Integrative analysis to identify genomic biomarkers in HPV positive oral cancer

项目摘要/摘要 人乳头瘤病毒阳性(HPV+)口腔癌(OC)，占口咽癌的70%以上 北美和欧洲的病例被发现更具侵袭性，转移倾向更高 与HPV阴性OC相比。人们认为，这种攻击性与其性质有关 人乳头瘤病毒感染引发的致癌机制人乳头瘤病毒编码两个有效的癌基因E6和E7 使关键抑癌基因pRb和p53失活，随后改变口腔中基因的表达谱 上皮细胞。为了确定人乳头瘤病毒致癌的分子机制，许多研究比较了 HPV+OC对正常口腔上皮、HPV阴性OC或其他癌症类型的(Epi)基因组图谱。这些 研究已经使用不同的方法(转录法、基因组法)生成了高通量测序数据集 和表观基因组)和细胞条件(正常、病毒感染和癌症)。然而，这些数据集是 由于缺乏可比较的分析平台来有效地审问他们，因此没有得到充分的探索，特别是在 在不同的研究中，异质性和批次效应很高。我们计划利用我们的数据科学经验 以及密切的湿实验室合作，以执行综合分析，以确定HPV特异性生物标记物 HPV+OC。我们建议整合(Epi)来自11个选定研究的基因组下一代测序数据集 涉及3种数据类型、13个细胞系、3个病毒感染阶段和2个 解剖学上相似的地方。分析的核心是消除潜在的批量效应和 整合数据集，并设置适当的对照来提名致癌生物标志物。为了验证这些发现，我们 建议用干实验和湿实验来评估候选生物标记物。从拟议研究中获得的见解 可以促进我们对口腔生物学的理解，并有可能转化为治疗HPV+OC的新疗法。

PROJECT SUMMARY/ABSTRACT Human papillomavirus positive (HPV+) oral cancer (OC), accounting for over 70% of oropharyngeal cancer cases in North America and Europe, was found to be more aggressive with a higher tendency of metastasis compared to HPV negative OC. It is believed that such aggressiveness is associated to the nature of its oncogenic mechanisms triggered by HPV infection. HPV encodes two potent oncogenes E6 and E7 that inactivate key tumor suppressors pRb and p53 and subsequently alter the expression spectrum of genes in oral epithelial cells. To identify the molecular mechanisms of HPV oncogenesis, numerous studies have compared the (epi)genomic profiles of HPV+ OC to normal oral epithelium, HPV negative OC, or other cancer types. These studies have generated high-throughput sequencing datasets using different methods (transcriptomic, genomic and epigenomic) and cellular conditions (normal, viral-infected and cancerous). However, these datasets were not fully explored due to lack of comparable analysis platform to efficiently interrogate them, especially when heterogeneity and batch effects are high across studies. We propose to leverage our data science experience as well as close wet-lab collaborations to perform integrative analysis to identify HPV-specific biomarkers in HPV+ OC. We propose to integrate (epi)genomic next generation sequencing datasets from 11 selected studies (with addition if more availability in the future), involving 3 data types, 13 cell lines, 3 viral infection stages and 2 anatomically similar sites. At the core of the analysis is to remove potential batch effects and biases of the integrated datasets and to set proper controls to nominate oncogenic biomarkers. To validate the findings, we propose both dry and wet-lab experiments to evaluate candidate biomarkers. Insights from the proposed study could advance our understanding of oral biology and potentially translate to novel therapeutics for HPV+ OC.