Low‐volume, high‐throughput sandwich immunoassays for profiling plasma proteins in mice: Identification of early‐stage systemic inflammation in a mouse model of intestinal cancer

Mouse models of human cancers may provide a valuable resource for the discovery of cancer biomarkers. We have developed a practical strategy for profiling specific proteins in mouse plasma using low‐volume sandwich‐immunoassays. We used this method to profile the levels of 14 different cytokines, acute‐phase reactants, and other cancer markers in plasma from mouse models of intestinal tumors and their wild‐type littermates, using as little as 1.5μl of diluted plasma per assay. Many of the proteins were significantly and consistently up‐regulated in the mutant mice. The mutant mice could be distinguished nearly perfectly from the wild‐type mice based on the combined levels of as few as three markers. Many of the proteins were up‐regulated even in the mutant mice with few or no tumors, suggesting the presence of a systemic host response at an early stage of cancer development. These results have implications for the study of host responses in mouse models of cancers and demonstrate the value of a new low‐volume, high‐throughput sandwich‐immunoassay method for sensitively profiling protein levels in cancer.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135703/1/mol2200712216-sup-mmc1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135703/2/mol2200712216.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135703/3/mol2200712216-sup-mmc2.pd

Heterogeneity of glycan biomarker clusters as an indicator of recurrence in pancreatic cancer

IntroductionOutcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence.MethodsWe probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections.ResultsThe tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells.ConclusionThus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome

Directive 02-14: Tax Obligations of Persons Purchasing Cigarettes in Interstate Commerce for which the Massachusetts Cigarette Excise Has Not Been Paid

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.</p

Glycogene Expression Alterations Associated with Pancreatic Cancer Epithelial-Mesenchymal Transition in Complementary Model Systems

The ability to selectively detect and target cancer cells that have undergone an epithelial-mesenchymal transition (EMT) may lead to improved methods to treat cancers such as pancreatic cancer. The remodeling of cellular glycosylation previously has been associated with cell differentiation and may represent a valuable class of molecular targets for EMT.As a first step toward investigating the nature of glycosylation alterations in EMT, we characterized the expression of glycan-related genes in three in-vitro model systems that each represented a complementary aspect of pancreatic cancer EMT. These models included: 1) TGFβ-induced EMT, which provided a look at the active transition between states; 2) a panel of 22 pancreatic cancer cell lines, which represented terminal differentiation states of either epithelial-like or mesenchymal-like; and 3) actively-migrating and stationary cells, which provided a look at the mechanism of migration. We analyzed expression data from a list of 587 genes involved in glycosylation (biosynthesis, sugar transport, glycan-binding, etc.) or EMT. Glycogenes were altered at a higher prevalence than all other genes in the first two models (p<0.05 and <0.005, respectively) but not in the migration model. Several functional themes were shared between the induced-EMT model and the cell line panel, including alterations to matrix components and proteoglycans, the sulfation of glycosaminoglycans; mannose receptor family members; initiation of O-glycosylation; and certain forms of sialylation. Protein-level changes were confirmed by Western blot for the mannose receptor MRC2 and the O-glycosylation enzyme GALNT3, and cell-surface sulfation changes were confirmed using Alcian Blue staining.Alterations to glycogenes are a major component of cancer EMT and are characterized by changes to matrix components, the sulfation of GAGs, mannose receptors, O-glycosylation, and specific sialylated structures. These results provide leads for targeting aggressive and drug resistant forms of pancreatic cancer cells

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al

Enhanced Discrimination of Malignant from Benign Pancreatic Disease by Measuring the CA 19-9 Antigen on Specific Protein Carriers

The CA 19-9 assay detects a carbohydrate antigen on multiple protein carriers, some of which may be preferential carriers of the antigen in cancer. We tested the hypothesis that the measurement of the CA 19-9 antigen on individual proteins could improve performance over the standard CA 19-9 assay. We used antibody arrays to measure the levels of the CA 19-9 antigen on multiple proteins in serum or plasma samples from patients with pancreatic adenocarcinoma or pancreatitis. Sample sets from three different institutions were examined, comprising 531 individual samples. The measurement of the CA 19-9 antigen on any individual protein did not improve upon the performance of the standard CA 19-9 assay (82% sensitivity at 75% specificity for early-stage cancer), owing to diversity among patients in their CA 19-9 protein carriers. However, a subset of cancer patients with no elevation in the standard CA 19-9 assay showed elevations of the CA 19-9 antigen specifically on the proteins MUC5AC or MUC16 in all sample sets. By combining measurements of the standard CA 19-9 assay with detection of CA 19-9 on MUC5AC and MUC16, the sensitivity of cancer detection was improved relative to CA 19-9 alone in each sample set, achieving 67–80% sensitivity at 98% specificity. This finding demonstrates the value of measuring glycans on specific proteins for improving biomarker performance. Diagnostic tests with improved sensitivity for detecting pancreatic cancer could have important applications for improving the treatment and management of patients suffering from this disease

Definitive characterization of CA 19-9 in resectable pancreatic cancer using a reference set of serum and plasma specimens

The validation of candidate biomarkers often is hampered by the lack of a reliable means of assessing and comparing performance. We present here a reference set of serum and plasma samples to facilitate the validation of biomarkers for resectable pancreatic cancer. The reference set includes a large cohort of stage I-II pancreatic cancer patients, recruited from 5 different institutions, and relevant control groups. We characterized the performance of the current best serological biomarker for pancreatic cancer, CA 19-9, using plasma samples from the reference set to provide a benchmark for future biomarker studies and to further our knowledge of CA 19-9 in early-stage pancreatic cancer and the control groups. CA 19-9 distinguished pancreatic cancers from the healthy and chronic pancreatitis groups with an average sensitivity and specificity of 70-74%, similar to previous studies using all stages of pancreatic cancer. Chronic pancreatitis patients did not show CA 19-9 elevations, but patients with benign biliary obstruction had elevations nearly as high as the cancer patients. We gained additional information about the biomarker by comparing two distinct assays. The two CA 9-9 assays agreed well in overall performance but diverged in measurements of individual samples, potentially due to subtle differences in antibody specificity as revealed by glycan array analysis. Thus, the reference set promises be a valuable resource for biomarker validation and comparison, and the CA 19-9 data presented here will be useful for benchmarking and for exploring relationships to CA 19-9