Alternative antibody for the detection of CA19-9 antigen: a European multicenter study for the evaluation of the analytical and clinical performance of the Access (R) GI Monitor assay on the UniCel (R) Dxl 800 Immunoassay System

Background: Gastrointestinal cancer antigen CA19-9 is known as a valuable marker for the management of patients with pancreatic cancer. Methods: The analytical and clinical performance of the Access(R) GI Monitor assay (Beckman Coulter) was evaluated on the UniCel(R) Dxl 800 Immunoassay System at five different European sites and compared with a reference method, defined as CA19-9 on the Elecsys System (Roche Diagnostics). Results: Total imprecision (%CV) of the GI Monitor ranged between 3.4% and 7.7%, and inter-laboratory reproducibility between 3.6% and 4.0%. Linearity upon dilution showed a mean recovery of 97.4% (SD+7.2%). Endogenous interferents had no influence on GI Monitor levels (mean recoveries: hemoglobin 103%, bilirubin 106%, triglycerides 106%). There was no high-dose hook effect up to 115,000 kU/L. Clinical performance investigated in sera from 1811 individuals showed a good correlation between the Access' GI Monitor and Elecsys CA19-9 (R = 0.959, slope = 1.004, intercept +0.17). GI Monitor serum levels were low in healthy individuals (n = 267, median = 6.0 kU/L, 95th percentile = 23.1 kU/L), higher in individuals with various benign diseases (n = 550, medians = 5.8-13.4 kU/L, 95th percentiles = 30.1-195.5 kU/L) and even higher in individuals suffering from various cancers (n = 995, medians = 8.4-233.8 kU/L, 95th percentiles = 53.7-13,902 kU/L). Optimal diagnostic accuracy for cancer detection against the relevant benign control group by the GI Monitor was found for pancreatic cancer {[}area under the curve (AUC) 0.83]. Results for the reference CA19-9 assay were comparable (AUC 0.85). Conclusions: The Access(R) GI Monitor provides very good methodological characteristics and demonstrates an excellent analytical and clinical correlation with the Elecsys CA19-9. The GI Monitor shows the best diagnostic accuracy in pancreatic cancer. Our results also suggest a clinical value of the GI Monitor in other cancers

Biomarkers in melanoma

Biomarkers are tumour- or host-related factors that correlate with tumour biological behaviour and patient prognosis. High-throughput analytical techniques--DNA and RNA microarrays--have identified numerous possible biomarkers, but their relevance to melanoma progression, clinical outcome and the selection of optimal treatment strategies still needs to be established. The review discusses a possible molecular basis for predictive tissue biomarkers such as melanoma thickness, ulceration and mitotic activity, and provides a list of promising new biomarkers identified from tissue microarrays that needs confirmation by independent, prospectively collected clinical data sets. In addition, common predictive serum biomarkers--lactate dehydrogenase, S100B and melanoma-inhibiting activity--as well as selected investigational serum biomarkers such as TA90IC and YKL-40 are also reviewed. A more accurate, therapeutically predictive classification of human melanomas and selection of patient populations that would profit from therapeutic interventions are among the major challenges expected to be addressed in the futur

Biomarkers in melanoma

Biomarkers are tumour- or host-related factors that correlate with tumour biological behaviour and patient prognosis. High-throughput analytical techniques—DNA and RNA microarrays—have identified numerous possible biomarkers, but their relevance to melanoma progression, clinical outcome and the selection of optimal treatment strategies still needs to be established. The review discusses a possible molecular basis for predictive tissue biomarkers such as melanoma thickness, ulceration and mitotic activity, and provides a list of promising new biomarkers identified from tissue microarrays that needs confirmation by independent, prospectively collected clinical data sets. In addition, common predictive serum biomarkers—lactate dehydrogenase, S100B and melanoma-inhibiting activity—as well as selected investigational serum biomarkers such as TA90IC and YKL-40 are also reviewed. A more accurate, therapeutically predictive classification of human melanomas and selection of patient populations that would profit from therapeutic interventions are among the major challenges expected to be addressed in the future

A new mathematical model for the interpretation of translational research evaluating six CTLA-4 polymorphisms in high-risk melanoma patients receiving adjuvant interferon

Adjuvant therapy of stage IIB/III melanoma with interferon reduces relapse and mortality by up to 33% but is accompanied by toxicity-related complications. Polymorphisms of the CTLA-4 gene associated with autoimmune diseases could help in identifying interferon treatment benefits. We previously genotyped 286 melanoma patients and 288 healthy (unrelated) individuals for six CTLA-4 polymorphisms (SNP). Previous analyses found no significant differences between the distributions of CTLA-4 polymorphisms in the melanoma population vs. controls, no significant difference in relapse free and overall survivals among patients and no correlation between autoimmunity and specific alleles. We report new analysis of these CTLA-4 genetic profiles, using Network Phenotyping Strategy (NPS). It is graph-theory based method, analyzing the SNP patterns. Application of NPS on CTLA-4 polymorphism captures allele relationship pattern for every patient into 6-partite mathematical graph P. Graphs P are combined into weighted 6-partite graph S, which subsequently decomposed into reference relationship profiles (RRP). Finally, every individual CTLA-4 genotype pattern is characterized by the graph distances of P from eight identified RRP's. RRP's are subgraphs of S, collecting equally frequent binary allele co-occurrences in all studied loci. If S topology represents the genetic "dominant model", the RRP's and their characteristic frequencies are identical to expectation-maximization derived haplotypes and maximal likelihood estimates of their frequencies. The graphrepresentation allows showing that patient CTLA-4 haplotypes are uniquely different from the controls by absence of specific SNP combinations. New function-related insight is derived when the 6-partite graph reflects allelic state of CTLA-4. We found that we can use differences between individual P and specific RRPs to identify patient subpopulations with clearly different polymorphic patterns relatively to controls as well as to identify patients with significantly different survival. © 2014 Pancoska et al