Discriminating patients with early-stage pancreatic cancer or chronic pancreatitis using serum electrospray mass profiling

Abstract

Blood tests are needed to aid in the early detection of pancreatic ductal adenocarcinoma (PDAC), and monitoring pancreatitis development into malignancy especially in high risk patients. This study exhibits efforts and progress toward developing such blood tests, using electrospray-mass spectrometry (MS) serum profiling to distinguish patients with early-stage PDAC or pancreatitis from each other and from controls. Identification of significant serum mass peak differences between these individuals was performed using t tests and “leave one out” cross validation. Serum mass peak distributions of control individuals were distinguished from those of patients with chronic pancreatitis or early-stage PDAC with P values <10−15, and patients with chronic pancreatitis were distinguished from those of patients with early-stage PDAC with a P value <10−12. Sera from 12 out of 12 patients with PDAC stages I, IIA and IIB were blindly validated from controls. Tandem MS/MS identified a cancer phenotype with elements of PDAC involved in early-stage PDAC/control discrimination. These studies indicate electrospray-MS mass profiling can detect serum changes in patients with pancreatitis or early-stage pancreatic cancer. Such technology has the potential to aid in early detection of pancreatic cancer, biomarker development, and in monitoring development of pancreatitis into PDAC.

Distinguishing patients with idiopathic epilepsy from solitary cysticercus granuloma epilepsy and biochemical phenotype assessment using a serum biomolecule profiling platform

“…Results reported here using ESI-MS were substantiated using an instrument with a different mass analyzer of lower resolution (Fig 3). These results support the hypothesis that the disease states induce biomolecular alterations that are reflected in the peripheral blood and have a role in identifying specific clinical groups [13,19,20,52]. This work focuses on the SCG and IE disease states and adds to our previous study using the ESI-MS platform that was able to distinguish the serum mass profile of a heterogeneous group of NCC (solitary, multiple, calcified, and healed cyst[s]) patients from individuals with IE [13].…”

“…Notably, the sensitivity test metric (true positive rate) of this LOOCV analysis exceeded that of serum EITB for SCG [13,50]. It is likely the positive results of these studies are due to the large number of different identifiers (mass peaks) applied by this approach, as opposed to more specific tests such as EITB and antigen ELISA [19,20,51]. Results reported here using ESI-MS were substantiated using an instrument with a different mass analyzer of lower resolution (Fig 3).…”

“…MS/ MS search-related settings were as follows: [enzyme name = no-enzyme (no digest)], precursor mass tolerance = 1.8 Da, fragment mass tolerance = 0.8 Da, b & y ions were scored, and dynamic modifications were noted for oxidation (C, M amino acids), phosphorylation (S, T, Y), methylation (C), all with maximum of 4 modifications per peptide. Protein identifications required a minimum of two unique peptides and a cross correlation range (Xcorr) �1.7, in line with previous studies [18][19][20]. Identified sequences were searched using Basic Local Alignment Search Tool (BLAST) analysis.…”

Serum discrimination and phenotype assessment of coronary artery disease patents with and without type 2 diabetes prior to coronary artery bypass graft surgery

“…The approach employed utilizes an all-liquid unfractionated serum mass profiling procedure to analyse serum mass peaks and biomolecules. This technology was previously successful in examining and distinguishing early stages of various cancers and neurological disorders [7][8][9][10]. This methodology is straightforward, involving serum isolation from peripheral blood, dilution and injection into an electrospray ionization mass spectrometer, followed by software mass peak analysis.…”

“…This mass spectrometry (MS) serum platform approach was successful in identifying patients with early-stage cancers, neurological infections, and traumatic brain injury (TBI) and related concussion disorders [7][8][9][10]. The specific disease monitoring and discriminating ability of this MS platform is likely due to the large number of distinguishing components analyzed at the same time [9][10][11].…”