AIM: A confirmed wild-type RAS tumor status is commonly required for prescribing anti-EGFR treatment for metastatic colorectal cancer. This noninterventional, observational research project estimated RAS mutation prevalence from real-world sources. MATERIALS & METHODS: Aggregate RAS mutation data were collected from 12 sources in three regions. Each source was analyzed separately; pooled prevalence estimates were then derived from meta-analyses. RESULTS: The pooled RAS mutation prevalence from 4431 tumor samples tested for RAS mutation status was estimated to be 43.6% (95% CI: 38.8-48.5%); ranging from 33.7% (95% CI: 28.4-39.3%) to 54.1% (95% CI: 51.7-56.5%) between sources. CONCLUSION: The RAS mutation prevalence estimates varied among sources. The reasons for this are not clear and highlight the need for further research.

• Klíčová slova
• anti-EGFR, mCRC, metastatic colorectal cancer, prevalence, real world,
• MeSH
• exony MeSH
• kolorektální nádory epidemiologie   mortalita   patologie   MeSH
• lidé MeSH
• míra přežití MeSH
• mutace MeSH
• nádorové biomarkery genetika   MeSH
• prevalence MeSH
• protoonkogenní proteiny B-Raf genetika   MeSH
• protoonkogenní proteiny p21(ras) genetika   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• práce podpořená grantem MeSH
• Názvy látek
• BRAF protein, human MeSH Prohlížeč
• nádorové biomarkery MeSH
• protoonkogenní proteiny B-Raf MeSH
• protoonkogenní proteiny p21(ras) MeSH

BACKGROUND: Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary. METHODS: We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays. RESULTS: We set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays. DISCUSSION: Byapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.

• Klíčová slova
• Bioinformatic pipeline, Chromothripsis, Chronic lymphocytic leukemia, Complex structural variants, Fusion gene, Gene expression, Leukemia, Next-generation sequencing, Statistics, Transcriptomics,
• Publikační typ
• časopisecké články MeSH