Topographic analysis of pancreatic cancer by TMA and digital spatial profiling reveals biological complexity with potential therapeutic implications

Abstract

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.

Keywords: Biomarkers; Digital spatial profiling; Image analysis; Pancreatic ductal adenocarcinoma; Topographic tissue microarrays; Tumour heterogeneity.

Figure 1

(A) H&E of TMA design from 12 PDAC blocks from which the described regions were selectively cored into a single recipient block. (i) Tumour rich, (ii) desmoplastic reaction, (iii) peri-neural invasion, (iv) peri-vascular extension, (v) tumour margin (epithelial-mesenchymal transition), (vi) lymphocytic inflammation and (vii) intravascular invasion, when obvious. (B) H&E and relevant immune markers to describe the accuracy of the TMA annotations (i–vii, as described above). (C) Line graph displaying the range of CD3 (i) and SMA (ii) in each of the topographical areas for cases 1–12. (D)(i) bar chart displaying the cumulative CD3 percentage positivity for cases 1–12 encompassing the topographic areas where data was available, with SMA data shown in (ii). (D)(iii) Expression of IDO1 in the tumour and stromal compartments, in different topographic areas for cases 1–12, T, tumour; S, stroma. 1E, focal IDO-1 IHC expression in a TMA core at × 4 and × 20 and TGF-β mRNA expression by RNA-ISH shown at × 20 and × 40.

Figure 2

(A)(i) Scatter plot displaying the correlation between IHC and RNA-ISH for C-Met expression across all cores where both data were available. (ii) Representative images of concordance and discordance of c-Met staining in the two hybridisation methodologies. (B)(i) bar chart displaying the cumulative c-Met IHC percentage positivity for cases 1–12 encompassing the topographic areas where data was available, with c-Met RNAScope data shown in (ii).

Figure 3

(A) Principle Component Analysis of the RNA data showing clustering. panCK status outlined in green. (B) Unsupervised hierarchical clustering of the 1000 most variable genes across all datasets clustered together based on the IHC status. Green box shows distinct panCK differences from two samples in case 5. The blue box highlights differences in the S100β+ sample from case 5, while the red box shows the difference panCK-ve/S100β− samples. (C) Displays the difference in heterogeneity across topograpahy within the same case (case 5), across perivascular extension, perineural invasion, and tumour margin. (D) Heat map showing the range of heterogeneity within the same core, where two samples were taken across the same topographical areas.