Nakkireddy Sumanth Reddy, Jang Inyeop, Kim Minji, Yin Linda X, Rivera Michael, Garcia Joaquin J, Bartemes Kathleen R, Routman David M, Moore Eric J, Abdel-Halim Chadi N, Ma Daniel J, Van Abel Kathryn M, Hwang Tae Hyun
Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Department of Otolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, MN, USA.
Commun Med (Lond). 2024 Oct 3;4(1):190. doi: 10.1038/s43856-024-00604-w.
Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images.
A retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes.
Analysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes.
Integrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC.
深度学习技术擅长在苏木精和伊红（H&E）染色的切片中识别肿瘤浸润淋巴细胞（TILs）和免疫表型。然而，它们阐明肿瘤免疫微环境（TME）中不同细胞表型详细功能特征的能力有限。我们旨在通过整合H&E与相邻的免疫组织化学（IHC）图像，增强对TME区域细胞组成和功能特征的理解，并改善患者分层。
对人乳头瘤病毒阳性口咽鳞状细胞癌（OPSCC）患者进行回顾性研究。使用配对的H&E和11种蛋白质的IHC切片，采用深度学习管道对TME中的肿瘤、基质和TILs进行定量。患者被分为免疫炎症（IN）、免疫排除（IE）或免疫荒漠（ID）表型。通过配准IHC和H&E切片，我们整合IHC数据以获取相应肿瘤区域的蛋白质表达。我们进一步根据这些蛋白质的丰度将患者分层为特定的免疫亚型，例如IN且CD8+细胞增加或减少。这种特征描述为基于H&E的亚型提供了功能见解。
对88例原发性肿瘤和70例受累淋巴结组织图像的分析显示，在原发性肿瘤中被分类为IN且CD8高表达和CD163低表达的患者预后改善（p = 0.007）。多变量Cox回归分析证实这些亚型的预后明显更好。
整合H&E和IHC数据可增强TME免疫表型的功能特征描述，并具有生物学可解释性，同时改善HPV（+）OPSCC患者的分层。
