SWATH-MS based proteomic profiling of pancreatic ductal adenocarcinoma tumours reveals the interplay between the extracellular matrix and related intracellular pathways
Abstract:Pancreatic cancer accounts for 2.8% of new cancer cases worldwide and is projected to become the second leading cause of cancer-related deaths by 2030. Patients of African ancestry appear to be at an increased risk for pancreatic ductal adenocarcinoma (PDAC), with more severe disease and outcomes. The purpose of this study was to map the proteomic and genomic landscape of a cohort of PDAC patients of African ancestry. Thirty tissues (15 tumours and 15 normal adjacent tissues) were obtained from consenting Sout… Show more
“… 64 Quantitative proteomics is increasingly adopted for identifying biomarkers of early pancreatic cancer, such as actinin-4, annexin A2, Bcl-2, H1.3, IGFBP2, IGFBP3, and galectin-1 ( Figure 5 ). 65 , 66 , 67 , 68 , 69 , 70 , 71 Figure 5 Quantitative proteomics adopted in the discovery of various cancer biomarkers Many biomarkers for different types of cancer are identified through quantitative proteomics. Biomarkers were found from cancer tissue (black), plasma/serum (orange), and exosome (green).…”
Section: Identifying Potential Biomarkers With Quantitative Proteomicsmentioning
confidence: 99%
“…Quantitative proteomics is a promising tool for revealing the molecular mechanisms of drug action. 58 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 Proteomics drug maps have greatly promoted the discovery of drug targets. After the quantitative analysis for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 cancer cell lines, the proteome activity landscapes were obtained.…”
Section: Discovering Drug Targets With Quantitative Proteomicsmentioning
Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.
“… 64 Quantitative proteomics is increasingly adopted for identifying biomarkers of early pancreatic cancer, such as actinin-4, annexin A2, Bcl-2, H1.3, IGFBP2, IGFBP3, and galectin-1 ( Figure 5 ). 65 , 66 , 67 , 68 , 69 , 70 , 71 Figure 5 Quantitative proteomics adopted in the discovery of various cancer biomarkers Many biomarkers for different types of cancer are identified through quantitative proteomics. Biomarkers were found from cancer tissue (black), plasma/serum (orange), and exosome (green).…”
Section: Identifying Potential Biomarkers With Quantitative Proteomicsmentioning
confidence: 99%
“…Quantitative proteomics is a promising tool for revealing the molecular mechanisms of drug action. 58 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 Proteomics drug maps have greatly promoted the discovery of drug targets. After the quantitative analysis for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 cancer cell lines, the proteome activity landscapes were obtained.…”
Section: Discovering Drug Targets With Quantitative Proteomicsmentioning
Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.
“…In PC, the dense TME architecture ensures that drug delivery is impeded. Our group recently showed using SWATH-MS, the intricate network of signaling pathways within the ECM of PC tumors highlighting different known and potentially novel associating proteins ( 27 ). Furthermore, mouse models of PC showed that CAFs accumulate gemcitabine and this may contribute to drug resistance ( 28 ).…”
Section: Chemoresistance In Pdac: Role Of the Tumor Microenvironmentmentioning
Pancreatic cancer is one of the most deadly cancers, ranking amongst the top leading cause of cancer related deaths in developed countries. Features such as dense stroma microenvironment, abnormal signaling pathways, and genetic heterogeneity of the tumors contribute to its chemoresistant characteristics. Amongst these features, growth factors have been observed to play crucial roles in cancer cell survival, progression, and chemoresistance. Here we review the role of the individual growth factors in pancreatic cancer chemoresistance. Importantly, the interplay between the tumor microenvironment and chemoresistance is explored in the context of pivotal role played by growth factors. We further describe current and future potential therapeutic targeting of these factors.
“…Sequential window of all theoretical mass spectra (SWATH-MS), a type of data-independent acquisition (DIA) LC–MS technique, also allows for reproducible analysis of prepared peptides in a systematic and unbiased manner [ 25 , 26 ]. Several studies, such as the one recently conducted by our group, have demonstrated the utility of SWATH-MS in proteomic profiling in a solid tumour for the identification of potential biomarkers [ 27 ].…”
Background
Gallbladder cancer (GBC) is a lethal cancer with a poor prognosis. The lack of specific and sensitive biomarkers results in delayed diagnosis with most patients presenting at late stages of the disease. Furthermore, there is little known about the molecular mechanisms associated with GBC, especially in patients of African ancestry. This study aimed to determine dysregulated proteins in South African GBC patients to identify potential mechanisms of the disease progression and plausible biomarkers.
Methods
Tissues (27 GBC, 13 Gallstone disease, and 5 normal tissues) and blood plasma (54 GBC and 73 Benign biliary pathology) were obtained from consenting patients. Protein extraction was performed on all tissues and liquid chromatography-mass spectrometry was used for proteomic profiling. A project-specific spectral library was built using the Pulsar search algorithm. Principal component and Spearman’s rank correlation analyses were performed using PAST (V4.07b). Pathway and Network analyses were conducted using REACTOME (v3.7) and stringAPP (v1.7.0), respectively.
Results
In the tissue sample group, there were 62 and 194 dysregulated proteins in GBC compared to normal and gallstone groups, respectively. In the plasma group, there were 33 altered proteins in GBC compared to the benign biliary pathology group. We found 9 proteins (APOA1, APOA2, RET4, TTR, HEMO, HBB, HBA, PIGR, and APOE) to be commonly dysregulated in both tissue and plasma. Furthermore, a subset analysis demonstrated that 2 proteins, S100A8 and S100A9, were downregulated in GBC patients with GD history compared to those without. Pathway analysis showed that the dysregulated proteins in GBC patients were enriched in pathways involved in smooth muscle contraction, metabolism, ECM organization, and integrin cell surface interactions.
Conclusion
The identified dysregulated proteins help in understanding GBC molecular mechanisms in our patient group. Furthermore, the alteration of specific proteins in both tissue and plasma samples suggests their potential utility as biomarkers of GBC in this sample cohort.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
