A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing

Pan-cancer discovery of somatic mutations from RNA sequencing data

Identification of somatic mutations (SMs) is essential for characterizing cancer genomes. While DNA-seq is the prevalent method for identifying SMs, RNA-seq provides an alternative strategy to discover tumor mutations in the transcribed genome. Here, we have developed a machine learning based pipeline to discover SMs based on RNA-seq data (designated as RNA-SMs). Subsequently, we have conducted a pan-cancer analysis to systematically identify RNA-SMs from over 8,000 tumors in The Cancer Genome Atlas (TCGA). In this way, we have identified over 105,000 novel SMs that had not been reported in previous TCGA studies. These novel SMs have significant clinical implications in designing targeted therapy for improved patient outcomes. Further, we have combined the SMs identified by both RNA-seq and DNA-seq analyses to depict an updated mutational landscape across 32 cancer types. This new online SM atlas, OncoDB ( https://oncodb.org ), offers a more complete view of gene mutations that underline the development and progression of various cancers.

Whole-Genome Sequencing Reveals Rare Off-Target Mutations in MC1R-Edited Pigs Generated by Using CRISPR-Cas9 and Somatic Cell Nuclear Transfer

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system has been widely used to create animal models for biomedical and agricultural use owing to its low cost and easy handling. However, the occurrence of erroneous cleavage (off-targeting) may raise certain concerns for the practical application of the CRISPR-Cas9 system. In this study, we created a melanocortin 1 receptor (MC1R)-edited pig model through somatic cell nuclear transfer (SCNT) by using porcine kidney cells modified by the CRISPR-Cas9 system. We then carried out whole-genome sequencing of two MC1R-edited pigs and two cloned wild-type siblings, together with the donor cells, to assess the genome-wide presence of single-nucleotide variants and small insertions and deletions (indels) and found only one candidate off-target indel in both MC1R-edited pigs. In summary, our study indicates that the minimal off-targeting effect induced by CRISPR-Cas9 may not be a major concern in gene-edited pigs created by SCNT.

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.

An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome

Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Quantification of rare somatic single nucleotide variants by droplet digital PCR using SuperSelective primers

Somatic single-nucleotide variants (SNVs) occur every time a cell divides, appearing even in healthy tissues at low frequencies. These mutations may accumulate as neutral variants during aging, or eventually, promote the development of neoplasia. Here, we present the SP-ddPCR, a droplet digital PCR (ddPCR) based approach that utilizes customized SuperSelective primers aiming at quantifying the proportion of rare SNVs. For that purpose, we selected five potentially pathogenic variants identified by whole-exome sequencing (WES) occurring at low variant allele frequency (VAF) in at-risk colon healthy mucosa of patients diagnosed with colorectal cancer or advanced adenoma. Additionally, two APC SNVs detected in two cancer lesions were added to the study for WES-VAF validation. SuperSelective primers were designed to quantify SNVs at low VAFs both in silico and in clinical samples. In addition to the two APC SNVs in colonic lesions, SP-ddPCR confirmed the presence of three out of five selected SNVs in the normal colonic mucosa with allelic frequencies ≤ 5%. Moreover, SP-ddPCR showed the presence of two potentially pathogenic variants in the distal normal mucosa of patients with colorectal carcinoma. In summary, SP-ddPCR offers a rapid and feasible methodology to validate next-generation sequencing data and accurately quantify rare SNVs, thus providing a potential tool for diagnosis and stratification of at-risk patients based on their mutational profiling.

LOGGIC Core BioClinical Data Bank: Added clinical value of RNA-Seq in an international molecular diagnostic registry for pediatric low-grade glioma patients

BACKGROUND

The international, multicenter registry LOGGIC Core BioClinical Data Bank aims to enhance the understanding of tumor biology in pediatric low-grade glioma (pLGG) and provide clinical and molecular data to support treatment decisions and interventional trial participation. Hence, the question arises whether implementation of RNA sequencing (RNA-Seq) using fresh frozen (FrFr) tumor tissue in addition to gene panel and DNA methylation analysis improves diagnostic accuracy and provides additional clinical benefit.

METHODS

Analysis of patients aged 0 to 21 years, enrolled in Germany between April 2019 and February 2021, and for whom FrFr tissue was available. Central reference histopathology, immunohistochemistry, 850k DNA methylation analysis, gene panel sequencing, and RNA-Seq were performed.

RESULTS

FrFr tissue was available in 178/379 enrolled cases. RNA-Seq was performed on 125 of these samples. We confirmed KIAA1549::BRAF-fusion (n = 71), BRAF V600E-mutation (n = 12), and alterations in FGFR1 (n = 14) as the most frequent alterations, among other common molecular drivers (n = 12). N = 16 cases (13%) presented rare gene fusions (eg, TPM3::NTRK1, EWSR1::VGLL1, SH3PXD2A::HTRA1, PDGFB::LRP1, GOPC::ROS1). In n = 27 cases (22%), RNA-Seq detected a driver alteration not otherwise identified (22/27 actionable). The rate of driver alteration detection was hereby increased from 75% to 97%. Furthermore, FGFR1 internal tandem duplications (n = 6) were only detected by RNA-Seq using current bioinformatics pipelines, leading to a change in analysis protocols.

CONCLUSIONS

The addition of RNA-Seq to current diagnostic methods improves diagnostic accuracy, making precision oncology treatments (MEKi/RAFi/ERKi/NTRKi/FGFRi/ROSi) more accessible. We propose to include RNA-Seq as part of routine diagnostics for all pLGG patients, especially when no common pLGG alteration was identified.

Mutation divergence over space in tumour expansion

Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.

Chromosomal instability and a deregulated cell cycle are intrinsic features of high-risk gastrointestinal stromal tumours with a metastatic potential

Patients with localised, high-risk gastrointestinal stromal tumours (GIST) benefit from adjuvant imatinib treatment. Still, approximately 40% of patients relapse within 3 years after adjuvant therapy and the clinical and histopathological features currently used for risk classification cannot precisely predict poor outcomes after standard treatment. This study aimed to identify genomic and transcriptomic profiles that could be associated with disease relapse and thus a more aggressive phenotype. Using a multi-omics approach, we analysed a cohort of primary tumours from patients with untreated, resectable high-risk GISTs. We compared patients who developed metastatic disease within 3 years after finishing adjuvant imatinib treatment and patients without disease relapse after more than 5 years of follow-up. Combining genomics and transcriptomics data, we identified somatic mutations and deregulated mRNA and miRNA genes intrinsic to each group. Our study shows that increased chromosomal instability (CIN), including chromothripsis and deregulated kinetochore and cell cycle signalling, separates high-risk samples according to metastatic potential. The increased CIN seems to be an intrinsic feature for tumours that metastasise and should be further validated as a novel prognostic biomarker for high-risk GIST.

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.

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.

What makes TMB an ambivalent biomarker for immunotherapy? A subtle mismatch between the sample-based design of variant callers and real clinical cohort

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

Generation and analysis of whole-genome sequencing data in human mammary epithelial cells

Breast cancer is the most common cancer worldwide, and advanced breast cancer with metastases is incurable mainly with currently available therapies. Therefore, it is essential to understand molecular characteristics during the progression of breast carcinogenesis. Here, we report a dataset of whole genomes from the human mammary epithelial cell system derived from a reduction mammoplasty specimen. This system comprises pre-stasis 184D cells, considered normal, and seven cell lines along cancer progression series that are immortalized or additionally acquired anchorage-independent growth. Our analysis of the whole-genome sequencing (WGS) data indicates that those seven cancer progression series cells have somatic mutations whose number ranges from 8,393 to 39,564 (with an average of 30,591) compared to 184D cells. These WGS data and our mutation analysis will provide helpful information to identify driver mutations and elucidate molecular mechanisms for breast carcinogenesis.

Is an SV caller compatible with sequencing data? An online recommendation tool to automatically recommend the optimal caller based on data features

A lot of bioinformatics tools were released to detect structural variants from the sequencing data during the past decade. For a data analyst, a natural question is about the selection of a tool fits for the data. Thus, this study presents an automatic tool recommendation method to facilitate data analysis. The optimal variant calling tool was recommended from a set of state-of-the-art bioinformatics tools by given a sequencing data. This recommendation method was implemented under a meta-learning framework, identifying the relationships between data features and the performance of tools. First, the meta-features were extracted to characterize the sequencing data and meta-targets were identified to pinpoint the optimal caller for the sequencing data. Second, a meta-model was constructed to bridge the meta-features and meta-targets. Finally, the recommendation was made according to the evaluation from the meta-model. A series of experiments were conducted to validate this recommendation method on both the simulated and real sequencing data. The results revealed that different SV callers often fit different sequencing data. The recommendation accuracy averaged more than 80% across all experimental configurations, outperforming the random- and fixed-pick strategy. To further facilitate the research community, we incorporated the recommendation method into an online cloud services for genomic data analysis, which is available at https://c.solargenomics.com/ via a simple registration. In addition, the source code and a pre-trained model is available at https://github.com/hello-json/CallerRecommendation for academic usages only.

Comparative analysis of tumor content estimation methods based on simu- lated tumor samples identified their impact on somatic variant detection in cancer whole genome sequencing

Whole genome sequencing (WGS) in cancer genomics has become widespread with recent technological innovations, and the amount and types of information obtained from WGS are increasing rapidly. Appropriate interpretation of results is becoming increasingly important in clinical applications. This study aimed to evaluate the accuracy of tumor content estimation and its impact on somatic variant detection, using 100 simulated tumor samples covering 10-100% tumor content constructed from the sequencing data of cell line models. Extensive analysis revealed that the estimation results varied among computational analytical methods. Notably, there was a large discrepancy in low tumor content (≤ 30%). The reproducibility decreased in cases wherein chromosome-scale copy number changes were observed in normal cells. The minimum tumor content required to detect somatic alterations was estimated to be 10-30%. Identification of whole genome doubling was achieved with the lowest tumor content, followed by single nucleotide variation/insertion or deletion, structural variation, and copy number variation. Tumor content had a significantly higher impact on the false negatives than the false positives in variant calls. Results should be interpreted cautiously for samples wherein tumor content is a concern. These results can form the basis of developing important guidelines for evaluating cancer WGS.

Japanese Regulatory Framework and Approach for Genome-edited Foods Based on Latest Scientific Findings

The food supply system is facing important challenges and its sustainability has to be considered. Genome-editing technology, which accelerates the development of new variety, could be used to achieve sustainable development goals, thereby protecting the environment and ensuring the stable production of food for an increasing global population. The most widely used genome-editing tool, CRISPR/Cas9, is easy to use, affordable, and versatile. Foods produced by genome-editing technologies have been developed worldwide to create novel traits. In the first half of the review, the latest scientific findings on genome-editing technologies are summarized, and the technical challenge in genome sequence analysis are clarified. CRISPR/Cas9 has versatile alternative techniques, such as base editor and prime editor. Genome sequencing technology has developed rapidly in recent years. However, it is still difficult to detect large deletions and structural variations. Long-read sequencing technology would solve this challenge. In the second part, regulatory framework and approach for genome-edited foods is introduced. The four government ministries, including the Ministry of Environment, the Ministry of Agriculture, Forestry and Fisheries, and the Ministry of Health, Labour and Welfare (MHLW), started to discuss how the regulation should be implemented in 2019. The SDN-1 technique is excluded from the current genetically modified organism (GMO) regulation. The Japanese regulatory framework includes pre-submission consultation and submission of notification form. In the last part of this review, transparency of regulatory framework and consumer confidence were described. Since maintaining consumer trust is vital, transparency of regulatory framework is a key to consumers. The information of notification process on approved genome-edited foods is made public immediately. This review will help regulators build regulatory frameworks, and lead to harmonization of the framework between the countries.

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.

Accurate somatic variant detection using weakly supervised deep learning

Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumor and matched normal DNA reads. VarNet is trained using image representations of 4.6 million high-confidence somatic variants annotated in 356 tumor whole genomes. We benchmark VarNet across a range of publicly available datasets, demonstrating performance often exceeding current state-of-the-art methods. Overall, our results demonstrate how a scalable deep learning approach could augment and potentially supplant human engineered features and heuristic filters in somatic variant calling.

Cancer Genomics

In the past decade, genomics has fundamentally changed our view of cancer biology, allowing comprehensive analyses of mutations, copy number alterations, structural variants, gene expression and DNA methylation profiles in large-scale studies across different cancer types. Efforts like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have fostered international collaborations for cancer genomic analyses and have generated public databases that give scientists around the world access to thoroughly curated data, which have been extensively used as a tool for further hypothesis driven research on several aspects of cancer biology. In parallel, some of these findings are being translated into specific clinical benefits for cancer patients. In this review, we provide a brief historical description of the evolution of international public cancer genome projects and related databases, as well as we discuss about their impact on general cancer research.

Quantum computing algorithms: getting closer to critical problems in computational biology

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

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.

DBFE: distribution-based feature extraction from structural variants in whole-genome data

MOTIVATION

Whole-genome sequencing has revolutionized biosciences by providing tools for constructing complete DNA sequences of individuals. With entire genomes at hand, scientists can pinpoint DNA fragments responsible for oncogenesis and predict patient responses to cancer treatments. Machine learning plays a paramount role in this process. However, the sheer volume of whole-genome data makes it difficult to encode the characteristics of genomic variants as features for learning algorithms.

RESULTS

In this article, we propose three feature extraction methods that facilitate classifier learning from sets of genomic variants. The core contributions of this work include: (i) strategies for determining features using variant length binning, clustering and density estimation; (ii) a programing library for automating distribution-based feature extraction in machine learning pipelines. The proposed methods have been validated on five real-world datasets using four different classification algorithms and a clustering approach. Experiments on genomes of 219 ovarian, 61 lung and 929 breast cancer patients show that the proposed approaches automatically identify genomic biomarkers associated with cancer subtypes and clinical response to oncological treatment. Finally, we show that the extracted features can be used alongside unsupervised learning methods to analyze genomic samples.

AVAILABILITY AND IMPLEMENTATION

The source code of the presented algorithms and reproducible experimental scripts are available on Github at https://github.com/MNMdiagnostics/dbfe.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The architecture of clonal expansions in morphologically normal tissue from cancerous and non-cancerous prostates

BACKGROUND

Up to 80% of cases of prostate cancer present with multifocal independent tumour lesions leading to the concept of a field effect present in the normal prostate predisposing to cancer development. In the present study we applied Whole Genome DNA Sequencing (WGS) to a group of morphologically normal tissue (n = 51), including benign prostatic hyperplasia (BPH) and non-BPH samples, from men with and men without prostate cancer. We assess whether the observed genetic changes in morphologically normal tissue are linked to the development of cancer in the prostate.

RESULTS

Single nucleotide variants (P = 7.0 × 10-03, Wilcoxon rank sum test) and small insertions and deletions (indels, P = 8.7 × 10-06) were significantly higher in morphologically normal samples, including BPH, from men with prostate cancer compared to those without. The presence of subclonal expansions under selective pressure, supported by a high level of mutations, were significantly associated with samples from men with prostate cancer (P = 0.035, Fisher exact test). The clonal cell fraction of normal clones was always higher than the proportion of the prostate estimated as epithelial (P = 5.94 × 10-05, paired Wilcoxon signed rank test) which, along with analysis of primary fibroblasts prepared from BPH specimens, suggests a stromal origin. Constructed phylogenies revealed lineages associated with benign tissue that were completely distinct from adjacent tumour clones, but a common lineage between BPH and non-BPH morphologically normal tissues was often observed. Compared to tumours, normal samples have significantly less single nucleotide variants (P = 3.72 × 10-09, paired Wilcoxon signed rank test), have very few rearrangements and a complete lack of copy number alterations.

CONCLUSIONS

Cells within regions of morphologically normal tissue (both BPH and non-BPH) can expand under selective pressure by mechanisms that are distinct from those occurring in adjacent cancer, but that are allied to the presence of cancer. Expansions, which are probably stromal in origin, are characterised by lack of recurrent driver mutations, by almost complete absence of structural variants/copy number alterations, and mutational processes similar to malignant tissue. Our findings have implications for treatment (focal therapy) and early detection approaches.

TMBcat: A multi-endpoint p-value criterion on different discrepancy metrics for superiorly inferring tumor mutation burden thresholds

Tumor mutation burden (TMB) is a widely recognized stratification biomarker for predicting the efficacy of immunotherapy; however, the number and universal definition of the categorizing thresholds remain debatable due to the multifaceted nature of efficacy and the imprecision of TMB measurements. We proposed a minimal joint p-value criterion from the perspective of differentiating the comprehensive therapeutic advantages, termed TMBcat, optimized TMB categorization across distinct cancer cohorts and surpassed known benchmarks. The statistical framework applies to multidimensional endpoints and is fault-tolerant to TMB measurement errors. To explore the association between TMB and various immunotherapy outcomes, we performed a retrospective analysis on 78 patients with non-small cell lung cancer and 64 patients with nasopharyngeal carcinomas who underwent anti-PD-(L)1 therapy. The stratification results of TMBcat confirmed that the relationship between TMB and immunotherapy is non-linear, i.e., treatment gains do not inherently increase with higher TMB, and the pattern varies across carcinomas. Thus, multiple TMB classification thresholds could distinguish patient prognosis flexibly. These findings were further validated in an assembled cohort of 943 patients obtained from 11 published studies. In conclusion, our work presents a general criterion and an accessible software package; together, they enable optimal TMB subgrouping. Our study has the potential to yield innovative insights into therapeutic selection and treatment strategies for patients.

Analytical demands to use whole-genome sequencing in precision oncology

Interrogating the tumor genome in its entirety by whole-genome sequencing (WGS) offers an unprecedented insight into the biology and pathogenesis of cancer, with potential impact on diagnostics, prognostication and therapy selection. WGS is able to detect sequence as well as structural variants and thereby combines central domains of cytogenetics and molecular genetics. Given the potential of WGS in directing targeted therapeutics and clinical decision-making, we envision a gradual transition of the method from research to clinical routine. This review is one out of three within this issue aimed at facilitating this effort, by discussing in-depth analytical validation, clinical interpretation and clinical utility of WGS. The review highlights the requirements for implementing, validating and maintaining a clinical WGS pipeline to obtain high-quality patient-specific data in accordance with the local regulatory landscape. Every step of the WGS pipeline, which includes DNA extraction, library preparation, sequencing, bioinformatics analysis, and data storage, is considered with respect to its logistics, necessities, potential pitfalls, and the required quality management. WGS is likely to drive clinical diagnostics and patient care forward, if requirements and challenges of the technique are recognized and met.

A Joint Model Considering Measurement Errors for Optimally Identifying Tumor Mutation Burden Threshold

Tumor mutation burden (TMB) is a recognized stratification biomarker for immunotherapy. Nevertheless, the general TMB-high threshold is unstandardized due to severe clinical controversies, with the underlying cause being inconsistency between multiple assessment criteria and imprecision of the TMB value. The existing methods for determining TMB thresholds all consider only a single dimension of clinical benefit and ignore the interference of the TMB error. Our research aims to determine the TMB threshold optimally based on multifaceted clinical efficacies accounting for measurement errors. We report a multi-endpoint joint model as a generalized method for inferring the TMB thresholds, facilitating consistent statistical inference using an iterative numerical estimation procedure considering mis-specified covariates. The model optimizes the division by combining objective response rate and time-to-event outcomes, which may be interrelated due to some shared traits. We augment previous works by enabling subject-specific random effects to govern the communication among distinct endpoints. Our simulations show that the proposed model has advantages over the standard model in terms of precision and stability in parameter estimation and threshold determination. To validate the feasibility of the proposed thresholds, we pool a cohort of 73 patients with non-small-cell lung cancer and 64 patients with nasopharyngeal carcinoma who underwent anti-PD-(L)1 treatment, as well as validation cohorts of 943 patients. Analyses revealed that our approach could grant clinicians a holistic efficacy assessment, culminating in a robust determination of the TMB screening threshold for superior patients. Our methodology has the potential to yield innovative insights into therapeutic selection and support precision immuno-oncology.

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM—GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines.

A multi-platform reference for somatic structural variation detection

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Prediction of risk-associated genes and high-risk liver cancer patients from their mutation profile: Benchmarking of mutation calling techniques

Identification of somatic mutations with high precision is one of the major challenges in the prediction of high-risk liver-cancer patients. In the past, number of mutations calling techniques have been developed that include MuTect2, MuSE, Varscan2, and SomaticSniper. In this study, an attempt has been made to benchmark the potential of these techniques in predicting the prognostic biomarkers for liver cancer. Initially, we extracted somatic mutations in liver cancer patients using Variant Call Format (VCF) and Mutation Annotation Format (MAF) files from the cancer genome atlas. In terms of size, the MAF files are 42 times smaller than VCF files and containing only high-quality somatic mutations. Further, machine learning based models have been developed for predicting high-risk cancer patients using mutations obtained from different techniques. The performance of different techniques and data files have been compared based on their potential to discriminate high and low-risk liver-cancer patients. Based on correlation analysis, we selected 80 genes having significant negative-correlation with the overall survival of liver cancer patients. The univariate survival analysis revealed the prognostic role of highly mutated genes. Single-gene based analysis showed that MuTect2 technique based MAF file has achieved maximum hazard ratio (HRLAMC3) of 9.25 with p-value 1.78E-06. Further, we developed various prediction models using risk-associated top-10 genes for each technique. Our results indicate that MuTect2 technique based VCF files outperform all other methods with maximum Area Under the Receiver-Operating Characteristic (AUROC) curve of 0.765 and HR 4.50 (p-value 3.83E-15). Eventually, VCF file generated using MuTect2 technique performs better among other mutation calling techniques for the prediction of high-risk liver cancer patients. We hope that our findings will provide a useful and comprehensive comparison of various mutation calling techniques for the prognostic analysis of cancer patients. In order to serve the scientific community, we have provided a Python-based pipeline to develop the prediction models using mutation profiles (VCF/MAF) of cancer patients. It is available on GitHub at https://github.com/raghavagps/mutation_bench.

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.

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.

Clinical whole-genome sequencing in cancer diagnosis

Characterizing the genomic landscape of cancers is a routine part of clinical care that began with the discovery of the Philadelphia chromosome and has since coevolved with genomic technologies. Genomic analysis of tumors at the nucleotide level using DNA sequencing has revolutionized the understanding of cancer biology and identified new molecular drivers of disease that have led to therapeutic advances and improved patient outcomes. However, the application of next-generation sequencing in the clinical laboratory has generally been limited until very recently to targeted analysis of selected genes. Recent technological innovations and reductions in sequencing costs are now able to deliver the long-promised goal of tumor whole-genome sequencing as a practical clinical assay.

Cost-Efficient Sequence-Based Nonextensible Oligonucleotide in Real-Time PCR and High-Throughput Sequencing

Molecular detection of disease-associated mutations, especially those with low abundance, is essential for academic research and clinical diagnosis. Certain variant detection methods reach satisfactory sensitivity and specificity in detecting rare mutations based on the introduction of blocking oligos to prevent the amplification of wild-type or unwanted templates, thus selectively amplifying and enriching the mutations. These blocking oligos usually suppress PCR amplification through the 3' chemical modifications, with high price, slow synthesis, and reduced purity. Herein, we introduce chemistry-free designs to block enzymatic extension during PCR by the steric hindrance from the secondary structures attached to the 3' end of the oligos (nonextensible oligonucleotide, NEO). We demonstrated that NEO efficiently prohibited the extension of both Taq and high-fidelity DNA polymerases. By further applying NEO as blockers in blocker displacement amplification (BDA) qPCR, multiplex BDA (mBDA) NGS, and quantitative BDA (QBDA) NGS methods, we showed that NEO blockers had performance comparable with previously validated chemical modifications. Comparison experiments using QBDA with NEO blockers and droplet digital PCR (ddPCR) on clinical formalin-fixed paraffin-embedded (FFPE) samples exhibited 100% concordance. Lastly, the ability of NEO to adjust plex uniformity through changes of PCR amplification efficiency was demonstrated in an 80-plex NGS panel.

[Application of high throughput sequencing in pediatric neurooncology]

Over the past decade, next generation sequencing (NGS) has become the standard method in research of cancer genomics; currently NGS is entering a new stage - direct usage in clinical oncology to improve diagnostics and establish personalized tumor treatments. NGS allows to read the genome and it is successfully applied to detect mutations and other somatic changes (translocations, inversions, insertions and deletions, copy number variants) leading to the development of a tumor. With a focus on transcriptome sequencing allows to clearly identify differences in gene expression, improve the classification of tumors and detect somatic chimeras. All these possibilities are especially relevant for pediatric neurooncology filed in view of the existing limitations in treatment and the need for the most accurate identification of the key factors of tumor development. In this article, we describe sequencing technology basis, its application on brain tumor materials to improve diagnostics, and other relevant possibilities that can be considered for direct usage in medicine.

National Genome Initiatives in Europe and the United Kingdom in the Era of Whole-Genome Sequencing: A Comprehensive Review

Identification of genomic variability in population plays an important role in the clinical diagnostics of human genetic diseases. Thanks to rapid technological development in the field of massive parallel sequencing technologies, also known as next-generation sequencing (NGS), complex genomic analyses are now easier and cheaper than ever before, which consequently leads to more effective utilization of these techniques in clinical practice. However, interpretation of data from NGS is still challenging due to several issues caused by natural variability of DNA sequences in human populations. Therefore, development and realization of projects focused on description of genetic variability of local population (often called "national or digital genome") with a NGS technique is one of the best approaches to address this problem. The next step of the process is to share such data via publicly available databases. Such databases are important for the interpretation of variants with unknown significance or (likely) pathogenic variants in rare diseases or cancer or generally for identification of pathological variants in a patient's genome. In this paper, we have compiled an overview of published results of local genome sequencing projects from United Kingdom and Europe together with future plans and perspectives for newly announced ones.

Multi-omic cross-sectional cohort study of pre-malignant Barrett's esophagus reveals early structural variation and retrotransposon activity

Barrett's esophagus is a pre-malignant lesion that can progress to esophageal adenocarcinoma. We perform a multi-omic analysis of pre-cancer samples from 146 patients with a range of outcomes, comprising 642 person years of follow-up. Whole genome sequencing reveals complex structural variants and LINE-1 retrotransposons, as well as known copy number changes, occurring even prior to dysplasia. The structural variant burden captures the most variance across the cohort and genomic profiles do not always match consensus clinical pathology dysplasia grades. Increasing structural variant burden is associated with: high levels of chromothripsis and breakage-fusion-bridge events; increased expression of genes related to cell cycle checkpoint, DNA repair and chromosomal instability; and epigenetic silencing of Wnt signalling and cell cycle genes. Timing analysis reveals molecular events triggering genomic instability with more clonal expansion in dysplastic samples. Overall genomic complexity occurs early in the Barrett's natural history and may inform the potential for cancer beyond the clinically discernible phenotype.

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

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

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.

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.

Design of Personalized Neoantigen RNA Vaccines Against Cancer Based on Next-Generation Sequencing Data

The good clinical results of immune checkpoint inhibitors (ICIs) in recent cancer therapy and the success of RNA vaccines against SARS-nCoV2 have provided important lessons to the scientific community. On the one hand, the efficacy of ICI depends on the number and immunogenicity of tumor neoantigens (TNAs) which unfortunately are not abundantly expressed in many cancer subtypes. On the other hand, novel RNA vaccines have significantly improved both the stability and immunogenicity of mRNA and its efficient delivery, this way overcoming past technique limitations and also allowing a quick vaccine development at the same time. These two facts together have triggered a resurgence of therapeutic cancer vaccines which can be designed to include individual TNAs and be synthesized in a timeframe short enough to be suitable for the tailored treatment of a given cancer patient.In this chapter, we explain the pipeline for the synthesis of TNA-carrying RNA vaccines which encompasses several steps such as individual tumor next-generation sequencing (NGS), selection of immunogenic TNAs, nucleic acid synthesis, drug delivery systems, and immunogenicity assessment, all of each step comprising different alternatives and variations which will be discussed.

Validation of HER2 Status in Whole Genome Sequencing Data of Breast Cancers with the Ploidy-Corrected Copy Number Approach

BACKGROUND AND OBJECTIVE

Human epidermal growth factor receptor 2 (HER2) protein overexpression is one of the most significant biomarkers for breast cancer diagnostics, treatment prediction, and prognostics. The high accessibility of HER2 inhibitors in routine clinical practice directly translates into the diagnostic need for precise and robust marker identification. Even though multigene next-generation sequencing methodologies have slowly taken over the field of single-biomarker molecular tests, the copy number alterations such as amplification of the HER2-coding ERBB2 gene are hard to validate on next-generation sequencing platforms as they are characterized by chromosomal structural heterogeneity, polysomy, and genomic context of ploidy. In our study, we tested the approach of using whole genome sequencing instead of next-generation sequencing panels to determine HER2 status in the clinical set-up.

METHODS

We used a large dataset of 876 patients with breast cancer whole genomes with curated clinical data and an additional set of 551 patients' external genomic data. We used the decision-tree-based algorithm for optimization of the diagnostic tool for HER2 status assessment by whole genome sequencing.

RESULTS

The most efficient approach to assess HER2 status in whole genome sequencing data was the ploidy-corrected copy number, utilizing ERBB2 copy number and mean tumor ploidy. The classifier achieved sensitivity of 91.18% and specificity of 98.69% on the internal validation dataset and 89.86% and 96.06% on the external data, which is similar to other next-generation sequencing methods, currently tested in the clinic.

CONCLUSIONS

We provide evidence that the HER2 status may be reliably determined by whole genome sequencing and is applicable across different laboratory protocols and pipelines. We suggest using the ploidy-corrected copy number for diagnostic purposes.

Global quantification exposes abundant low-level off-target activity by base editors

Base editors are dedicated engineered deaminases that enable directed conversion of specific bases in the genome or transcriptome in a precise and efficient manner, and hold promise for correcting pathogenic mutations. A major concern limiting application of this powerful approach is the issue of off-target edits. Several recent studies have shown substantial off-target RNA activity induced by base editors and demonstrated that off-target mutations may be suppressed by improved deaminases versions or optimized guide RNAs. Here, we describe a new class of off-target events that are invisible to the established methods for detection of genomic variations and were thus far overlooked. We show that nonspecific, seemingly stochastic, off-target events affect a large number of sites throughout the genome or the transcriptome, and account for the majority of off-target activity. We develop and employ a different, complementary approach that is sensitive to the stochastic off-target activity and use it to quantify the abundant off-target RNA mutations due to current, optimized deaminase editors. We provide a computational tool to quantify global off-target activity, which can be used to optimize future base editors. Engineered base editors enable directed manipulation of the genome or transcriptome at single-base resolution. We believe that implementation of this computational approach would facilitate design of more specific base editors.

Genetics of somatic auto-inflammatory disorders

Systemic autoinflammatory disorders (SAIDs) encompass a heterogeneous group of monogenic disorders characterized by recurrent episodes of systemic and organ-specific inflammation. Genetic studies have facilitated the identification of Mendelian forms of SAIDs but many patients still remain without a diagnosis. Recent studies have uncovered that somatic (acquired) mutations can cause later-onset SAIDs. In this review, we will discuss the current knowledge surrounding the genetics of these acquired auto-inflammatory disorders (AAIDs), with a focus on VEXAS, NLRP3-associated AAIDs and Schnitzler's syndrome and provide suggestions for future research in this field.

Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) provides new insights to address biological and medical questions, and it will benefit more from the ultralow input RNA or subcellular sequencing.

RESULTS

Here, we present a highly sensitive library construction protocol for ultralow input RNA sequencing (ulRNA-seq). We systematically evaluate experimental conditions of this protocol, such as reverse transcriptase, template-switching oligos (TSO), and template RNA structure. It was found that Maxima H Minus reverse transcriptase and rN modified TSO, as well as all RNA templates capped with m7G improved the sequencing sensitivity and low abundance gene detection ability. RNA-seq libraries were successfully prepared from total RNA samples as low as 0.5 pg, and more than 2000 genes have been identified.

CONCLUSIONS

The ability of low abundance gene detection and sensitivity were largely enhanced with this optimized protocol. It was also confirmed in single-cell sequencing, that more genes and cell markers were identified compared to conventional sequencing method. We expect that ulRNA-seq will sequence and transcriptome characterization for the subcellular of disease tissue, to find the corresponding treatment plan.

Radiation-induced gliomas represent H3-/IDH-wild type pediatric gliomas with recurrent PDGFRA amplification and loss of CDKN2A/B

Long-term complications such as radiation-induced second malignancies occur in a subset of patients following radiation-therapy, particularly relevant in pediatric patients due to the long follow-up period in case of survival. Radiation-induced gliomas (RIGs) have been reported in patients after treatment with cranial irradiation for various primary malignancies such as acute lymphoblastic leukemia (ALL) and medulloblastoma (MB). We perform comprehensive (epi-) genetic and expression profiling of RIGs arising after cranial irradiation for MB (n = 23) and ALL (n = 9). Our study reveals a unifying molecular signature for the majority of RIGs, with recurrent PDGFRA amplification and loss of CDKN2A/B and an absence of somatic hotspot mutations in genes encoding histone 3 variants or IDH1/2, uncovering diagnostic markers and potentially actionable targets.

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.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

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

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Canine tumor mutational burden is correlated with TP53 mutation across tumor types and breeds

Spontaneous canine cancers are valuable but relatively understudied and underutilized models. To enhance their usage, we reanalyze whole exome and genome sequencing data published for 684 cases of >7 common tumor types and >35 breeds, with rigorous quality control and breed validation. Our results indicate that canine tumor alteration landscape is tumor type-dependent, but likely breed-independent. Each tumor type harbors major pathway alterations also found in its human counterpart (e.g., PI3K in mammary tumor and p53 in osteosarcoma). Mammary tumor and glioma have lower tumor mutational burden (TMB) (median < 0.5 mutations per Mb), whereas oral melanoma, osteosarcoma and hemangiosarcoma have higher TMB (median ≥ 1 mutations per Mb). Across tumor types and breeds, TMB is associated with mutation of TP53 but not PIK3CA, the most mutated genes. Golden Retrievers harbor a TMB-associated and osteosarcoma-enriched mutation signature. Here, we provide a snapshot of canine mutations across major tumor types and breeds.