Development of a Prognostic Compound Immunoscore for Head and Neck Cancer

项目总结 最近临床试验的知识表明，超过80%的头颈癌(HNC)是 低免疫原性冷肿瘤和对免疫检查点受体(ICR)阻断无反应。与 对于感冒的新兴组合策略，对这组肿瘤的准确识别对于 最佳治疗方案的选择。然而，没有一致的算法可用来评估 HNC的全球免疫概况。目前的大多数免疫评分方法都是基于免疫组织化学的。 (IHC)对有限的生物标记物进行染色，这阻止了对肿瘤图景的准确注释- 浸润性淋巴细胞(TIL)IHC方法在技术上是敏感的，可能会出现机构间和 病理学家之间的差异。此外，目前的免疫核心只侧重于少数T细胞亚群，并且 没有整合调节肿瘤对免疫杀伤反应的癌症基因组特征。事实上，很强 有证据表明，I型干扰素(IFN-I)途径在HNC应答中起着重要作用 效应器免疫细胞。因此，利用全球TIL图谱和癌症基因组特征提供了 根据免疫原性对HNC进行分类的前所未有的机会。当前稳健的方法可用于 细胞去卷积对异常值很敏感，这种异常值经常出现在整个肿瘤的RNA序列中。 数据集。我们最近的研究表明，一种新的机器学习工具快速而稳健地去卷积 表达轮廓(FARDEEP)自适应地检测和删除异常值，在 免疫细胞去卷积。准确地说，这个项目的总体假设是 整合FARDEEP辅助的TIL去卷积和癌症基因组学的复合免疫核心可以 有效识别冷HNC。为了实现这一目标，我们立即采取的两个步骤是：**(1)我们将制定一项 用于识别TIL致癌途径的稳健无模型方法**(2)我们将构建一种化合物 整合癌症基因组特征和TIL图谱的免疫核心以识别寒冷的HNC。这些研究将 开发一种新的“适用于分析全基因组数据的统计方法”，并提供“统计” 对一种NIDCR优先疾病的现有全基因组数据的分析。该项目将完善一个强大的和 新型免疫细胞去卷积机器学习工具和表征中心致癌途径的转移 瓷砖景观。新的免疫基因组学算法将简化免疫评分方法，以 有效地对HNC进行分层，有助于组合处理的精确选择。

PROJECT SUMMARY Knowledge from the recent clinical trials suggests that over 80% of head and neck cancer (HNC) are hypo-immunogenic cold tumors and non-responsive to immune checkpoint receptors (ICR) blockade. With the emerging combinatorial strategies for cold cancer, precise identification of this group of tumors is essential for the selection of optimal treatment protocols. However, there is no consistent algorithm available to assess the global immune profile of HNC. Most of the current immunoscore methods are based on immunohistochemical (IHC) staining of a limited panel of biomarkers, which prevents a precise annotation of the landscape of tumor- infiltrating lymphocytes (TIL). The IHC method is technically sensitive, and may present inter-institutional and inter-pathologists variations. Moreover, the current immunoscore only emphasizes on a few T-cell subsets, and does not integrate cancer genomic features that modulate tumor response to immune killing. In fact, strong evidence suggests that the type I interferon (IFN-I) pathway plays a fundamental role in HNC response to effector immune cells. Thus, leveraging global TIL profiles and cancer genomic features offers an unprecedented opportunity to classify HNC based on its immunogenicity. The current robust methods for cellular deconvolution are sensitive to outliers, which are frequently observed in the whole tumor RNA-Seq datasets. Our recent studies show that a novel machine learning tool Fast And Robust DEconcolution of Expression Profiles (FARDEEP), which adaptively detects and removes outliers, exhibits superior accuracy in immune cell deconvolution. In precise alignment with the FOA, the overarching hypothesis of this project is that a compound immunoscore integrating FARDEEP-assisted TIL deconvolution and cancer genomics can effectively identify cold HNC. To achieve this goal, our two immediate next steps are: **(1) We will develop a robust model-free approach to identify TIL-driving oncogenic pathways; **(2) We will construct a compound immunoscore integrating cancer genomic features and TIL profiles to identify cold HNC. These studies will develop a novel “statistical methodology appropriate for analyzing genome-wide data” and provide “statistical analysis of existing genome-wide data” for an NIDCR priority disease. This project will refine a robust and novel immune-cell deconvolution machine learning tool and characterize central oncogenic pathways that shift the TIL landscape. The new immunogenomics algorithms will streamline the immunoscoring method to effectively stratify HNC and contribute to the precision selection of combinatorial treatments.