Feasibility of serodiagnosis of ovarian cancer by mass spectrometry

The emergence of new biological disease markers from mass spectrometric studies of serum proteomes has been quite limited. There are challenges regarding the analytical and statistical procedures, preanalytical variability, and study designs. In this serological study of ovarian cancer, we apply classification methods in a strictly designed study with standardized sample collection procedures. A total of 265 sera from women admitted with symptoms of a pelvic mass were used for model building. We developed a rigorous approach for building classification models suitable for the highly multivariate data and illustrate how to evaluate and ensure data quality and optimize data preprocessing and data reduction. We document time dependent changes in peak profiles up to 15 months after sampling even when storing samples at -20 degrees C. The developed classification model was validated using completely independent samples and a cross validation procedure which we call cross mode; validation was applied to get realistic performance values. The best models were able to classify with 79% specificity and 56% sensitivity, i.e., an analytical accuracy of 68%. However, the existing serum marker (CA-125) alone gave a better analytical accuracy (81%) in the same sample set Also, the combination of mass spectrometric data and levels of CA-125 data did not improve the predictive performance of models. In conclusion, proteomic approaches to biomarker discovery are not necessarily yielding straightforward diagnostic leads but lay the foundation for more work

MicroRNA Expression in Formalin-fixed Paraffin-embedded Cancer Tissue:Identifying Reference MicroRNAs and Variability

BACKGROUND: Archival formalin-fixed paraffin-embedded (FFPE) cancer tissue samples are a readily available resource for microRNA (miRNA) biomarker identification. No established standard for reference miRNAs in FFPE tissue exists. We sought to identify stable reference miRNAs for normalization of miRNA expression in FFPE tissue samples from patients with colorectal (CRC) and pancreatic (PC) cancer and to quantify the variability associated with sample age and fixation. METHODS: High-throughput miRNA profiling results from 203 CRC and 256 PC FFPE samples as well as from 37 paired frozen/FFPE samples from nine other CRC tumors (methodological samples) were used. Candidate reference miRNAs were identified by their correlation with global mean expression. The stability of reference genes was analyzed according to published methods. The association between sample age and global mean miRNA expression was tested using linear regression. Variability was described using correlation coefficients and linear mixed effects models. Normalization effects were determined by changes in standard deviation and by hierarchical clustering. RESULTS: We created lists of 20 miRNAs with the best correlation to global mean expression in each cancer type. Nine of these miRNAs were present in both lists, and miR-103a-3p was the most stable reference miRNA for both CRC and PC FFPE tissue. The optimal number of reference miRNAs was 4 in CRC and 10 in PC. Sample age had a significant effect on global miRNA expression in PC (50 % reduction over 20 years) but not in CRC. Formalin fixation for 2–6 days decreased miRNA expression 30–65 %. Normalization using global mean expression reduced variability for technical and biological replicates while normalization using the expression of the identified reference miRNAs reduced variability only for biological replicates. Normalization only had a minor impact on clustering results. CONCLUSIONS: We identified suitable reference miRNAs for future miRNA expression experiments using CRC- and PC FFPE tissue samples. Formalin fixation decreased miRNA expression considerably, while the effect of increasing sample age was estimated to be negligible in a clinical setting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-015-2030-2) contains supplementary material, which is available to authorized users