Tumour cell-induced platelet aggregation revealed different phenotypes between PDAC plasticity subtypes – A proteomic comparison between two pancreatic cancer cell lines

Platelets have been shown to play an important role in metastasis and tumour-cell proliferation. In this context, the interaction of platelets with tumour cells is another prerequisite for successful metastatic dissemination. To investigate the interplay between pancreatic cancer cells and platelets (TCIPA), we compared the tumour-cell and platelet proteome during platelet aggregation respecting distinct plastic PDAC subtypes.

We isolated platelets from healthy donors and followed their crosstalk after calcification and incubation with either TRAP (+ control), only CaCl2 (- control), and one of the SILAC-labelled pancreatic cancer cell lines BxPC3 or PanC1. Aggregation was analysed using aggregometry, immunochemistry, and nanoparticle tracking analysis (NTA). Samples were subsequently prepared for data-independent acquisition parallel accumulation-serial fragmentation (diaPASEF) mass spectrometry to perform proteomic analysis. Machine learning algorithms, principal component analyses, and gene set enrichment analyses were employed to identify differential expressed protein features.

Although both cell lines were able to induce aggregation, we found that BxPC3 (epithelial subtype) induced faster and earlier platelet aggregation compared to PanC1 (mesenchymal subtype). After TCIPA, 22 proteins were significantly upregulated in BxPC3 compared to PanC1, whereas 58 proteins were significantly upregulated in PanC1 compared to BxPC3 (q-value £ 0.05 and log2 fold-change ³ 1. We also found 106 proteins significantly upregulated in both cell lines after TCIPA, which are part of platelet adhesion and aggregation pathways.

The results highlight the identification of important proteins involved in the interaction between platelets and pancreatic cancer cells for the first time. Additionally, different pancreatic subtypes revealed distinct TCIPAs that could be used to characterise PDAC progression.