Оформление конструкторской документации на печатные платы в условиях автоматизированного проектирования и подготовки производства

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Comprehensive evaluation of methodology to assess abundance of immune infiltrates in breast cancer

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Determining sensitivity and specificity of HER2 testing in breast cancer using a tissue micro-array approach

INTRODUCTION: Overexpression of the human epidermal growth factor receptor 2 (HER2) as a result of HER2 gene amplification is associated with a relatively poor prognosis in breast cancer and is predictive of HER2-targeting therapy response. False-positive rates of up to 20% for HER2 testing have been described. HER2-testing laboratories are therefore encouraged to participate in external quality control schemes in order to improve HER2-testing standardization. METHODS: This study investigated the feasibility of retesting large numbers of invasive breast cancers for HER2 status on tissue micro-array (TMA) as part of a quality control scheme. For this assessment different HER2 testing methods were used including HER2 detecting antibodies SP3, 4B5, Herceptest and mono color silver in situ hybridization (SISH) and dual color SISH. Final HER2 status for each tumor on the TMA was compared to the local testing result for the same tumor. Discordances between these two results were investigated further by staining whole tumor sections. RESULTS: For this study, 1,210 invasive breast carcinomas of patients treated in six hospitals between 2006 and 2008 were evaluated. Results from the three immunohistochemistry (IHC) and two in situ hybridization (ISH) assays performed on the TMAs were compared. The final HER2 status on TMA was determined with SP3, 4B5 and mono color SISH. Concordance between local HER2 test results and TMA retesting was 98.0%. Discordant results between local and TMA retesting were found in 20 tumors (2.0%). False positive HER2 IHC results were identified in 13 (1.3%) tumors; false negative IHC results in seven (0.7%) tumors. CONCLUSIONS: Retesting large volumes of HER2 classified breast carcinomas was found to be feasible and can be reliably performed by staining TMAs with SP3, 4B5 and mono color SISH in combination with full-sized slides for discordant cases. The frequency of false-positive results was lower than previously reported in the literature. This method is now offered to other HER2-testing laboratories

Prognostic and predictive value of human equilibrative nucleoside transporter 1 (hENT1) in extrahepatic cholangiocarcinoma: a translational study

Introduction: Effective (neo) adjuvant chemotherapy for cholangiocarcinoma is lacking due to chemoresistance and the absence of predictive biomarkers. Human equilibrative nucleoside transporter 1 (hENT1) has been described as a potential prognostic and predictive biomarker. In this study, the potential of rabbit-derived (SP120) and murine-derived (10D7G2) antibodies to detect hENT1 expression was compared in tissue samples of patients with extrahepatic cholangiocarcinoma (ECC), and the predictive value of hENT1 was investigated in three ECC cell lines. Methods: Tissues of 71 chemonaïve patients with histological confirmation of ECC were selected and stained with SP120 or 10D7G2 to assess the inter-observer variability for both antibodies and the correlation with overall survival. Concomitantly, gemcitabine sensitivity after hENT1 knockdown was assessed in the ECC cell lines EGI-1, TFK-1, and SK-ChA-1 using sulforhodamine B assays. Results: Scoring immunohistochemistry for hENT1 expression with the use of SP120 antibody resulted in the highest interobserver agreement but did not show a prognostic role of hENT1. However, 10D7G2 showed a prognostic role for hENT1, and a potential predictive role for gemcitabine sensitivity in hENT1 in SK-ChA-1 and TFK-1 cells was found. Discussion: These findings prompt further studies for both preclinical validation of the role of hENT1 and histochemical standardization in cholangiocarcinoma patients treated with gemcitabine-based chemotherapy

Processed pseudogenes acquired somatically during cancer development

Cancer evolves by mutation, with somatic reactivation of retrotransposons being one such mutational process. Germline retrotransposition can cause processed pseudogenes, but whether this occurs somatically has not been evaluated. Here we screen sequencing data from 660 cancer samples for somatically acquired pseudogenes. We find 42 events in 17 samples, especially non-small cell lung cancer (5/27) and colorectal cancer (2/11). Genomic features mirror those of germline LINE element retrotranspositions, with frequent target-site duplications (67%), consensus TTTTAA sites at insertion points, inverted rearrangements (21%), 5′ truncation (74%) and polyA tails (88%). Transcriptional consequences include expression of pseudogenes from UTRs or introns of target genes. In addition, a somatic pseudogene that integrated into the promoter and first exon of the tumour suppressor gene, MGA, abrogated expression from that allele. Thus, formation of processed pseudogenes represents a new class of mutation occurring during cancer development, with potentially diverse functional consequences depending on genomic context

Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed