Early detection and risk of head and neck cancer through immune based spatial omics

摘要 头颈部鳞状细胞癌(HNSCC)是全球第七大常见癌症。在口头上 空洞，大多数鳞状细胞癌开始时是世界卫生组织分类的先兆病变 世界卫生组织(WHO)将其列为“口腔潜在恶性疾病”。口腔白斑是最常见的 OPMD，全球患病率为4.1%，恶变率在0.1%-34.0%之间，以及 口腔异型增生恶变率高达40%。这些范围广泛的恶性 转化率表明，尚未满足开发能够更好地区分预后的生物标志物的需求 预测癌前病变转化为侵袭性病变的风险 癌症。在癌症的发展过程中，免疫编辑发生在肿瘤的微环境中。 免疫编辑包括三个阶段：消除、平衡和逃逸。我们假设有定义的 皮损微环境中与癌前病变转化相关的免疫特征 根据已知的免疫编辑阶段，将细胞转化为浸润性癌症。目前的护理工具标准是 从癌前病变建立良性口腔病变包括常规苏木精/伊红(HE)和单一 免疫组织化学染色(IHC)，它限制了可在任何时候评估的免疫细胞的数量 就一次。因此，我们开发了一种创新的空间组学技术(SAFE)，它促进了 对整个组织切片进行全面和深度的多路传输，并将人工智能和 加速口腔内~45分子和免疫信号分析的机器学习方法 在临床上合适的时间范围内的损害。我们建议进行单细胞RNA测序来鉴定 独特的细胞和免疫转录程序，区分口腔良性和癌前病变 损伤(N=60)，并将通过SAFE评估相关蛋白的表达和空间定位(目标1)。 随后，在目标2中，我们将评估定义分子和/或免疫学特征(S)在唯一的 一组患者(N=55)，有连续活检记录从癌前转化为癌症超过20 针对单独的良性和癌前病变队列(N=155)。从我们的数据集中，我们将 首次开发包含宿主免疫反应的口腔癌进展模型 将恶性进展的风险评估改进到比目前可能的程度更高的程度。

ABSTRACT Head and neck squamous cell carcinoma (HNSCC) is the seventh most common cancer worldwide. In the oral cavity, most cases of squamous cell carcinoma begin as a precursor lesion classified by the World Health Organization (WHO) as an “oral potentially malignant disorder (OPMD).” Oral leukoplakia is the most common OPMD, with a global prevalence of 4.1% and a malignant transformation rate between 0.1%-34.0%, and the malignant transformation rate increases to 40% in oral dysplasia. These wide ranges of malignant transformation rates suggest an unmet need to develop prognostic biomarkers that can better differentiate benign from premalignant lesions and predict the risk of transformation of premalignant lesions to invasive cancer. During the development of cancer, immunoediting occurs within the tumor microenvironment. Immunoediting consists of three phases: elimination, equilibrium, and escape. We posit that there are defined immune signatures within a lesional microenvironment that correlate with the transformation of precancerous cells to invasive cancer based on the known phases of immunoediting. The current standard of care tools to establish benign from premalignant oral lesions include conventional hematoxylin/eosin (HE) and single staining immunohistochemistry (IHC), which limits the number of immune cells which can be evaluated at any one time. Thus, we developed an innovative spatial omics technology (SAFE) that facilitates the comprehensive and deep multiplexing of whole tissue sections and incorporates artificial intelligence and machine learning approaches to accelerate the analysis of ~45 molecular and immune signatures within oral lesions in a clinically appropriate timeframe. We propose to perform single-cell RNA sequencing to identify the unique cellular and immunological transcriptional programs that distinguish benign and premalignant oral lesions (N=60) and will assess relevant protein expression and spatial localization via SAFE (Aim 1). Subsequently, in Aim 2, we will evaluate the defining molecular and/or immunological signature(s) in a unique set of patients (N=55) with serial biopsies documenting transformation from premalignancy to cancer over 20 years against a separate cohort of benign and premalignant lesions (N=155). From our dataset, we will develop an oral cancer progression model that incorporates the host immune response for the first time to improve risk assessment for malignant progression to a degree superior to what is currently possible.