Integration of epidemiology, pathology, immunology and outcomes in colorectal cancer

摘要 机器学习有可能改变病理诊断，并解决非常有限的可访问性。 低收入国家的病理学专家。实体瘤的常规组织学图像包含大量 视觉特征可以被机器学习等人工智能工具提取和处理， 擅长基本的图像分析任务，如肿瘤检测。此外，机器学习还可以预测 直接来自组织学图像的临床相关特征，包括微卫星不稳定性和免疫特征 独立预测对治疗的预后反应。这个庞大的、多元文化的、种族和民族多样的 研究使用常规收集的临床标本的整个载玻片的图像，并应用计算机 病理学方法和数字空间表达谱，以量化地改善CRC诊断、预后和 预测模型以及临床、流行病学和遗传学数据。研究目标将得以实现 通过三个具体目标。在目标1中，我们将从整个幻灯片图像中应用新的机器学习算法 在种族/人种中可重复地鉴定结直肠癌的MSI、组织病理学和免疫特征， 不同的人群。我们将研究6，751例CRC病例的H&E幻灯片，将5，551例CRC的现有幻灯片数字化 例和1，200例新的CRC病例，并具有同期的临床和流行病学数据。然后，我们将申请 深度学习方法，以准确识别CRC的组织病理学特征和免疫特征。我们 将使用强大的培训验证和测试设计（70%/15%/15%），以确保 我们的发现在目标2中，我们将测试预测MSI和免疫特征的机器学习算法是否 临床、流行病学和生殖系遗传学数据的加入，改善了与CRC预后相关的基因。我们将 使用机器学习统计方法来测试目标1中开发的算法是否可以提高对 总生存期和对治疗的反应，以及除完整数字载玻片外的补充信息 图像.最后，在目标3中，我们将比较从表达的数字空间剖面中获得的信息， 结直肠肿瘤中的蛋白质与来自淋巴细胞免疫评分定量的信息 在肿瘤中心（CT）和浸润边缘（IM）的人群，并探讨这些措施是否改善 目标1和目标2中在样本子集中开发的模型。我们将执行GeoMx数字空间分析， 在150例I-III期TNM结直肠癌中表达的56种蛋白质，以比较数字空间 免疫评分是一种完全依赖于CD 3+和CD 8 + T细胞表达模式的评分系统。 这项研究利用了来自美国的病理学、流行病学、临床、免疫学和生殖系遗传学数据， 来自加州、底特律、纽约、佛罗里达、波多黎各、以色列和西班牙的种族/人种多样化的CRC患者。 我们的首要目标是通过具有临床影响力的免疫学方法来提高结直肠癌的有效诊断。 数据区.

ABSTRACT Machine learning has the potential to transform pathologic diagnosis and to address very limited accessibility of expert pathology in low-income countries. Routine histology images of solid tumors contain an immense number of visual features that can be extracted and processed by artificial intelligence tools like machine learning, which excels at basic image analysis tasks such as tumor detection. In addition, machine learning can also predict clinically relevant features directly from histology images including microsatellite instability and immune features that independently predict prognosis response to therapy. This large, multicultural, racially and ethnically diverse study uses images of whole slides from routinely collected clinical specimens and applies computational pathology methods and digital spatial expression profiling to quantifiably improve CRC diagnosis, prognosis and predictive models together with clinical, epidemiologic and genetic data. The study goals will be accomplished through three specific aims. In Aim 1, we will apply novel machine learning algorithms from whole slide images to reproducibly identify MSI, histopathologic and immune features of colorectal cancer in racially/ethnically diverse populations. We will study H&E slides from 6,751 CRC cases, digitizing existing slides from 5,551 CRC cases and 1,200 new cases of CRC with contemporaneous clinical and epidemiologic data. Then, we will apply deep learning methods to accurately identify histopathologic features and immune characteristics of CRC. We will use a robust training validation, and testing design (70%/15%/15%) to ensure the rigor and reproducibility of our findings. In Aim 2, we will test whether machine learning algorithms that predict MSI and immune features related to CRC prognosis improve with the addition of clinical, epidemiologic, and germline genetic data. We will use machine learning statistical methods to test whether algorithms developed in Aim 1 improve prediction of overall survival and response to therapy with the addition of supplemental information beyond whole slide digital images. Finally, in Aim 3, we will compare the information derived from digital spatial profiling of expressed proteins in colorectal tumors with the information derived from Immunoscore quantification of lymphocyte populations at the tumor center (CT) and the invasive margin (IM), and explore whether these measures improve the models developed in Aims 1 and 2 in a subset of samples. We will perform GeoMx digital spatial profiling of 56 proteins expressed in 150 Stage I-III TNM colorectal cancers to compare the performance of digital spatial profiling to Immunoscore, a scoring system relying exclusively on expression patterns of CD3+ and CD8+ T cells. This study takes advantage of pathologic, epidemiologic, clinical, immunologic and germline genetic data from racially/ethnically diverse CRC patients from California, Detroit, New York, Florida, Puerto Rico, Israel and Spain. Our overarching goal is to improve the efficient diagnosis of colorectal cancer with clinically impactful immune profiles.