Proteomics based on mass spectrometry is a molecular biology tool that measures thousands of proteins from complex samples and can be used to generate quantitative protein profiles. It is important to consider the proteomic landscape of cells, with a particular interest in cancer cells, in order to fully identify molecular pathways.
Downstream transcriptomics often miss to capture cancer mutations, a phenomenon that occurs because while gene transcription or RNA messenger (mRNA) levels often correspond to total protein levels for a particular gene, there is a lack of wide-linkage between proteins and their mRNAs due to differences in translation. . In addition, proteins are further altered through post-transcriptional modifications – such as phosphorylation kinases that regulate cancer-signaling pathways.
In the new study, researchers from Baylor College of Medicine sought to combine proteomic data with transcription data to gain a more complete view of the complex management mechanisms underlying cancer. They assumed that invasive cancers are different due to altered pathways expressed at the protein level, and that the differences may help identify therapeutic targets of action.
“We analyzed protein data that included tens of thousands of proteins from approximately 800 tumors covering seven different types of cancer – breast, colon, lung, kidney, ovarian, uterine and pediatric glioma – available from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics dataset based on mass spectrometry, ”said author Chad Creighton, PhD, professor of medicine and co-director of Cancer Bioinformatics at the Dan L. Duncan Comprehensive Cancer Center at Baylor.
In total, 14,586 proteins and 44,763 phosphoproteins were detected across the seven cancers, an average of 9,700 proteins per cancer. In general, the researchers found hundreds of differently expressed proteins and phosphoproteins at a higher level (histologic parameter specifying the differentiation rate of cancer cells) or stage (clinical parameter indicating as the extent to which the mound has spread outside the original site).
They found that the results from protein analysis often correlated with corresponding mRNA analysis. However, proteomic patterns were often not seen in transcription patterns (gene transcription) or vice versa. This indicates that proteomics data could capture biological information lost in gene expression profile studies (measurement of RNA transcripts).
Cancers caused by DNA damage arise from single-nucleotide changes. Some of the level or phase-associated proteins identified in the analysis showed changes in somatic copy number changes in the cancer genus.
Sub-set associations of the proteomic stage signatures with copy number variation patterns may represent proteins that are selectively amplified or missing in the more aggressive cancers. For example, lower-sensitivity proteins associated with higher-grade cancers were identified by frequently missed genes.
In contrast, elevated protein levels in cancers were associated with increased metabolic pathways including glycolysis, lipid synthesis, the Krebs cycle, and the Warburg effect.
Functional study of protein kinases in invasive cancers
The team also wanted to validate the notion that identifying overexpressed kinases using proteomic analysis of tumor glands is a useful way to identify novel therapeutic drug targets.
“That’s exactly what we were able to do with this new, very powerful data set,” said author Diana Monsivais, PhD, associate professor of pathology and immunology at Baylor. “We focused on the uterine cancer data for which the computer analysis identified changes in several proteins associated with invasive cancer. We selected protein kinases, enzymes that would represent stronger candidates for chemotherapy.”
The team analyzed the uterine data for potential targets for functional studies in uterine endometrial cell lines. From a set of 347 protein kinases, they selected four to study in functional studies.
These protein kinases, along with more than 20 others, were associated with higher-grade uterine cancer as well as other types of invasive cancer. They found that treatment of some kinases reduced survival or migratory capacity for some uterine cancer cells.
In particular, they found that mitogen-activated cell kinase kinase 2 (MAP3K2), which activates the 5-extracellular protein-regulated cell kinase 5 (MEK5 / ERK5) cell signaling role, plays a specific role. , a process by which cancer spreads to other cigarettes.
“Our experiments have hypothesized that proteomics analysis is a useful strategy not only to better understand what is driving cancer, but to identify new ways to control it. or destroy it, “Monsivais said.
Creighton said researchers have historically only created transcription data.
“Looking at the protein data itself, which is available with the CPTAC, will allow researchers to extract a new set of information from these cancers,” Creighton said. , we compared mRNA and protein signatures and, although they were involved in many cases, about half of the proteins in the proteomic signatures were not included. to the corresponding mRNA signature, suggesting that mRNA and protein data must be included in cancer studies. . “
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