Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing

Germline whole genome sequencing in adults with multiple primary tumors

Multiple primary tumors (MPTs) are a harbinger of hereditary cancer syndromes. Affected individuals often fit genetic testing criteria for a number of hereditary cancer genes and undergo multigene panel testing. Other genomic testing options, such as whole exome (WES) and whole genome sequencing (WGS) are available, but the utility of these genomic approaches as a second-tier test for those with uninformative multigene panel testing has not been explored. Here, we report our germline sequencing results from WGS in 9 patients with MPTs who had non-informative multigene panel testing. Following germline WGS, sequence (agnostic or 735 selected genes) and copy number variant (CNV) analysis was performed according to the American College of Medical Genetics (ACMG) standards and guidelines for interpreting sequence variants and reporting CNVs. In this cohort, WGS, as a second-tier test, did not identify additional pathogenic or likely pathogenic variants in cancer predisposition genes. Although we identified a CHEK2 likely pathogenic variant and a MUTYH pathogenic variant, both were previously identified in the multigene panels and were not explanatory for the presented type of tumors. CNV analysis also failed to identify any pathogenic or likely pathogenic variants in cancer predisposition genes. In summary, after multigene panel testing, WGS did not reveal any additional pathogenic variants in patients with MPTs. Our study, based on a small cohort of patients with MPT, suggests that germline gene panel testing may be sufficient to investigate these cases. Future studies with larger sample sizes may further elucidate the additional utility of WGS in MPTs.

Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38-0.83) and quantitatively (Kendall tau-b = 0.18-0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9-24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution.

Defining the single base importance of human mRNAs and lncRNAs

As the fundamental unit of a gene and its transcripts, nucleotides have enormous impacts on the gene function and evolution, and thus on phenotypes and diseases. In order to identify the key nucleotides of one specific gene, it is quite crucial to quantitatively measure the importance of each base on the gene. However, there are still no sequence-based methods of doing that. Here, we proposed Base Importance Calculator (BIC), an algorithm to calculate the importance score of each single base based on sequence information of human mRNAs and long noncoding RNAs (lncRNAs). We then confirmed its power by applying BIC to three different tasks. Firstly, we revealed that BIC can effectively evaluate the pathogenicity of both genes and single bases through single nucleotide variations. Moreover, the BIC score in The Cancer Genome Atlas somatic mutations is able to predict the prognosis of some cancers. Finally, we show that BIC can also precisely predict the transmissibility of SARS-CoV-2. The above results indicate that BIC is a useful tool for evaluating the single base importance of human mRNAs and lncRNAs.

NOTCH2 gene mutation and gamma-secretase inhibitor in mediating the malignancy of ovarian cancer

The carcinogenic mechanisms by which serous ovarian cancer (OC) occurs remain to be explored. Currently, we have conducted whole-exome sequencing (WES) and targeted deep sequencing to validate new molecular markers, including NOTCH2, that impede the progression of cell malignancy in ovarian cancer (OC). Following NOTCH2 P2113S mutation and NOTCH signaling pathway inhibitor N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT) treatment, the cell proliferation, migration, and invasion of A2780 and SKOV3 OC cells were examined in vitro. WES identified the P2113S point mutation in NOTCH2. The NOTCH2 mutation rate was 26.67 % among the 75 OC cases. The NOTCH2 P2113S mutation and DAPT treatment downregulated Notch-2 protein levels in the two OC cells. Functionally, interfering with NOTCH2 expression promoted the migrative, proliferative, and invasive capacities of OC cells. Western blotting further confirmed that NOTCH2-mediated tumorigenesis lies in reducing apoptosis through dysregulation of Bax/Bcl2, affecting repair of DNA damage through reducing DNA-PK and blocking the transcription factor Hes1 along with increasing immune regulator p65. Furthermore, the NOTCH2-mediated tumorigenesis was mostly reversed after NF-κB inhibitor Bay11-7082 treatment. These findings identified the NOTCH2 P2113S mutation in ovarian carcinogenesis, and NOTCH2 P2113S is a potential target in treating OC.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

VCFshiny: an R/Shiny application for interactively analyzing and visualizing genetic variants

Summary

Next-generation sequencing generates variants that are typically documented in variant call format (VCF) files. However, comprehensively examining variant information from VCF files can pose a significant challenge for researchers lacking bioinformatics and programming expertise. To address this issue, we introduce VCFshiny, an R package that features a user-friendly web interface enabling interactive annotation, interpretation, and visualization of variant information stored in VCF files. VCFshiny offers two annotation methods, Annovar and VariantAnnotation, to add annotations such as genes or functional impact. Annotated VCF files are deemed acceptable inputs for the purpose of summarizing and visualizing variant information. This includes the total number of variants, overlaps across sample replicates, base alterations of single nucleotides, length distributions of insertions and deletions (indels), high-frequency mutated genes, variant distribution in the genome and of genome features, variants in cancer driver genes, and cancer mutational signatures. VCFshiny serves to enhance the intelligibility of VCF files by offering an interactive web interface for analysis and visualization.

Availability and implementation

The source code is available under an MIT open source license at https://github.com/123xiaochen/VCFshiny with documentation at https://123xiaochen.github.io/VCFshiny.

A critical spotlight on the paradigms of FFPE-DNA sequencing

In the late 19th century, formalin fixation with paraffin-embedding (FFPE) of tissues was developed as a fixation and conservation method and is still used to this day in routine clinical and pathological practice. The implementation of state-of-the-art nucleic acid sequencing technologies has sparked much interest for using historical FFPE samples stored in biobanks as they hold promise in extracting new information from these valuable samples. However, formalin fixation chemically modifies DNA, which potentially leads to incorrect sequences or misinterpretations in downstream processing and data analysis. Many publications have concentrated on one type of DNA damage, but few have addressed the complete spectrum of FFPE-DNA damage. Here, we review mitigation strategies in (I) pre-analytical sample quality control, (II) DNA repair treatments, (III) analytical sample preparation and (IV) bioinformatic analysis of FFPE-DNA. We then provide recommendations that are tested and illustrated with DNA from 13-year-old liver specimens, one FFPE preserved and one fresh frozen, applying target-enriched sequencing. Thus, we show how DNA damage can be compensated, even when using low quantities (50 ng) of fragmented FFPE-DNA (DNA integrity number 2.0) that cannot be amplified well (Q129 bp/Q41 bp = 5%). Finally, we provide a checklist called 'ERROR-FFPE-DNA' that summarises recommendations for the minimal information in publications required for assessing fitness-for-purpose and inter-study comparison when using FFPE samples.

Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data

BACKGROUND

High-throughput sequencing (HTS) offers unprecedented opportunities for the discovery of causative gene variants in multiple human disorders including cancers, and has revolutionized clinical diagnostics. However, despite more than a decade of use of HTS-based assays, extracting relevant functional information from whole-exome sequencing (WES) data remains challenging, especially for non-specialists lacking in-depth bioinformatic skills.

RESULTS

To address this limitation, we developed Var∣Decrypt, a web-based tool designed to greatly facilitate WES data browsing and analysis. Var∣Decrypt offers a wide range of gene and variant filtering possibilities, clustering and enrichment tools, providing an efficient way to derive patient-specific functional information and to prioritize gene variants for functional analyses. We applied Var∣Decrypt on WES datasets of 10 acute erythroid leukemia patients, a rare and aggressive form of leukemia, and recovered known disease oncogenes in addition to novel putative drivers. We additionally validated the performance of Var∣Decrypt using an independent dataset of ~ 90 multiple myeloma WES, recapitulating the identified deregulated genes and pathways, showing the general applicability and versatility of Var∣Decrypt for WES analysis.

CONCLUSION

Despite years of use of WES in human health for diagnosis and discovery of disease drivers, WES data analysis still remains a complex task requiring advanced bioinformatic skills. In that context, there is a need for user-friendly all-in-one dedicated tools for data analysis, to allow biologists and clinicians to extract relevant biological information from patient datasets. Here, we provide Var∣Decrypt (trial version accessible here: https://vardecrypt.com/app/vardecrypt ), a simple and intuitive Rshiny application created to fill this gap. Source code and detailed user tutorial are available at https://gitlab.com/mohammadsalma/vardecrypt .

Exploring the Potential Driver Gene Mutations That Promote Renal Cancer Cell Metastasis and Implantation Based on Circulating Tumor Cells Culture

Studies have shown that the circulating tumor cell (CTC) is a necessary condition for the invasion and distant metastasis of renal cell carcimona (RCC). However, few CTCs-related gene mutations have been developed which could promote the metastasis and implantation of RCC. The objective of this study is to explore the potential driver gene mutations that promote RCC metastasis and implantation based on CTCs culture. Fifteen patients with primary mRCC and three healthy subjects were included, and peripheral blood was obtained. After the preparation of synthetic biological scaffolds, peripheral blood CTCs were cultured. Successful cultured CTCs were applied to construct CTCs-derived xenograft (CDX) models, followed by DNA extraction, whole exome sequencing (WES) and bioinformatics analysis. Synthetic biological scaffolds were constructed based on previously applied techniques, and peripheral blood CTCs culture was successfully performed. We then constructed CDX models and performed WES, and explored the potential driver gene mutations that may promote RCC metastasis and implantation. Bioinformatics analysis showed that KAZN and POU6F2 may be closely related to the prognosis of RCC. We successfully performed the culture of peripheral blood CTCs and, on this basis we initially explored the potential driver mutations for the metastasis and implantation of RCC.

Skeletal muscle omics signatures in cancer cachexia: perspectives and opportunities

Cachexia is a life-threatening complication of cancer that occurs in up to 80% of patients with advanced cancer. Cachexia reflects the systemic consequences of cancer and prominently features unintended weight loss and skeletal muscle wasting. Cachexia impairs cancer treatment tolerance, lowers quality of life, and contributes to cancer-related mortality. Effective treatments for cancer cachexia are lacking despite decades of research. High-throughput omics technologies are increasingly implemented in many fields including cancer cachexia to stimulate discovery of disease biology and inform therapy choice. In this paper, we present selected applications of omics technologies as tools to study skeletal muscle alterations in cancer cachexia. We discuss how comprehensive, omics-derived molecular profiles were used to discern muscle loss in cancer cachexia compared with other muscle-wasting conditions, to distinguish cancer cachexia from treatment-related muscle alterations, and to reveal severity-specific mechanisms during the progression of cancer cachexia from early toward severe disease.

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.

Whole-exome mutational landscape and molecular marker study in mucinous and clear cell ovarian cancer cell lines 3AO and ES2

BACKGROUND

Ovarian cancer is one of the most lethal cancers in women because it is often diagnosed at an advanced stage. The molecular markers investigated thus far have been unsatisfactory.

METHODS

We performed whole-exome sequencing on the human ovarian cancer cell lines 3AO and ES2 and the normal ovarian epithelial cell line IOSE-80. Molecular markers of ovarian cancer were screened from shared mutation genes and copy number variation genes in the 6q21-qter region.

RESULTS

We found that missense mutations were the most common mutations in the gene (93%). The MUC12, FLG and MUC16 genes were highly mutated in 3AO and ES2 cells. Copy number amplification occurred mainly in 4p16.1 and 11q14.3, and copy number deletions occurred in 4q34.3 and 18p11.21. A total of 23 hub genes were screened, of which 16 were closely related to the survival of ovarian cancer patients. The three genes CCDC170, THBS2 and COL14A1 are most significantly correlated with the survival and prognosis of ovarian cancer. In particular, the overall survival of ovarian cancer patients with high CCDC170 gene expression was significantly prolonged (P < 0.001). The expression of CCDC170 in normal tissues was significantly higher than that in ovarian cancer tissues (P < 0.05), and its expression was significantly decreased in advanced ovarian cancer. Western blotting and immunofluorescence assays also showed that the expression of CCDC170 in ovarian cancer cells was significantly lower than that in normal cells (P < 0.001, P < 0.01).

CONCLUSIONS

CCDC170 is expected to become a new diagnostic molecular target and prognostic indicator for ovarian cancer patients, which can provide new ideas for the design of antitumor drugs.

Targeted nanopore sequencing enables complete characterisation of structural deletions initially identified using exon-based short-read sequencing strategies

BACKGROUND

The widespread adoption of exome sequencing has greatly increased the rate of genetic diagnosis for inherited conditions. However, the detection and validation of large deletions remains challenging. While numerous bioinformatics approaches have been developed to detect deletions from whole - exome sequencing and targeted panels, further work is typically required to define the physical breakpoints or integration sites. Accurate characterisation requires either expensive follow - up whole - genome sequencing or the time - consuming, laborious process of PCR walking, both of which are challenging when dealing with the repeat sequences which frequently intersect deletion breakpoints. The aim of this study was to develop a cost-effective, long-range sequencing method to characterise deletions.

METHODS

Genomic DNA was amplified with primers spanning the deletion using long-range PCR and the products purified. Sequencing was performed on MinION flongle flowcells. The resulting fast5 files were basecalled using Guppy, trimmed using Porechop and aligned using Minimap2. Filtering was performed using NanoFilt. Nanopore sequencing results were verified by Sanger sequencing.

RESULTS

Four cases with deletions detected following comparative read-depth analysis of targeted short-read sequencing were analysed. Nanopore sequencing defined breakpoints at the molecular level in all cases including homozygous breakpoints in EYS, CNGA1 and CNGB1 and a heterozygous deletion in PRPF31. All breakpoints were verified by Sanger sequencing.

CONCLUSIONS

In this study, a quick, accurate and cost - effective method is described to characterise deletions identified from exome, and similar data, using nanopore sequencing.

Review the progression of ovarian clear cell carcinoma from the perspective of genomics and epigenomics

Ovarian clear cell carcinoma (OCCC) is a rare subtype of epithelial ovarian cancer with unique molecular characteristics, specific biological and clinical behavior, poor prognosis and high resistance to chemotherapy. Pushed by the development of genome-wide technologies, our knowledge about the molecular features of OCCC has been considerably advanced. Numerous studies are emerging as groundbreaking, and many of them are promising treatment strategies. In this article, we reviewed studies about the genomics and epigenetics of OCCC, including gene mutation, copy number variations, DNA methylation and histone modifications.

Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies

BACKGROUND

The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples.

RESULTS

We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy.

CONCLUSIONS

A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

Personalized genome assembly for accurate cancer somatic mutation discovery using tumor-normal paired reference samples

BACKGROUND

The use of a personalized haplotype-specific genome assembly, rather than an unrelated, mosaic genome like GRCh38, as a reference for detecting the full spectrum of somatic events from cancers has long been advocated but has never been explored in tumor-normal paired samples. Here, we provide the first demonstrated use of de novo assembled personalized genome as a reference for cancer mutation detection and quantifying the effects of the reference genomes on the accuracy of somatic mutation detection.

RESULTS

We generate de novo assemblies of the first tumor-normal paired genomes, both nuclear and mitochondrial, derived from the same individual with triple negative breast cancer. The personalized genome was chromosomal scale, haplotype phased, and annotated. We demonstrate that it provides individual specific haplotypes for complex regions and medically relevant genes. We illustrate that the personalized genome reference not only improves read alignments for both short-read and long-read sequencing data but also ameliorates the detection accuracy of somatic SNVs and SVs. We identify the equivalent somatic mutation calls between two genome references and uncover novel somatic mutations only when personalized genome assembly is used as a reference.

CONCLUSIONS

Our findings demonstrate that use of a personalized genome with individual-specific haplotypes is essential for accurate detection of the full spectrum of somatic mutations in the paired tumor-normal samples. The unique resource and methodology established in this study will be beneficial to the development of precision oncology medicine not only for breast cancer, but also for other cancers.

Clinical Applications of Liquid Biopsy in Colorectal Cancer Screening: Current Challenges and Future Perspectives

Colorectal cancer (CRC) represents the third most prevalent cancer worldwide and a leading cause of mortality among the population of western countries. However, CRC is frequently a preventable malignancy due to various screening tests being available. While failing to obtain real-time data, current screening methods (either endoscopic or stool-based tests) also require disagreeable preparation protocols and tissue sampling through invasive procedures, rendering adherence to CRC screening programs suboptimal. In this context, the necessity for novel, less invasive biomarkers able to identify and assess cancer at an early stage is evident. Liquid biopsy comes as a promising minimally invasive diagnostic tool, able to provide comprehensive information on tumor heterogeneity and dynamics during carcinogenesis. This review focuses on the potential use of circulating tumor cells (CTCs), circulating nucleic acids (CNAs) and extracellular vesicles as emerging liquid biopsy markers with clinical application in the setting of CRC screening. The review also examines the opportunity to implement liquid biopsy analysis during everyday practice and provides highlights on clinical trials researching blood tests designed for early cancer diagnosis. Additionally, the review explores potential applications of liquid biopsies in the era of immunotherapy.

Towards increased accuracy and reproducibility in SARS-CoV-2 next generation sequence analysis for public health surveillance

During the COVID-19 pandemic, SARS-CoV-2 surveillance efforts integrated genome sequencing of clinical samples to identify emergent viral variants and to support rapid experimental examination of genome-informed vaccine and therapeutic designs. Given the broad range of methods applied to generate new viral genomes, it is critical that consensus and variant calling tools yield consistent results across disparate pipelines. Here we examine the impact of sequencing technologies (Illumina and Oxford Nanopore) and 7 different downstream bioinformatic protocols on SARS-CoV-2 variant calling as part of the NIH Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Tracking Resistance and Coronavirus Evolution (TRACE) initiative, a public-private partnership established to address the COVID-19 outbreak. Our results indicate that bioinformatic workflows can yield consensus genomes with different single nucleotide polymorphisms, insertions, and/or deletions even when using the same raw sequence input datasets. We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations. Such consistency among bioinformatic pipelines is fundamental to SARS-CoV-2 and future pathogen surveillance efforts. The application of analysis standards is necessary to more accurately document phylogenomic trends and support data-driven public health responses.

DETexT: An SNV detection enhancement for low read depth by integrating mutational signatures into TextCNN

Detecting SNV at very low read depths helps to reduce sequencing requirements, lowers sequencing costs, and aids in the early screening, diagnosis, and treatment of cancer. However, the accuracy of SNV detection is significantly reduced at read depths below ×34 due to the lack of a sufficient number of read pairs to help filter out false positives. Many recent studies have revealed the potential of mutational signature (MS) in detecting true SNV, understanding the mutational processes that lead to the development of human cancers, and analyzing the endogenous and exogenous causes. Here, we present DETexT, an SNV detection method better suited to low read depths, which classifies false positive variants by combining MS with deep learning algorithms to mine correlation information around bases in individual reads without relying on the support of duplicate read pairs. We have validated the effectiveness of DETexT on simulated and real datasets and conducted comparative experiments. The source code has been uploaded to https://github.com/TrinaZ/extra-lowRD for academic use only.

PEcnv: accurate and efficient detection of copy number variations of various lengths

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb–1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv

RNA-SSNV: A Reliable Somatic Single Nucleotide Variant Identification Framework for Bulk RNA-Seq Data

The usage of expressed somatic mutations may have a unique advantage in identifying active cancer driver mutations. However, accurately calling mutations from RNA-seq data is difficult due to confounding factors such as RNA-editing, reverse transcription, and gap alignment. In the present study, we proposed a framework (named RNA-SSNV, https://github.com/pmglab/RNA-SSNV) to call somatic single nucleotide variants (SSNV) from tumor bulk RNA-seq data. Based on a comprehensive multi-filtering strategy and a machine-learning classification model trained with comprehensively curated features, RNA-SSNV achieved the best precision–recall rate (0.880–0.884) in a testing dataset and robustly retained 0.94 AUC for the precision–recall curve in three validation adult-based TCGA (The Cancer Genome Atlas) datasets. We further showed that the somatic mutations called by RNA-SSNV tended to have a higher functional impact and therapeutic power in known driver genes. Furthermore, VAF (variant allele fraction) analysis revealed that subclonal harboring expressed mutations had evolutional selection advantage and RNA had higher detection power to rescue DNA-omitted mutations. In sum, RNA-SSNV will be a useful approach to accurately call expressed somatic mutations for a more insightful analysis of cancer drive genes and carcinogenic mechanisms.

Detection of a novel panel of 24 genes with high frequencies of mutation in gastric cancer based on next-generation sequencing

BACKGROUND

Gastric cancer is a leading cause of cancer-related mortality worldwide. Many somatic mutations have been identified based on next-generation sequencing; they likely play a vital role in cancer treatment selection. However, next-generation sequencing has not been widely used to diagnose and treat gastric cancer in the clinic.

AIM

To test the mutant gene frequency as a guide for molecular diagnosis and personalized therapy in gastric cancer by use of next-generation sequencing.

METHODS

We constructed a panel of 24 mutant genes to detect somatic nucleotide variations and copy number variations based on a next-generation sequencing technique. Our custom panel included high-mutation frequency cancer driver and tumour suppressor genes. Mutated genes were also analyzed using the cBioPortal database. The clinical annotation of important variant mutation sites was evaluated in the ClinVar database. We searched for candidate drugs for targeted therapy and immunotherapy from the OncoKB database.

RESULTS

In our study, the top 16 frequently mutated genes were TP53(58%), ERBB2(28%), BRCA2 (23%), NF1 (19%), PIK3CA (14%), ATR (14%), MSH2 (12%), FBXW7 (12%), BMPR1A (12%), ERBB3 (11%), ATM (9%), FGFR2 (8%), MET (8%), PTEN (6%), CHD4 (6%), and KRAS (5%). TP53 is a commonly mutated gene in gastric cancer and has a similar frequency to that in the cBioPortal database. 33 gastric cancer patients (51.6%) with microsatellite stability and eight patients (12.5%) with microsatellite instability-high were investigated. Enrichment analyses demonstrated that high-frequency mutated genes had transmembrane receptor protein kinase activity. We discovered that BRCA2, PIK3CA, and FGFR2 gene mutations represent promising biomarkers in gastric cancer.

CONCLUSION

We developed a powerful panel of 24 genes with high frequencies of mutation that could detect common somatic mutations. The observed mutations provide potential targets for the clinical treatment of gastric cancer.

Detection of oncogenic and clinically actionable mutations in cancer genomes critically depends on variant calling tools

Motivation

The analysis of cancer genomes provides fundamental information about its etiology, the processes driving cell transformation or potential treatments. While researchers and clinicians are often only interested in the identification of oncogenic mutations, actionable variants or mutational signatures, the first crucial step in the analysis of any tumor genome is the identification of somatic variants in cancer cells (i.e. those that have been acquired during their evolution). For that purpose, a wide range of computational tools have been developed in recent years to detect somatic mutations in sequencing data from tumor samples. While there have been some efforts to benchmark somatic variant calling tools and strategies, the extent to which variant calling decisions impact the results of downstream analyses of tumor genomes remains unknown.

Results

Here, we quantify the impact of variant calling decisions by comparing the results obtained in three important analyses of cancer genomics data (identification of cancer driver genes, quantification of mutational signatures and detection of clinically actionable variants) when changing the somatic variant caller (MuSE, MuTect2, SomaticSniper and VarScan2) or the strategy to combine them (Consensus of two, Consensus of three and Union) across all 33 cancer types from The Cancer Genome Atlas. Our results show that variant calling decisions have a significant impact on these analyses, creating important differences that could even impact treatment decisions for some patients. Moreover, the Consensus of three calling strategy to combine the output of multiple variant calling tools, a very widely used strategy by the research community, can lead to the loss of some cancer driver genes and actionable mutations. Overall, our results highlight the limitations of widespread practices within the cancer genomics community and point to important differences in critical analyses of tumor sequencing data depending on variant calling, affecting even the identification of clinically actionable variants.

Availability and implementation

Code is available at https://github.com/carlosgarciaprieto/VariantCallingClinicalBenchmark.

Supplementary information

Supplementary data are available at Bioinformatics online.

V(D)J Recombination: Recent Insights in Formation of the Recombinase Complex and Recruitment of DNA Repair Machinery

V(D)J recombination is an essential mechanism of the adaptive immune system, producing a diverse set of antigen receptors in developing lymphocytes via regulated double strand DNA break and subsequent repair. DNA cleavage is initiated by the recombinase complex, consisting of lymphocyte specific proteins RAG1 and RAG2, while the repair phase is completed by classical non-homologous end joining (NHEJ). Many of the individual steps of this process have been well described and new research has increased the scale to understand the mechanisms of initiation and intermediate stages of the pathway. In this review we discuss 1) the regulatory functions of RAGs, 2) recruitment of RAGs to the site of recombination and formation of a paired complex, 3) the transition from a post-cleavage complex containing RAGs and cleaved DNA ends to the NHEJ repair phase, and 4) the potential redundant roles of certain factors in repairing the break. Regulatory (non-core) domains of RAGs are not necessary for catalytic activity, but likely influence recruitment and stabilization through interaction with modified histones and conformational changes. To form long range paired complexes, recent studies have found evidence in support of large scale chromosomal contraction through various factors to utilize diverse gene segments. Following the paired cleavage event, four broken DNA ends must now make a regulated transition to the repair phase, which can be controlled by dynamic conformational changes and post-translational modification of the factors involved. Additionally, we examine the overlapping roles of certain NHEJ factors which allows for prevention of genomic instability due to incomplete repair in the absence of one, but are lethal in combined knockouts. To conclude, we focus on the importance of understanding the detail of these processes in regards to off-target recombination or deficiency-mediated clinical manifestations.

Consistency and reproducibility of large panel next-generation sequencing: Multi-laboratory assessment of somatic mutation detection on reference materials with mismatch repair and proofreading deficiency

INTRODUCTION

Clinical precision oncology increasingly relies on accurate genome-wide profiling using large panel next generation sequencing; however, difficulties in accurate and consistent detection of somatic mutation from individual platforms and pipelines remain an open question.

OBJECTIVES

To obtain paired tumor-normal reference materials that can be effectively constructed and interchangeable with clinical samples, and evaluate the performance of 56 panels under routine testing conditions based on the reference samples.

METHODS

Genes involved in mismatch repair and DNA proofreading were knocked down using the CRISPR-Cas9 technology to accumulate somatic mutations in a defined GM12878 cell line. They were used as reference materials to comprehensively evaluate the reproducibility and accuracy of detection results of oncopanels and explore the potential influencing factors.

RESULTS

In total, 14 paired tumor-normal reference DNA samples from engineered cell lines were prepared, and a reference dataset comprising 168 somatic mutations in a high-confidence region of 1.8 Mb were generated. For mutations with an allele frequency (AF) of more than 5% in reference samples, 56 panels collectively reported 1306 errors, including 729 false negatives (FNs), 179 false positives (FPs) and 398 reproducibility errors. The performance metric varied among panels with precision and recall ranging from 0.773 to 1 and 0.683 to 1, respectively. Incorrect and inadequate filtering accounted for a large proportion of false discovery (including FNs and FPs), while low-quality detection, cross-contamination and other sequencing errors during the wet bench process were other sources of FNs and FPs. In addition, low AF (<5%) considerably influenced the reproducibility and comparability among panels.

CONCLUSIONS

This study provided an integrated practice for developing reference standard to assess oncopanels in detecting somatic mutations and quantitatively revealed the source of detection errors. It will promote optimization, validation, and quality control among laboratories with potential applicability in clinical use.

miRMut: Annotation of mutations in miRNA genes from human whole-exome or whole-genome sequencing

Here, we present the miRMut protocol to annotate mutations found in miRNA genes based on whole-exome sequencing (WES) or whole-genome sequencing (WGS) results. The pipeline assigns mutation characteristics, including miRNA gene IDs (miRBase and MirGeneDB), mutation localization within the miRNA precursor structure, potential RNA-binding motif disruption, the ascription of mutation according to Human Genome Variation Society (HGVS) nomenclature, and miRNA gene characteristics, such as miRNA gene confidence and miRNA arm balance. The pipeline includes creating tabular and graphical summaries. For complete details on the use and execution of this protocol, please refer to Urbanek-Trzeciak et al. (2020).

Towards accurate and reliable resolution of structural variants for clinical diagnosis

Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts.

Whole-exome sequencing analysis of a Japanese patient with hyperinsulinemia and liver dysfunction

Hyperinsulinemia is often observed in obese subjects because of insulin resistance, but it may occur in nonobese subjects with unknown etiology. A 72-year-old man was admitted to our hospital for the examination of hyperinsulinemia, reactive hypoglycemia, and liver dysfunction. The patient’s body mass index was 23.7 kg/m², but he had an elevated visceral fat area (125 cm²). His laboratory data showed mildly elevated liver enzymes, whereas plasma fasting glucose and serum insulin levels were 91 mg/dL and 52.3 μU/mL, respectively. In a 75-g oral glucose tolerance test, the serum insulin level reached the highest value of 1124 μU/mL at 180 minutes. There was no obvious etiology except for mild liver steatosis shown by liver biopsy. We suspected genetic abnormalities related to hyperinsulinemia. We performed whole-exome sequencing (WES) analyses and identified a heterozygous nonsense variant p.R924X in the insulin receptor (INSR) gene, a novel heterozygous missense variant p.V416M in the AKT1 gene, and a novel hemizygous missense variant p.R310Q in the PHKA2 gene, which is the causative gene of hepatic injury as glycogen storage disease type IX. It was speculated that the INSR gene variant, in addition to visceral fat accumulation, was the main cause of hyperinsulinemia and reactive hypoglycemia, and the remaining 2 variants were also partly responsible for hyperinsulinemia. WES analysis revealed candidate gene variants of hyperinsulinemia and hepatic-type glycogenosis. Thus, WES analysis may be a useful tool for clarifying the etiology when unexplained genetic pathophysiological conditions are suspected.

Comparison of somatic mutation landscapes in Chinese versus European breast cancer patients

Recent genomic studies suggest that Asian breast cancer (BC) may have distinct somatic features; however, most comparisons of BC genomic features across populations did not account for differences in age, subtype, and sequencing methods. In this study, we analyzed whole-exome sequencing (WES) data to characterize somatic copy number alterations (SCNAs) and mutation profiles in 98 Hong Kong BC (HKBC) patients and compared with those from The Cancer Genome Atlas of European ancestry (TCGA-EA, N = 686), which had similar distributions of age at diagnosis and PAM50 subtypes as in HKBC. We developed a two-sample Poisson model to compare driver gene selection pressure, which reflects the effect sizes of cancer driver genes, while accounting for differences in sample size, sequencing platforms, depths, and mutation calling methods. We found that somatic mutation and SCNA profiles were overall very similar between HKBC and TCGA-EA. The selection pressure for small insertions and deletions (indels) in GATA3 (false discovery rate (FDR) corrected p < 0.01) and single-nucleotide variants (SNVs) in TP53 (nominal p = 0.02, FDR corrected p = 0.28) was lower in HKBC than in TCGA-EA. Among the 13 signatures of single-base substitutions (SBS) that are common in BC, we found a suggestively higher contribution of SBS18 and a lower contribution of SBS1 in HKBC than in TCGA-EA, while the two APOBEC-induced signatures showed similar prevalence. Our results suggest that the genomic landscape of BC was largely very similar between HKBC and TCGA-EA, despite suggestive differences in some driver genes and mutational signatures that warrant future investigations in large and diverse Asian populations.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

BACKGROUND

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data.

RESULTS

In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance.

CONCLUSIONS

The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

BACKGROUND

Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.

RESULTS

To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×.

CONCLUSIONS

Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.