Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data

Benchmarking whole exome sequencing in the German network for personalized medicine

INTRODUCTION

Whole Exome Sequencing (WES) has emerged as an efficient tool in clinical cancer diagnostics to broaden the scope from panel-based diagnostics to screening of all genes and enabling robust determination of complex biomarkers in a single analysis.

METHODS

To assess concordance, six formalin-fixed paraffin-embedded (FFPE) tissue specimens and four commercial reference standards were analyzed by WES as matched tumor-normal DNA at 21 NGS centers in Germany, each employing local wet-lab and bioinformatics. Somatic and germline variants, copy-number alterations (CNAs), and complex biomarkers were investigated. Somatic variant calling was performed in 494 diagnostically relevant cancer genes. The raw data were collected and re-analyzed with a central bioinformatic pipeline to separate wet- and dry-lab variability.

RESULTS

The mean positive percentage agreement (PPA) of somatic variant calling was 76 % while the positive predictive value (PPV) was 89 % in relation to a consensus list of variants found by at least five centers. Variant filtering was identified as the main cause for divergent variant calls. Adjusting filter criteria and re-analysis increased the PPA to 88 % for all and 97 % for the clinically relevant variants. CNA calls were concordant for 82 % of genomic regions. Homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) status were concordant for 94 %, 93 %, and 93 % of calls, respectively. Variability of CNAs and complex biomarkers did not decrease considerably after harmonization of the bioinformatic processing and was hence attributed mainly to wet-lab differences.

CONCLUSION

Continuous optimization of bioinformatic workflows and participating in round robin tests are recommended.

Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES)

OBJECTIVES

Tumor mutational burden (TMB) is a significant biomarker for predicting immune checkpoint inhibitor response, but the clinical performance of whole-exome sequencing (WES)-based TMB estimation has received less attention compared to panel-based methods. This study aimed to assess the reliability and comparability of WES-based TMB analysis among laboratories under routine testing conditions.

METHODS

A multicenter study was conducted involving 24 laboratories in China using in silico reference data sets. The accuracy and comparability of TMB estimation were evaluated using matched tumor-normal data sets. Factors such as accuracy of variant calls, limit of detection (LOD) of WES test, size of regions of interest (ROIs) used for TMB calculation, and TMB cutoff points were analyzed.

RESULTS

The laboratories consistently underestimated the expected TMB scores in matched tumor-normal samples, with only 50% falling within the ±30% TMB interval. Samples with low TMB score (<2.5) received the consensus interpretation. Accuracy of variant calls, LOD of the WES test, ROI, and TMB cutoff points were important factors causing interlaboratory deviations.

CONCLUSIONS

This study highlights real-world challenges in WES-based TMB analysis that need to be improved and optimized. This research will aid in the selection of more reasonable analytical procedures to minimize potential methodologic biases in estimating TMB in clinical exome sequencing tests. Harmonizing TMB estimation in clinical testing conditions is crucial for accurately evaluating patients' response to immunotherapy.

Genomic Insights into Idiopathic Granulomatous Mastitis through Whole-Exome Sequencing: A Case Report of Eight Patients

Idiopathic granulomatous mastitis (IGM) is a rare condition characterised by chronic inflammation and granuloma formation in the breast. The aetiology of IGM is unclear. By focusing on the protein-coding regions of the genome, where most disease-related mutations often occur, whole-exome sequencing (WES) is a powerful approach for investigating rare and complex conditions, like IGM. We report WES results on paired blood and tissue samples from eight IGM patients. Samples were processed using standard genomic protocols. Somatic variants were called with two analytical pipelines: nf-core/sarek with Strelka2 and GATK4 with Mutect2. Our WES study of eight patients did not find evidence supporting a clear genetic component. The discrepancies between variant calling algorithms, along with the considerable genetic heterogeneity observed amongst the eight IGM cases, indicate that common genetic drivers are not readily identifiable. With only three genes, CHIT1, CEP170, and CTR9, recurrently altering in multiple cases, the genetic basis of IGM remains uncertain. The absence of validation for somatic variants by Sanger sequencing raises further questions about the role of genetic mutations in the disease. Other potential contributors to the disease should be explored.

Recommendations for Tumor Mutational Burden Assay Validation and Reporting: A Joint Consensus Recommendation of the Association for Molecular Pathology, College of American Pathologists, and Society for Immunotherapy of Cancer

Tumor mutational burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in several tumor types. Several laboratories offer TMB testing, but there is significant variation in how TMB is calculated, reported, and interpreted among laboratories. TMB standardization efforts are underway, but no published guidance for TMB validation and reporting is currently available. Recognizing the current challenges of clinical TMB testing, the Association for Molecular Pathology convened a multidisciplinary collaborative working group with representation from the American Society of Clinical Oncology, the College of American Pathologists, and the Society for the Immunotherapy of Cancer to review the laboratory practices surrounding TMB and develop recommendations for the analytical validation and reporting of TMB testing based on survey data, literature review, and expert consensus. These recommendations encompass pre-analytical, analytical, and postanalytical factors of TMB analysis, and they emphasize the relevance of comprehensive methodological descriptions to allow comparability between assays.

A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

Advancing bladder cancer management: development of a prognostic model and personalized therapy

Background

Bladder cancer (BLCA) was recognized as a significant public health challenge due to its high incidence and mortality rates. The influence of molecular subtypes on treatment outcomes was well-acknowledged, necessitating further exploration of their characterization and application. This study was aimed at enhancing the understanding of BLCA by mapping its molecular heterogeneity and developing a robust prognostic model using single-cell and bulk RNA sequencing data. Additionally, immunological characteristics and personalized treatment strategies were investigated through the risk score.

Methods

Single-cell RNA sequencing (scRNA-seq) data from GSE135337 and bulk RNA-seq data from several sources, including GSE13507, GSE31684, GSE32894, GSE69795, and TCGA-BLCA, were utilized. Molecular subtypes, particularly the basal-squamous (Ba/Sq) subtype associated with poor prognosis, were identified. A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype. this model was validated across internal and external datasets to ensure predictive accuracy. High- and low-risk groups based on the risk score derived from TCGA-BLCA data were analyzed to examine their immune-related molecular profiles and treatment responses.

Results

Six molecular subtypes were identified, with the Ba/Sq subtype being consistently associated with poor prognosis. The prognostic model, based on basal-squamous subtype-related genes (BSSRGs), was shown to have strong predictive performance across diverse clinical settings with AUC values at 1, 3, and 5 years indicating robust predictability in training, testing, and entire datasets. Analysis of the different risk groups revealed distinct immune infiltration and microenvironments. Generally higher tumor mutation burden (TMB) scores and lower tumor immune dysfunction and exclusion (TIDE) scores were exhibited by the low-risk group, suggesting varied potentials for systemic drug response between the groups. Finally, significant differences in potential systemic drug response rates were also observed between risk groups.

Conclusions

The study introduced and validated a new prognostic model for BLCA based on BSSRGs, which was proven effective in prognosis prediction. The potential for personalized therapy, optimized by patient stratification and immune profiling, was highlighted by our risk score, aiming to improve treatment efficacy. This approach was promised to offer significant advancements in managing BLCA, tailoring treatments based on detailed molecular and immunological insights.

DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin embedded tissue with high accuracy

Formalin-fixed, paraffin-embedded (FFPE) tissue specimens are routinely used in pathological diagnosis, but their large number of artifactual mutations complicate the evaluation of companion diagnostics and analysis of next-generation sequencing data. Identification of variants with low allele frequencies is challenging because existing FFPE filtering tools label all low-frequency variants as artifacts. To address this problem, we aimed to develop DEEPOMICS FFPE, an AI model that can classify a true variant from an artifact. Paired whole exome sequencing data from fresh frozen and FFPE samples from 24 tumors were obtained from public sources and used as training and validation sets at a ratio of 7:3. A deep neural network model with three hidden layers was trained with input features using outputs of the MuTect2 caller. Contributing features were identified using the SHapley Additive exPlanations algorithm and optimized based on training results. The performance of the final model (DEEPOMICS FFPE) was compared with those of existing models (MuTect filter, FFPolish, and SOBDetector) by using well-defined test datasets. We found 41 discriminating properties for FFPE artifacts. Optimization of property quantification improved the model performance. DEEPOMICS FFPE removed 99.6% of artifacts while maintaining 87.1% of true variants, with an F1-score of 88.3 in the entire dataset not used for training, which is significantly higher than those of existing tools. Its performance was maintained even for low-allele-fraction variants with a specificity of 0.995, suggesting that it can be used to identify subclonal variants. Different from existing methods, DEEPOMICS FFPE identified most of the sequencing artifacts in the FFPE samples while retaining more of true variants, including those of low allele frequencies. The newly developed tool DEEPOMICS FFPE may be useful in designing capture panels for personalized circulating tumor DNA assay and identifying candidate neoepitopes for personalized vaccine design. DEEPOMICS FFPE is freely available on the web ( http://deepomics.co.kr/ffpe ) for research.

Integration of single-cell and bulk RNA-sequencing to analyze the heterogeneity of hepatocellular carcinoma and establish a prognostic model

BACKGROUND

The highly heterogeneous nature of hepatocellular carcinoma (HCC) results in different responses and prognoses to the same treatment in patients with similar clinical stages.

AIMS

Thus, it is imperative to investigate the association between HCC tumor heterogeneity and treatment response and prognosis.

METHODS AND RESULTS

At first, we downloaded scRNA-seq, bulk RNA-seq, and clinical data from TCGA and GEO databases. We conducted quality control, normalization using SCTransform, dimensionality reduction using PCA, batch effect removal using Harmony, dimensionality reduction using UMAP, and cell annotation-based marker genes on the scRNA-seq data. We recognized tumor cells, identified tumor-related genes (TRGs), and performed cell communication analysis. Next, we developed a prognostic model using univariable Cox, LASSO, and multivariate Cox analyses. The signature was evaluated using survival analysis, ROC curves, C-index, and nomogram. Last, we studied the predictability of the signature in terms of prognosis and immunotherapeutic response for HCC, assessed a variety of drugs for clinical treatment, and used the qRT-PCR analysis to validate the mRNA expression levels of prognostic TRGs.

CONCLUSION

To conclude, this study expounded upon the influence of tumor cell heterogeneity on the prediction of treatment outcomes and prognosis in HCC. This, in turn, enhances the predictive ability of the TNM staging system and furnishes novel perspectives on the prognostic assessment and therapy of HCC.

Performance analysis of conventional and AI-based variant callers using short and long reads

BACKGROUND

The accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use.

RESULTS

In this study, we evaluated five of the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies (Illumina, PacBio HiFi, and ONT) for the same set of samples from the Genome In A Bottle project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons.

CONCLUSION

This study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

Oligodontia in the Clinical Spectrum of Syndromes: A Systematic Review

The aim of this systematic review was to describe the clinical and genetic features of syndromes showing oligodontia as a sign. The review was performed according to the PRISMA 2020 checklist guidelines, and the search was conducted using PubMed, Scopus, Lilacs, Web of science, Livivo, and EMBASE and supplemented by a gray literature search on Google Scholar and ProQuest, applying key terms relevant to the research questions. The systematic review identified 47 types of syndromes in 83 studies, and the most common was hypohidrotic ectodermal dysplasia, which was reported in 24 patients in 22 studies. Other common syndromes that reported oligodontia included Axenfeld-Rieger syndrome, Witkop's syndrome, Ellis-van Creveld syndrome, blepharocheilodontic syndrome, and oculofaciocardiodental syndrome. The X-linked mode of inheritance was the most reported (n = 13 studies), followed by the autosomal dominant (n = 13 studies). The review describes the main syndromes that may have oligodontia as a clinical sign and reinforces the need for orodental-facial examining for adequate diagnosis and treatment of the affected patients. Molecular analysis in order to better understand the occurrence of oligodontia is imperative.

Evaluating the performance of low-frequency variant calling tools for the detection of variants from short-read deep sequencing data

Detection of low-frequency variants with high accuracy plays an important role in biomedical research and clinical practice. However, it is challenging to do so with next-generation sequencing (NGS) approaches due to the high error rates of NGS. To accurately distinguish low-level true variants from these errors, many statistical variants calling tools for calling low-frequency variants have been proposed, but a systematic performance comparison of these tools has not yet been performed. Here, we evaluated four raw-reads-based variant callers (SiNVICT, outLyzer, Pisces, and LoFreq) and four UMI-based variant callers (DeepSNVMiner, MAGERI, smCounter2, and UMI-VarCal) considering their capability to call single nucleotide variants (SNVs) with allelic frequency as low as 0.025% in deep sequencing data. We analyzed a total of 54 simulated data with various sequencing depths and variant allele frequencies (VAFs), two reference data, and Horizon Tru-Q sample data. The results showed that the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers regarding detection limit. Sequencing depth had almost no effect on the UMI-based callers but significantly influenced on the raw-reads-based callers. Regardless of the sequencing depth, MAGERI showed the fastest analysis, while smCounter2 consistently took the longest to finish the variant calling process. Overall, DeepSNVMiner and UMI-VarCal performed the best with considerably good sensitivity and precision of 88%, 100%, and 84%, 100%, respectively. In conclusion, the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers in terms of sensitivity and precision. We recommend using DeepSNVMiner and UMI-VarCal for low-frequency variant detection. The results provide important information regarding future directions for reliable low-frequency variant detection and algorithm development, which is critical in genetics-based medical research and clinical applications.

Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients

A growing number of druggable targets and national initiatives for precision oncology necessitate broad genomic profiling for many cancer patients. Whole exome sequencing (WES) offers unbiased analysis of the entire coding sequence, segmentation-based detection of copy number alterations (CNAs), and accurate determination of complex biomarkers including tumor mutational burden (TMB), homologous recombination repair deficiency (HRD), and microsatellite instability (MSI). To assess the inter-institution variability of clinical WES, we performed a comparative pilot study between German Centers of Personalized Medicine (ZPMs) from five participating institutions. Tumor and matched normal DNA from 30 patients were analyzed using custom sequencing protocols and bioinformatic pipelines. Calling of somatic variants was highly concordant with a positive percentage agreement (PPA) between 91 and 95% and a positive predictive value (PPV) between 82 and 95% compared with a three-institution consensus and full agreement for 16 of 17 druggable targets. Explanations for deviations included low VAF or coverage, differing annotations, and different filter protocols. CNAs showed overall agreement in 76% for the genomic sequence with high wet-lab variability. Complex biomarkers correlated strongly between institutions (HRD: 0.79-1, TMB: 0.97-0.99) and all institutions agreed on microsatellite instability. This study will contribute to the development of quality control frameworks for comprehensive genomic profiling and sheds light onto parameters that require stringent standardization.

Technological and computational approaches to detect somatic mosaicism in epilepsy

Lesional epilepsy is a common and severe disease commonly associated with malformations of cortical development, including focal cortical dysplasia and hemimegalencephaly. Recent advances in sequencing and variant calling technologies have identified several genetic causes, including both short/single nucleotide and structural somatic variation. In this review, we aim to provide a comprehensive overview of the methodological advancements in this field while highlighting the unresolved technological and computational challenges that persist, including ultra-low variant allele fractions in bulk tissue, low availability of paired control samples, spatial variability of mutational burden within the lesion, and the issue of false-positive calls and validation procedures. Information from genetic testing in focal epilepsy may be integrated into clinical care to inform histopathological diagnosis, postoperative prognosis, and candidate precision therapies.

Construction of a telomere-related gene signature to predict prognosis and immune landscape for glioma

Background

Glioma is one of the commonest malignant tumors of the brain. However, glioma present with a poor clinical prognosis. Therefore, specific detection markers and therapeutic targets need to be explored as a way to promote the survival rate of BC patients. Therefore, we need to search for quality immune checkpoints to support the efficacy of immunotherapy for glioma.

Methods

We first recognized differentially expressed telomere-related genes (TRGs) and accordingly developed a risk model by univariate and multivariate Cox analysis. The accuracy of the model is then verified. We evaluated the variations in immune function and looked at the expression levels of immune checkpoint genes. Finally, to assess the anti-tumor medications often used in the clinical treatment of glioma, we computed the half inhibitory concentration of pharmaceuticals.

Results

We finally identified nine TRGs and built a risk model. Through the validation of the model, we found good agreement between the predicted and observed values. Then, we found 633 differentially expressed genes between various risk groups to identify the various molecular pathways between different groups. The enrichment of CD4+ T cells, CD8+ T cells, fibroblasts, endothelial cells, macrophages M0, M1, and M2, mast cells, myeloid dendritic cells, and neutrophils was favorably correlated with the risk score, but the enrichment of B cells and NK cells was negatively correlated with the risk score. The expression of several immune checkpoint-related genes differed significantly across the risk groups. Finally, in order to create individualized treatment plans for diverse individuals, we searched for numerous chemotherapeutic medications for patients in various groups.

Conclusion

The findings of this research provide evidence that TRGs may predict a patient's prognosis for glioma, assist in identifying efficient targets for glioma immunotherapy, and provide a foundation for an efficient, customized approach to treating glioma patients.

Machine learning integrations develop an antigen-presenting-cells and T-Cells-Infiltration derived LncRNA signature for improving clinical outcomes in hepatocellular carcinoma

As a highly heterogeneous cancer, the prognostic stratification and personalized management of hepatocellular carcinoma (HCC) are still challenging. Recently, Antigen-presenting-cells (APCs) and T-cells-infiltration (TCI) have been reported to be implicated in modifying immunology in HCC. Nevertheless, the clinical value of APCs and TCI-related long non-coding RNAs (LncRNAs) in the clinical outcomes and precision treatment of HCC is still obscure. In this study, a total of 805 HCC patients were enrolled from three public datasets and an external clinical cohort. 5 machine learning (ML) algorithms were transformed into 15 kinds of ML integrations, which was used to construct the preliminary APC-TCI related LncRNA signature (ATLS). According to the criterion with the largest average C-index in the validation sets, the optimal ML integration was selected to construct the optimal ATLS. By incorporating several vital clinical characteristics and molecular features for comparison, ATLS was demonstrated to have a relatively more significantly superior predictive capacity. Additionally, it was found that the patients with high ATLS score had dismal prognosis, relatively high frequency of tumor mutation, remarkable immune activation, high expression levels of T cell proliferation regulators and anti-PD-L1 response as well as extraordinary sensitivity to Oxaliplatin/Fluorouracil/Lenvatinib. In conclusion, ATLS may serve as a robust and powerful biomarker for improving the clinical outcomes and precision treatment of HCC.

Four-Year Laboratory Performance of the First College of American Pathologists In Silico Next-Generation Sequencing Bioinformatics Proficiency Testing Surveys

CONTEXT.—

In 2016, the College of American Pathologists (CAP) launched the first next-generation sequencing (NGS) in silico bioinformatics proficiency testing survey to evaluate the performance of clinical laboratory bioinformatics pipelines for the detection of oncology-associated variants at varying allele fractions. This survey focused on 2 commonly used oncology panels, the Illumina TruSeq Amplicon Cancer Panel and the Thermo Fisher Ion AmpliSeq Cancer Hotspot v2 Panel.

OBJECTIVE.—

To review the analytical performance of laboratories participating in the CAP NGS bioinformatics (NGSB) surveys, comprising NGSB1 for Illumina users and NGSB2 for Thermo Fisher Ion Torrent users, between 2016 and 2019.

DESIGN.—

Responses from 78 laboratories were analyzed for accuracy and associated performance characteristics.

RESULTS.—

The analytical sensitivity was 90.0% (1901 of 2112) for laboratories using the Illumina platform and 94.8% (2153 of 2272) for Thermo Fisher Ion Torrent users. Variant type and variant allele fraction were significantly associated with performance. False-negative results were seen mostly for multi-nucleotide variants and variants engineered at variant allele fractions of less than 25%. Analytical specificity for all participating laboratories was 99.8% (9303 of 9320). There was no statistically significant association between deletion-insertion length and detection rate.

CONCLUSIONS.—

These results demonstrated high analytical sensitivity and specificity, supporting the feasibility and utility of using in silico mutagenized NGS data sets as a supplemental challenge to CAP surveys for oncology-associated variants based on physical samples. This program demonstrates the opportunity and challenges that can guide future surveys inclusive of customized in silico programs.

Challenges in neoantigen-directed therapeutics

A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.

Detection and evolutionary dynamics of somatic FAS variants in autoimmune lymphoproliferative syndrome: Diagnostic implications

Autoimmune lymphoproliferative syndrome (ALPS) is a rare primary immune disorder characterized by impaired apoptotic homeostasis. The clinical characteristics include lymphoproliferation, autoimmunity (mainly cytopenia), and an increased risk of lymphoma. A distinctive biological feature is accumulation (&gt;2.5%) of an abnormal cell subset composed of TCRαβ+ CD4-CD8- T cells (DNTs). The most common genetic causes of ALPS are monoallelic pathogenic variants in the FAS gene followed by somatic FAS variants, mainly restricted to DNTs. Identification of somatic FAS variants has been typically addressed by Sanger sequencing in isolated DNTs. However, this approach can be costly and technically challenging, and may not be successful in patients with normal DNT counts receiving immunosuppressive treatment. In this study, we identified a novel somatic mutation in FAS (c.718_719insGTCG) by Sanger sequencing on purified CD3+ cells. We then followed the evolutionary dynamics of the variant along time with an NGS-based approach involving deep amplicon sequencing (DAS) at high coverage (20,000-30,000x). Over five years of clinical follow-up, we obtained six blood samples for molecular study from the pre-treatment (DNTs&gt;7%) and treatment (DNTs&lt;2%) periods. DAS enabled detection of the somatic variant in all samples, even the one obtained after five years of immunosuppressive treatment (DNTs: 0.89%). The variant allele frequency (VAF) range was 4%-5% in pre-treatment samples and &lt;1.5% in treatment samples, and there was a strong positive correlation between DNT counts and VAF (Pearson’s R: 0.98, p=0.0003). We then explored whether the same approach could be used in a discovery setting. In the last follow-up sample (DNT: 0.89%) we performed somatic variant calling on the FAS exon 9 DAS data from whole blood and purified CD3+ cells using VarScan 2. The c.718_719insGTCG variant was identified in both samples and showed the highest VAF (0.67% blood, 1.58% CD3+ cells) among &gt;400 variants called. In summary, our study illustrates the evolutionary dynamics of a somatic FAS mutation before and during immunosuppressive treatment. The results show that pathogenic somatic FAS variants can be identified with the use of DAS in whole blood of ALPS patients regardless of their DNT counts.

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning

MOTIVATION

Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-exome sequencing is considered the gold standard for determining TMB, it is difficult to be applied in clinical practice due to its high cost. There are also a few DNA panel-based methods to estimate TMB; however, their detection cost is also high, and the associated wet-lab experiments usually take days, which emphasize the need for faster and cheaper alternatives.

RESULTS

In this study, we propose a multi-modal deep learning model based on a residual network (ResNet) and multi-modal compact bilinear pooling to predict TMB status (i.e. TMB high (TMB_H) or TMB low(TMB_L)) directly from histopathological images and clinical data. We applied the model to CRC data from The Cancer Genome Atlas and compared it with four other popular methods, namely, ResNet18, ResNet50, VGG19 and AlexNet. We tested different TMB thresholds, namely, percentiles of 10%, 14.3%, 15%, 16.3%, 20%, 30% and 50%, to differentiate TMB_H and TMB_L.For the percentile of 14.3% (i.e. TMB value 20) and ResNet18, our model achieved an area under the receiver operating characteristic curve of 0.817 after 5-fold cross-validation, which was better than that of other compared models. In addition, we also found that TMB values were significantly associated with the tumor stage and N and M stages. Our study shows that deep learning models can predict TMB status from histopathological images and clinical information only, which is worth clinical application.

Towards an accurate and robust analysis pipeline for somatic mutation calling

Accurate and robust somatic mutation detection is essential for cancer treatment, diagnostics and research. Various analysis pipelines give different results and thus should be systematically evaluated. In this study, we benchmarked 5 commonly-used somatic mutation calling pipelines (VarScan, VarDictJava, Mutect2, Strelka2 and FANSe) for their precision, recall and speed, using standard benchmarking datasets based on a series of real-world whole-exome sequencing datasets. All the 5 pipelines showed very high precision in all cases, and high recall rate in mutation rates higher than 10%. However, for the low frequency mutations, these pipelines showed large difference. FANSe showed the highest accuracy (especially the sensitivity) in all cases, and VarScan and VarDictJava outperformed Mutect2 and Strelka2 in low frequency mutations at all sequencing depths. The flaws in filter was the major cause of the low sensitivity of the four pipelines other than FANSe. Concerning the speed, FANSe pipeline was 8.8∼19x faster than the other pipelines. Our benchmarking results demonstrated performance of the somatic calling pipelines and provided a reference for a proper choice of such pipelines in cancer applications.

The comparison of cancer gene mutation frequencies in Chinese and U.S. patient populations

Knowing the mutation frequency of cancer genes in China is crucial for reducing the global health burden. We integrate the tumor epidemiological statistics with cancer gene mutation rates identified in 11,948 cancer patients to determine their weighted proportions within a Chinese cancer patient cohort. TP53 (51.4%), LRP1B (13.4%), PIK3CA (11.6%), KRAS (11.1%), EGFR (10.6%), and APC (10.5%) are identified as the top mutated cancer genes in China. Additionally, 18 common cancer types from both China and U.S. cohorts are analyzed and classified into three patterns principally based upon TP53 mutation rates: TP53-Top, TP53-Plus, and Non-TP53. Next, corresponding similarities and prominent differences are identified upon comparing the mutational profiles from both cohorts. Finally, the potential population-specific and environmental risk factors underlying the disparities in cancer gene mutation rates between the U.S. and China are analyzed. Here, we show and compare the mutation rates of cancer genes in Chinese and U.S. population cohorts, for a better understanding of the associated etiological and epidemiological factors, which are important for cancer prevention and therapy.

Development and validation of a novel necroptosis-related score to improve the outcomes of clear cell renal cell carcinoma

Necroptosis has been indicated as a key regulator of tumor progression. However, the prognostic regulatory role of necroptosis in clear cell renal cell carcinoma (ccRCC) needs to be further investigated. In this study, necroptosis-related subtypes were identified by mining the public cohort (n = 530) obtained from The Cancer Genome Atlas. By applying Principal Component Analysis (PCA), the necroptosis-related scores (N-Score) were developed to assess the prognosis procession of ccRCC. The results were further validated by an external clinical cohort (n = 116) obtained from the First Affiliated Hospital of Wenzhou Medical University. It has been found that N-Score could precisely distinguish the prognostic outcomes of patients as an independent risk factor (Hazard ratio = 4.990, 95% confidence interval (CI) = 2.007–12.403, p < 0.001). In addition, changes in N-Score were associated with differences in tumor mutational burden as well as immune infiltration characterization. Moreover, higher N-Scores were also correlated significantly molecular drug sensitivity and stronger immune checkpoint activity. Notably, the prognosis of ccRCC could be effectively guided by combining the N-Scores and external clinical indicators. In conclusion, N-Scores could be served as a robust and effective biomarker to improve the prognosis outcomes and targeted therapy of ccRCC.

Bias and inconsistency in the estimation of tumour mutation burden

BACKGROUND

Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB.

METHODS

We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth.

RESULTS

We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity.

CONCLUSION

Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

Postzygotic mutations and where to find them - Recent advances and future implications in the field of non-neoplastic somatic mosaicism

The technological progress of massively parallel sequencing (MPS) has triggered a remarkable development in the research on postzygotic mutations. Although the overwhelming majority of studies in the field focus on oncogenesis, non-neoplastic diseases are attracting more and more attention. The aim of this review was to summarize some of the most recent findings in the field of somatic mosaicism in diseases other than neoplastic events. We discuss the abundance and role of postzygotic mutations, with a special emphasis on disorders which occur only in a mosaic form (obligatory mosaic diseases; OMDs). Based on the list of OMDs compiled from the published literature and three databases (OMIM, Orphanet and MosaicBase), we demonstrate the prevalence of cancer-related genes across OMDs and suggest other sources to further explore OMDs and OMD-related genes. Additionally, we comment on some practical aspects related to mosaic diseases, such as approaches to tissue sampling, the MPS coverage required to detect variants at a very low frequency, as well as on bioinformatic and molecular tools dedicated to detect somatic mutations in MPS data.

RDscan: A New Method for Improving Germline and Somatic Variant Calling Based on Read Depth Distribution

Several tools have been developed for calling variants from next-generation sequencing (NGS) data. Although they are generally accurate and reliable, most of them have room for improvement, especially regarding calling variants in datasets with low read depth. In addition, the somatic variants predicted by several somatic variant callers tend to have very low concordance rates. In this study, we developed a new method (RDscan) for improving germline and somatic variant calling in NGS data. RDscan removes misaligned reads, repositions reads, and calculates RDscore based on the read depth distribution. With RDscore, RDscan improves the precision of variant callers by removing false-positive variant calls. When we tested our new tool using the latest variant calling algorithms and data from the 1000 Genomes Project and Illumina's public datasets, accuracy was improved for most of the algorithms. After screening variants with RDscan, calling accuracies increased for germline variants in 11 of 12 cases and for somatic variants in 21 of 24 cases. RDscan is simple to use and can effectively remove false-positive variants while maintaining a low computation load. Therefore, RDscan, along with existing variant callers, should contribute to improvements in genome analysis.

Benchmarking Low-Frequency Variant Calling With Long-Read Data on Mitochondrial DNA

Background: Sequencing quality has improved over the last decade for long-reads, allowing for more accurate detection of somatic low-frequency variants. In this study, we used mixtures of mitochondrial samples with different haplogroups (i.e., a specific set of mitochondrial variants) to investigate the applicability of nanopore sequencing for low-frequency single nucleotide variant detection.

Methods: We investigated the impact of base-calling, alignment/mapping, quality control steps, and variant calling by comparing the results to a previously derived short-read gold standard generated on the Illumina NextSeq. For nanopore sequencing, six mixtures of four different haplotypes were prepared, allowing us to reliably check for expected variants at the predefined 5%, 2%, and 1% mixture levels. We used two different versions of Guppy for base-calling, two aligners (i.e., Minimap2 and Ngmlr), and three variant callers (i.e., Mutserve2, Freebayes, and Nanopanel2) to compare low-frequency variants. We used F₁ score measurements to assess the performance of variant calling.

Results: We observed a mean read length of 11 kb and a mean overall read quality of 15. Ngmlr showed not only higher F₁ scores but also higher allele frequencies (AF) of false-positive calls across the mixtures (mean F₁ score = 0.83; false-positive allele frequencies < 0.17) compared to Minimap2 (mean F₁ score = 0.82; false-positive AF < 0.06). Mutserve2 had the highest F₁ scores (5% level: F₁ score >0.99, 2% level: F₁ score >0.54, and 1% level: F₁ score >0.70) across all callers and mixture levels.

Conclusion: We here present the benchmarking for low-frequency variant calling with nanopore sequencing by identifying current limitations.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Somatic Mutations Detected in Parkinson Disease Could Affect Genes With a Role in Synaptic and Neuronal Processes

The role of somatic mutations in complex diseases, including neurodevelopmental and neurodegenerative disorders, is becoming increasingly clear. However, to date, no study has shown their relation to Parkinson disease’s phenotype. To explore the relevance of embryonic somatic mutations in sporadic Parkinson disease, we performed whole-exome sequencing in blood and four brain regions of ten patients. We identified 59 candidate somatic single nucleotide variants (sSNVs) through sensitive calling and a careful filtering strategy (COSMOS). We validated 27 of them with amplicon-based ultra-deep sequencing, with a 70% validation rate for the highest-confidence variants. The identified sSNVs are in genes with synaptic functions that are co-expressed with genes previously associated with Parkinson disease. Most of the sSNVs were only called in blood but were also found in the brain tissues with ultra-deep amplicon sequencing, demonstrating the strength of multi-tissue sampling designs.

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.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Early Breast Cancer Evolution by Autosomal Broad Copy Number Alterations

The availability of comprehensive genomic datasets across patient populations enables the application of novel methods for reconstructing tumor evolution within individual patients. To this end, we propose studying autosomal broad copy number alterations (CNAs) as a framework to better understand early tumor evolution. We compared the broad CNAs and somatic mutations of patients with 1 to 10 autosomal broad CNAs against the full set of patients, using data from The Cancer Genome Atlas breast cancer project. We reveal here that the frequency of a chromosome arm obtaining a broad CNA and a genome acquiring somatic mutations changes as autosomal broad CNAs accumulate. Therefore, we propose that the number of autosomal broad CNAs is an important characteristic of breast tumors that needs to be taken into consideration when studying breast tumors. To investigate this idea more in-depth, we next studied the frequency that specific chromosome arms acquire broad CNAs in patients with 1 to 10 broad CNAs. With this process, we identified the broad CNAs that exhibit the fastest rates of accumulation across all patients. This finding suggests a likely order of occurrence of these alterations in patients, which is apparent when we consider a subset of patients with few broad CNAs. Here, we lay the foundation for future studies to build upon our findings and use autosomal broad CNAs as a method to monitor breast tumor progression in vivo to further our understanding of how early tumor evolution unfolds.

gcMECM: graph clustering of mutual exclusivity of cancer mutations

Background

Next-generation sequencing platforms allow us to sequence millions of small fragments of DNA simultaneously, revolutionizing cancer research. Sequence analysis has revealed that cancer driver genes operate across multiple intricate pathways and networks with mutations often occurring in a mutually exclusive pattern. Currently, low-frequency mutations are understudied as cancer-relevant genes, especially in the context of networks.

Results

Here we describe a tool, gcMECM, that enables us to visualize the functionality of mutually exclusive genes in the subnetworks derived from mutation associations, gene–gene interactions, and graph clustering. These subnetworks have revealed crucial biological components in the canonical pathway, especially those mutated at low frequency. Examining the subnetwork, and not just the impact of a single gene, significantly increases the statistical power of clinical analysis and enables us to build models to better predict how and why cancer develops.

Conclusions

gcMECM uses a computationally efficient and scalable algorithm to identify subnetworks in a canonical pathway with mutually exclusive mutation patterns and distinct biological functions.

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost

Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exomes for tumor samples only without the paired normal samples. To include these types of data for extensive studies on the process of tumorigenesis, it is necessary to develop an approach for identifying somatic mutations using tumor exome sequencing data only. In this study, we designed a machine learning approach using Deep Neural Network (DNN) and XGBoost to identify somatic mutations in tumor-only exome sequencing data and we integrated this into a pipeline called DNN-Boost. The XGBoost algorithm is used to extract the features from the results of variant callers and these features are then fed into the DNN model as input. The XGBoost algorithm resolves issues of missing values and overfitting. We evaluated our proposed model and compared its performance with other existing benchmark methods. We noted that the DNN-Boost classification model outperformed the benchmark method in classifying somatic mutations from paired tumor-normal exome data and tumor-only exome data.

Development and validation of an expanded targeted sequencing panel for non-invasive prenatal diagnosis of sporadic skeletal dysplasia

Background

Skeletal dysplasia (SD) is one of the most common inherited neonatal disorders worldwide, where the recurrent pathogenic mutations in the FGFR2, FGFR3, COL1A1, COL1A2 and COL2A1 genes are frequently reported in both non-lethal and lethal SD. The traditional prenatal diagnosis of SD using ultrasonography suffers from lower accuracy and performed at latter gestational stage. Therefore, it remains in desperate need of precise and accurate prenatal diagnosis of SD in early pregnancy. With the advancements of next-generation sequencing (NGS) technology and bioinformatics analysis, it is feasible to develop a NGS-based assay to detect genetic defects in association with SD in the early pregnancy.

Methods

An ampliseq-based targeted sequencing panel was designed to cover 87 recurrent hotspots reported in 11 common dominant SD and run on both Ion Proton and NextSeq550 instruments. Thirty-six cell-free and 23 genomic DNAs were used for assay developed. Spike-in DNA prepared from standard sample harboring known mutation and normal sample were also employed to validate the established SD workflow. Overall performances of coverage, uniformity, and on-target rate, and the detecting limitations on percentage of fetal fraction and read depth were evaluated.

Results

The established targeted-seq workflow enables a single-tube multiplex PCR for library construction and shows high amplification efficiency and robust reproducibility on both Ion Proton and NextSeq550 platforms. The workflow reaches 100% coverage and both uniformity and on-target rate are > 96%, indicating a high quality assay. Using spike-in DNA with different percentage of known FGFR3 mutation (c.1138 G > A), the targeted-seq workflow demonstrated the ability to detect low-frequency variant of 2.5% accurately. Finally, we obtained 100% sensitivity and 100% specificity in detecting target mutations using established SD panel.

Conclusions

An expanded panel for rapid and cost-effective genetic detection of SD has been developed. The established targeted-seq workflow shows high accuracy to detect both germline and low-frequency variants. In addition, the workflow is flexible to be conducted in the majority of the NGS instruments and ready for routine clinical application. Taken together, we believe the established panel provides a promising diagnostic or therapeutic strategy for prenatal genetic testing of SD in routine clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01063-1.

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.

Assessment of the gene mosaicism burden in blood and its implications for immune disorders

There are increasing evidences showing the contribution of somatic genetic variants to non-cancer diseases. However, their detection using massive parallel sequencing methods still has important limitations. In addition, the relative importance and dynamics of somatic variation in healthy tissues are not fully understood. We performed high-depth whole-exome sequencing in 16 samples from patients with a previously determined pathogenic somatic variant for a primary immunodeficiency and tested different variant callers detection ability. Subsequently, we explored the load of somatic variants in the whole blood of these individuals and validated it by amplicon-based deep sequencing. Variant callers allowing low frequency read thresholds were able to detect most of the variants, even at very low frequencies in the tissue. The genetic load of somatic coding variants detectable in whole blood is low, ranging from 1 to 2 variants in our dataset, except for one case with 17 variants compatible with clonal haematopoiesis under genetic drift. Because of the ability we demonstrated to detect this type of genetic variation, and its relevant role in disorders such as primary immunodeficiencies, we suggest considering this model of gene mosaicism in future genetic studies and considering revisiting previous massive parallel sequencing data in patients with negative results.

Detailed evaluation of cancer sequencing pipelines in different microenvironments and heterogeneity levels

The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.

Artificial Intelligence in Cancer Research and Precision Medicine

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.

Neoantigen prediction in human breast cancer using RNA sequencing data

Neoantigens have attracted attention as biomarkers or therapeutic targets. However, accurate prediction of neoantigens is still challenging, especially in terms of its accuracy and cost. Variant detection using RNA sequencing (RNA-seq) data has been reported to be a low-accuracy but cost-effective tool, but the feasibility of RNA-seq data for neoantigen prediction has not been fully examined. In the present study, we used whole-exome sequencing (WES) and RNA-seq data of tumor and matched normal samples from six breast cancer patients to evaluate the utility of RNA-seq data instead of WES data in variant calling to detect neoantigen candidates. Somatic variants were called in three protocols using: (i) tumor and normal WES data (DNA method, Dm); (ii) tumor and normal RNA-seq data (RNA method, Rm); and (iii) combination of tumor RNA-seq and normal WES data (Combination method, Cm). We found that the Rm had both high false-positive and high false-negative rates because this method depended greatly on the expression status of normal transcripts. When we compared the results of Dm with those of Cm, only 14% of the neoantigen candidates detected in Dm were identified in Cm, but the majority of the missed candidates lacked coverage or variant allele reads in the tumor RNA. In contrast, about 70% of the neoepitope candidates with higher expression and rich mutant transcripts could be detected in Cm. Our results showed that Cm could be an efficient and a cost-effective approach to predict highly expressed neoantigens in tumor samples.

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy

BACKGROUND

Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor rejection. Since it is impractical to experimentally assess all candidate neoepitopes prior to vaccination, developing accurate methods for predicting tumor-rejection mediating neoepitopes (TRMNs) is critical for enabling routine clinical use of cancer vaccines.

RESULTS

In this paper we introduce Positive-unlabeled Learning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based classifiers. PLATO generates a set of high confidence positive calls by applying a stringent filter to model-based predictions, then rescores remaining candidates by using positive-unlabeled learning. To achieve robust performance on clinical samples with large patient-to-patient variation, PLATO further integrates AutoML hyper-parameter tuning, classification threshold selection based on spies, and support for bootstrapping.

CONCLUSIONS

Experimental results on real datasets demonstrate that PLATO has improved performance compared to model-based approaches for two key steps in TRMN prediction, namely somatic variant calling from exome sequencing data and peptide identification from MS/MS data.

Improvement of Disease Resistance in Livestock: Application of Immunogenomics and CRISPR/Cas9 Technology

Disease occurrence adversely affects livestock production and animal welfare, and have an impact on both human health and public perception of food-animals production. Combined efforts from farmers, animal scientists, and veterinarians have been continuing to explore the effective disease control approaches for the production of safe animal-originated food. Implementing the immunogenomics, along with genome editing technology, has been considering as the key approach for safe food-animal production through the improvement of the host genetic resistance. Next-generation sequencing, as a cutting-edge technique, enables the production of high throughput transcriptomic and genomic profiles resulted from host-pathogen interactions. Immunogenomics combine the transcriptomic and genomic data that links to host resistance to disease, and predict the potential candidate genes and their genomic locations. Genome editing, which involves insertion, deletion, or modification of one or more genes in the DNA sequence, is advancing rapidly and may be poised to become a commercial reality faster than it has thought. The clustered regulatory interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) [CRISPR/Cas9] system has recently emerged as a powerful tool for genome editing in agricultural food production including livestock disease management. CRISPR/Cas9 mediated insertion of NRAMP1 gene for producing tuberculosis resistant cattle, and deletion of CD163 gene for producing porcine reproductive and respiratory syndrome (PRRS) resistant pigs are two groundbreaking applications of genome editing in livestock. In this review, we have highlighted the technological advances of livestock immunogenomics and the principles and scopes of application of CRISPR/Cas9-mediated targeted genome editing in animal breeding for disease resistance.

Highly accurate long-read HiFi sequencing data for five complex genomes

The PacBio^® HiFi sequencing method yields highly accurate long-read sequencing datasets with read lengths averaging 10–25 kb and accuracies greater than 99.5%. These accurate long reads can be used to improve results for complex applications such as single nucleotide and structural variant detection, genome assembly, assembly of difficult polyploid or highly repetitive genomes, and assembly of metagenomes. Currently, there is a need for sample data sets to both evaluate the benefits of these long accurate reads as well as for development of bioinformatic tools including genome assemblers, variant callers, and haplotyping algorithms. We present deep coverage HiFi datasets for five complex samples including the two inbred model genomes Mus musculus and Zea mays, as well as two complex genomes, octoploid Fragaria × ananassa and the diploid anuran Rana muscosa. Additionally, we release sequence data from a mock metagenome community. The datasets reported here can be used without restriction to develop new algorithms and explore complex genome structure and evolution. Data were generated on the PacBio Sequel II System.

Measurement(s)	DNA • genome • Metagenome
Technology Type(s)	DNA sequencing • PacBio Sequel System
Factor Type(s)	organism that had its genome sequenced
Sample Characteristic - Organism	Mus musculus • Rana muscosa • Fragaria x ananassa • Zea mays

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12855527

Comparative assessments of indel annotations in healthy and cancer genomes with next-generation sequencing data

BACKGROUND

Insertion and deletion (indel) is one of the major variation types in human genomes. Accurate annotation of indels is of paramount importance in genetic variation analysis and investigation of their roles in human diseases. Previous studies revealed a high number of false positives from existing indel calling methods, which limits downstream analyses of the effects of indels on both healthy and disease genomes. In this study, we evaluated seven commonly used general indel calling programs for germline indels and four somatic indel calling programs through comparative analysis to investigate their common features and differences and to explore ways to improve indel annotation accuracy.

METHODS

In our comparative analysis, we adopted a more stringent evaluation approach by considering both the indel positions and the indel types (insertion or deletion sequences) between the samples and the reference set. In addition, we applied an efficient way to use a benchmark for improved performance comparisons for the general indel calling programs RESULTS: We found that germline indels in healthy genomes derived by combining several indel calling tools could help remove a large number of false positive indels from individual programs without compromising the number of true positives. The performance comparisons of somatic indel calling programs are more complicated due to the lack of a reliable and comprehensive benchmark. Nevertheless our results revealed large variations among the programs and among cancer types.

CONCLUSIONS

While more accurate indel calling programs are needed, we found that the performance for germline indel annotations can be improved by combining the results from several programs. In addition, well-designed benchmarks for both germline and somatic indels are key in program development and evaluations.

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network

It is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistical or machine learning approaches have been developed to detect somatic small variants. However, they take into consideration only limited information about the reference and potential variant allele in both tumor and normal samples at a candidate somatic site. Also, they differ in how biological and technological noises are addressed. Hence, they are expected to produce divergent outputs. To overcome the drawbacks of existing somatic callers, we develop a deep learning-based tool called DeepSSV, which employs a convolutional neural network (CNN) model to learn increasingly abstract feature representations from the raw data in higher feature layers. DeepSSV creates a spatially oriented representation of read alignments around the candidate somatic sites adapted for the convolutional architecture, which enables it to expand to effectively gather scattered evidence. Moreover, DeepSSV incorporates the mapping information of both reference allele-supporting and variant allele-supporting reads in the tumor and normal samples at a genomic site that are readily available in the pileup format file. Together, the CNN model can process the whole alignment information. Such representational richness allows the model to capture the dependencies in the sequence and identify context-based sequencing artifacts. We fitted the model on ground truth somatic mutations and did benchmarking experiments on simulated and real tumors. The benchmarking results demonstrate that DeepSSV outperforms its state-of-the-art competitors in overall F1 score.

Best practices for variant calling in clinical sequencing

Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the “best practice” principles in this review should be relevant to clinical variant calling in the long term.

Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays

CONTEXT.—

Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories.

OBJECTIVE.—

To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay.

DATA SOURCES.—

This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation.

CONCLUSIONS.—

This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.