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; 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 头颈部鳞状细胞癌（HNSCC）是全球第七个最常见的癌症。在口头 腔，大多数鳞状细胞癌的病例是由世界健康归类的前体病变 组织（WHO）是“口腔潜在的恶性疾病（OPMD）”。口服白细胞是最常见的 OPMD，全球患病率为4.1％，恶性转化率在0.1％-34.0％，并且 口腔发育不良的恶性转化率增加到40％。这些宽阔的恶性范围 转换率表明未满足的需要开发预后生物标志物，可以更好地区分 良性病变良性，预测前病变转化为侵入性的风险 癌症。在癌症的发展过程中，免疫编辑发生在肿瘤微环境中。 免疫程序包括三个阶段：消除，平衡和逃脱。我们指出有定义 与癌前转化相关的病变微环境中的免疫特征 细胞基于已知的免疫编辑阶段来进行侵入性癌症。当前护理工具的标准 从口服前病变中建立良性包括常规苏木精/曙红（HE）和单一 染色免疫组织化学（IHC），它限制了可以在任何情况下评估的免疫细胞的数量 一度。那就是我们开发了一种创新的空间轨迹技术（安全），以促进 整个组织部分的全面和深度多路复用，并结合了人工智能和 机器学习方法以加速口服内〜45个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。我们建议执行单细胞RNA测序以识别 独特的蜂窝和免疫转录程序，可区分良性和预先口服 病变（n = 60），将通过安全（AIM 1）评估相关的蛋白质表达和空间定位。 随后，在AIM 2中，我们将评估独特的定义分子和/或免疫信号 一组患者（n = 55），有串行活检的文献记录转化从预先命名到癌症超过20 与单独的良性和预临界病变的单独队列（n = 155）。从我们的数据集中，我们将 开发一个口腔癌进度模型，该模型首次结合了宿主免疫反应 改善恶性发展的风险评估，比当前可能的程度优于程度。