Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, BT9 7AE, UK.
Cellular Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK.
Sci Rep. 2024 May 18;14(1):11361. doi: 10.1038/s41598-024-62031-0.
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer.
胰腺导管腺癌（PDAC）仍然是人类最致命的恶性肿瘤之一。组织微阵列（TMA）是一种高通量生物标志物检测的成熟方法，但可能无法捕获具有潜在生物学相关性的癌症组织学特征。代表癌症病理生理特征的拓扑 TMA（T-TMA）是从具有代表性的回顾性 PDAC 诊断材料构建的，包括 72 个单独的核心组织样本。使用基于组织杂交的实验对 T-TMA 进行了检测，以确认拓扑采样的准确性、肿瘤发生前和癌症免疫介质的表达，总共进行了超过 750 项单独的生物标志物分析。还采用了定制设计的下一代测序（NGS）面板和空间分布特异性转录组评估。癌症病理生理特征的形态学选择通过蛋白特异性表达得到了证实。定量分析确定了 IDO/TGF-β 轴中表达的拓扑特异性模式；在标志性区域内炎症和纤维变性存在异质关系，c-MET 的蛋白和基因表达普遍但可变。NGS 结果突出了样本内潜在的遗传异质性，这可能对特定生物标志物的表达产生干扰。T-TMA 与定量生物标志物数字评分相结合，是识别胰腺癌中生物标志物标志性表达的有用工具。
