Integration of Immunology and Microbiology into Molecular Pathological Epidemiology of Colorectal Cancer

摘要 结直肠癌是一种异质性疾病，受体细胞突变、微生物区系、宿主免疫、 和风险因素暴露，需要综合研究方法来更好地了解病因。 拟议的项目计划将免疫学和微生物学整合到分子病理学中。 流行病学(MPE)，深入了解暴露、微生物区系、免疫细胞和 结直肠癌和腺瘤中的肿瘤细胞。该项目还计划开发新的统计和 用于这类研究的计算方法。该项目利用各种不同的肿瘤组织数据集 人群，包括护士健康研究(NHS)，NHS2，卫生专业人员随访研究， 结直肠癌联合会(GECCO)遗传学和流行病学，多民族队列研究(MEC)， 合作伙伴结肠镜检查队列(PCC)和CALGB/SWOG 80702试验。我们的多层次数据库有 长期生活方式因素和结直肠癌(CRC)肿瘤特征的累积信息 腺瘤。我们的研究涉及对肿瘤组织中各种免疫细胞和微生物区系的评估。 使用多光谱分析结合数字图像分析和机器学习算法。 肿瘤组织中分子病理、微生物和免疫的特征将有助于阐明 致癌过程。将评估饮食和生活方式因素与结直肠癌(或 腺瘤)根据微生物和/或免疫特征进行亚型，并与这些肿瘤的临床转归有关 子类型。MPE方法有望揭示结直肠癌目前未知的风险和预后因素。 和早发性结直肠癌，其发病率在全球范围内有所增加，原因不明。此外， 研究可改变的生活方式因素对肿瘤组织中免疫细胞和微生物的影响 开辟新的途径，制定个性化的预防策略。将努力复制调查结果。 尽可能使用其他独立的数据集。新的统计方法将解决分析问题 例如连续亚型、缺少生物标记物数据、瘤内空间变异、高维肿瘤 数据、中介分析和预测模型。此外，新的计算算法将是 用于破译肿瘤中免疫细胞亚群和肿瘤细胞的空间模式 微环境。免疫学和微生物学的综合流行病学分析将产生 为探索抗肿瘤免疫和微生物区系在结直肠癌中的作用提供科学依据 发展。此外，该项目还将通过开发新的 统计和计算方法。

Summary Colorectal cancer is a heterogenous disease influenced by somatic mutations, microbiota, host immunity, and risk factor exposures, necessitating integrative research approaches to better understand the etiology. The proposed project plans to integrates immunology and microbiology into molecular pathological epidemiology (MPE) to gain insights into the interactive role of exposures, microbiota, immune cells, and tumor cells in colorectal cancer and adenoma. This project also plans to develop novel statistical and computational methods for such research. This project leverages tumor tissue datasets in various populations, including the Nurses' Health Study (NHS), NHS2, Health Professionals Follow-up Study, Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), Multiethnic Cohort Study (MEC), Partners Colonoscopy Cohort (PCC), and CALGB/SWOG 80702 trials. Our multi-level databases have accumulated information on long-term lifestyle factors and tumoral features of colorectal cancer (CRC) and adenoma. Our research involves the assessment of various immune cells and microbiota in tumor tissue using multispectral assays combined with digital image analyses and machine learning algorithms. Characteristics of molecular pathology, microorganisms, and immunity in tumor tissue will shed light on the carcinogenic process. Diet and lifestyle factors will be assessed in relation to incidence of CRC (or adenoma) subtyped by microbial and/or immune features, and in relation to clinical outcomes of these tumor subtypes. The MPE approach is expected to reveal currently unknown risk and prognostic factors for CRC and early-onset CRC, the incidence of which has increased globally for uncertain reasons. Moreover, examining effects of modifiable lifestyle factors on immune cells and microorganisms in tumor tissue will open new ways to develop personalized preventive strategies. Efforts will be made to replicate findings using other independent datasets whenever possible. New statistical methods will address analytical issues such as continuous subtyping, missing biomarker data, intratumor spatial variations, high-dimensional tumor data, mediation analyses, and prediction models. Furthermore, novel computational algorithms will be developed to decipher the spatial patterns of immune cell subsets and tumor cells in the tumor microenvironment. Integrative epidemiological analyses of immunology and microbiology will generate provide the scientific foundation for exploring roles of anti-tumor immunity and microbiota in CRC development. In addition, this project will advance population cancer sciences via the development of new statistical and computational methods.