In situ validation of an intestinal stem cell signature in colorectal cancer.

OBJECTIVE: Wnt/Tcf, Lgr5, Ascl2 and/or Bmi1 signalling is believed to define the mouse intestinal stem cell niche(s) from which adenomas arise. The aim of this study was to determine the relevance of these putative intestinal stem cell markers to human colorectal cancer. DESIGN: 19 putative intestinal stem cell markers, including Ascl2 and Lgr5, were identified from published data and an evaluation of a human colorectal gene expression database. Associations between these genes were assessed by isotopic in situ hybridisation (ISH) in 57 colorectal adenocarcinomas. Multiplex fluorescent ISH and chromogenic non-isotopic ISH were performed to confirm expression patterns. The prognostic significance of Lgr5 was assessed in 891 colorectal adenocarcinomas. RESULTS: Ascl2 and Lgr5 were expressed in 85% and 74% of cancers respectively, and expression was positively correlated (p=0.003). Expression of Bmi1 was observed in 47% of cancers but was very weak in 98% of cases with expression. Both Ascl2 and/or Lgr5 were positively correlated with the majority of genes in the signature but neither was correlated with Cdk6, Gpx2, Olfm4 or Tnfrsf19. Lgr5 did not have prognostic significance. CONCLUSION: These data suggest that 74-85% of colorectal cancers express a Lgr5/Ascl2 associated signature and support the hypothesis that they derive from Lgr5(+)/Ascl2(+) crypt stem cells, not Bmi1(+) stem cells. However, Olfm4 was not found to be a useful marker of Lgr5(+) cells in normal colon or tumours. In this large series, Lgr5 expression is not associated with increased tumour aggressiveness, as might be expected from a cancer stem cell marker.

Prognostic and predictive significance of proliferation in 867 colorectal cancers.

AIM: Recently, the Oncotype DX recurrence score, which measures a gene expression signature including markers of tumour proliferation, was validated as a prognostic signature in colorectal cancer. This study aimed to evaluate whether the Ki67 proliferation index can provide similar prognostic and predictive information. METHODS: Tissue microarrays were constructed from triplicate cores of colorectal cancer. Immunohistochemistry for Ki67 was performed with the SP6 antibody and the percentage of positive tumour cells scored. Prognostic significance was evaluated in 867 cancers (601 events) using Cox proportional hazards models. RESULTS: The Ki67 labelling index, divided at the median, was not a statistically or clinically significant prognostic factor in univariate analyses of 5-year overall survival (HR 0.98, 95% CI 0.84 to 1.15, p=0.84). Multivariate analyses were similarly non-significant. However, in Dukes' stage C patients, the high Ki67 subgroup derived a greater 5-year overall survival benefit from chemotherapy (HR 0.32, 95% CI 0.21 to 0.51, p<0.0001) than the low subgroup (HR 0.57, 95% CI 0.37 to 0.89, p=0.011). CONCLUSIONS: The Ki67 proliferation index is not a useful prognostic factor in colorectal cancer, but deserves further evaluation as a predictive factor for the incremental benefit derived from adjuvant chemotherapy.

Cluster analysis of host cytokine responses to biodefense pathogens in a whole blood ex vivo exposure model (WEEM).

BACKGROUND: Rapid detection and therapeutic intervention for infectious and emerging diseases is a major scientific goal in biodefense and public health. Toward this end, cytokine profiles in human blood were investigated using a human whole blood ex vivo exposure model, called WEEM. RESULTS: Samples of whole blood from healthy volunteers were incubated with seven pathogens including Yersinia pseudotuberculosis, Yersinia enterocolitica, Bacillus anthracis, and multiple strains of Yersinia pestis, and multiplexed protein expression profiling was conducted on supernatants of these cultures with an antibody array to detect 30 cytokines simultaneously. Levels of 8 cytokines, IL-1α, IL-1ß, IL-6, IL-8, IL-10, IP-10, MCP-1 and TNFα, were significantly up-regulated in plasma after bacterial exposures of 4 hours. Statistical clustering was applied to group the pathogens based on the host response protein expression profiles. The nearest phylogenetic neighbors clustered more closely than the more distant pathogens, and all seven pathogens were clearly differentiated from the unexposed control. In addition, the Y. pestis and Yersinia near neighbors were differentiated from the B. anthracis strains. CONCLUSIONS: Cluster analysis, based on host response cytokine profiles, indicates that distinct patterns of immunomodulatory proteins are induced by the different pathogen exposures and these patterns may enable further development into biomarkers for diagnosing pathogen exposure.

Statistical analysis of variation in the human plasma proteome.

Quantifying the variation in the human plasma proteome is an essential prerequisite for disease-specific biomarker detection. We report here on the longitudinal and individual variation in human plasma characterized by two-dimensional difference gel electrophoresis (2-D DIGE) using plasma samples from eleven healthy subjects collected three times over a two week period. Fixed-effects modeling was used to remove dye and gel variability. Mixed-effects modeling was then used to quantitate the sources of proteomic variation. The subject-to-subject variation represented the largest variance component, while the time-within-subject variation was comparable to the experimental variation found in a previous technical variability study where one human plasma sample was processed eight times in parallel and each was then analyzed by 2-D DIGE in triplicate. Here, 21 protein spots had larger than 50% CV, suggesting that these proteins may not be appropriate as biomarkers and should be carefully scrutinized in future studies. Seventy-eight protein spots showing differential protein levels between different individuals or individual collections were identified by mass spectrometry and further characterized using hierarchical clustering. The results present a first step toward understanding the complexity of longitudinal and individual variation in the human plasma proteome, and provide a baseline for improved biomarker discovery.

Statistical analysis of the experimental variation in the proteomic characterization of human plasma by two-dimensional difference gel electrophoresis.

The complexity of human plasma presents a number of challenges to the efficient and reproducible proteomic analysis of differential expression in response to disease. Before individual variation and disease-specific protein biomarkers can be identified from human plasma, the experimental variability inherent in the protein separation and detection techniques must be quantified. We report on the variation found in two-dimensional difference gel electrophoresis (2-D DIGE) analysis of human plasma. Eight aliquots of a human plasma sample were subjected to top-6 highest abundant protein depletion and were subsequently analyzed in triplicate for a total of 24 DIGE samples on 12 gels. Spot-wise standard deviation estimates indicated that fold changes greater than 2 can be detected with a manageable number of replicates in simple ANOVA experiments with human plasma. Mixed-effects statistical modeling quantified the effect of the dyes, and segregated the spot-wise variance into components of sample preparation, gel-to-gel differences, and random error. The gel-to-gel component was found to be the largest source of variation, followed by the sample preparation step. An improved protocol for the depletion of the top-6 high-abundance proteins is suggested, which, along with the use of statistical modeling and future improvements in gel quality and image processing, can further reduce the variation and increase the efficiency of 2-D DIGE proteomic analysis of human plasma.

Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyder.

MOTIVATION: The DeCyder software (GE Healthcare) is the current state-of-the-art commercial product for the analysis of two-dimensional difference gel electrophoresis (2D DIGE) experiments. Analyses complementing DeCyder are suggested by incorporating recent advances from the microarray data analysis literature. A case study on the effect of smallpox vaccination is used to compare the results obtained from DeCyder with the results obtained by applying moderated t-tests adjusted for multiple comparisons to DeCyder output data that was additionally normalized. RESULTS: Application of the more stringent statistical tests applied to the normalized 2D DIGE data decreased the number of potentially differentially expressed proteins from the number obtained from DeCyder and increased the confidence in detecting differential expression in human clinical studies.