Recent advances and opportunities in proteomic analyses of tumour heterogeneity

Abstract

Solid tumour malignancies comprise a highly variable admixture of tumour and non-tumour cellular populations, forming a complex cellular ecosystem and tumour microenvironment. This tumour heterogeneity is not incidental, and is known to correlate with poor patient prognosis for many cancer types. Indeed, non-malignant cell populations, such as vascular endothelial and immune cells, are known to play key roles supporting and, in some cases, driving aggressive tumour biology, and represent targets of emerging therapeutics, such as antiangiogenesis and immune checkpoint inhibitors. The biochemical interplay between these cellular populations and how they contribute to molecular tumour heterogeneity remains enigmatic, particularly from the perspective of the tumour proteome. This review focuses on recent advances in proteomic methods, namely imaging mass spectrometry, single-cell proteomic techniques, and preanalytical sample processing, that are uniquely positioned to enable detailed analysis of discrete cellular populations within tumours to improve our understanding of tumour proteomic heterogeneity. This review further emphasizes the opportunity afforded by the application of these techniques to the analysis of tumour heterogeneity in formalin-fixed paraffin-embedded archival tumour tissues, as these represent an invaluable resource for retrospective analyses that is now routinely accessible, owing to recent technological and methodological advances in tumour tissue proteomics. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

“…Several prognostic gene signatures for PDAC have been proposed [17][18][19][20][21][22][23][24]115]. However, we found no gene overlapping with our set of secreted-related genes with these previous signatures from the literature.…”

“…When we compared our list of 39 secretome proteins with PDAC gene signatures from the literature, we found no overlap between these previous studies [17][18][19][20][21][22][23][24]110]. This may be because global gene transcription levels insufficiently reflect global protein levels or due to limitations related to the sensibility of proteomics techniques, which point to the need to integrate transcriptomic and proteomic analyses to identify critical molecular changes of cancer in its essence [112,[115][116][117][118]. Interestingly, our results show that tumor transcription levels of our set of proteins that are expressed and secreted in pancreatic cancer are useful as potential prognostic biomarkers when compared to previously proposed transcript-based signatures for PDAC.…”

“…We found no protein shared between all secretome and proteome studies. This discrepancy may be due to the diversity of proteomic techniques used by different studies; or limitations in proteomic analysis related to sample preparation and sample heterogeneity, proteome complexity to be analyzed, especially regarding protein expression levels; and limitations of the analytical methods [112][113][114][115]. To overcome these limitations, we selected the proteins presented in two or more studies, aiming to increase the possibility of detecting proteins involved in pancreatic cancer.…”

“…However, we found no gene overlapping with our set of secreted-related genes with these previous signatures from the literature. We also compared the performance of our signature with nine gene expression signatures [17][18][19][20][21][22][23][24]115] in predicting worse survival in PDAC, which was tested in four different PDAC datasets (TCGA, ICGC, GSE21501, GSE28735) available in the SurvExpress tool. These signatures were able to separate risk-groups based on the gene expression profiles (data are not shown).…”

“…However, it is important to emphasize our dataset comprises proteins that are expressed and secreted in PDAC, constituting a rich source of biomarkers. [17][18][19][20][21][22][23][24]115]. The color of the circles in the heat scatter plot represents the agreement index, while the size of the circle is based on the log-rank p-value of the risk group separation based on the SurvExpress tool.…”