Liu Bojing, Polack Meaghan, Coudray Nicolas, Quiros Adalberto Claudio, Sakellaropoulos Theodore, Crobach Augustinus S L P, van Krieken J Han J M, Yuan Ke, Tollenaar Rob A E M, Mesker Wilma E, Tsirigos Aristotelis
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.
Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, New York, USA.
bioRxiv. 2024 Mar 21:2024.02.26.582106. doi: 10.1101/2024.02.26.582106.
Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival was confirmed in an independent clinical trial cohort (N=1213 WSIs). This unbiased atlas resulted in 47 HPCs displaying unique and sharing clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analysis of these HPCs, including immune landscape and gene set enrichment analysis, and association to clinical outcomes, we shed light on the factors influencing survival and responses to treatments like standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil new insights and aid decision-making and personalized treatments for colon cancer patients.
自监督学习（SSL）可自动从未经标注的苏木精-伊红染色全切片图像（WSIs）中提取并解释组织病理学特征。我们在435张TCGA结肠腺癌WSIs上训练了一个SSL巴洛双胞胎编码器，以从小图像块中提取特征。然后，莱顿社区检测将切片分组为组织形态学表型簇（HPCs）。在一个独立的临床试验队列（N = 1213张WSIs）中证实了HPCs对总生存期的可重复性和预测能力。这个无偏图谱产生了47个HPCs，它们显示出独特且具有临床意义的组织形态学特征，突出了组织类型、数量和结构，特别是在肿瘤基质方面。通过对这些HPCs进行深入分析，包括免疫图谱和基因集富集分析以及与临床结果的关联，我们揭示了影响生存以及对标准辅助化疗和实验性治疗等治疗反应的因素。对HPCs的进一步探索可能会揭示新的见解，并有助于为结肠癌患者进行决策和个性化治疗。
