An evaluation of copy number variation detection tools for cancer using whole exome sequencing data

Excitotoxicity, Oxytosis/Ferroptosis, and Neurodegeneration: Emerging Insights into Mitochondrial Mechanisms

Mitochondrial dysfunction plays a pivotal role in the development of age-related diseases, particularly neurodegenerative disorders. The etiology of mitochondrial dysfunction involves a multitude of factors that remain elusive. This review centers on elucidating the role(s) of excitotoxicity, oxytosis/ferroptosis and neurodegeneration within the context of mitochondrial bioenergetics, biogenesis, mitophagy and oxidative stress and explores their intricate interplay in the pathogenesis of neurodegenerative diseases. The effective coordination of mitochondrial turnover processes, notably mitophagy and biogenesis, is assumed to be critically important for cellular resilience and longevity. However, the age-associated decrease in mitophagy impedes the elimination of dysfunctional mitochondria, consequently impairing mitochondrial biogenesis. This deleterious cascade results in the accumulation of damaged mitochondria and deterioration of cellular functions. Both excitotoxicity and oxytosis/ferroptosis have been demonstrated to contribute significantly to the pathophysiology of neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's Disease (HD), Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). Excitotoxicity, characterized by excessive glutamate signaling, initiates a cascade of events involving calcium dysregulation, energy depletion, and oxidative stress and is intricately linked to mitochondrial dysfunction. Furthermore, emerging concepts surrounding oxytosis/ferroptosis underscore the importance of iron-dependent lipid peroxidation and mitochondrial engagement in the pathogenesis of neurodegeneration. This review not only discusses the individual contributions of excitotoxicity and ferroptosis but also emphasizes their convergence with mitochondrial dysfunction, a key driver of neurodegenerative diseases. Understanding the intricate crosstalk between excitotoxicity, oxytosis/ferroptosis, and mitochondrial dysfunction holds potential to pave the way for mitochondrion-targeted therapeutic strategies. Such strategies, with a focus on bioenergetics, biogenesis, mitophagy, and oxidative stress, emerge as promising avenues for therapeutic intervention.

Copy number variation introduced by a massive mobile element facilitates global thermal adaptation in a fungal wheat pathogen

Copy number variation (CNV) can drive rapid evolution in changing environments. In microbial pathogens, such adaptation is a key factor underpinning epidemics and colonization of new niches. However, the genomic determinants of such adaptation remain poorly understood. Here, we systematically investigate CNVs in a large genome sequencing dataset spanning a worldwide collection of 1104 genomes from the major wheat pathogen Zymoseptoria tritici. We found overall strong purifying selection acting on most CNVs. Genomic defense mechanisms likely accelerated gene loss over episodes of continental colonization. Local adaptation along climatic gradients was likely facilitated by CNVs affecting secondary metabolite production and gene loss in general. One of the strongest loci for climatic adaptation is a highly conserved gene of the NAD-dependent Sirtuin family. The Sirtuin CNV locus localizes to an ~68-kb Starship mobile element unique to the species carrying genes highly expressed during plant infection. The element has likely lost the ability to transpose, demonstrating how the ongoing domestication of cargo-carrying selfish elements can contribute to selectable variation within populations. Our work highlights how standing variation in gene copy numbers at the global scale can be a major factor driving climatic and metabolic adaptation in microbial species.

Unveiling novel genetic variants in 370 challenging medically relevant genes using the long read sequencing data of 41 samples from 19 global populations

BACKGROUND

A large number of challenging medically relevant genes (CMRGs) are situated in complex or highly repetitive regions of the human genome, hindering comprehensive characterization of genetic variants using next-generation sequencing technologies. In this study, we employed long-read sequencing technology, extensively utilized in studying complex genomic regions, to characterize genetic alterations, including short variants (single nucleotide variants and short insertions and deletions) and copy number variations, in 370 CMRGs across 41 individuals from 19 global populations.

RESULTS

Our analysis revealed high levels of genetic variants in CMRGs, with 68.73% exhibiting copy number variations and 65.20% containing short variants that may disrupt protein function across individuals. Such variants can influence pharmacogenomics, genetic disease susceptibility, and other clinical outcomes. We observed significant differences in CMRG variation across populations, with individuals of African ancestry harboring the highest number of copy number variants and short variants compared to samples from other continents. Notably, 15.79% to 33.96% of short variants were exclusively detectable through long-read sequencing. While the T2T-CHM13 reference genome significantly improved the assembly of CMRG regions, thereby facilitating variant detection in these regions, some regions still lacked resolution.

CONCLUSION

Our results provide an important reference for future clinical and pharmacogenetic studies, highlighting the need for a comprehensive representation of global genetic diversity in the reference genome and improved variant calling techniques to fully resolve medically relevant genes.

In vitro evolution and whole genome analysis to study chemotherapy drug resistance in haploid human cells

In vitro evolution and whole genome analysis has proven to be a powerful method for studying the mechanism of action of small molecules in many haploid microbes but has generally not been applied to human cell lines in part because their diploid state complicates the identification of variants that confer drug resistance. To determine if haploid human cells could be used in MOA studies, we evolved resistance to five different anticancer drugs (doxorubicin, gemcitabine, etoposide, topotecan, and paclitaxel) using a near-haploid cell line (HAP1) and then analyzed the genomes of the drug resistant clones, developing a bioinformatic pipeline that involved filtering for high frequency alleles predicted to change protein sequence, or alleles which appeared in the same gene for multiple independent selections with the same compound. Applying the filter to sequences from 28 drug resistant clones identified a set of 21 genes which was strongly enriched for known resistance genes or known drug targets (TOP1, TOP2A, DCK, WDR33, SLCO3A1). In addition, some lines carried structural variants that encompassed additional known resistance genes (ABCB1, WWOX and RRM1). Gene expression knockdown and knockout experiments of 10 validation targets showed a high degree of specificity and accuracy in our calls and demonstrates that the same drug resistance mechanisms found in diverse clinical samples can be evolved, discovered and studied in an isogenic background.

Somatic Tumor Testing in Prostate Cancer: Experience of a Tertiary Care Center Including Pathologist-Driven Reflex Testing of Localized Tumors at Diagnosis

Somatic tumor testing in prostate cancer (PCa) can guide treatment options by identifying clinically actionable variants in DNA damage repair genes, including acquired variants not detected using germline testing alone. Guidelines currently recommend performing somatic tumor testing in metastatic PCa, whereas there is no consensus on the role of testing in regional disease, and the optimal testing strategy is only evolving. This study evaluates the frequency, distribution, and pathologic correlates of somatic DNA damage repair mutations in metastatic and localized PCa following the implementation of pathologist-driven reflex testing at diagnosis. A cohort of 516 PCa samples were sequenced using a custom next-generation sequencing panel including homologous recombination repair and mismatch repair genes. Variants were classified based on the Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists guidelines. In total, 183 (35.5%) patients had at least one variant, which is as follows: 72 of 516 (13.9%) patients had at least 1 tier I or tier II variant, whereas 111 of 516 (21.5%) patients had a tier III variant. Tier I/II variant(s) were identified in 27% (12/44) of metastatic biopsy samples and 13% (61/472) of primary samples. Overall, 12% (62/516) of patients had at least 1 tier I/II variant in a homologous recombination repair gene, whereas 2.9% (10/516) had at least 1 tier I/II variant in a mismatch repair gene. The presence of a tier I/II variant was not significantly associated with the grade group (GG) or presence of intraductal/cribriform carcinoma in the primary tumor. Among the 309 reflex-tested hormone-naive primary tumors, tier I/II variants were identified in 10% (31/309) of cases, which is as follows: 9.2% (9/98) GG2; 9% (9/100) GG3; 9.1% (4/44) GG4; and 13.4% (9/67) GG5 cases. Our findings confirm the use of somatic tumor testing in detecting variants of clinical significance in PCa and provide insights that can inform the design of testing strategies. Pathologist-initiated reflex testing streamlines the availability of the results for clinical decision-making; however, pathologic parameters such as GG and the presence of intraductal/cribriform carcinoma may not be reliable to guide patient selection.

Exome sequencing reveals genetic heterogeneity and clinically actionable findings in children with cerebral palsy

Cerebral palsy (CP) is the most common motor disability in children. To ascertain the role of major genetic variants in the etiology of CP, we conducted exome sequencing on a large-scale cohort with clinical manifestations of CP. The study cohort comprised 505 girls and 1,073 boys. Utilizing the current gold standard in genetic diagnostics, 387 of these 1,578 children (24.5%) received genetic diagnoses. We identified 412 pathogenic and likely pathogenic (P/LP) variants across 219 genes associated with neurodevelopmental disorders, and 59 P/LP copy number variants. The genetic diagnostic rate of children with CP labeled at birth with perinatal asphyxia was higher than the rate in children without asphyxia (P = 0.0033). Also, 33 children with CP manifestations (8.5%, 33 of 387) had findings that were clinically actionable. These results highlight the need for early genetic testing in children with CP, especially those with risk factors like perinatal asphyxia, to enable evidence-based medical decision-making.

Detection of elusive DNA copy-number variations in hereditary disease and cancer through the use of noncoding and off-target sequencing reads

Copy-number variants (CNVs) play a substantial role in the molecular pathogenesis of hereditary disease and cancer, as well as in normal human interindividual variation. However, they are still rather difficult to identify in mainstream sequencing projects, especially involving exome sequencing, because they often occur in DNA regions that are not targeted for analysis. To overcome this problem, we developed OFF-PEAK, a user-friendly CNV detection tool that builds on a denoising approach and the use of "off-target" DNA reads, which are usually discarded by sequencing pipelines. We benchmarked OFF-PEAK on data from targeted sequencing of 96 cancer samples, as well as 130 exomes of individuals with inherited retinal disease from three different populations. For both sets of data, OFF-PEAK demonstrated excellent performance (>95% sensitivity and >80% specificity vs. experimental validation) in detecting CNVs from in silico data alone, indicating its immediate applicability to molecular diagnosis and genetic research.

Long-insert sequence capture detects high copy numbers in a defence-related beta-glucosidase gene βglu-1 with large variations in white spruce but not Norway spruce

Conifers are long-lived and slow-evolving, thus requiring effective defences against their fast-evolving insect natural enemies. The copy number variation (CNV) of two key acetophenone biosynthesis genes Ugt5/Ugt5b and βglu-1 may provide a plausible mechanism underlying the constitutively variable defence in white spruce (Picea glauca) against its primary defoliator, spruce budworm. This study develops a long-insert sequence capture probe set (Picea_hung_p1.0) for quantifying copy number of βglu-1-like, Ugt5-like genes and single-copy genes on 38 Norway spruce (Picea abies) and 40 P. glauca individuals from eight and nine provenances across Europe and North America respectively. We developed local assemblies (Piabi_c1.0 and Pigla_c.1.0), full-length transcriptomes (PIAB_v1 and PIGL_v1), and gene models to characterise the diversity of βglu-1 and Ugt5 genes. We observed very large copy numbers of βglu-1, with up to 381 copies in a single P. glauca individual. We observed among-provenance CNV of βglu-1 in P. glauca but not P. abies. Ugt5b was predominantly single-copy in both species. This study generates critical hypotheses for testing the emergence and mechanism of extreme CNV, the dosage effect on phenotype, and the varying copy number of genes with the same pathway. We demonstrate new approaches to overcome experimental challenges in genomic research in conifer defences.

On the core segmentation algorithms of copy number variation detection tools

Shotgun sequencing is a high-throughput method used to detect copy number variants (CNVs). Although there are numerous CNV detection tools based on shotgun sequencing, their quality varies significantly, leading to performance discrepancies. Therefore, we conducted a comprehensive analysis of next-generation sequencing-based CNV detection tools over the past decade. Our findings revealed that the majority of mainstream tools employ similar detection rationale: calculates the so-called read depth signal from aligned sequencing reads and then segments the signal by utilizing either circular binary segmentation (CBS) or hidden Markov model (HMM). Hence, we compared the performance of those two core segmentation algorithms in CNV detection, considering varying sequencing depths, segment lengths and complex types of CNVs. To ensure a fair comparison, we designed a parametrical model using mainstream statistical distributions, which allows for pre-excluding bias correction such as guanine-cytosine (GC) content during the preprocessing step. The results indicate the following key points: (1) Under ideal conditions, CBS demonstrates high precision, while HMM exhibits a high recall rate. (2) For practical conditions, HMM is advantageous at lower sequencing depths, while CBS is more competitive in detecting small variant segments compared to HMM. (3) In case involving complex CNVs resembling real sequencing, HMM demonstrates more robustness compared with CBS. (4) When facing large-scale sequencing data, HMM costs less time compared with the CBS, while their memory usage is approximately equal. This can provide an important guidance and reference for researchers to develop new tools for CNV detection.

ECOLE: Learning to call copy number variants on whole exome sequencing data

Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE .

Soft Tissue and Bone Tumor Diagnostics: Harnessing the Power of Molecular Techniques

Since the introduction of new molecular techniques, the diagnostic landscape of soft tissue and bone tumors has expanded greatly over the past few years. The use of new molecular techniques has led to the identification of new genetic alterations and, therefore, to a better understanding of tumorigenesis, tumor detection and classification. Furthermore, methylation profiling has emerged as a classification tool for soft tissue and bone tumors. Molecular pathology also plays an important role in the determination of patient prognosis and in the identification of targets that can be used for targeted therapy. As a result, molecular pathology has gained a more prominent role in the daily practice of the surgical pathologist. This review delves into various molecular techniques applied in the surgical pathology of soft tissue and bone tumors. It highlights their applications through the analysis of five specific cases.

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.

Systematic assessment of the contribution of structural variants to inherited retinal diseases

Despite increasing success in determining genetic diagnosis for patients with inherited retinal diseases (IRDs), mutations in about 30% of the IRD cases remain unclear or unsettled after targeted gene panel or whole exome sequencing. In this study, we aimed to investigate the contributions of structural variants (SVs) to settling the molecular diagnosis of IRD with whole-genome sequencing (WGS). A cohort of 755 IRD patients whose pathogenic mutations remain undefined were subjected to WGS. Four SV calling algorithms including include MANTA, DELLY, LUMPY and CNVnator were used to detect SVs throughout the genome. All SVs identified by any one of these four algorithms were included for further analysis. AnnotSV was used to annotate these SVs. SVs that overlap with known IRD-associated genes were examined with sequencing coverage, junction reads and discordant read pairs. Polymerase Chain Reaction (PCR) followed by Sanger sequencing was used to further confirm the SVs and identify the breakpoints. Segregation of the candidate pathogenic alleles with the disease was performed when possible. A total of 16 candidate pathogenic SVs were identified in 16 families, including deletions and inversions, representing 2.1% of patients with previously unsolved IRDs. Autosomal dominant, autosomal recessive and X-linked inheritance of disease-causing SVs were observed in 12 different genes. Among these, SVs in CLN3, EYS and PRPF31 were found in multiple families. Our study suggests that the contribution of SVs detected by short-read WGS is about 0.25% of our IRD patient cohort and is significantly lower than that of single nucleotide changes and small insertions and deletions.

Genomic and transcriptomic profiling reveal molecular characteristics of parathyroid carcinoma

Genomic and transcriptomic profiling has enhanced the diagnostic and treatment options for many cancers. However, the molecular characteristics of parathyroid cancer remain largely unexplored, thereby limiting the development of new therapeutic interventions. Herein, we conducted genomic and transcriptomic sequencing of 50 parathyroid tissues (12 carcinomas, 28 adenomas, and 10 normal tissues) to investigate the intrinsic and comparative molecular features of parathyroid carcinoma. We confirmed multiple two-hit mutation patterns in cell division cycle 73 (CDC73) that converged to biallelic inactivation, calling into question the presence of a second hit in other genes. In addition, allele-specific repression of CDC73 in copies with germline-truncating variants suggested selective pressure prior to tumorigenesis. Transcriptomic analysis identified upregulation of the expression of E2F targets, KRAS and TNF-alpha signaling, and epithelial-mesenchymal transition pathways in carcinomas compared to adenomas and normal tissues. A molecular classification model based on carcinoma-specific genes clearly separated carcinomas from adenomas and normal tissues, the clinical utility of which was demonstrated in two patients with uncertain malignant potential. A deeper analysis of gene expression and functional prediction suggested that Wilms tumor 1 (WT1) is a potential biomarker for CDC73-mutant parathyroid carcinoma, which was further validated through immunohistochemistry. Overall, our study revealed the genomic and transcriptomic profiles of parathyroid carcinoma and may help direct future precision diagnostic and therapeutic improvements.

A comparison of algorithms for identifying copy number variants in family-based whole-exome sequencing data and its implications in inheritance pattern analysis

There remain challenges in accurately identifying constitutional or germline copy number variants (gCNVs) based on whole-exome sequencing data that have implications for genetic diagnosis for 'rare undiagnosed disease' in the clinical setting. Although multiple algorithms have been proposed, a systematic comparison of these algorithms for calling gCNVs and analyzing inherited pattern have yet to be fully conducted. Therefore, we empirically compared seven exome-based algorithms, including XHMM, CLAMMS, CODEX2, ExomeDepth, DECoN, CN.MOPS, and GATK gCNV, for calling gCNVs in 151 individuals from 44 pedigrees, together with the gold standard of genotyping-derived gCNVs in the same cohort for the performance assessment. These algorithms demonstrated varied powers in identifying gCNVs, although the distribution of gCNVs size was similar. The number of shared gCNVs across these algorithms was limited (e.g., only four gCNVs shared among seven algorithms); however, several algorithms showed varying degrees of consistency (e.g., 1,843 gCNVs shared between DECoN and ExomeDepth). CLAMMS and CODEX2 outperformed the remaining algorithms according to a relatively higher F-score (i.e., 0.145 and 0.152, respectively). In addition, these algorithms exhibited different Mendelian inconsistencies of gCNVs and significant challenges remained in inheritance pattern analysis. In conclusion, selecting good algorithms may have important implications in gCNVs-based inheritance pattern analysis for family-based studies.

Germline VWF/MPRIP and somatoplasm FGA variants synergically confer susceptibility to non-traumatic osteonecrosis of the femoral head

Non-traumatic osteonecrosis of the femoral head (ONFH) relies on multiple pathogenic factors, including intravascular coagulation, osteoporosis and lipid metabolism disorders. Despite extensively explored from various aspects, genetic mechanism underlying non-traumatic ONFH has not been fully elucidated. We randomly collected blood and necrotic tissue samples from 32 patients with non-traumatic ONFH as well as blood samples from 30 healthy individuals for whole exome sequencing (WES). Germline mutation and somatic mutation were analyzed to identify new potential pathogenic genes responsible for non-traumatic ONFH. Three genes might correlate with non-traumatic ONFH: VWF, MPRIP (germline mutations) and FGA (somatic mutations). Germline or somatic mutations in VWF, MPRIP and FGA correlate with intravascular coagulation, thrombosis, and consequently, ischemic necrosis of the femoral head.

Systematic assessment of the contribution of structural variants to inherited retinal diseases

Despite increasing success in determining genetic diagnosis for patients with inherited retinal diseases (IRDs), mutations in about 30% of the IRD cases remain unclear or unsettled after targeted gene panel or whole exome sequencing. In this study, we aimed to investigate the contributions of structural variants (SVs) to settling the molecular diagnosis of IRD with whole-genome sequencing (WGS). A cohort of 755 IRD patients whose pathogenic mutations remain undefined was subjected to WGS. Four SV calling algorithms including include MANTA, DELLY, LUMPY, and CNVnator were used to detect SVs throughout the genome. All SVs identified by any one of these four algorithms were included for further analysis. AnnotSV was used to annotate these SVs. SVs that overlap with known IRD-associated genes were examined with sequencing coverage, junction reads, and discordant read pairs. PCR followed by Sanger sequencing was used to further confirm the SVs and identify the breakpoints. Segregation of the candidate pathogenic alleles with the disease was performed when possible. In total, sixteen candidate pathogenic SVs were identified in sixteen families, including deletions and inversions, representing 2.1% of patients with previously unsolved IRDs. Autosomal dominant, autosomal recessive, and X-linked inheritance of disease-causing SVs were observed in 12 different genes. Among these, SVs in CLN3, EYS, PRPF31 were found in multiple families. Our study suggests that the contribution of SVs detected by short-read WGS is about 0.25% of our IRD patient cohort and is significantly lower than that of single nucleotide changes and small insertions and deletions.

BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants

Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.

Big data in basic and translational cancer research

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.

Tangent normalization for somatic copy-number inference in cancer genome analysis

MOTIVATION

Somatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas, the Broad Institute Genome Characterization Center developed the Tangent normalization method to generate copy-number profiles using data from single-nucleotide polymorphism (SNP) arrays and whole-exome sequencing (WES) technologies for over 10 000 pairs of tumors and matched normal samples. Here, we describe the Tangent method, which uses a unique linear combination of normal samples as a reference for each tumor sample, to subtract systematic errors that vary across samples. We also describe a modification of Tangent, called Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available.

RESULTS

Tangent normalization substantially increases signal-to-noise ratios (SNRs) compared to conventional normalization methods in both SNP array and WES analyses. Tangent and Pseudo-Tangent normalizations improve the SNR by reducing noise with minimal effect on signal and exceed the contribution of other steps in the analysis such as choice of segmentation algorithm. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data.

AVAILABILITY AND IMPLEMENTATION

Tangent is available at https://github.com/broadinstitute/tangent and as a Docker image (https://hub.docker.com/r/broadinstitute/tangent). Tangent is also the normalization method for the copy-number pipeline in Genome Analysis Toolkit 4 (GATK4).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation, we introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that by using CNAViz, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling.

Copy-number aberrations (CNAs) are large genetic alterations that are pervasive in cancer and, therefore, have been the focus of several cancer research studies. Copy-number segmentation is a key step in the process of CNA identification, which consist in partitioning the genome into genomic segments with the same copy-number state. However, segmentation is challenging and the limitations of current segmentation algorithms lead to inaccuracies in the characterization of CNAs. In this paper, we introduce CNAViz, an interactive web-based tool that enables the user to edit segmentation solutions and overcome current limitations. We demonstrate the ability of a user to use CNAViz to improve segmentation solutions on both simulated and real data, analyzing six published bulk DNA sequencing samples from three breast cancer patients. Finally, we demonstrate that these improvements in segmentation solutions improve accuracy in downstream copy-number calling, enabling more accurate analyses of intra-tumor heterogeneity.

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

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

Whole-exome analysis of 177 pediatric patients with undiagnosed diseases

Recently, whole-exome sequencing (WES) has been used for genetic diagnoses of patients who remain otherwise undiagnosed. WES was performed in 177 Japanese patients with undiagnosed conditions who were referred to the Tokai regional branch of the Initiative on Rare and Undiagnosed Diseases (IRUD) (TOKAI-IRUD). This study included only patients who had not previously received genome-wide testing. Review meetings with specialists in various medical fields were held to evaluate the genetic diagnosis in each case, which was based on the guidelines of the American College of Medical Genetics and Genomics. WES identified diagnostic single-nucleotide variants in 66 patients and copy number variants (CNVs) in 11 patients. Additionally, a patient was diagnosed with Angelman syndrome with a complex clinical phenotype upon detection of a paternally derived uniparental disomy (UPD) [upd(15)pat] wherein the patient carried a homozygous DUOX2 p.E520D variant in the UPD region. Functional analysis confirmed that this DUOX2 variant was a loss-of-function missense substitution and the primary cause of congenital hypothyroidism. A significantly higher proportion of genetic diagnoses was achieved compared to previous reports (44%, 78/177 vs. 24-35%, respectively), probably due to detailed discussions and the higher rate of CNV detection.

Application of next-generation sequencing in the diagnosis of gastric cancer

Objectives: Gastric cancer (GC) is a disease with high mortality, poor prognosis and numerous risk factors. GC has an asymptomatic nature in early stages of the diseases, making timely diagnosis complicated using common conventional approaches, namely pathological examinations and imaging tests. Recently, molecular profiling of GC using next generation sequencing (NGS) has opened new doors to efficient prognostic, diagnostic, and therapeutic strategies. The current review aims to thoroughly discuss and compare the current NGS techniques and commercial platforms utilized for GC diagnosis and treatment, highlighting the most recent NGS-based GC studies. Furthermore, this review addresses the challenges of clinical implementation of NGS in GC.Materials and methods: This review was conducted according to the eligible studies identified via search of Web of Science, PubMed, Scopus, Embase and the Cochrane Library. In the present study, data on gastric cancer patients and NGS methods used to diagnose the disease were reviewed.Conclusion: Given the ever-rising advancements in NGS technologies, bioinformatics, healthcare guidelines and refined classifications, it is hoped that these technologies can actualize their advantages and optimize GC patients' experience.

Polishing copy number variant calls on exome sequencing data via deep learning

Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results with mostly valid statistical assumptions, copy number detection on whole-exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data are cost-efficient, compact, and relatively ubiquitous. The bottleneck is primarily due to the noncontiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data, and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can efficiently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model consistently improves the performance independent of (1) sequencing technology, (2) exome capture kit, and (3) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets.

TADeus2: a web server facilitating the clinical diagnosis by pathogenicity assessment of structural variations disarranging 3D chromatin structure

In recent years great progress has been made in identification of structural variants (SV) in the human genome. However, the interpretation of SVs, especially located in non-coding DNA, remains challenging. One of the reasons stems in the lack of tools exclusively designed for clinical SVs evaluation acknowledging the 3D chromatin architecture. Therefore, we present TADeus2 a web server dedicated for a quick investigation of chromatin conformation changes, providing a visual framework for the interpretation of SVs affecting topologically associating domains (TADs). This tool provides a convenient visual inspection of SVs, both in a continuous genome view as well as from a rearrangement’s breakpoint perspective. Additionally, TADeus2 allows the user to assess the influence of analyzed SVs within flaking coding/non-coding regions based on the Hi-C matrix. Importantly, the SVs pathogenicity is quantified and ranked using TADA, ClassifyCNV tools and sampling-based P-value. TADeus2 is publicly available at https://tadeus2.mimuw.edu.pl.

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.

Distinguishing excess mutations and increased cell death based on variant allele frequencies

Tumors often harbor orders of magnitude more mutations than healthy tissues. The increased number of mutations may be due to an elevated mutation rate or frequent cell death and correspondingly rapid cell turnover, or a combination of the two. It is difficult to disentangle these two mechanisms based on widely available bulk sequencing data, where sequences from individual cells are intermixed and, thus, the cell lineage tree of the tumor cannot be resolved. Here we present a method that can simultaneously estimate the cell turnover rate and the rate of mutations from bulk sequencing data. Our method works by simulating tumor growth and finding the parameters with which the observed data can be reproduced with maximum likelihood. Applying this method to a real tumor sample, we find that both the mutation rate and the frequency of death may be high.

Tumors frequently harbor an elevated number of mutations, compared to healthy tissue. These extra mutations may be generated either by an increased mutation rate or the presence of cell death resulting in increased cellular turnover and additional cell divisions for tumor growth. Separating the effects of these two factors is a nontrivial problem. Here we present a method which can simultaneously estimate cell turnover rate and genomic mutation rate from bulk sequencing data. Our method is based on the estimation of the parameters of a generative model of tumor growth and mutations. Applying our method to a human hepatocellular carcinoma sample reveals an elevated per cell division mutation rate and high cell turnover.

Guiding the global evolution of cytogenetic testing for hematologic malignancies

Cytogenetics has long represented a critical component in the clinical evaluation of hematologic malignancies. Chromosome banding studies provide a simultaneous snapshot of genome-wide copy number and structural variation, which have been shown to drive tumorigenesis, define diseases, and guide treatment. Technological innovations in sequencing have ushered in our present-day clinical genomics era. With recent publications highlighting novel sequencing technologies as alternatives to conventional cytogenetic approaches, we, an international consortium of laboratory geneticists, pathologists, and oncologists, describe herein the advantages and limitations of both conventional chromosome banding and novel sequencing technologies and share our considerations on crucial next steps to implement these novel technologies in the global clinical setting for a more accurate cytogenetic evaluation, which may provide improved diagnosis and treatment management. Considering the clinical, logistic, technical, and financial implications, we provide points to consider for the global evolution of cytogenetic testing.

Allele-specific expression reveals genes with recurrent cis-regulatory alterations in high-risk neuroblastoma

BACKGROUND

Neuroblastoma is a pediatric malignancy with a high frequency of metastatic disease at initial diagnosis. Neuroblastoma tumors have few recurrent protein-coding mutations but contain extensive somatic copy number alterations (SCNAs) suggesting that mutations that alter gene dosage are important drivers of tumorigenesis. Here, we analyze allele-specific expression in 96 high-risk neuroblastoma tumors to discover genes impacted by cis-acting mutations that alter dosage.

RESULTS

We identify 1043 genes with recurrent, neuroblastoma-specific allele-specific expression. While most of these genes lie within common SCNA regions, many of them exhibit allele-specific expression in copy neutral samples and these samples are enriched for mutations that are predicted to cause nonsense-mediated decay. Thus, both SCNA and non-SCNA mutations frequently alter gene expression in neuroblastoma. We focus on genes with neuroblastoma-specific allele-specific expression in the absence of SCNAs and find 26 such genes that have reduced expression in stage 4 disease. At least two of these genes have evidence for tumor suppressor activity including the transcription factor TFAP2B and the protein tyrosine phosphatase PTPRH.

CONCLUSIONS

In summary, our allele-specific expression analysis discovers genes that are recurrently dysregulated by both large SCNAs and other cis-acting mutations in high-risk neuroblastoma.

Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders

Mitochondria are major contributors to ATP synthesis, generating more than 90% of the total cellular energy production through oxidative phosphorylation (OXPHOS): metabolite oxidation, such as the β-oxidation of fatty acids, and the Krebs’s cycle. OXPHOS inadequacy due to large genetic lesions in mitochondrial as well as nuclear genes and homo- or heteroplasmic point mutations in mitochondrially encoded genes is a characteristic of heterogeneous, maternally inherited genetic disorders known as mitochondrial disorders that affect multisystemic tissues and organs with high energy requirements, resulting in various signs and symptoms. Several traditional diagnostic approaches, including magnetic resonance imaging of the brain, cardiac testing, biochemical screening, variable heteroplasmy genetic testing, identifying clinical features, and skeletal muscle biopsies, are associated with increased risks, high costs, a high degree of false-positive or false-negative results, or a lack of precision, which limits their diagnostic abilities for mitochondrial disorders. Variable heteroplasmy levels, mtDNA depletion, and the identification of pathogenic variants can be detected through genetic sequencing, including the gold standard Sanger sequencing. However, sequencing can be time consuming, and Sanger sequencing can result in the missed recognition of larger structural variations such as CNVs or copy-number variations. Although each sequencing method has its own limitations, genetic sequencing can be an alternative to traditional diagnostic methods. The ever-growing roster of possible mutations has led to the development of next-generation sequencing (NGS). The enhancement of NGS methods can offer a precise diagnosis of the mitochondrial disorder within a short period at a reasonable expense for both research and clinical applications.

KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data

Copy number variation (CNV) is a well-known type of genomic mutation that is associated with the development of human cancer diseases. Detection of CNVs from the human genome is a crucial step for the pipeline of starting from mutation analysis to cancer disease diagnosis and treatment. Next-generation sequencing (NGS) data provides an unprecedented opportunity for CNVs detection at the base-level resolution, and currently, many methods have been developed for CNVs detection using NGS data. However, due to the intrinsic complexity of CNVs structures and NGS data itself, accurate detection of CNVs still faces many challenges. In this paper, we present an alternative method, called KNNCNV (K-Nearest Neighbor based CNV detection), for the detection of CNVs using NGS data. Compared to current methods, KNNCNV has several distinctive features: 1) it assigns an outlier score to each genome segment based solely on its first k nearest-neighbor distances, which is not only easy to extend to other data types but also improves the power of discovering CNVs, especially the local CNVs that are likely to be masked by their surrounding regions; 2) it employs the variational Bayesian Gaussian mixture model (VBGMM) to transform these scores into a series of binary labels without a user-defined threshold. To evaluate the performance of KNNCNV, we conduct both simulation and real sequencing data experiments and make comparisons with peer methods. The experimental results show that KNNCNV could derive better performance than others in terms of F1-score.

Pan-cancer analysis of biological events on cell cycle instability in gastrointestinal cancers with integrative scoring method

Omics data integration plays an essential role in manifesting hidden cancer insights. To detect the main combinatorial/parallel impact of cancer events, integrative approaches in pan-cancer studies must be used. Here, we assessed gastrointestinal (GI) cancers from several perspectives of genomics, transcriptomics, epigenomics, and also combinatorial impacts using a novel integrative approach to score genes. Next, scores were diffused on a signaling network and extracted subnetworks. We also implemented our new scoring method to compare upper-/lower-GI cancers, investigate the regulatory mechanisms of lncRNAs, and detect amplifications/deletions between GI and non-GI cancers. The integrative subnetwork indicated the interplay among essential protein families in the cell cycle. The copy-number-variation-related subnetwork revealed minor cell cycle and immune effects, whereas the methylation-related subnetwork revealed significant immune effects. The top-score lncRNAs indicated a distinct regulatory pattern for lower-/upper-, and accessory-GI categories. In summary, cell cycle dysfunction might be largely the consequence of combinatorial abnormalities.

VarGenius-HZD Allows Accurate Detection of Rare Homozygous or Hemizygous Deletions in Targeted Sequencing Leveraging Breadth of Coverage

Homozygous deletions (HDs) may be the cause of rare diseases and cancer, and their discovery in targeted sequencing is a challenging task. Different tools have been developed to disentangle HD discovery but a sensitive caller is still lacking. We present VarGenius-HZD, a sensitive and scalable algorithm that leverages breadth-of-coverage for the detection of rare homozygous and hemizygous single-exon deletions (HDs). To assess its effectiveness, we detected both real and synthetic rare HDs in fifty exomes from the 1000 Genomes Project obtaining higher sensitivity in comparison with state-of-the-art algorithms that each missed at least one event. We then applied our tool on targeted sequencing data from patients with Inherited Retinal Dystrophies and solved five cases that still lacked a genetic diagnosis. We provide VarGenius-HZD either stand-alone or integrated within our recently developed software, enabling the automated selection of samples using the internal database. Hence, it could be extremely useful for both diagnostic and research purposes.

Response to Comment on "Reintroduction of the archaic variant of NOVA1 in cortical organoids alters neurodevelopment"

Maricic et al. performed an undisclosed in silico-only whole-exome sequencing analysis of our data and found genomic alterations previously undetected in some clones. Some of the predicted alterations, if true, could change the original genotype of the clones. We failed to experimentally validate all but one of these genomic alterations, which did not affect our previous results or data interpretation.

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases

BACKGROUND

Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation.

METHODS

We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed.

RESULTS

GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases.

CONCLUSIONS

GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

A Local Outlier Factor-Based Detection of Copy Number Variations From NGS Data

Copy number variation (CNV) is a major type of genomic structural variations that play an important role in human disorders. Next generation sequencing (NGS) has fueled the advancement in algorithm design to detect CNVs at base-pair resolution. However, accurate detection of CNVs of low amplitudes remains a challenging task. This paper proposes a new computational method, CNV-LOF, to identify CNVs of full-range amplitudes from NGS data. CNV-LOF is distinctly different from traditional methods, which mainly consider aberrations from a global perspective and rely on some assumed distribution of NGS read depths. In contrast, CNV-LOF takes a local view on the read depths and assigns an outlier factor to each genome segment. With the outlier factor profile, CNV-LOF uses a boxplot procedure to declare CNVs without the reliance of any distribution assumptions. Simulation experiments indicate that CNV-LOF outperforms five existing methods with respect to F1-measure, sensitivity, and precision. CNV-LOF is further validated on real sequencing samples, yielding highly consistent results with peer methods. CNV-LOF is able to detect CNVs of low and moderate amplitudes where the other existing methods fail, and it is expected to become a routine approach for the discovery of novel CNVs on whole sequencing genome.

Technical and biological constraints on ctDNA-based genotyping

Circulating tumor DNA (ctDNA) enables real-time genomic profiling of cancer without the need for tissue biopsy. ctDNA-based technology is seeing rapid uptake in clinical practice due to the potential to inform patient management from diagnosis to advanced disease. In metastatic disease, ctDNA can identify somatic mutations, copy-number variants (CNVs), and structural rearrangements that are predictive of therapy response. However, the ctDNA fraction (ctDNA%) is unpredictable and confounds variant detection strategies, undermining confidence in liquid biopsy results. Assay design also influences which types of genomic alterations are identifiable. Here, we describe the relationships between ctDNA%, methodology, and sensitivity-specificity for major classes of genomic alterations in prostate cancer. We provide recommendations to navigate the technical complexities that constrain the detection of clinically relevant genomic alterations in ctDNA.

Prognostic Value of Germline Copy Number Variants and Environmental Exposures in Non-small Cell Lung Cancer

Germline copy number variant (gCNV) has been studied as a genetic determinant for prognosis of several types of cancer, but little is known about how it affects non-small cell lung cancer (NSCLC) prognosis. We aimed to develop a prognostic nomogram for NSCLC based on gCNVs. Promising gCNVs that are associated with overall survival (OS) of NSCLC were sorted by analyzing the TCGA data and were validated in a small Chinese population. Then the successfully verified gCNVs were determined in a training cohort (n = 570) to develop a prognostic nomogram, and in a validation cohort (n = 465) to validate the nomogram. Thirty-five OS-related gCNVs were sorted and were reduced to 15 predictors by the Lasso regression analysis. Of them, only CNVR395.1 and CNVR2239.1 were confirmed to be associated with OS of NSCLC in the Chinese population. High polygenic risk score (PRS), which was calculated by the hazard effects of CNVR395.1 and CNVR2239.1, exerted a significantly higher death rate in the training cohort (HR = 1.41, 95%CI: 1.16-1.74) and validation cohort (HR = 1.42, 95%CI: 1.13-1.77) than low PRS. The nomogram incorporating PRS and surrounding factors, achieved admissible concordance indexes of 0.678 (95%CI: 0.664-0.693) and 0.686 (95%CI: 0.670-0.702) in predicting OS in the training and validation cohorts, respectively, and had well-fitted calibration curves. Moreover, an interaction between PRS and asbestos exposure was observed on affecting OS (P interaction = 0.042). Our analysis developed a nomogram that achieved an admissible prediction of NSCLC survival, which would be beneficial to the personalized intervention of NSCLC.

Shall genomic correlation structure be considered in copy number variants detection?

Copy number variation has been identified as a major source of genomic variation associated with disease susceptibility. With the advent of whole-exome sequencing (WES) technology, massive WES data have been generated, allowing for the identification of copy number variants (CNVs) in the protein-coding regions with direct functional interpretation. We have previously shown evidence of the genomic correlation structure in array data and developed a novel chromosomal breakpoint detection algorithm, LDcnv, which showed significantly improved detection power through integrating the correlation structure in a systematic modeling manner. However, it remains unexplored whether the genomic correlation exists in WES data and how such correlation structure integration can improve the CNV detection accuracy. In this study, we first explored the correlation structure of the WES data using the 1000 Genomes Project data. Both real raw read depth and median-normalized data showed strong evidence of the correlation structure. Motivated by this fact, we proposed a correlation-based method, CORRseq, as a novel release of the LDcnv algorithm in profiling WES data. The performance of CORRseq was evaluated in extensive simulation studies and real data analysis from the 1000 Genomes Project. CORRseq outperformed the existing methods in detecting medium and large CNVs. In conclusion, it would be more advantageous to model genomic correlation structure in detecting relatively long CNVs. This study provides great insights for methodology development of CNV detection with NGS data.

Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes

Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected "driver" genes with limited application to rare drivers and other genomic elements. In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

CNV_IFTV: An Isolation Forest and Total Variation-Based Detection of CNVs from Short-Read Sequencing Data

Accurate detection of copy number variations (CNVs) from short-read sequencing data is challenging due to the uneven distribution of reads and the unbalanced amplitudes of gains and losses. The direct use of read depths to measure CNVs tends to limit performance. Thus, robust computational approaches equipped with appropriate statistics are required to detect CNV regions and boundaries. This study proposes a new method called CNV_IFTV to address this need. CNV_IFTV assigns an anomaly score to each genome bin through a collection of isolation trees. The trees are trained based on isolation forest algorithm through conducting subsampling from measured read depths. With the anomaly scores, CNV_IFTV uses a total variation model to smooth adjacent bins, leading to a denoised score profile. Finally, a statistical model is established to test the denoised scores for calling CNVs. CNV_IFTV is tested on both simulated and real data in comparison to several peer methods. The results indicate that the proposed method outperforms the peer methods. CNV_IFTV is a reliable tool for detecting CNVs from short-read sequencing data even for low-level coverage and tumor purity. The detection results on tumor samples can aid to evaluate known cancer genes and to predict target drugs for disease diagnosis.

A practical guide to cancer subclonal reconstruction from DNA sequencing

Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.

Genetic testing in dementia - utility and clinical strategies

Techniques for clinical genetic testing in dementia disorders have advanced rapidly but remain to be more widely implemented in practice. A positive genetic test offers a precise molecular diagnosis, can help members of an affected family to determine personal risk, provides a basis for reproductive choices and can offer options for clinical trials. The likelihood of identifying a specific genetic cause of dementia depends on the clinical condition, the age at onset and family history. Attempts to match phenotypes to single genes are mostly inadvisable owing to clinical overlap between the dementias, genetic heterogeneity, pleiotropy and concurrent mutations. Currently, the appropriate genetic test in most cases of dementia is a next-generation sequencing gene panel, though some conditions necessitate specific types of test such as repeat expansion testing. Whole-exome and whole-genome sequencing are becoming financially feasible but raise or exacerbate complex issues such as variants of uncertain significance, secondary findings and the potential for re-analysis in light of new information. However, the capacity for data analysis and counselling is already restricting the provision of genetic testing. Patients and their relatives need to be given reliable information to enable them to make informed choices about tests, treatments and data sharing; the ability of patients with dementia to make decisions must be considered when providing this information.

Droplet digital PCR for large genomic rearrangements detection: A promising strategy in tissue BRCA1 testing

BACKGROUND AND AIMS

With the introduction of Olaparib as target therapy for High Grade Serous Ovarian Cancer (HGSOC) patients with germline and somatic BRCA1/2 mutations, the genetic test performed on tumor tissue has become important like the germline test. In somatic testing the evaluation of Large Genomic Rearrangements (LGRs) represents the main challenge. We describe a droplet digital PCR (ddPCR) assay for the evaluation of target BRCA1 LGRs on blood and formalin-fixed paraffin-embedded (FFPE)/Fresh Frozen Tissue (FFT) samples.

MATERIALS AND METHODS

We analyzed blood, FFPE and FFT samples in a validation setting of n = 78 HGSOC patients. We applied the ddPCR to BRCA1 exons 2, 20 and 21 as some of the most common BRCA1 exons involved in LGRs in our cohort of patients.

RESULTS

The ddPCR custom assays allowed the identification of LGRs in all sample types, including FFPE specimens. Moreover, we were able to clearly detect LGRs accounted as somatic event.

CONCLUSION

The introduction of ddPCR in a comprehensive workflow, encompassing both germline and somatic tests, represents an improvement in BRCA1/2 testing. ddPCR can overcome challenges related to BRCA testing, especially on FFPE analysis. Finally, ddPCR represents a promising alternative strategy to the established standard methods currently used in clinical setting.