Detection of pancreatic cancer using serum protein profiling

Velstra, Berit; Bonsing, Bert A.; Mertens, Bart; Burgt, Yuri E. M. van der; Huijbers, A.; Vasen, Hans F. A.; Mesker, Wilma E.; Deelder, André M.; Tollenaar, Rob A.E.M.

doi:10.1111/hpb.12017

Cited by 7 publications

(12 citation statements)

References 47 publications

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“…Blood specimens from both the discovery and validation cohorts were collected and processed according to a standardized protocol. 35 Briefly, all blood samples were drawn by antecubital venipuncture. Approximately 8 mL of venous blood was collected in a 10 mL BD vacutainer SST II advance and centrifuged for 10 minutes at 1000g.…”

Section: Serum Sample Collection and Plate Designmentioning

confidence: 99%

“…Before measurements (ie, serum N-glycome analysis) took place, each sample was aliquoted into 60 μL tubes. 35 One aliquot of each sample was then relocated into a 96-well plate format according to a plate design, thus keeping cases and their age-and sex-matched controls on the same plate. Additionally, for technical quality control (QC) of the spectra, each plate contained a minimum of six in-house standards and two blanks.…”

Section: Serum Sample Collection and Plate Designmentioning

confidence: 99%

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Serum N‐Glycome analysis reveals pancreatic cancer disease signatures

et al. 2020

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Background &Aims: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type with loco-regional spread that makes the tumor surgically unresectable. Novel diagnostic tools are needed to improve detection of PDAC and increase patient survival. In this study we explore serum protein N-glycan profiles from PDAC patients with regard to their applicability to serve as a disease biomarker panel. Methods: Total serum N-glycome analysis was applied to a discovery set (86 PDAC cases/84 controls) followed by independent validation (26 cases/26 controls) using in-house collected serum specimens. Protein N-glycan profiles were obtained using ultrahigh resolution mass spectrometry and included linkage-specific sialic acid information. N-glycans were relatively quantified and case-control classification performance was evaluated based on glycosylation traits such as branching, fucosylation, and sialylation. Results: In PDAC patients a higher level of branching (OR 6.19, P-value 9.21 × 10 −11) and (antenna)fucosylation (OR 13.27, P-value 2.31 × 10 −9) of N-glycans was found. Furthermore, the ratio of α2,6-vs α2,3-linked sialylation was higher in patients compared to healthy controls. A classification model built with three glycosylation traits was used for discovery (AUC 0.88) and independent validation (AUC 0.81), with sensitivity and specificity values of 0.85 and 0.71 for the discovery set and 0.75 and 0.72 for the validation set. Conclusion: Serum N-glycome analysis revealed glycosylation differences that allow classification of PDAC patients from healthy controls. It was demonstrated that glycosylation traits rather than single N-glycan structures obtained in this clinical glycomics study can serve as a basis for further development of a blood-based diagnostic test. 2 | VREEKER Et al. How to cite this article: Vreeker GCM, Hanna-Sawires RG, Mohammed Y, et al. Serum N-Glycome analysis reveals pancreatic cancer disease signatures.

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Section: Serum Sample Collection and Plate Designmentioning

confidence: 99%

Section: Serum Sample Collection and Plate Designmentioning

confidence: 99%

Serum N‐Glycome analysis reveals pancreatic cancer disease signatures

et al. 2020

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“… Various peptide and protein signatures have been reported based on a single-step sample clean-up procedure using a combination of a carrier (depicted in the inner shell ) with capture material (depicted in the middle shell ) and a mass spectrometer (depicted in the outer shell ). Previously, our group has reported signatures for PC based on weak-cation exchange with MALDI time-of-flight [13] , and reversed-phase C18 with MALDI time-of-flight [14] . In the current study, an ultrahigh-resolution reversed-phase C18-MALDI-FTICR signature is used that was obtained in a case-control calibration and validation design ( left-hand side ) [15] .…”

Section: Figurementioning

confidence: 99%

“…In our center, a discriminating PC biomarker signature was recently identified by following a serum peptide and protein profiling strategy based on a combination of automated single-step sample clean-up and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) [13] , [14] . The most detailed protein signatures were obtained using an ultrahigh-resolution MALDI Fourier transform ion cyclotron resonance (FTICR) MS platform that provided case-control classifications with a sensitivity and specificity both well above 85% [15] .…”

Section: Introductionmentioning

confidence: 99%

Application of a Serum Protein Signature for Pancreatic Cancer to Separate Cases from Controls in a Pancreatic Surveillance Cohort

Potjer

Mertens

Nicolardi

et al. 2016

Translational Oncology

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BACKGROUND: Pancreatic cancer (PC) surveillance is currently offered to individuals with a genetic predisposition to PC, but routinely used radiological screening modalities are not entirely reliable in detecting early-stage PC or its precursor lesions. We recently identified a discriminating PC biomarker signature in a sporadic patient cohort. In this study, we investigated if protein profiling can accurately distinguish PC from non-PC in a pancreatic surveillance cohort of genetically predisposed individuals. METHODS: Serum samples of 66 individuals with a CDKN2A germline mutation who participated in the pancreatic surveillance program (5 cases, 61 controls) were obtained following a standardized protocol. After sample clean-up, peptide and protein profiles were obtained on an ultrahigh-resolution matrix-assisted laser desorption/ionization–Fourier transform ion cyclotron resonance mass spectrometry platform. A discriminant score for each sample was calculated with a previously designed prediction rule, and the median discriminant scores of cases and controls were compared. Individuals with precursor lesions of PC (n = 4) and individuals with a recent diagnosis of melanoma (n = 4) were also separately considered. RESULTS: Cases had a higher median discriminant score than controls (0.26 vs 0.016; P = .001). The only individual with pathologically confirmed precursor lesions of PC could also be clearly distinguished from controls, and having a (recent) medical history of melanoma did not influence the protein signatures. CONCLUSIONS: Peptide and protein signatures are able to accurately distinguish PC cases from controls in a pancreatic surveillance setting. Mass spectrometry–based protein profiling therefore seems to be a promising candidate for implementation in the pancreatic surveillance program as an additional screening modality.

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“…Nevertheless, research continues in this arena, and subsequent studies have had to be conscientious about addressing reproducibility issues. Recently, studies have emerged that claim some success in using mass spectra-based proteome profiling to diagnose head and neck squamous cell carcinoma [108], lung cancer [109] and pancreatic cancer [110], but these tests are far from ready for clinical use. In the short term, the future of MS in the clinic seems to lie in assaying combinations of separately validated, discrete biomarkers.…”

Section: Expert Commentarymentioning

confidence: 99%

Cancer proteomics: developments in technology, clinical use and commercialization

Yeat

Lin

Sager

et al. 2015

Expert Review of Proteomics

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In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

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Detection of pancreatic cancer using serum protein profiling

Abstract: Serum profiles of healthy controls and PC can be discrimated between. Further research is warranted to evaluate specificity and whether this biosignature can be used for early detection in a high risk population.

Cited by 7 publications

References 47 publications

Serum N‐Glycome analysis reveals pancreatic cancer disease signatures

Serum N‐Glycome analysis reveals pancreatic cancer disease signatures

Application of a Serum Protein Signature for Pancreatic Cancer to Separate Cases from Controls in a Pancreatic Surveillance Cohort

Cancer proteomics: developments in technology, clinical use and commercialization

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