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.

抽象的 头颈鳞状细胞癌（HNSCC）是全球第七大常见的口腔癌症。 大多数鳞状细胞癌病例都是以世界卫生组织分类的前体病变开始的 组织（WHO）将其定义为“口腔潜在恶性疾病（OPMD）”。 OPMD，全球患病率为4.1%，恶变率在0.1%-34.0%之间， 口腔异型增生的恶变率高达40%。 转化率表明开发能够更好区分的预后生物标志物的需求尚未得到满足 良性病变与癌前病变的区别，并预测癌前病变转变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微环境中。 免疫编辑由三个阶段组成：消除、平衡和逃逸。 病变微环境中的免疫特征与癌前病变的转化相关 基于免疫编辑的已知阶段，将细胞转化为侵袭性癌症。当前的护理工具标准。 从癌前口腔建立良性包括常规苏木精/伊红 (HE) 和单一病变 免疫组织化学染色 (IHC)，限制了可在任何时间评估的免疫细胞数量 因此，我们开发了一种创新的空间组学技术（SAFE），以促进 对整个组织切片进行全面和深度的多重分析，并结合人工智能和 机器学习方法可加速口腔内约 45 种分子和免疫特征的分析 我们建议在临床上适当的时间范围内进行单细胞 RNA 测序来识别病变。 独特的细胞和免疫转录程序，可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（目标 1）。 随后，在目标 2 中，我们将以独特的方式评估定义的分子和/或免疫学特征 一组患者（N = 55）进行了连续活检，记录了超过 20 岁从癌前病变到癌症的转变 根据我们的数据集，我们将针对一组良性和癌前病变（N = 155）进行数年的研究。 开发口腔癌进展模型，首次将宿主免疫反应纳入其中 将恶性进展的风险评估提高到比目前可能的水平更高的程度。