Genetic and phenotypic characteristics of Russian patients with BRAF-mutated colorectal cancer.

Colorectal cancer (CRC) is one of the most common malignancies in the world. It's estimated about 1.8 M new CRC cases worldwide per year. A somatic mutation in the BRAF gene in the tumor is a negative prognostic factor. This work is aimed at studying the clinical and genetic characteristics of Russian CRC patients with the BRAF mutation. The BRAF mutations were studied by Sanger sequencing and digital droplet PCR in 489 patients and found in 34 (7%) cases. The most common mutation was p.V600E (82%). Also, rare variants were found: p.K601E, p.N581I, p.G596R, and p.D594N. All the patients with rare mutations were characterized by an unfavorable prognosis of the disease. The clinical features of the patients with BRAF mutations in the study include the predominant primary tumor site in the rectum, in addition to the right colon. Then, most of the cases were diagnosed in the advanced stages of the disease and were represented by high-grade adenocarcinomas. This article demonstrates the feasibility of analysis of the entire exon 15 of BRAF gene in CRC patients regardless of tumor localization.

An approach for feature selection with data modelling in LC-MS metabolomics.

The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. The proposed approach requires only an annotation-free peak table and produces an extremely reduced set of the most relevant features together with validation via Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.