Comprehensive outline of whole exome sequencing data analysis tools available in clinical oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

Következő generációs szekvenálási technológiák kifejlődése és alkalmazásai

In the past ten years the development of next generation sequencing technologies brought a new era in the field of quick and efficient DNA sequencing. In our study we give an overview of the methodological achievements from Sanger's chain-termination sequencing in 1975 to those allowing real-time DNA sequencing today. Sequencing methods that utilize clonal amplicons for parallel multistrand sequencing comprise the basics of currently available next generation sequencing techniques. Nowadays next generation sequencing is mainly used for basic research in functional genomics, providing quintessential information in the meta-analyses of data from signal transduction pathways, onthologies, proteomics and metabolomics. Although next generation sequencing is yet sparsely used in clinical practice, cardiology, oncology and epidemiology already show an immense need for the additional knowledge obtained by this new technology. The main barrier of its spread is the lack of standardization of analysis evaluation methods, which obscure objective assessment of the results. Orv. Hetil., 2011, 152, 55-62

Colon cancer subtypes: Concordance, effect on survival and selection of the most representative preclinical models

Multiple gene-expression-based subtypes have been proposed for the molecular subdivision of colon cancer in the last decade. We aimed to cross-validate these classifiers to explore their concordance and their power to predict survival. A gene-chip-based database comprising 2,166 samples from 12 independent datasets was set up. A total of 22 different molecular subtypes were re-trained including the CCHS, CIN25, CMS, ColoGuideEx, ColoGuidePro, CRCassigner, MDA114, Meta163, ODXcolon, Oncodefender, TCA19, and V7RHS classifiers as well as subtypes established by Budinska, Chang, DeSousa, Marisa, Merlos, Popovici, Schetter, Yuen, and Watanabe (first authors). Correlation with survival was assessed by Cox proportional hazards regression for each classifier using relapse-free survival data. The highest efficacy at predicting survival in stage 2-3 patients was achieved by Yuen (p = 3.9e-05, HR = 2.9), Marisa (p = 2.6e-05, HR = 2.6) and Chang (p = 9e-09, HR = 2.35). Finally, 61 colon cancer cell lines from four independent studies were assigned to the closest molecular subtype. © 2016 The Author(s)

Reanalysis of genotype distributions published in Neurology between 1999 and 2002

The authors tested 123 genotypes described in 54 papers published in the journal Neurology between 1999 and 2002 to ascertain whether these genotype distributions deviated from Hardy - Weinberg equilibrium (HWE). Unreported deviations from HWE in 19 genotype distributions described in 11 of the papers were discovered. The authors also report additional information that could have been extracted after calculating HWE and conclude that HWE values should be mandatory in population genetic studies published in Neurology

Prognostic value of PDCD-1 and CTLA-4 in ovarian cancer patients

Therapeutic effectiveness of treatments for ovarian cancer is not optimal. PDCD-1 and CTLA-4 offers the potential as a prognostic marker in addition to being a target for therapy. To assess the prognostic roles of PDCD-1 and CTLA-4 Gene in ovarian cancer, we utilized the Kaplan Meier plotter, a biomarker assessment tool with large quantities of data. The relationship between PDCD-1 and overall survival (OS) as well as CTLA-4 and OS were presented using Hazard Ratio, 95% CI and logrank P value. Then gene expression level was compared using H-Test and U test. The results were as follows: PDCD-1 and CTLA-4 gene expressions among 1582 ovarian cancer patients were shown with median gene expression value as the cut-off. Expression of PDCD-1 and CTLA-4 did not differ with regard to stages and P53 gene mutation. But the expression of CTLA-4 was higher in endometrioid than in serous cancer patients. Different grades of both PDCD-1 and CTLA-4 had different mean values. Higher expression of the PDCD-1 was not significantly correlated with better OS with HR 0.88 (95% CI: 0.77-1.01, P=0.061) but higher CTLA-4 was associated with better survival with HR 0.84 (95% CI: 0.73-0.96, P=0.0099) on the transcriptome level. In conclusion, lower expression of CTLA-4, but not PDCD-1 predicts worse survival