Challenges in detecting pre-malignant pancreatic lesions during acute pancreatitis using a serum microRNA assay: a study based on KrasG12D transgenic mice.

Caerulein-induced acute pancreatitis accelerates the progression of pancreatic intraepithelial neoplasia (PanIN) lesions in a pancreas-specific KrasG12D mouse model. The purpose of this study was to explore whether serum microRNAs (miRNAs) can serve as sensitive biomarkers to detect occult PanIN in the setting of acute pancreatitis. Serum miRNA profiles were quantified by an array-based method and normalized by both Variance Stabilization Normalization (VSN) and invariant methods. Individual miRNAs were validated by TaqMan real-time PCR with synthetic spike-in C. elegans miRNAs as external controls. Serum miRNA profiles distinguished KrasG12D mice with pancreatitis from wild-type mice without pancreatitis, but failed to differentiate KrasG12D mice with pancreatitis from wild-type mice with pancreatitis. Most individual miRNAs that increased in KrasG12D mice with pancreatitis were not significantly different between KrasG12D mice without pancreatitis and wild-type mice without pancreatitis. Mechanistically, Gene Set Enrichment Analysis (GSEA) of the mRNA array data and immunohistochemical assays showed that caerulein-induced acute pancreatitis involved acinar cell loss and immune cell infiltration, which might contribute to serum miRNA profile changes. This study highlighted the challenges in using sensitive serum miRNA biomarker screening for the early detection of pancreatic malignancies during acute pancreatitis.

Classifying patients for breast cancer by detection of autoantibodies against a panel of conformation-carrying antigens.

Patients with breast cancer elicit an autoantibody response against cancer proteins, which reflects and amplifies the cellular changes associated with tumorigenesis. Detection of autoantibodies in plasma may provide a minimally invasive mechanism for early detection of breast cancer. To identify cancer proteins that elicit a humoral response, we generated a cDNA library enriched for breast cancer genes that encode membrane and secreted proteins, which are more likely to induce an antibody response compared with intracellular proteins. To generate conformation-carrying antigens that are efficiently recognized by patients' antibodies, a eukaryotic expression strategy was established. Plasma from 200 patients with breast cancer and 200 age-matched healthy controls were measured for autoantibody activity against 20 different antigens designed to have conformational epitopes using ELISA. A conditional logistic regression model was used to select a combination of autoantibody responses against the 20 different antigens to classify patients with breast cancer from healthy controls. The best combination included ANGPTL4, DKK1, GAL1, MUC1, GFRA1, GRN, and LRRC15; however, autoantibody responses against GFRA1, GRN, and LRRC15 were inversely correlated with breast cancer. When the autoantibody responses against the 7 antigens were added to the base model, including age, BMI, race and current smoking status, the assay had the following diagnostic capabilities: c-stat (95% CI), 0.82 (0.78-0.86); sensitivity, 73%; specificity, 76%; and positive likelihood ratio (95% CI), 3.04 (2.34-3.94). The model was calibrated across risk deciles (Hosmer-Lemeshow, P = 0.13) and performed well in specific subtypes of breast cancer including estrogen receptor positive, HER-2 positive, invasive, in situ and tumor sizes >1 cm.