RESUMO
OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) has limited therapeutic options, particularly with immune checkpoint inhibitors. Highly chemoresistant 'stem-like' cells, known as cancer stem cells (CSCs), are implicated in PDAC aggressiveness. Thus, comprehending how this subset of cells evades the immune system is crucial for advancing novel therapies. DESIGN: We used the KPC mouse model (LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx-1-Cre) and primary tumour cell lines to investigate putative CSC populations. Transcriptomic analyses were conducted to pinpoint new genes involved in immune evasion. Overexpressing and knockout cell lines were established with lentiviral vectors. Subsequent in vitro coculture assays, in vivo mouse and zebrafish tumorigenesis studies, and in silico database approaches were performed. RESULTS: Using the KPC mouse model, we functionally confirmed a population of cells marked by EpCAM, Sca-1 and CD133 as authentic CSCs and investigated their transcriptional profile. Immune evasion signatures/genes, notably the gene peptidoglycan recognition protein 1 (PGLYRP1), were significantly overexpressed in these CSCs. Modulating PGLYRP1 impacted CSC immune evasion, affecting their resistance to macrophage-mediated and T-cell-mediated killing and their tumourigenesis in immunocompetent mice. Mechanistically, tumour necrosis factor alpha (TNFα)-regulated PGLYRP1 expression interferes with the immune tumour microenvironment (TME) landscape, promoting myeloid cell-derived immunosuppression and activated T-cell death. Importantly, these findings were not only replicated in human models, but clinically, secreted PGLYRP1 levels were significantly elevated in patients with PDAC. CONCLUSIONS: This study establishes PGLYRP1 as a novel CSC-associated marker crucial for immune evasion, particularly against macrophage phagocytosis and T-cell killing, presenting it as a promising target for PDAC immunotherapy.
RESUMO
Cancer stem cells (CSCs) undergo epithelial-mesenchymal transition (EMT) to drive metastatic dissemination in experimental cancer models. However, tumour cells undergoing EMT have not been observed disseminating into the tissue surrounding human tumour specimens, leaving the relevance to human cancer uncertain. We have previously identified both EpCAM and CD24 as CSC markers that, alongside the mesenchymal marker Vimentin, identify EMT CSCs in human oral cancer cell lines. This afforded the opportunity to investigate whether the combination of these three markers can identify disseminating EMT CSCs in actual human tumours. Examining disseminating tumour cells in over 12,000 imaging fields from 74 human oral tumours, we see a significant enrichment of EpCAM, CD24 and Vimentin co-stained cells disseminating beyond the tumour body in metastatic specimens. Through training an artificial neural network, these predict metastasis with high accuracy (cross-validated accuracy of 87-89%). In this study, we have observed single disseminating EMT CSCs in human oral cancer specimens, and these are highly predictive of metastatic disease.
When oral cancers metastasise that is, when tumour cells invade other parts of the body they typically do so by first colonizing the lymph nodes present in the neck. As this event significantly reduces chances of survival, oral cancer patients often have their neck lymph nodes removed to prevent the spread of the disease. However, this surgery carries risks and leads to longer hospital stays, stressing the need for better ways to predict which oral tumours will metastasise. Evidence from lab-grown cells and mice studies suggest that, in oral cancer, metastasis occurs when some cells in the original tumour go through a process called the epithelial-mesenchymal transition (EMT for short). This transformation allows the cells to detach from the tumour and become invasive. However, it has so far been difficult to observe this process in actual human tumours; this is partly because cells undergoing EMT stop producing the proteins that scientists rely on to distinguish cancer and healthy cells. To address this knowledge gap, Youssef et al. focused on three proteins: two tumour markers, EpCAM and CD24; and Vimentin, which is produced in greater quantities in the invasive mesenchymal state. Previous work had shown that a specific population of oral tumour cells can continue to express all three proteins even when adopting a mesenchymal identity through EMT. Based on this knowledge, Youssef et al. hypothesised that tracking Vimentin, EpCAM and CD24 using fluorescence microscopy would allow them to identify metastasising cells in human samples. An analysis of over 12,000 images from 74 tumours obtained from surgeries revealed that, in the metastatic samples, the cells detaching from primary tumours were more likely to express these three proteins. Finally, Youssef et al. used these images to train a machine learning algorithm. When applied to data from new oral cancer patients, the programme was able to predict whether their tumours were likely to spread with 89% accuracy. If confirmed by further work, and in particular on larger samples, these findings could in the future help clinicians decide which patients with oral cancer would benefit the most from surgery to remove neck lymph nodes.