Abstract：
Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning to summarize a latent space across 147 local (e.g., fiber length, solidity) and global (e.g., fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Furthermore, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive versus outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.
摘要：
胰腺导管腺癌（PDAC）是发病率增加的唯一癌症之一，通常与肿瘤内和肿瘤周围的瘢痕形成有关。称为结缔组织增生。这种瘢痕形成在细胞外基质(ECM)结构中是高度异质的,并且在尚未完全理解的肿瘤生物学和临床结果两者中起复杂的作用。使用苏木精和伊红（H&E），现有临床工作流程中使用的常规组织学染色，我们量化了85例患者样本中的ECM结构,以评估促纤维增生性结构与临床结局(如生存时间和疾病复发)之间的关系.通过利用无监督机器学习(ML)来总结147个局部的潜在空间(例如光纤长度，坚固性)和全局(例如纤维分支，孔隙度)基于H&E的特征，我们发现了一系列与生存率和复发率差异相关的组织学结构.Further,我们通过indexing(CODEX)参考图集将H&E架构映射到CO-Detection，揭示与结果阳性和阳性相关的基于细胞和蛋白质的局部生态位肿瘤微环境中的结果阴性瘢痕形成。总的来说,我们的研究利用标准H&E染色来揭示促纤维化组织和PDAC结局之间的临床相关关联，提供了一个可翻译的管道来支持预后决策和空间生物因素的蓝图，用于通过组织工程方法进行建模。
