Anti-MS4A1 monoclonal antibody, clone BJTC23 Functional Grade

Purpose: We applied a unique method to identify genes expressed in whole blood that can serve as biomarkers to detect colorectal cancer (CRC). Experimental Design:Total RNA was isolated from 211 blood samples (110 non-CRC, 101 CRC). Microarray and quantitative real-time PCR were used for biomarker screening and validation, respectively. Results: From a set of 31 RNA samples (16 CRC, 15 controls), we selected 37 genes from analyzed microarray data that differed significantly between CRC samples and controls (P < 0.05). We tested these genes with a second set of 115 samples (58 CRC, 57 controls) using quantitative real-time PCR, validating 17 genes as differentially expressed. Five of these genes were selected for logistic regression analysis, of which two were the most up-regulated (CDA and MGC20553) and three were the most down-regulated (BANK1, BCNP1, and MS4A1) in CRC patients. Logit (P) of the five-gene panel had an area under the curve of 0.88 (95% confidence interval, 0.81-0.94). At a cutoff of logit (P) >+0.5 as disease (high risk), <-0.5 as control (low risk), and in between as an intermediate zone, the five-gene biomarker combination yielded a sensitivity of 94% (47 of 50) and a specificity of 77% (33 of 43). The intermediate zone contained 22 samples. We validated the predictive power of these five genes with a novel third set of 92 samples, correctly identifying 88% (30 of 34) of CRC samples and 64% (27 of 42) of non-CRC samples. The intermediate zone contained 16 samples. Conclusion: Our results indicate that the five-gene biomarker panel can be used as a novel blood-based test for CRC.

Aims: In multiple sclerosis patients, treatment with recombinant IFN-beta (rIFN-beta) is partially efficient in reducing clinical exacerbations. However, its molecular mechanism of action is still under scrutiny. Materials & methods: We used DNA microarrays (Affymetrix, CA, USA) and peripheral mononuclear blood cells from 25 relapsing remitting multiple sclerosis patients to analyze the longitudinal transcriptional profile within 2 years of rIFN-beta administration. Sets of differentially expressed genes were attained by applying a combination of independent criteria, thereby providing efficient data curation and gene filtering that accounted for technical and biological noise. Gene ontology term-association analysis and scientific literature text mining were used to explore evidence of gene interaction. Results: Post-therapy initiation, we identified 42 (day 2), 175 (month 1), 103 (month 12) and 108 (month 24) differentially expressed genes. Increased expression of established IFN-beta marker genes, as well as differential expression of circulating IFN-beta-responsive candidate genes, were observed. MS4A1 (CD20), a known target of B-cell depletion therapy, was significantly downregulated after one month. CMPK2, FCER1A, and FFAR2 appeared as hitherto unrecognized multiple sclerosis treatment-related differentially expressed genes that were consistently modulated over time. Overall, 84 interactions between 54 genes were attained, of which two major gene networks were identified at an earlier stage of therapy: the first (n = 15 genes) consisted of mostly known IFN-beta-activated genes, whereas the second (n = 12) mainly contained downregulated genes that to date have not been associated with IFN-beta effects in multiple sclerosis array research. Conclusion: We achieved both a broadening of the knowledge of IFN-beta mechanism-of-action-related constituents and the identification of time-dependent interactions between IFN-beta regulated genes.