Application of a Serum Protein Signature for Pancreatic Cancer to Separate Cases from Controls in a Pancreatic Surveillance Cohort.
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.
Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: t.p.potjer@lumc.nl.
Thomas P
TP
Potjer
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands.
Bart J
BJ
Mertens
Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands.
Simone
S
Nicolardi
Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands.
Yuri E M
YE
van der Burgt
Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands.
Bert A
BA
Bonsing
Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands.
Wilma E
WE
Mesker
Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands.
Rob A E M
RA
Tollenaar
Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands.
Hans F A
HF
Vasen