Human papillomavirus (HPV) is a known driving oncogenic factor in oropharyngeal cancer, as well as a significant prognostic biomarker for patient survival. Retrospective studies conducted by the International Head and Neck Cancer Epidemiology Consortium (INHANCE) have demonstrated that clinical biomarkers have prognostic value in helping stratify OPSCC patients into groups with differing risks of death or disease progression. However, HPV-positive oropharyngeal cancer patients have similar rates of metastatic spread to HPV-negative patients. The same is true for patient groups stratified with other clinical biomarkers. More robust prognostic biomarkers are needed to accurately stratify patients for optimally effective treatment. MicroRNAs (miRNAs) are a family of small non-coding RNA molecules that collectively control the expression of thousands of protein-coding genes. Multiple studies indicate that miRNAs are promising cancer biomarkers and play critical regulatory roles in oropharyngeal cancer. Imaging features extracted from medical images are an exciting new class of cancer biomarkers for characterizing tumor habitats. For several tumor sites, imaging biomarkers have shown promise in accurately separating favorable and unfavorable prognosis patients. However, current efforts to utilize high-dimensional multimodal biomarkers for treatment outcome prediction have been compromised by small patient numbers relative to the feature space dimensionality; feature redundancy, heterogeneity, and uncertainty; and patient cohorts with unbalanced outcomes. The correlation, independence, and complementary nature of multimodal biomarkers (imaging, miRNA, HPV, clinical, and histopathologic biomarkers) remains unexplored as well. The major goal of this research is to develop a multimodal biomarker-based model that can reliably predict subsets of OPSCC patients with low and high risks for treatment failure. The model will serve as a clinical decision-making tool. Specifically, we propose a novel principle and systematic machine learning-based strategy to effectively identify and seamlessly combine prognostic information carried by multimodal biomarkers. Aim 1: Identify prognostic multimodal biomarkers, given OPSCC patient data. Aim 2: Develop and test a comprehensive multimodal biomarker-based model for predicting OPSCC treatment outcomes. Aim 3: Assess the clinical benefit of the model for OPSCC patient stratification and individualized treatment.