Mutation Calling Research Articles

SpikeSeq: A rapid, cost efficient and simple method to identify SARS-CoV-2 variants of concern by Sanger sequencing part of the spike protein gene

In 2020, the novel coronavirus, SARS-CoV-2, caused a pandemic, which is still raging at the time of writing this. Here, we present results from SpikeSeq, the first published Sanger sequencing-based method for the detection of Variants of Concern (VOC) and key mutations, using a 1 kb amplicon from the recognized ARTIC Network primers. The proposed setup relies entirely on materials and methods already in use in diagnostic RT-qPCR labs and on existing commercial infrastructure offering sequencing services. For data analysis, we provide an automated, open source, and browser-based mutation calling software (https://github.com/kblin/covid-spike-classification, https://ssi.biolib.com/covid-spike-classification). We validated the setup on 195 SARS-CoV-2 positive samples, and we were able to profile 85% of RT-qPCR positive samples, where the last 15% largely stemmed from samples with low viral count. We compared the SpikeSeq results to WGS results. SpikeSeq has been used as the primary variant identification tool on > 10.000 SARS-CoV-2 positive clinical samples during 2021. At approximately 4€ per sample in material cost, minimal hands-on time, little data handling, and a short turnaround time, the setup is simple enough to be implemented in any SARS-CoV-2 RT-qPCR diagnostic lab. Our protocol provides results that can be used to choose antibodies in a clinical setting and for the tracking and surveillance of all positive samples for new variants and known ones such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1) Delta (B.1.617.2), Omicron BA.1(B.1.1.529), BA.2, BA.4/5, BA.2.75.x, and many more, as of October 2022.

5. IDH1 R132H, IDH2 R172K and BRAF V600E mutation detection by OncoScan CNV Plus in brain tumors

The OncoScan CNV Plus (OC+) assay is a single-nucleotide polymorphism-based microarray designed to detect genome-wide copy-number imbalance and loss of heterozygosity. This assay is also designed to detect 64 mutations in 9 cancer genes (BRAF, EGFR, IDH1, IDH2, KRAS, NRAS, PIK3CA, PTEN and TP53). We evaluated the accuracy of OC+ mutation calling in comparison to mutation testing by a 118-gene targeted amplicon-based neuro-oncology next-generation sequencing (NGS) panel for three brain tumor clinically relevant mutations: IDH1 R132H, IDH2 R172K and BRAF V600E. Cases included 636 brain tumor samples with tumor purity of at least 20%. IDH1 R132H mutation was called by OC+ in 138 cases; 137 cases were confirmed by NGS (sensitivity: 95.8%) and the single false positive case had an IDH1 R132L mutation. Among the 498 cases called negative for IDH1 R132H mutation by OC+, 6 were false negatives and shown to harbor an IDH1 R132H mutation by NGS (specificity: 99.8%). False negative cases had low variant allele frequency (20% or less), low DNA input and/or suboptimal OC+ quality metrics. All IDH2 R172K (n=14) and BRAF V600E (n=20) mutations called by OC+ were confirmed by NGS; no additional cases were detected by NGS, with sensitivity and specificity of 100% for calling of these 2 mutations by OC+. Our results indicate that OC+ mutation calling is accurate for IDH2 R172K and BRAF V600E mutations, and very specific but relatively less sensitive for IDH1 R132H mutation, suggesting that OC+ may be suitable for direct mutation calling for these three mutations. The OncoScan CNV Plus (OC+) assay is a single-nucleotide polymorphism-based microarray designed to detect genome-wide copy-number imbalance and loss of heterozygosity. This assay is also designed to detect 64 mutations in 9 cancer genes (BRAF, EGFR, IDH1, IDH2, KRAS, NRAS, PIK3CA, PTEN and TP53). We evaluated the accuracy of OC+ mutation calling in comparison to mutation testing by a 118-gene targeted amplicon-based neuro-oncology next-generation sequencing (NGS) panel for three brain tumor clinically relevant mutations: IDH1 R132H, IDH2 R172K and BRAF V600E. Cases included 636 brain tumor samples with tumor purity of at least 20%. IDH1 R132H mutation was called by OC+ in 138 cases; 137 cases were confirmed by NGS (sensitivity: 95.8%) and the single false positive case had an IDH1 R132L mutation. Among the 498 cases called negative for IDH1 R132H mutation by OC+, 6 were false negatives and shown to harbor an IDH1 R132H mutation by NGS (specificity: 99.8%). False negative cases had low variant allele frequency (20% or less), low DNA input and/or suboptimal OC+ quality metrics. All IDH2 R172K (n=14) and BRAF V600E (n=20) mutations called by OC+ were confirmed by NGS; no additional cases were detected by NGS, with sensitivity and specificity of 100% for calling of these 2 mutations by OC+. Our results indicate that OC+ mutation calling is accurate for IDH2 R172K and BRAF V600E mutations, and very specific but relatively less sensitive for IDH1 R132H mutation, suggesting that OC+ may be suitable for direct mutation calling for these three mutations.

39. Associations between somatically altered genes and recurrence outcomes in estrogen receptor positive breast cancer

The relationships between somatic mutations, copy number alterations (CNA) and outcomes in estrogen receptor positive (ER+) breast cancer is understudied. We have earlier published associations between rare and low frequency mutations and clinical outcomes in ER+ disease (PMID:30181556). As an extension of the study, FFPE blocks were obtained from 889 additional patients from the TransATAC study, an Anastrozole (A) or Tamoxifen (T) alone or combined randomized trial with 10-year follow-up. Tumor samples were reviewed, DNA isolated, and targeted sequencing performed for 83 genes. Mutation calling methods were previously reported. Average Copy number (CN) ratios were estimated for 875 tumors and 94 unmatched normal breast tissues using CNVkit. 841 samples met minimum quality and coverage criteria. For a given gene, gains and losses were defined as tumor CN-ratio above or below 95% confidence-interval of the distribution of normal CN-ratios. Hazard ratio was calculate for recurrence free survival (RFS) for somatically altered genes. TP53 mutations was one of the most important prognostic markers in this cohort. Non-silent mutations in ATM, ERBB3, ERBB4, KMT2C and NF1 associated with poor RFS. Co-deletion and co-amplification of genes respectively on chromosomal region 1p (MAGI3, JAK1, ARID1A, MTOR) and 20q13 (MYBL2, NCOA3, AURKA) associated with poor RFS. Deletion of FZD7 and PPP2R2A and amplification of MAP3K4, MAP3K13, LYN and BRAF associated with poor RFS. In summary, we present the prognostic role of multiple recurrently altered genes and their association with poor RFS in ER+ breast cancer. This warrants further investigation within clinical subtypes and treatment-specific sub-populations.

Feasibility of a novel non-invasive swab technique for serial whole-exome sequencing of cervical tumors during chemoradiation therapy.

BackgroundClinically relevant genetic predictors of radiation response for cervical cancer are understudied due to the morbidity of repeat invasive biopsies required to obtain genetic material. Thus, we aimed to demonstrate the feasibility of a novel noninvasive cervical swab technique to (1) collect tumor DNA with adequate throughput to (2) perform whole-exome sequencing (WES) at serial time points over the course of chemoradiation therapy (CRT).MethodsCervical cancer tumor samples from patients undergoing chemoradiation were collected at baseline, at week 1, week 3, and at the completion of CRT (week 5) using a noninvasive swab-based biopsy technique. Swab samples were analyzed with whole-exome sequencing (WES) with mutation calling using a custom pipeline optimized for shallow whole-exome sequencing with low tumor purity (TP). Tumor mutation changes over the course of treatment were profiled.Results216 samples were collected and successfully sequenced for 70 patients (94% of total number of tumor samples collected). A total of 33 patients had a complete set of samples at all four time points. The mean mapping rate was 98% for all samples, and the mean target coverage was 180. Estimated TP was greater than 5% for all samples. Overall mutation frequency decreased during CRT but mapping rate and mean target coverage remained at >98% and >180 reads at week 5.ConclusionThis study demonstrates the feasibility and application of a noninvasive swab-based technique for WES analysis which may be applied to investigate dynamic tumor mutational changes during treatment to identify novel genes which confer radiation resistance.

Primary resistance against integrase strand transfer inhibitors in integrase strand transfer inhibitor-naive patients failing first- and second-line ART in Tanzania.

Sub-Saharan African countries are introducing integrase strand transfer inhibitors (INSTIs) in their ART programmes as the preferred first-line regimen, and dolutegravir is the INSTI of choice due to its potency, tolerability and high genetic barrier to resistance. Dolutegravir was introduced into the first-line ART regimen in Tanzania in 2019. However, there is a paucity of data on the occurrence of mutations in HIV lineages circulating in Tanzania. This study aimed to determine the prevalence of INSTI primary resistance mutations in Tanzanian patients exposed to ART but not INSTIs. Plasma samples from 50 INSTI-naive patients failing first- or second-line ART [median (IQR) age: 40 (21.93-46.41) years; 68% women] were subjected to Sanger sequencing of the HIV integrase gene. Participants had been on ART for a median (IQR) duration of 7.32 (4.73-9.29) years, with 80% and 20% failing first- and second-line ART, respectively. No major INSTI mutations were found, but 2 (4%) participants had the accessory mutation T97A. Using the REGA HIV-1 subtyping tool, HIV subtype A1 (53.1%) was found to be dominant, followed by subtypes C (30.6%) and D (16.3%). This study found no current evidence for transmitted resistance against INSTIs among unexposed patients failing ART and supports the scale-up of INSTI-based regimens. However, the presence of accessory mutations calls for the surveillance of INSTI resistance mutations to ensure that the anticipated long-term desired outcomes are achieved.

109P Improved accuracy of somatic variant detection from high throughput sequencing (HTS) data via site-specific noise estimation

109P Improved accuracy of somatic variant detection from high throughput sequencing (HTS) data via site-specific noise estimation

Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence.

MotivationTumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient’s tumour composition and evolutionary history is key for personalized therapies. Single-cell sequencing (SCS) now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the complexity of the underlying evolutionary process.ResultsBy modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour SCS data.Availability and implementationSCIN is available at https://github.com/cbg-ethz/SCIPhIN.Supplementary information Supplementary data are available at Bioinformatics online.

Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA

Mutational signatures accumulate in somatic cells as an admixture of endogenous and exogenous processes that occur during an individual’s lifetime. Since dividing cells release cell-free DNA (cfDNA) fragments into the circulation, we hypothesize that plasma cfDNA might reflect mutational signatures. Point mutations in plasma whole genome sequencing (WGS) are challenging to identify through conventional mutation calling due to low sequencing coverage and low mutant allele fractions. In this proof of concept study of plasma WGS at 0.3–1.5x coverage from 215 patients and 227 healthy individuals, we show that both pathological and physiological mutational signatures may be identified in plasma. By applying machine learning to mutation profiles, patients with stage I-IV cancer can be distinguished from healthy individuals with an Area Under the Curve of 0.96. Interrogating mutational processes in plasma may enable earlier cancer detection, and might enable the assessment of cancer risk and etiology.

Clonal evolution in primary breast cancers under sequential epirubicin and docetaxel monotherapy

BackgroundSubclonal evolution during primary breast cancer treatment is largely unexplored. We aimed to assess the dynamic changes in subclonal composition of treatment-naïve breast cancers during neoadjuvant chemotherapy.MethodsWe performed whole exome sequencing of tumor biopsies collected before, at therapy switch, and after treatment with sequential epirubicin and docetaxel monotherapy in 51 out of 109 patients with primary breast cancer, who were included in a prospectively registered, neoadjuvant single-arm phase II trial.ResultsThere was a profound and differential redistribution of subclones during epirubicin and docetaxel treatment, regardless of therapy response. While truncal mutations and main subclones persisted, smaller subclones frequently appeared or disappeared. Reassessment of raw data, beyond formal mutation calling, indicated that the majority of subclones seemingly appearing during treatment were in fact present in pretreatment breast cancers, below conventional detection limits. Likewise, subclones which seemingly disappeared were still present, below detection limits, in most cases where tumor tissue remained. Tumor mutational burden (TMB) dropped during neoadjuvant therapy, and copy number analysis demonstrated specific genomic regions to be systematically lost or gained for each of the two chemotherapeutics.ConclusionsSequential epirubicin and docetaxel monotherapy caused profound redistribution of smaller subclones in primary breast cancer, while early truncal mutations and major subclones generally persisted through treatment.Trial registrationClinicalTrials.gov, NCT00496795, registered on July 4, 2007.

Analysis of the mutation dynamics of SARS-CoV-2 genome in the samples from Georgia State of the United States

BackgroundThe COVID-19 is caused by a novel coronavirus SARS-CoV-2, which started from China. It spread rapidly throughout the world and was later declared a pandemic by the WHO. Over the course of time, SARS-CoV-2 has mutated for survival advantages, and this led to multiple variants. Multiple studies on mutations identification in SARS-CoV2 have been published covering extensive sample areas. The purpose of this study was to limit the sample area to the Georgia state in the U.S. and to analyze the genome sequences for mutation profiling across the genome and origin of variants. MethodsThe genome sequences (n = 3,970) were obtained from the NCBI database as of June 12, 2021, with the filter of being complete sequenced genomes, homo-sapiens host, and only from Georgia State of the U.S. NextClade, an online tool was used for the analysis of the sequences using Wuhan-Hu-1/2019 as a reference genome. The algorithm was sequence alignment, translation, mutation calling, phylogenetic placement, clade assignment, and quality control (QC). Thirty-six samples with bad QC were removed from the mutational analysis. ResultsA total 117,743 mutations in the nucleotides were identified (averaging 31.5 mutations per sample). The mutations A23403G, C3037T, C241T, and C14408T were detected in 98% of the samples. Also, a total of 75,517 mutations in the amino acid were identified (averaging 20.2 mutations per sample). The mutations D614G and P314L were identified in >97% samples whereas R203K, G204R, P681H, and N501Y were detected in >50% samples. Analysis also revealed 16 different clades with 20I (49.6%). Clades 20G (24.2%) and 20A (5.5%) being the most abundant, showed that SARS-CoV-2 in the Georgia State originated mainly from Southeast England, other parts of the U.S., and several countries in Western Europe. ConclusionLooking at the three most common variants in Georgia State of the U.S., we could determine the primary locations of transmission or origin for the virus, and our analyses indicates that majority of the cases originated from Southeast England (Clade 20I), the U.S. itself (Clade 20G), and from Western Europe (Clade 20C).

Pediatric-Type Indolent B-Cell Lymphomas With Overlapping Clinical, Pathologic, and Genetic Features.

Pediatric-type follicular lymphoma (PTFL) and pediatric nodal marginal zone lymphoma (PNMZL) are rare pediatric-type indolent B-cell lymphomas (PedIBCL) that differ clinicopathologically from their adult counterparts. Accurate diagnosis is important to avoid overtreatment but is often challenging. The mutational landscape of PTFL is known and may aid diagnosis, but the genetic features of PNMZL are not well understood. We analyzed 21 cases of PedIBCL according to their clinicopathologic findings and classified them into PTFL (n=11), PNMZL (n=2), and “mixed type” tumors (n=8) showing ambiguous histology. We also analyzed 2 cases of adult B-cell lymphomas showing features of PedIBCL. Targeted sequencing of 121 lymphoma-related genes was performed. The median age of PedIBCL patients was 16 years (range: 3 to 47), and all but 1 PTFL patient were male. All patients presented with limited-stage disease, and only 1 relapsed. There were no significant differences in clinical features among the 3 PedIBCL groups. The most frequently mutated genes were MAP2K1, TNFRSF14, KMT2C, IRF8, and NOTCH2. The genetic features of all groups were similar to the established mutational landscape of PTFL. The 2 adult B-cell lymphomas cases also had MAP2K1, TNFRSF14, and IRF8 mutations, but the clinical features were not typical of PedIBCL. In summary, this study demonstrated that PTFL and PNMZL are similar diseases with overlapping clinical, pathologic, and genetic features; mixed type tumors can also occur. Atypical adult cases with similar histologic features were also observed. Therefore, the disease spectrum of PedIBCL may be much broader than is currently believed.

Abstract LB543: tmHRD: Accurately estimating the homologous recombination deficiency status from clinical panel sequencing data

Abstract Estimating the status of homologous recombination deficiency (HRD) is of clinical interest because the homologous recombination-deficient cells are reported sensitive to poly-ADP ribose polymerase (PARP) inhibitors. As HRD status becomes a critical indicator, many approaches have been proposed to measure the HRD status, among which estimating HRD status from cancer sequencing data is the popular strategy, where the representative methods include HRDetect, SigMA, FoundationFocusCDx, MyChoiceHRD, etc. Although sequencing data is the same input of these bioinformatics tools, algorithms prefer different aspects. Some methods focus on germline variants in BRCA1/2 genes and somatic mutational signatures, while some others capture the genomic scars, including loss of heterozygosity (LOH), telomere allele imbalance (TAI) and large-scale state transition (LST). However, there are two major controversial points when applying these tools on clinical sequencing data. First, panel sequencing is the most popular sequencing plan in clinic, but gene panels usually carry only hundreds to a thousand genes. Thus, the panel sequencing can neither capture a large number of mutations to calculate mutational signatures, nor obtain large genomic scars, such as TAI and LST. Second, the associations between the mutations and HRD status is still unclear. Thus, scoring mutations and genomic scars is not reasonable. To solving these issues, we here proposed a machine learning-based method, called tmHRD, which estimates HRD status according to limited mutations and LOHs captured by clinical panel sequencing data. It consists of a semi-supervised learning (TRI) model and a multi-instance learning (MIL) model. The TRI model filters the false positives from mutation calls. Then, the filtered mutation calls from one patient is defined as a package, where each mutation call is an instance in the package. When training the MIL model, in each iteration, a package with known HRD status (label) is given to MIL model. MIL model calculates the correlations among instances and assigns an adjusted weight for each. Thus, the HRD status is only contributed by those mutations associated with. We tested tmHRD on several datasets obtained by the clinical trials we participated. Compared to the existing methods, tmHRD achieved better performance. The average recognition rate of tmHRD was approaching 90%, while the comparison methods show around 80%. The software package, tmHRD, is freely available at https://github.com/Sherwin-xjtu/TriMILhrd/ for academic usages only. Citation Format: Xuwen Wang, Ying Xu, Yanfang Guan, Xin Yi, Jiayin Wang. tmHRD: Accurately estimating the homologous recombination deficiency status from clinical panel sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB543.

Abstract 6253: RNA-seq based somatic variant calling and gene expression analysis reveals tumor heterogeneity and metastatic potential in colorectal cancers

Abstract Colorectal cancer (CRC) is the third common malignant tumor and the second most responsible for worldwide cancer deaths. Also, RNAseq technology has been used for two purposes: to find exonic regions on the genome and quantify the expression level of the gene. We tried to validate the pipeline for identifying somatic variants from RNA-seq data, mainly following GATK4 somatic calling pipelines with some optimizing modifications. It is intended to examine whether somatic mutations driven from RNAseq data are associated with the biological behaviors of the individual colorectal cancer cells. We found that key genes (i.e., tumor suppressor genes such as APC and p53) are mostly mutated by T to C (in the non-coding region) or C to T (in the coding region) transitions, so that causes missense and nonsense mutations. However, highly mutated genes did not show significant expressional changes compared to the normal tissue. Otherwise, genes related to the metastatic potentials were observed to have highly increased expression. Our results can substantially verify the reliability of the somatic mutation calling approach using RNAseq data to call cancer-driving mutation and confirm an increase of extracellular matrix metabolism in the CRC. Citation Format: Ji Won Choi, Kwangsung Ahn, Sangsoo Kim, Dong-Il Park, Soo-kyung Park. RNA-seq based somatic variant calling and gene expression analysis reveals tumor heterogeneity and metastatic potential in colorectal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6253.

Abstract 810: HRD is inversely correlated with MSI and identifies immunologically cold tumors in most cancer types

Abstract Homologous recombination deficiency (HRD) leads to DNA double strand breaks and can be exploited by the use of PARP inhibitors to induce synthetic lethality of cancer cells. Over the last years, the clinical utility of this therapeutic concept was successfully demonstrated in ovarian, breast, prostate and pancreatic cancer. Currently, the role of HRD is investigated in trials testing immune checkpoint blockers alone or in combination with PARP inhibitors. But the relationship between HRD and immune cell contexture in cancer is incompletely understood. Here, we analyzed the association of HRD with tumor mutational burden (TMB), immune cell composition and gene expression in 10,000 tumors of 32 solid cancer types from the TCGA project. For each of the tumors, the HRD sum score (HRDsum) was calculated from allele-specific copy numbers (derived from genotyping data) and the TMB was calculated from the missense mutation calls (derived from WES data). HRDsum and tumor mutational burden (TMB) correlated positively pan-cancer (R=0.42) and within most cancer types. By contrast, HRD was absent in ultra-hypermutated (TMB >= 100 mut/Mb) tumors. Ultra-hypermutation was typically associated with microsatellite instability (MSI) or POLE/POLD1 mutation. Significant positive correlation of the HRDsum and immune cell infiltration was observed pan-cancer, but only within a few cancer types. Significant positive correlation of HRDsum and the T-cell inflamed gene expression profile was observed only in 7 of 32 cancer types: in breast cancer, ovarian cancer, low grade glioma, testicular germ cell tumors and three kinds of kidney cancer. A functional genomics analysis was carried out by correlating genome-wide gene expression data (derived from RNA-Seq) with HRDsum. The resulting lists of significantly correlating genes were analyzed for enrichment of 50 hallmark gene sets (catalog H, MSigDB). We detected simultaneous enrichment of two proliferation-related categories, E2F_TARGETS and G2M_checkpoint, for 13 of the 32 cancer types (ACC, BLCA, BRCA, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, MESO, PRAD, SARC, THYM). The study shows that HRD is associated with immunological activation of the tumor microenvironment only in a minority of cancer types. Our data support further studies of immune activating therapies in combination with immune checkpoint blockade in the not intrinsically activated cancer types and advocate to combine different genomic and transcriptomic biomarkers (including HRD and TMB) for comprehensive molecular diagnostics and therapy guidance. Citation Format: Jan Budczies, Klaus Kluck, Susanne Beck, Iordanis Ouralidis, Michael Allgäuer, Michael Menzel, Eugen Rempel, Daniel Kazdal, Lukas Perkhofer, Alexander Kleger, Peter Schirmacher, Thomas Seufferlein, Albrecht Stenzinger. HRD is inversely correlated with MSI and identifies immunologically cold tumors in most cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 810.

Empirical estimates of the mutation rate for an alphabaculovirus.

Mutation rates are of key importance for understanding evolutionary processes and predicting their outcomes. Empirical mutation rate estimates are available for a number of RNA viruses, but few are available for DNA viruses, which tend to have larger genomes. Whilst some viruses have very high mutation rates, lower mutation rates are expected for viruses with large genomes to ensure genome integrity. Alphabaculoviruses are insect viruses with large genomes and often have high levels of polymorphism, suggesting high mutation rates despite evidence of proofreading activity by the replication machinery. Here, we report an empirical estimate of the mutation rate per base per strand copying (s/n/r) of Autographa californica multiple nucleopolyhedrovirus (AcMNPV). To avoid biases due to selection, we analyzed mutations that occurred in a stable, non-functional genomic insert after five serial passages in Spodoptera exigua larvae. Our results highlight that viral demography and the stringency of mutation calling affect mutation rate estimates, and that using a population genetic simulation model to make inferences can mitigate the impact of these processes on estimates of mutation rate. We estimated a mutation rate of μ = 1×10-7 s/n/r when applying the most stringent criteria for mutation calling, and estimates of up to μ = 5×10-7 s/n/r when relaxing these criteria. The rates at which different classes of mutations accumulate provide good evidence for neutrality of mutations occurring within the inserted region. We therefore present a robust approach for mutation rate estimation for viruses with stable genomes, and strong evidence of a much lower alphabaculovirus mutation rate than supposed based on the high levels of polymorphism observed.

Enabling tandem repeat sequence genotype analysis across capture kits.

e18700 Background: Previous studies on mutation calling have documented capture kit batch effects in Whole Exome Sequencing (WES) data from The Cancer Genome Atlas (TCGA) database, hindering direct comparison between samples from different capture kits. For example, in classification, a cancer type exclusively sampled by a specific capture kit in the training set would have very low accuracy if the testing set was sampled by another capture kit. To enable cross-capture-kit between-cancer genotype analyses with the TCGA dataset, a novel read count transformation algorithm is developed to remove capture kit batch effects. This algorithm was tested with our Machine Learning model which uses Tandem Repeat Sequence (TRS) mutation markers as training features. Methods: The proposed algorithm transforms TRS read count data to remove low quality samples, read depth differences, and capture kit batch effects from the dataset. Results: 1) TRS read count of WES samples are investigated. Particularly, we show that TRS site read counts do not correlate across capture kits but correlate within capture kits. This suggests that WES read count is largely independent from an exon’s location in the genome and is more strongly correlated with capture kit probes. 2) TRS detection rate for each sample within each capture kit is found to be normally distributed. Outliers with very low TRS detection rate can be used for quality filtering. 3) The transformation algorithm effectively removes capture kit batch effects from the dataset. At the same time, it retains cancer-specific signals in the samples. Before applying the transformation algorithm, cancer type classification accuracy is low (̃0-25%) if the testing data set uses a different capture kit from the training data set. We show that applying the transformation algorithm allows cancer type classification accuracy to improve by over 65%. Conclusions: We demonstrated that direct comparison of WES TRS read count data across capture kits is possible after application of our transformation algorithm. This opens the path to cross-capture-kit between-cancer genotype analyses with the TCGA dataset, which were previously unfeasible due to capture kit batch effects.

OMEN: network-based driver gene identification using mutual exclusivity.

Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed. We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark dataset derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways. The source code is freely available for download at www.github.com/DriesVanDaele/OMEN. The dataset is archived at https://doi.org/10.5281/zenodo.6419097 and the code at https://doi.org/10.5281/zenodo.6419764. Supplementary data are available at Bioinformatics online.

Establishment of four head and neck squamous cell carcinoma cell lines: importance of reference DNA for accurate genomic characterisation.

There is significant interest in developing early passage cell lines with matched normal reference DNA to facilitate a precision medicine approach in assessing drug response. This study aimed to establish early passage cell lines, and perform whole exome sequencing and short tandem repeat profiling on matched normal reference DNA, primary tumour and corresponding cell lines. A cell culture based, in vitro study was conducted of patients with primary human papillomavirus positive and human papillomavirus negative tumours. Four early passage cell lines were established. Two cell lines were human papillomavirus positive, confirmed by sequencing and p16 immunoblotting. Short tandem repeat profiling confirmed that all cell lines were established from their index tumours. Whole exome sequencing revealed that the matched normal reference DNA was critical for accurate mutational analysis: a high rate of false positive mutation calls were excluded (87.6 per cent). Early passage cell lines were successfully established. Patient-matched reference DNA is important for accurate cell line mutational calls.

Urine- and Blood-Based Molecular Profiling of Human Prostate Cancer.

ObjectiveProstate cancer (PCa) is one of the most common malignant tumors, accounting for 20% of total tumors ranked first in males. PCa is usually asymptomatic at the early stage and the specificity of the current biomarkers for the detection of PCa is low. The present study evaluates circulating tumor DNA (ctDNA) in blood or urine, which can be used as biomarkers of PCa and the combination of these markers may increase the sensitivity and specificity of the detection of PCa.MethodsTissue, blood, and urine samples were collected from patients with PCa. All prostate tissue specimens underwent pathological examination. A hybrid-capture-based next-generation sequencing assay was used for plasma and urinary ctDNA profiling. Sequencing data were analyzed by an in-house pipeline for mutation calling. Mutational profiles of PCa and BPH were compared in both plasma and urine samples. Associations of detected mutations and clinical characteristics were statistically analyzed.ResultsA significant association of mutation allele frequencies (MAFs) in the blood samples with patients with metastatic PCa rather than patients with primary PCa, and MAFs are changed after treatment in patients with PCa. Further, the number of mutations in urine is not associated with clinical characteristics of PCa patients, but the frequencies of mutation alleles in the urine are associated with patient age. Comparison of cfDNA aberration profiles between urine and blood reveals more alterations in urine than in blood, including TP53, AR, ATM, MYC, and SPOP mutations.ConclusionThis work provides the potential clinical application of urine, in addition to blood, as a powerful and convenient non-invasive approach in personalized medicine for patients with PCa.

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2

Current somatic mutation callers are biased against repetitive regions, preventing the identification of potential driver alterations in these loci. We developed a mutation caller for repetitive regions, and applied it to study repetitive non protein-coding genes in more than 2200 whole-genome cases. We identified a recurrent mutation at position c.28 in the gene encoding the snRNA U2. This mutation is present in B-cell derived tumors, as well as in prostate and pancreatic cancer, suggesting U2 c.28 constitutes a driver candidate associated with worse prognosis. We showed that the GRCh37 reference genome is incomplete, lacking the U2 cluster in chromosome 17, preventing the identification of mutations in this gene. Furthermore, the 5′-flanking region of WDR74, previously described as frequently mutated in cancer, constitutes a functional copy of U2. These data reinforce the relevance of non-coding mutations in cancer, and highlight current challenges of cancer genomic research in characterizing mutations affecting repetitive genes.