Investigating the prognostic role of intrinsic immune evasion mechanisms in HNSCC

摘要 头颈癌是世界上最普遍的侵袭性癌症之一，其中大多数是 鳞状细胞癌（SCC）或HNSCC。众所周知的风险因素与发病有关， HNSCC包括人乳头瘤病毒和环境因素，包括吸烟和饮酒。尽管 对HNSCC的分子遗传学研究已经超过二十年，仍然缺乏预后评估。 这些生物标志物可以指导HNSCC患者的当前治疗和管理。以前的研究和我们的 初步分析显示，最重要的免疫中心生物标志物具有最有希望的预后价值 对于HNSCC患者，与其他免疫治疗有利的癌症如皮肤癌和肺癌相比。在 在R 01项目中，我们提出了对现有转录组学和遗传数据进行大规模二次分析， 阐明内在免疫逃避机制在头颈癌中的预后作用。的 潜在的中心假设是肿瘤内在和宿主内在免疫信号传导共调节免疫应答。 HNSCC中现有疗法的有效性;以及阐明靶向药物之间的相互作用。 基因组/遗传变异和背景肿瘤免疫签名通过有效的统计和机器 学习方法可以产生新的预后生物标志物以及潜在的治疗靶点。本 最后，我们提出三个具体目标。在第一个目标中，我们将产生HNSCC特异性免疫特征 通过使用新的机器学习方法整合单细胞和大块肿瘤转录组数据， 由我们的团队开发。在第二个目标中，我们将发现并优先考虑与免疫相关的长非编码 RNA（lncRNA）生物标志物，通过调用现有的转录组数据，并应用有效的全基因组 筛选方法在第三个目标中，我们将联合收割机基线种系变异与肿瘤免疫 签名，并进一步检查其在接受免疫检查点阻断治疗的患者中的预后价值 （由多个机构提供，包括莫菲特，俄勒冈州立大学和密歇根大学）。总的来说，我们提出了最广泛的 迄今为止在HNSCC中进行的研究调查了基因组学来源的未调查元素， 临床和肿瘤免疫表型。新的组学数据资源和由此产生的生物标志物，特别是 我们研究中的免疫相关生物标志物有可能影响诊断， HNSCC患者的预后，并可以指导更有效和个性化的免疫治疗选择， 未来

ABSTRACT Head and neck cancer is one of the most prevalent aggressive cancers worldwide, with the majority being squamous cell carcinoma (SCC), or HNSCC. The well-known risk factors associated with the genesis of HNSCC include human papillomavirus and environment factors including tobacco and alcohol use. Despite more than two decades of research into the molecular genetics of HNSCC, there is still lack of prognostic biomarkers that can guide current treatment and management of HNSCC patients. Previous studies and our preliminary analysis showed that top immune-centric biomarkers had the most promising prognostic value for HNSCC patients compared to other immunotherapy-favorable cancers such as skin and lung cancers. In this R01 project, we propose a large-scale secondary analysis of existing transcriptomic and genetic data to elucidate the prognostic role of intrinsic immune evasion mechanisms in head and neck cancer. The underlying central hypothesis is that tumor-intrinsic and host-intrinsic immune signaling co-modulate the effectiveness of existing therapies in HNSCC; and elucidation of the interaction between targeted genomic/genetic alternations and background tumor-immune signatures via efficient statistical and machine learning methods can lead to novel prognostic biomarkers, as well as potential therapeutic targets. To this end, we propose three specifics aims. In the first aim, we will generate HNSCC-specific immune signatures by integrating single-cell and bulk-tumor transcriptomic data using novel machine learning methods developed by our team. In the second aim, we will discover and prioritize immune-related long non-coding RNA (lncRNA) biomarkers by recalling existing transcriptome data and applying an efficient genome-wide screening method. In the third aim, we will combine baseline germline variants with tumor immune signatures and further examine its prognostic value in patients treated by immune checkpoint blockade (provide by multiple institutes including Moffitt, OSU and UNC). Collectively, we propose the most extensive study to date in HNSCC investigating under-investigated element of the genomics source for predicting clinical and tumor immune phenotypes. The new omics data resource and resulted biomarkers, especially the immune-related biomarkers, from our research have potential to impact upon the diagnosis and prognosis of HNSCC patients and could guide more efficient and personalized immunotherapy selection in the future.