Integrative analysis to identify genomic biomarkers in HPV positive oral cancer

项目总结/摘要 人乳头瘤病毒阳性（HPV+）口腔癌（OC），占口咽癌的70%以上 在北美和欧洲的病例中，发现其更具侵袭性，具有更高的转移倾向 与HPV阴性OC相比。据信，这种侵略性与其性质有关。 HPV感染引发的致癌机制。HPV编码两种有效的癌基因E6和E7， 关键肿瘤抑制因子pRb和p53改变口腔癌基因表达谱 上皮细胞为了确定HPV肿瘤发生的分子机制，许多研究比较了 HPV+ OC与正常口腔上皮、HPV阴性OC或其他癌症类型的（epi）基因组谱。这些 研究已经使用不同的方法（转录组学、基因组学）产生了高通量测序数据集 和表观基因组）和细胞状况（正常、病毒感染和癌性）。然而，这些数据集 由于缺乏可比较的分析平台来有效地询问他们，特别是当 研究间的异质性和批次效应较高。我们建议利用我们的数据科学经验 以及密切的湿实验室合作，进行综合分析，以确定HPV特异性生物标志物， HPV+ OC。我们建议从11个选定的研究中整合（EPI）基因组下一代测序数据集 (with另外，如果将来有更多可用性），涉及3种数据类型，13种细胞系，3种病毒感染阶段和2种 解剖学上相似的部位。分析的核心是消除潜在的批次效应和偏差， 整合数据集，并设置适当的控制来提名致癌生物标志物。为了验证这些发现，我们 提出干和湿实验室实验来评估候选生物标志物。拟议研究的见解 可以促进我们对口腔生物学的理解，并可能转化为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.