Development of a Prognostic Compound Immunoscore for Head and Neck Cancer

头颈癌预后复合免疫评分的开发

• 批准号: 9766266
• 负责人: Yu Leo Lei
• 金额: $ 16.2万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766266
• 关键词: Algorithms; Automobile Driving; Cancer Vaccines; Cells; Clinic; Clinical Trials; Complement; Data; Data Set; Development; Dimensions; Disease; Engineering; Exclusion; Exhibits; Expression Profiling; Gene Expression; Genes; Glean; Goals; Group Identifications; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Immune; Immunity; Immunogenomics; Immunohistochemistry; Infiltration; Institution; Interferon Type I; Interferon Type II; Interferons; Knowledge; Learning; Lymphocyte Subset; Machine Learning; Malignant Neoplasms; Methodology; Methods; Modeling; Mutate; Mutation; National Institute of Dental and Craniofacial Research; Oncogenic; Outcome; Pathologist; Pathway interactions; Patients; Play; Population; Receptor Inhibition; Receptor Signaling; Role; Selection for Treatments; Signal Transduction; Squamous cell carcinoma; Staging System; Stains; Statistical Data Interpretation; T-Lymphocyte Subsets; Techniques; Time; Treatment Protocols; Tumor-Infiltrating Lymphocytes; Tumor-infiltrating immune cells; Variant; Weight; base; biomarker panel; cancer biomarkers; cancer genomics; checkpoint receptors; chemoradiation; combinatorial; genome-wide; genome-wide analysis; immune checkpoint; immunogenic; immunogenicity; inhibitor/antagonist; inter-institutional; novel; optimal treatments; patient stratification; prevent; prognostic; prognostic performance; public health relevance; receptor expression; recruit; response; statistics; tool; transcriptome sequencing; tumor

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.

项目概要 最近的临床试验表明，超过 80% 的头颈癌 (HNC) 是由 低免疫原性冷肿瘤和对免疫检查点受体（ICR）阻断无反应。 新兴的冷癌组合策略，精确识别这组肿瘤对于治疗癌症至关重要 然而，没有一致的算法可用于评估最佳治疗方案。 HNC 的整体免疫谱目前大多数免疫评分方法都是基于免疫组织化学。 （IHC）对有限的一组生物标志物进行染色，这阻碍了对肿瘤景观的精确注释 IHC 方法在技术上很敏感，可能会出现机构间和浸润性淋巴细胞。 此外，目前的免疫评分仅强调少数 T 细胞亚群，并且 事实上，没有整合调节肿瘤对免疫杀伤反应的癌症基因组特征。 有证据表明，I 型干扰素 (IFN-I) 通路在 HNC 应答中发挥着重要作用 因此，利用全局 TIL 谱和癌症基因组特征提供了一种方法。 根据 HNC 的免疫原性对 HNC 进行分类是前所未有的机会。 细胞反卷积对异常值敏感，这些异常值在整个肿瘤 RNA-Seq 中经常观察到 我们最近的研究表明，一种新颖的机器学习工具可以快速且鲁棒地解构。 表达谱 (FARDEEP) 可自适应检测和去除异常值，在以下方面表现出卓越的准确性： 与 FOA 精确一致，该项目的总体假设是： 整合 FARDEEP 辅助 TIL 反卷积和癌症基因组学的复合免疫评分可以 有效识别感冒 HNC 为实现这一目标，我们接下来的两个步骤是： **(1) 我们将开发一个 **(2) 我们将建造一个大院 这些研究将整合癌症基因组特征和 TIL 谱的免疫评分来识别冷 HNC。 开发一种新颖的“适合分析全基因组数据的统计方法”并提供“统计 对 NIDCR 优先疾病的现有全基因组数据进行分析” 该项目将完善一个强大而稳健的方法。 新型免疫细胞反卷积机器学习工具，并描述了转变的中心致癌途径 新的免疫基因组学算法将简化免疫评分方法 有效地对 HNC 进行分层，有助于组合治疗的精确选择。