Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

Abstract

BACKGROUND: Endoscopic ultrasound-guided fine-needle aspiration fails to diagnose up to 25% of patients with pancreatic ductal adenocarcinoma (PDAC). Proteomics can help to overcome this clinical dilemma. We hypothesized that soluble protein signatures can differentiate PDAC from benign tissues. STUDY DESIGN: Tissues were obtained from resected surgical specimens, lysed, and homogenates collected for analysis with a 41-protein multiplex assay. Analyte concentrations were normalized to total protein. Statistical analysis was performed to evaluate for differences in PDAC vs benign tissue. RESULTS: Tissues were obtained from 159 patients, 82 patients with PDAC naïve to therapy and 77 with benign pancreatic pathology. Fourteen analytes had a receiver operating characteristic curve area of >0.75 for predicting PDAC vs benign tissue. A recursive partitioning model using only 2 analytes, interleukin 1 receptor antagonist and transforming growth factor-a, provided an accuracy, sensitivity, and specificity of 91.2%, 90.2%, and 92.2%, respectively. A penalized logistic regression model found 12 analytes that provide diagnostic value to a protein signature. The mean area under the receiver operating characteristic after 50 tenfold cross-validations was 0.951. Accuracy, sensitivity, and specificity of this model were 91.2%, 87.8%, and 94.8%, respectively. Applying the scenario of 80% disease prevalence in patients undergoing endoscopic ultrasound with fine-needle aspiration for a pancreatic head mass, positive predictive value is 98.5% (95% CI 93.0% to 99.7%) and negative predictive value is 66.0% (95% CI 54.9% to 75.6%). CONCLUSIONS: Protein signatures from pancreatic specimens can differentiate PDAC from benign tissue. Additional work to validate these findings in a unique sample set is warranted.

K-ras point mutation detection as an ancillary diagnostic biomarker: 1 step forward and 2 steps back?

“…The putative biomarkers, which are most ideally applied to specimens interpreted as atypical or suspicious, include, but are not limited to, Ca19-9, immunohistochemistry (CK17 and loss of SMAD4 expression), fluorescent in situ hybridization, microRNAs, osteopontin, epigenetic markers, protein signatures, and K-ras mutation detection. 2,[5][6][7][8] An international cohort of 3594 PDAC samples from primary and metastatic deposits, of which 96% of cases passed presequencing and postsequencing quality control, reflected as expected that K-ras, TP53, CDKN2A, and SMAD4 were the most frequently altered genes. Kras mutations were identified in 88% of specimens.…”

Infiltration of CD3+ and CD8+ lymphocytes in association with inflammation and survival in pancreatic cancer

“…Homogenates were probed for 41 unique analytes using a commercially available multiplex analysis per the manufactures protocol (catalog no. HCYTMAG-60K-PX41; Millipore Sigma) and as described previously [ 15 ]. The multiplex assay was selected for its large inflammatory panel and used as exploratory analysis as previously described [ 15 ].…”

“…HCYTMAG-60K-PX41; Millipore Sigma) and as described previously [ 15 ]. The multiplex assay was selected for its large inflammatory panel and used as exploratory analysis as previously described [ 15 ]. Data were acquired with the MAGPIX System (Luminex Corp) and analyzed using MILLIPLEX Analyst 5.1 (Millipore Sigma).…”

Endoscopic Ultrasound-Guided Sampling for Personalized Pancreatic Cancer Treatment

“…The amount and quality of tissues extracted using EUS-guided sampling are frequently insufficient for histopathological diagnosis, despite improved puncture techniques and needles. New diagnostic methods in addition to EUS-guided sampling, such as microRNA-based tests [ 78 ], confocal laser endomicroscopy [ 79 ], and proteomics-based tests [ 80 ], have been reported to have potential for future clinical application. A recent development in this field is the use of lipid profiling by probe electrospray ionization mass spectrometry and machine learning, including artificial intelligence for diagnosis.…”