False Calls Research Articles

Evaluation of the impact of ‘Act FAST’ stroke awareness campaign on calls to Emergency Medical Services and ambulance dispatches in Qatar

Background: The ‘Act FAST’ campaign is a major international stroke prevention program aiming to raise knowledge and awareness of stroke signs and symptoms and encouraging calling emergency medical service (EMS) providers as soon as symptoms are recognized.1 We aim to evaluate the impact of the campaign by analyzing EMS stroke calls in Qatar as studies on the impact of such initiative on behavioral response at onset are limited.2Methods: This is a retrospective time series evaluation utilizing monthly Hamad Medical Corporation (HMC) EMS call data for strokes in Qatar. HMC Ambulance Service (HMCAS) uses the Medical Priority Dispatch System (MPDS) and International Academies of Emergency Dispatch (IAED) protocols. Emergency calls are thoroughly analyzed for stroke diagnosis. We will evaluate pre- and post-campaign the relative change in stroke calls volume and classify them into true stroke and stroke mimic calls based on Emergency Department and Stroke Unit data, and make a comparison in total proportion of true stroke calls. We will perform multivariable regression to measure the effect of campaign exposure on the volume of stroke-related calls and also describe any significant association between specific patient characteristics with increased ambulance usage, after controlling for confounders. Anticipated results: Following the signs and symptoms of the stroke awareness campaign and suggested behavioral response at stroke onset, a relative increase in the number of stroke calls to EMS is expected as reported by Bray et al., following a similar campaign in Australia3. Conclusion: The impact of the Act FAST stroke campaign on the population of Qatar will be determined by evaluating a relative change in stroke calls post-campaign and misclassifications. The percentage change in false alarms and calls without stroke specific symptoms in a population group will help identify campaign related contributing factors thereby providing evidence to tailor future campaigns.

A detailed characteristics of bias associated with long runs of homozygosity identification based on medium density SNP microarrays.

In the present study, runs of homozygosity (ROH) detected with the use of a standard bovine 54k single nucleotide polymorphism (SNP) genotyping assay and two different ROH detection approaches, based on 50 (M1) or 15 (M2) consecutive SNPs, were compared with results of whole genome sequencing. Both microarray-based methods accurately recognised medium-sized ROH, however, it was found that M2 method seemed to better than M1 identify short ROH, but highly overestimated their number, leading to numerous false positive calls. Moreover, long ROH identified with microarray data tended to break into shorter segments in sequencing data because of the presence of regions with high heterozygosity within the ROH sequences. This may indicate, that these long ROH are formed by closely positioned shorter homozygous segments that may be of older origin or may be created by two similar but not identical haplotypes, showing minor internal recombination signs. Such finding also suggests that at least some of the results of previous studies in regard to long ROH may be biased leading to inaccurate estimations of genomes autozygosity via ROH classification into length categories.

Emergency Number 112: Its Nature and the Need to Educate the Public in Poland

BACKGROUND: According to statistical data, since 2014, only about 20% of incoming calls to the emergency call centre have been actual emergency calls, others are classified as non-emergency or false calls. This leads to the question: Why is this situation happening in the Republic of Poland? OBJECTIVES: The aim of this article is to show the public’s awareness with regard to the purpose of 112 emergency number and to indicate the causes and effects of the discussed phenomenon as well as proposals aimed at changing the existing state of affairs. METHODOLOGY: Theoretical and empirical methods were used. An analysis was made of, inter alia, available literature on security and education for safety, sources of law and reports on the functioning of the emergency communication system. Interviews with experts, who are the 112 emergency number operators, were also conducted. CONCLUSIONS: There are many problems generated by the Polish citizens that prevent the proper functioning of the 112 emergency telephone number. The reasons lie primarily in two areas: the lack of adequate education within the context of all social groups and the lack of strict penalties for deliberate performing non-emergency calls or blocking the emergency line.

Identification and characterization of occult human-specific LINE-1 insertions using long-read sequencing technology.

Long Interspersed Element-1 (LINE-1) retrotransposition contributes to inter- and intra-individual genetic variation and occasionally can lead to human genetic disorders. Various strategies have been developed to identify human-specific LINE-1 (L1Hs) insertions from short-read whole genome sequencing (WGS) data; however, they have limitations in detecting insertions in complex repetitive genomic regions. Here, we developed a computational tool (PALMER) and used it to identify 203 non-reference L1Hs insertions in the NA12878 benchmark genome. Using PacBio long-read sequencing data, we identified L1Hs insertions that were absent in previous short-read studies (90/203). Approximately 81% (73/90) of the L1Hs insertions reside within endogenous LINE-1 sequences in the reference assembly and the analysis of unique breakpoint junction sequences revealed 63% (57/90) of these L1Hs insertions could be genotyped in 1000 Genomes Project sequences. Moreover, we observed that amplification biases encountered in single-cell WGS experiments led to a wide variation in L1Hs insertion detection rates between four individual NA12878 cells; under-amplification limited detection to 32% (65/203) of insertions, whereas over-amplification increased false positive calls. In sum, these data indicate that L1Hs insertions are often missed using standard short-read sequencing approaches and long-read sequencing approaches can significantly improve the detection of L1Hs insertions present in individual genomes.

Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias.

Data normalization is a critical step in RNA sequencing (RNA-seq) analysis, aiming to remove systematic effects from the data to ensure that technical biases have minimal impact on the results. Analyzing numerous RNA-seq datasets, we detected a prevalent sample-specific length effect that leads to a strong association between gene length and fold-change estimates between samples. This stochastic sample-specific effect is not corrected by common normalization methods, including reads per kilobase of transcript length per million reads (RPKM), Trimmed Mean of M values (TMM), relative log expression (RLE), and quantile and upper-quartile normalization. Importantly, we demonstrate that this bias causes recurrent false positive calls by gene-set enrichment analysis (GSEA) methods, thereby leading to frequent functional misinterpretation of the data. Gene sets characterized by markedly short genes (e.g., ribosomal protein genes) or long genes (e.g., extracellular matrix genes) are particularly prone to such false calls. This sample-specific length bias is effectively removed by the conditional quantile normalization (cqn) and EDASeq methods, which allow the integration of gene length as a sample-specific covariate. Consequently, using these normalization methods led to substantial reduction in GSEA false results while retaining true ones. In addition, we found that application of gene-set tests that take into account gene–gene correlations attenuates false positive rates caused by the length bias, but statistical power is reduced as well. Our results advocate the inspection and correction of sample-specific length biases as default steps in RNA-seq analysis pipelines and reiterate the need to account for intergene correlations when performing gene-set enrichment tests to lessen false interpretation of transcriptomic data.

Empirical design of a variant quality control pipeline for whole genome sequencing data using replicate discordance

The success of next-generation sequencing depends on the accuracy of variant calls. Few objective protocols exist for QC following variant calling from whole genome sequencing (WGS) data. After applying QC filtering based on Genome Analysis Tool Kit (GATK) best practices, we used genotype discordance of eight samples that were sequenced twice each to evaluate the proportion of potentially inaccurate variant calls. We designed a QC pipeline involving hard filters to improve replicate genotype concordance, which indicates improved accuracy of genotype calls. Our pipeline analyzes the efficacy of each filtering step. We initially applied this strategy to well-characterized variants from the ClinVar database, and subsequently to the full WGS dataset. The genome-wide biallelic pipeline removed 82.11% of discordant and 14.89% of concordant genotypes, and improved the concordance rate from 98.53% to 99.69%. The variant-level read depth filter most improved the genome-wide biallelic concordance rate. We also adapted this pipeline for triallelic sites, given the increasing proportion of multiallelic sites as sample sizes increase. For triallelic sites containing only SNVs, the concordance rate improved from 97.68% to 99.80%. Our QC pipeline removes many potentially false positive calls that pass in GATK, and may inform future WGS studies prior to variant effect analysis.

Evaluation of computational genotyping of structural variation for clinical diagnoses.

BackgroundStructural variation (SV) plays a pivotal role in genetic disease. The discovery of SVs based on short DNA sequence reads from next-generation DNA sequence methods is error-prone, with low sensitivity and high false discovery rates. These shortcomings can be partially overcome with extensive orthogonal validation methods or use of long reads, but the current cost precludes their application for routine clinical diagnostics. In contrast, SV genotyping of known sites of SV occurrence is relatively robust and therefore offers a cost-effective clinical diagnostic tool with potentially few false-positive and false-negative results, even when applied to short-read DNA sequence data.ResultsWe assess 5 state-of-the-art SV genotyping software methods, applied to short-read sequence data. The methods are characterized on the basis of their ability to genotype different SV types, spanning different size ranges. Furthermore, we analyze their ability to parse different VCF file subformats and assess their reliance on specific metadata. We compare the SV genotyping methods across a range of simulated and real data including SVs that were not found with Illumina data alone. We assess sensitivity and the ability to filter initial false discovery calls. We determined the impact of SV type and size on the performance for each SV genotyper. Overall, STIX performed the best on both simulated and GiaB based SV calls, demonstrating a good balance between sensitivity and specificty.ConclusionOur results indicate that, although SV genotyping software methods have superior performance to SV callers, there are limitations that suggest the need for further innovation.

Amplimap: a versatile tool to process and analyze targeted NGS data.

Summaryamplimap is a command-line tool to automate the processing and analysis of data from targeted next-generation sequencing experiments with PCR-based amplicons or capture-based enrichment systems. From raw sequencing reads, amplimap generates output such as read alignments, annotated variant calls, target coverage statistics and variant allele counts and frequencies for each target base pair. In addition to its focus on user-friendliness and reproducibility, amplimap supports advanced features such as consensus base calling for read families based on unique molecular identifiers and filtering false positive variant calls caused by amplification of off-target loci.Availability and implementationamplimap is available as a free Python package under the open-source Apache 2.0 License. Documentation, source code and installation instructions are available at https://github.com/koelling/amplimap.

Abstract 3522: Enzymatic DNA repair enables high quality library preparation and accurate sequencing from highly damaged DNA inputs

Abstract High-throughput sequencing is key for clinical diagnostic applications such as disease mutation profiling and identifying pathogens with high taxonomic specificity. Preparation of high quality next-generation sequencing (NGS) libraries is critical for obtaining accurate patient data, but clinical scientists are often limited by input DNA quality. In cancer genomics, formalin-fixed, paraffin-embedded (FFPE) tissue from patient biopsy is a common source of DNA, but the extracted DNA is often poor in both yield and quality due to fixation- and storage-induced damage. Additionally, these damage-induced sequencing artifacts raise the background level of mutations, making it difficult to discern true, low frequency, disease-related variants from noise. Even relatively high-quality DNA inputs contain nicks and gaps limiting library yields and read lengths for long read sequencing platforms including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio). We developed a second-generation DNA repair enzyme mix (V2) that efficiently repairs the most prevalent damage types found in FFPE DNA and further improves the quality and yield of NGS libraries through efficient nick and gap repair. Illumina library yield and quality were improved when DNA samples were treated with FFPE DNA Repair V2 before library preparation. Target enrichment and whole exome enrichment experiments showed that the V2 repair mix did not alter the overall frequency of variants identified, thus it did not introduce bias, but significantly improved the sequencing accuracy by reducing the number of false variant calls due to damage-induced sequence artifacts. True mutation frequencies were validated using droplet digital PCR (ddPCR). Repaired ONT libraries using DNA inputs of varying quality also showed improved library quality as well as greater read lengths. Therefore, enzymatic DNA repair prior to NGS library preparation is a critical first step for improving the quality and accuracy of short- and long-read sequencing libraries. Citation Format: Margaret R. Heider, Lixin Chen, Chen Song, Luo Sun, Pingfang Liu, Laurence Ettwiller, Lauren Higgins, Eileen Dimalanta, Theodore Davis, Thomas Evans. Enzymatic DNA repair enables high quality library preparation and accurate sequencing from highly damaged DNA inputs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3522.

Abstract 3533: Low-frequency variant detection in cell-free DNA by integrating double-stranded unique molecular identifiers with ultrafast amplicon-based library preparations

Abstract The use of liquid biopsy has seen tremendous growth in recent years as cell-free DNA (cfDNA) is a noninvasive and easily obtainable sample type with many diagnostics and prognostic values. While liquid biopsy can potentially enable new applications, such as early cancer detection, treatment monitoring, and drug resistance screening, it also presents challenges to accurate variant detection due to the low fraction of mutant DNA present in theses samples, which can be easily buried by the artifacts and background noise from systematic PCR and sequencing errors. While some success has been made using adapters with unique molecular identifiers (UMIs) in hybrid capture-based methods and performing deep sequencing, such approaches suffer from long, tricky, and tedious workflows and disappointingly low on-target rates. Additionally, the large amount of cfDNA required for hybrid capture workflows is not realistic to obtain from patients. We have developed the CleanPlex® UMI technology to provide a fast, simple, and reliable NGS solution for low-frequency variant detection. The technology features three simple steps to generate molecular-barcoded and target-enriched NGS libraries in under 4 hours. This amplicon-based method consists of a multiplex PCR reaction for molecular barcoding, followed by a biochemical reaction for removal of redundant PCR products, and finally a second round of PCR to add Illumina adapter sequences and sample indexes. To validate this new method, we designed an NGS panel targeting frequently mutated hotspots in 23 genes associated with lung cancer. NGS libraries were prepared with commercial reference cfDNA at 0.1% to 0.5% minor allele frequencies (mAF) and sequenced on an Illumina NextSeq®. Overall, we obtain high detection sensitivity for low-frequency alleles even at low DNA inputs. When including UMI in the analysis, we observe significant reductions in false positive calls. Nearly all known mutations covered by the CleanPlex UMI panel (12 out of 13) are detected at 0.1% mAF using 50ng of the reference cfDNA. At 0.25% mAF, all known mutants are detected with 100% PPV using 50ng of DNA. With only 20 ng of cfDNA, the method can achieve the same 100% detection at 0.5% mAF. The CleanPlex® UMI technology demonstrates high sensitivity with low false positive rate, for the detection of low-frequency alleles. Citation Format: Lucie S. Lee, Yang Lily Liu, Kathryn Pendleton, Jeffrey Liu, Lifeng Phil Lin, Guoying Liu, Zhitong Liu. Low-frequency variant detection in cell-free DNA by integrating double-stranded unique molecular identifiers with ultrafast amplicon-based library preparations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3533.

Abstract 2474: Automated somatic variant classifier to reduce false positives identified by tumor normal variant callers

Abstract Accurate identification of somatic mutations is key to targeted cancer therapies. However, due to the complexity of next-generation sequencing, there are many variables that give rise to sequencing and alignment artifacts. Most sequencing workflows using tumor normal paired samples rely on the intersection or union of mutations by more than one somatic variant caller such as Mutect2, Strelka, VarScan2, Muse, and SomaticSniper. Yet, they still contain a number of false positive and false negative calls. Therefore, there is a need for a manual review of variants using Integrative Genomics Viewer (IGV) to incorporate information that is difficult to take into account in automated workflows. To alleviate the burden of manual inspection, we present an automated classification method to pre classify detected somatic variants into true or false classes for further manual review. Methods: We created a false positive classifier that utilizes automated IGV visualization of somatic variants. Our somatic workflow involves tumor normal DNA sequencing, BWAMEM alignment, deduplication of reads via Picard, somatic calling using two somatic callers, followed by the union of the detected variants, SnpEff annotation, and extracting nonsilent variants with variant allele fractions greater than 5 percent. We used the data for 1,413 nonsilent variants from a set of 10 tumor normal pairs of various cancer types. The variants were scored by cancer analysts using IGV. Initial false positive rate was 28 percent. We fine tuned Residual Deep Neural Network which utilizes weights from the network with the same architecture trained on ImageNet. Discriminative fine tuning was performed in two stages: first unfreezing the last fully connected layer only, and then unfreezing all the layers and training with differential learning rates. Results and Conclusion: Our method achieves 93 percent overall accuracy. The classifier greatly reduces the false positive rate for the set of variant calls from 28 percent down to 4 percent at the expense of calling 3 percent of true variants false. Previously, deep neural networks have been shown to yield the state of the art performance in multiple domains including image processing, speech, and text processing. In this work, we describe a way to fine tune Residual Neural Network trained on a large image data set to perform false positive variant detection in tumor-normal variant alignment snapshots from IGV. Our general approach that significantly reduces false positive rate of putative variants can be extended to any subset of somatic callers. Citation Format: Alena S. Harley, Corine K. Lau, Eve Shinbrot. Automated somatic variant classifier to reduce false positives identified by tumor normal variant callers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2474.

Mode and Tempo of Microsatellite Length Change in a Malaria Parasite Mutation Accumulation Experiment.

Malaria parasites have small extremely AT-rich genomes: microsatellite repeats (1–9 bp) comprise 11% of the genome and genetic variation in natural populations is dominated by repeat changes in microsatellites rather than point mutations. This experiment was designed to quantify microsatellite mutation patterns in Plasmodium falciparum. We established 31 parasite cultures derived from a single parasite cell and maintained these for 114–267 days with frequent reductions to a single cell, so parasites accumulated mutations during ∼13,207 cell divisions. We Illumina sequenced the genomes of both progenitor and end-point mutation accumulation (MA) parasite lines in duplicate to validate stringent calling parameters. Microsatellite calls were 99.89% (GATK), 99.99% (freeBayes), and 99.96% (HipSTR) concordant in duplicate sequence runs from independent sequence libraries, whereas introduction of microsatellite mutations into the reference genome revealed a low false negative calling rate (0.68%). We observed 98 microsatellite mutations. We highlight several conclusions: microsatellite mutation rates (3.12 × 10−7 to 2.16 × 10−8/cell division) are associated with both repeat number and repeat motif like other organisms studied. However, 41% of changes resulted from loss or gain of more than one repeat: this was particularly true for long repeat arrays. Unlike other eukaryotes, we found no insertions or deletions that were not associated with repeats or homology regions. Overall, microsatellite mutation rates are among the lowest recorded and comparable to those in another AT-rich protozoan (Dictyostelium). However, a single infection (>1011 parasites) will still contain over 2.16 × 103 to 3.12 × 104 independent mutations at any single microsatellite locus.

The Evaluation of Effectiveness and Outcomes of Code Blue System in a New Tertiary Care Hospital

Objective: The code blue call is used to alert the Code Blue team for patients with cardiac or respiratory arrest. The purpose of this study is to evaluate the time, the locations, and the outcomes of code blue calls. We also aimed to determine the rate of false code blue calls, and demographic data of the patients. Material and Methods: In this study, we retrospectively scanned the code blue call forms in our hospital between January 2017 and January 2018. The demographic data of the patients, the arrival time of the team, the time, the locations, and the outcomes of the calls, and the rate of false code blue calls were recorded. Results: We had 225 code blue calls in the study period. The mean arrival time of the team was 1.97±0.72 min. Most of the code blue calls were given in Palliative Care Unit (76 patients, 33.77 %), followed by Internal Medicine Services (54 patients, 24 %), and Department of Pulmonary Diseases (36 patients, 16%). The rate of false code blue calls was found to be 13.33 %. Most of the code blue calls (140 calls, 62.22 %) were during off times. A hundred patients had died (44.44 %); 88 patients had been admitted to the ICU (39.11 %); and 7 had been continued care in ward (3.11 %) by a successful intervention. Conclusion: Giving more blue code calls during off-hours and the absence of night duty doctor at the services, especially in rural hospitals like ours, emphasizes the importance of this system.

Intraspecific DNA contamination distorts subtle population structure in a marine fish: Decontamination of herring samples before restriction-site associated sequencing and its effects on population genetic statistics.

Wild specimens are often collected in challenging field conditions, where samples may be contaminated with the DNA of conspecific individuals. This contamination can result in false genotype calls, which are difficult to detect, but may also cause inaccurate estimates of heterozygosity, allele frequencies and genetic differentiation. Marine broadcast spawners are especially problematic, because population genetic differentiation is low and samples are often collected in bulk and sometimes from active spawning aggregations. Here, we used contaminated and clean Pacific herring (Clupea pallasi) samples to test (a) the efficacy of bleach decontamination, (b) the effect of decontamination on RAD genotypes and (c) the consequences of contaminated samples on population genetic analyses. We collected fin tissue samples from actively spawning (and thus contaminated) wild herring and nonspawning (uncontaminated) herring. Samples were soaked for 10min in bleach or left untreated, and extracted DNA was used to prepare DNA libraries using a restriction site-associated DNA (RAD) approach. Our results demonstrate that intraspecific DNA contamination affects patterns of individual and population variability, causes an excess of heterozygotes and biases estimates of population structure. Bleach decontamination was effective at removing intraspecific DNA contamination and compatible with RAD sequencing, producing high-quality sequences, reproducible genotypes and low levels of missing data. Although sperm contamination may be specific to broadcast spawners, intraspecific contamination of samples may be common and difficult to detect from high-throughput sequencing data and can impact downstream analyses.

Hg19KIndel: ethnicity normalized human reference genome

BackgroundThe most widely used human genome reference assembly hg19 harbors minor alleles at 2.18 million positions as revealed by 1000 Genome Phase 3 dataset. Although this is less than 2% of the 89 million variants reported, it has been shown that the minor alleles can result in 30% false positives in individual genomes, thus misleading and burdening downstream interpretation. More alarming is the fact that, significant percentage of variants that are homozygous recessive for these minor alleles, with potential disease implications, are masked from reporting.ResultsWe have demonstrated that the false positives (FP) and false negatives (FN) can be corrected for by simply replacing nucleotides at the minor allele positions in hg19 with corresponding major allele. Here, we have effectively replaced 2.18 million minor alleles Single Nucleotide Polymorphism (SNPs), Insertion and Deletions (INDELs), Multiple Nucleotide Polymorphism (MNPs) in hg19 with the corresponding major alleles to create an ethnically normalized reference genome called hg19KIndel. In doing so, hg19KIndel has both corrected for sequencing errors acknowledged to be present in hg19 and has improved read alignment near the minor alleles in hg19.ConclusionWe have created and made available a new version human reference genome called hg19KIndel. It has been shown that variant calling using hg19KIndel, significantly reduces false positives calls, which in-turn reduces the burden from downstream analysis and validation. It also improved false negative variants call, which means that the variants which were getting missed due to the presence of minor alleles in hg19, will now be called using hg19KIndel. Using hg19KIndel, one even gets a better mapping percentage when compared to currently available human reference genome. hg19KIndel reference genome and its auxiliary datasets are available at https://doi.org/10.5281/zenodo.2638113

Passenger mutations accurately classify human tumors.

Determining the cancer type and molecular subtype has important clinical implications. The primary site is however unknown for some malignancies discovered in the metastatic stage. Moreover liquid biopsies may be used to screen for tumoral DNA, which upon detection needs to be assigned to a site-of-origin. Classifiers based on genomic features are a promising approach to prioritize the tumor anatomical site, type and subtype. We examined the predictive ability of causal (driver) somatic mutations in this task, comparing it against global patterns of non-selected (passenger) mutations, including features based on regional mutation density (RMD). In the task of distinguishing 18 cancer types, the driver mutations–mutated oncogenes or tumor suppressors, pathways and hotspots–classified 36% of the patients to the correct cancer type. In contrast, the features based on passenger mutations did so at 92% accuracy, with similar contribution from the RMD and the trinucleotide mutation spectra. The RMD and the spectra covered distinct sets of patients with predictions. In particular, introducing the RMD features into a combined classification model increased the fraction of diagnosed patients by 50 percentage points (at 20% FDR). Furthermore, RMD was able to discriminate molecular subtypes and/or anatomical site of six major cancers. The advantage of passenger mutations was upheld under high rates of false negative mutation calls and with exome sequencing, even though overall accuracy decreased. We suggest whole genome sequencing is valuable for classifying tumors because it captures global patterns emanating from mutational processes, which are informative of the underlying tumor biology.

Association between sample characteristics and tumor mutational burden estimated by next-generation sequencing-based multiplex gene panel assay.

148 Background: Tumor mutational burden (TMB) measured via next-generation sequencing (NGS)-based gene panel testing is a promising biomarker for response to immune checkpoint inhibitors (ICIs) in solid tumors. However, little is known about the characteristics of tissue sample that can affect the TMB score. Methods: Data of 199 patients with solid tumors who underwent NGS-based gene panel assay were reviewed. Associations between the TMB score and sample characteristics, including sample DNA quality, sample type, sampling site, and storage period, were analyzed. Clinical outcomes of patients with a high TMB score (≥10 mutations/megabase) who received anti-PD-1 antibodies (n = 22) were also analyzed. Results: TMB score was significantly higher in samples that yielded a DNA library concentration of < 5 nM and in samples obtained from formalin-fixed paraffin-embedded tissue (FFPE) compared to fresh frozen tissue. The sample storage period was also positively correlated with TMB score. Of the 22 patients with a high TMB score, partial response and stable disease were observed in two (9.1%) and five (22.7%) cases, respectively. Among 15 non-responders, 11 (73.3%) had a low DNA library concentration, thereby indicating that a high TMB score might be attributable to false positive mutation calls. Conclusions: Our results suggest that the TMB score estimated via NGS-based gene panel assay could be affected by the DNA library concentration, sample type and storage period.

BagGMM: Calling copy number variation by bagging multiple Gaussian mixture models from tumor and matched normal next-generation sequencing data

Copy number variations (CNVs) contribute significantly to human genomic variability, some of which lead to diseases. However, effective detection of CNVs from whole genome next generation sequencing data (NGS) remains challenging. Here, we present BagGMM, a new method to call CNVs using tumor-normal matched samples from NGS data. BagGMM extracts read depth ratios of tumor samples to normal samples, divides the genomic sequences into segments by sliding windows to count the average coverage ratio of each segment, filters candidate deletions and duplications based on a coarse criterion of coverage ratio, and then builds Gaussian Mixture Model (GMM) for remaining ratios to identify the remaining ambiguous copy number states after filtration. Bagging multiple GMMs makes false positive calls descent instead of using one GMM, thus enhancing the detection power of BagGMM. Considering the computation speed of GMMs and false positive calls, we employ a segmentation procedure “large window and then small windows”, which is also helpful to determine boundary of CNV regions. We apply BagGMM to three simulation datasets and two groups of human whole genome sequencing (WGS) data for breast cancer patients and ovarian cancer patients to identify CNVs, respectively. All performed experiments demonstrate that BagGMM has the capability of robustly identification of CNVs with different sizes and states. The performance of this tool is compared to four peer existing CNV detection methods. BagGMM shows a significant improvement in both sensitivity and specificity for detecting both copy number gains and losses.

OUN's anti-Soviet resistance and the activities of Ukrainians from Eastern Galicia to preserve national foundations in Lemkivshchyna in 1939–1941

The article outlines events that are indirectly and directly related to the OUN and the activities of the intelligentsia from Eastern Galicia in the territory of Lemkivshchyna during the first Sovietization of Western Ukraine. The outbreak of World War II became a new test for the population of the Ukrainian-Polish-Slovak borderlands in the context of the ethno-national transformations of Central and Eastern Europe. The methodological basis is a comprehensive approach to the analysis of this problem. Since the beginning of the Sovietization of Galicia in 1939 the authorities have resorted to increased propaganda among the Lemkos for their departure to the USSR while taking advantage of the deplorable social situation of the locals and their low level of self-consciousness. Simultaneously, the most active figures of the region together with the local representatives of the OUN underground tried in various ways, from agitation to deliberately provoked false calls, to prevent the Lemkos from departure to the USSR. In the study based on source materials revealed the peculiarities of counteraction of local representatives of the nationalist underground to the Bolshevik propaganda in Lemkivshchyna of the departure to the Ukrainian SSR. At the same time the article reveals the nature of the activity of the intelligentsia from Eastern Galicia among the Lemkos. It is emphasized that immediately since the beginning of the World War ІІ considering the Bilshovyks occupation of Eastern Galicia, conscious and committed Ukrainians, began to arrive in Galician Lemkivschyna. They led an active public activity among Lemkos in schools, cooperatives, in cultural and educational institutions. It has been proved that the emigrants-refugees of the local intelligentsia from Eastern Galicia, despite the short stay in the Lemkos territories, played an important role in consolidating the region's national forces, actively working to raise the level of self-consciousness of the population of the region, thereby affirming the strong national foundations of the Lemkos region. The prospects of further research are to find out how the Soviet regime prepared the ground for violent deportation, initially through voluntary resettlement of Lemkos. The lessons and consequences of this event show that important state-building processes require thorough preparation and unity of all national forces. Keywords Lemkivshchyna, Lemkos, OUN, Sovietization, resettlement, national-cultural movement, intelligentsia, Lemkivshchyna, Lemkos, Eastern Galicia, Bolshevik propaganda.

Discovering functional impacts of miRNAs in cancers using a causal deep learning model

BackgroundMicro-RNAs (miRNAs) play a significant role in regulating gene expression under physiological and pathological conditions such as cancers. However, it remains a challenging problem to discover the target messenger RNAs (mRNAs) of a miRNA in a data driven fashion. On one hand, sequence-based methods for predicting miRNA targets tend to make too many false positive calls. On the other hand, analyzing expression correlation between miRNAs and mRNAs cannot establish whether relationship between a pair of correlated miRNA and mRNA is causal.MethodsIn this study, we designed a deep learning model, referred to as miRNA causal deep net (mCADET), which aims to explicitly represent two types of statistical relationships between miRNAs and mRNAs: correlation resulting from confounded co-regulation and correlation as a result of causal regulation. The model utilizes a deep neural network to simulate transcription mechanism that leads to co-expression of miRNA and mRNA, and, in addition, it also contains directed edges from miRNAs to mRNAs to capture causal relationships among them.ResultsWe trained the mCADET model using pan-cancer miRNA and mRNA data from The Cancer Genome Atlas (TCGA) project to investigate mechanism of co-expression and causal interactions between miRNAs and mRNAs. Quantitative analyses of the results indicate that the mCADET significantly outperforms conventional deep learning models when modeling combined miRNA and mRNA expression data, indicating its superior capability of capturing the high-order statistical structures in the data. Qualitative analysis of predicted targets of miRNAs indicate that predictions by mCADET agree well with existing knowledge. Finally, the predictions by mCADET have a significantly lower false discovery rate and better overall accuracy in comparison to sequence-based and correlation-based methods when comparing to experimental results.ConclusionThe mCADET model can simultaneously infer the states of cellular signaling system regulating co-expression of miRNAs and mRNAs, while capturing their causal relationships in a data-driven fashion.