Multimodal Biomarkers For Oropharyngeal Cancer

摘要 头颈部癌症是美国第五大常见癌症类型，总体生存率 率低于50%。虽然头颈部癌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）病例的数量稳步上升， 显著增加。大多数OPSCC患者接受标准癌症治疗。4然而， 结果差异很大，难以预测。在治疗早期预测肿瘤是否可能 对治疗的反应是提供个体化癌症护理中最困难但最重要的任务之一。 人乳头瘤病毒（HPV）是口咽癌中已知的驱动致癌因子，也是一种致癌因子。 对患者生存有重要的预后生物标志物。国际负责人进行的回顾性研究 和颈部癌症流行病学联盟（INHANCE）已经证明，临床生物标志物具有 有助于将OPSCC患者分成具有不同死亡或疾病风险的组的预后价值 进展然而，HPV阳性口咽癌患者的转移性扩散率与 HPV阴性患者。对于用其他临床生物标志物分层的患者组也是如此。更稳健 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。 microRNA（miRNAs）是一类非编码RNA小分子，它们共同控制着细胞内的蛋白质， 成千上万的蛋白质编码基因的表达。多项研究表明，miRNAs是有希望的癌症 生物标志物，并在口咽癌中发挥关键的调节作用。从医学图像中提取图像特征 图像是用于表征肿瘤生境的令人兴奋的新类别的癌症生物标志物。对于几种肿瘤 影像学生物标志物在准确区分有利和不利预后方面显示出了希望 患者然而，目前利用高维多模式生物标志物用于治疗结果的努力， 相对于特征空间维度，患者数量较少会影响预测; 特征冗余、异质性和不确定性;以及具有不平衡结局的患者队列。的 多模式生物标志物（成像，miRNA，HPV， 临床和组织病理学生物标志物）也未被探索。 本研究的主要目标是开发一种基于生物标志物的多模态模型， 具有治疗失败的低风险和高风险的OPSCC患者的子集。该模型将作为临床 决策工具。具体来说，我们提出了一种新的原则和系统的机器学习为基础的 有效识别和无缝联合收割机携带预测信息的策略 生物标志物。目的1：根据OPSCC患者数据，确定预后多模式生物标志物。目标2：发展和 测试用于预测OPSCC治疗结果的综合多模式生物标志物模型。目标3： 评估OPSCC患者分层和个体化治疗模型的临床获益。

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.