Monabbati Shayan, Corredor Germán, Pathak Tilak, Peacock Craig, Yang Kailin, Koyfman Shlomo, Scacheri Peter, Lewis James, Madabhushi Anant, Viswanath Satish E, Gryder Berkley
Dept. of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Dept. of Biomedical Engineering, Emory University School of Medicine, Atlanta, GA, USA; Mayo Clinic, AZ, USA.
Eur J Cancer. 2025 May 15;221:115429. doi: 10.1016/j.ejca.2025.115429. Epub 2025 Apr 14.
Measuring the chromatin state of a tumor provides a powerful map of its epigenetic commitments; however, as these are generally bulk measurements, it has not yet been possible to connect changes in chromatin accessibility to the pathological signatures of complex tumors. In parallel, recent advances in computational pathology have enabled the identification of spatial features and immune cells within oral cavity tumors and their microenvironment.
Here, we present pathogenomic fingerprinting (PaGeFin), a novel method that integrates morphological tumor features with chromatin states using ATAC-seq. This framework links spatial morphologic and epigenetic features, offering insights into tumor progression and immune evasion within and across tumors. Morphologic features describing spatial relationships between tumor and lymphocyte cells that are prognostic of oral cavity squamous cell carcinoma (OSCC) were identified through AI-driven pathology analysis. These pathomic features were spatially colocalized within the epigenome of 4 distinct sections of 4 OSCC tumors.
These key features pinpointed chromatin regions responsible for critical immune cell function through peak locations and enrichment analysis, highlighting loci of CD27+ memory B cells, helper CD4+ T cells, and cytotoxic CD8 naïve T cells that likely drive morphologic changes in the distribution of lymphocytes in the tumor microenvironment and promote aggressive tumor behavior. Gene Ontology analysis revealed that the CTLA4, CD79A, CD3D, and CCR7 genes were embedded in these regions.
This computational approach is the first to assess the correlation between pathomic and epigenetic features in the context of cancer.
测量肿瘤的染色质状态可提供其表观遗传特征的强大图谱；然而，由于这些通常是整体测量，尚未能够将染色质可及性的变化与复杂肿瘤的病理特征联系起来。与此同时，计算病理学的最新进展使得能够识别口腔肿瘤及其微环境中的空间特征和免疫细胞。
在此，我们提出了病原体组指纹识别（PaGeFin），这是一种使用ATAC-seq将肿瘤形态特征与染色质状态整合的新方法。该框架将空间形态和表观遗传特征联系起来，为肿瘤进展以及肿瘤内部和之间的免疫逃逸提供见解。通过人工智能驱动的病理分析确定了描述肿瘤与淋巴细胞之间空间关系且对口腔鳞状细胞癌（OSCC）具有预后意义的形态特征。这些病理特征在4个OSCC肿瘤的4个不同切片的表观基因组中在空间上共定位。
这些关键特征通过峰位置和富集分析确定了负责关键免疫细胞功能的染色质区域，突出了CD27 + 记忆B细胞、辅助性CD4 + T细胞和细胞毒性初始CD8 T细胞的基因座，这些基因座可能驱动肿瘤微环境中淋巴细胞分布的形态变化并促进侵袭性肿瘤行为。基因本体分析表明，CTLA4、CD79A、CD3D和CCR7基因嵌入在这些区域中。
这种计算方法是首次在癌症背景下评估病理特征与表观遗传特征之间的相关性。
