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

通过基于免疫的空间组学早期发现头颈癌并降低风险

• 批准号: 10766467
• 负责人: Sara Isabel Pai
• 金额: --
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2023-09-07
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10766467
• 关键词: Acceleration; Address; Algorithms; Antitumor Response; Artificial Intelligence; Atlases; Benign; Biological Markers; Biopsy; Biopsy Specimen; Cells; Characteristics; Classification; Clinical; Data Set; Development; Diagnostic tests; Disease; Early Diagnosis; Early Intervention; Enrollment; Epithelium; Equilibrium; Genetic; Genetic Transcription; Goals; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Hematoxylin and Eosin Staining Method; Heterogeneity; Histologic; Immune; Immune response; Immune system; Immunohistochemistry; Immunologic Markers; Immunologics; Incidence; Lesion; Loss of Heterozygosity; Machine Learning; Malignant - descriptor; Malignant Epithelial Cell; Malignant Neoplasms; Modeling; Modification; Molecular; Oral; Oral Characters; Oral Leukoplakia; Oral Stage; Oral cavity; Patients; Phase; Population; Premalignant Cell; Prevalence; Prognostic Marker; Resected; Risk; Risk Assessment; Risk Factors; Sampling; Smoking History; Squamous cell carcinoma; Stains; Stromal Cells; System; Techniques; Technology; Testing; Time; Tissues; Tobacco; Tobacco use; United States; Update; Variant; World Health Organization; cancer cell; cancer diagnosis; cancer invasiveness; cancer prevention; cancer therapy; carcinogenesis; clinical application; cohort; computerized tools; experience; high risk; immune resistance; immunogenicity; improved; innovation; insight; malignant mouth neoplasm; new technology; oral dysplasia; oral lesion; predictive marker; premalignant; prognostic; programs; prospective; protein expression; response; risk prediction; single-cell RNA sequencing; standard of care; tool; tumor; tumor heterogeneity; tumor microenvironment; tumor progression; tumorigenesis

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.

摘要 头颈部鳞状细胞癌（HNSCC）是全球第七大常见癌症。口服 大多数鳞状细胞癌开始是世界卫生组织分类的前驱病变， 世界卫生组织（WHO）将其称为“口腔潜在恶性疾病（OPMD）”。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， 口腔发育不良恶变率高达40%。这些广泛的恶性肿瘤 转化率表明，开发能够更好地区分 从癌前病变转化为良性病变，并预测癌前病变转化为浸润性病变的风险 癌在癌症的发展过程中，免疫编辑发生在肿瘤微环境中。 免疫编辑包括三个阶段：消除、平衡和逃逸。我们认为， 与癌前病变转化相关的病变微环境中的免疫特征 基于免疫编辑的已知阶段，将细胞转化为侵袭性癌症。目前的标准护理工具， 从恶变前口腔病变建立良性病变，包括常规苏木精/伊红（HE）和单 免疫组织化学染色（IHC），这限制了可以在任何时间评估的免疫细胞的数量。 一次性因此，我们开发了一种创新的空间组学技术（SAFE）， 全组织切片的全面和深度多路复用，并结合人工智能， 机器学习方法加速口腔内~45种分子和免疫特征的分析 在临床上适当的时间范围内的病变。我们建议进行单细胞RNA测序，以确定 独特的细胞和免疫转录程序，区分良性和癌前口腔 病变（N=60），并将通过SAFE评估相关蛋白表达和空间定位（目的1）。 随后，在目标2中，我们将在一个独特的实验中评估定义的分子和/或免疫学特征。 一组患者（N=55），其连续活检记录了20年以上从癌前病变转变为癌症 良性和癌前病变的单独队列（N=155）。根据我们的数据集，我们将 开发口腔癌进展模型，首次纳入宿主免疫反应， 改善恶性进展风险评估，使其达到上级程度。