CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

Biallelic structural variants in three patients with ERCC8-related Cockayne syndrome and a potential pitfall of copy number variation analysis

Cockayne syndrome (CS) is a rare autosomal recessive disorder caused by mutations in ERCC8 or ERCC6. Most pathogenic variants in ERCC8 are single nucleotide substitutions. Structural variants (SVs) have been reported in patients with ERCC8-related CS. However, comprehensive molecular detection, including SVs of ERCC8, in CS patients remains problematic. Herein, we present three Japanese patients with ERCC8-related CS in whom causative SVs were identified using whole-exome-based copy number variation (CNV) detection tools. One patient showed compound heterozygosity for a 259-kb deletion and a deletion of exon 4 which has previously been reported as an Asia-specific variant. The other two patients were homozygous for the same exon 4 deletion. The exon 4 deletion was detected only by the ExomeDepth software. Intrigued by the discrepancy in the detection capability of various tools for the SVs, we evaluated the analytic performance of four whole-exome-based CNV detection tools using an exome data set from 337 healthy individuals. A total of 1,278,141 exons were predicted as being affected by the 4 CNV tools. Interestingly 95.1% of these affected exons were detected by one tool alone. Thus, we expect that the use of multiple tools may improve the detection rate of SVs from aligned exome data.

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.

Haplotype information of large neuromuscular disease genes provided by linked-read sequencing has a potential to increase diagnostic yield

Rare or novel missense variants in large genes such as TTN and NEB are frequent in the general population, which hampers the interpretation of putative disease-causing biallelic variants in patients with sporadic neuromuscular disorders. Often, when the first initial genetic analysis is performed, the reconstructed haplotype, i.e. phasing information of the variants is missing. Segregation analysis increases the diagnostic turnaround time and is not always possible if samples from family members are lacking. To overcome this difficulty, we investigated how well the linked-read technology succeeded to phase variants in these large genes, and whether it improved the identification of structural variants. Linked-read sequencing data of nemaline myopathy, distal myopathy, and proximal myopathy patients were analyzed for phasing, single nucleotide variants, and structural variants. Variant phasing was successful in the large muscle genes studied. The longest continuous phase blocks were gained using high-quality DNA samples with long DNA fragments. Homozygosity increased the number of phase blocks, especially in exome sequencing samples lacking intronic variation. In our cohort, linked-read sequencing added more information about the structural variation but did not lead to a molecular genetic diagnosis. The linked-read technology can support the clinical diagnosis of neuromuscular and other genetic disorders.

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 .

Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility

Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.

Long-read genome sequencing identifies cryptic structural variants in congenital aniridia cases

BACKGROUND

Haploinsufficiency of the transcription factor PAX6 is the main cause of congenital aniridia, a genetic disorder characterized by iris and foveal hypoplasia. 11p13 microdeletions altering PAX6 or its downstream regulatory region (DRR) are present in about 25% of patients; however, only a few complex rearrangements have been described to date. Here, we performed nanopore-based whole-genome sequencing to assess the presence of cryptic structural variants (SVs) on the only two unsolved "PAX6-negative" cases from a cohort of 110 patients with congenital aniridia after unsuccessfully short-read sequencing approaches.

RESULTS

Long-read sequencing (LRS) unveiled balanced chromosomal rearrangements affecting the PAX6 locus at 11p13 in these two patients and allowed nucleotide-level breakpoint analysis. First, we identified a cryptic 4.9 Mb de novo inversion disrupting intron 7 of PAX6, further verified by targeted polymerase chain reaction amplification and sequencing and FISH-based cytogenetic analysis. Furthermore, LRS was decisive in correctly mapping a t(6;11) balanced translocation cytogenetically detected in a second proband with congenital aniridia and considered non-causal 15 years ago. LRS resolved that the breakpoint on chromosome 11 was indeed located at 11p13, disrupting the DNase I hypersensitive site 2 enhancer within the DRR of PAX6, 161 Kb from the causal gene. Patient-derived RNA expression analysis demonstrated PAX6 haploinsufficiency, thus supporting that the 11p13 breakpoint led to a positional effect by cleaving crucial enhancers for PAX6 transactivation. LRS analysis was also critical for mapping the exact breakpoint on chromosome 6 to the highly repetitive centromeric region at 6p11.1.

CONCLUSIONS

In both cases, the LRS-based identified SVs have been deemed the hidden pathogenic cause of congenital aniridia. Our study underscores the limitations of traditional short-read sequencing in uncovering pathogenic SVs affecting low-complexity regions of the genome and the value of LRS in providing insight into hidden sources of variation in rare genetic diseases.

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.

Revisiting Genetic Epidemiology with a Refined Targeted Gene Panel for Hereditary Hearing Impairment in the Taiwanese Population

Hearing impairment is one of the most common sensory disorders in children, and targeted next-generation sequencing (NGS)-based genetic examinations can assist in its prognostication and management. In 2020, we developed a simplified 30-gene NGS panel from the original 214-gene NGS version based on Taiwanese genetic epidemiology data to increase the accessibility of NGS-based examinations. In this study, we evaluated the diagnostic performance of the 30-gene NGS panel and compared it with that of the original 214-gene NGS panel in patient subgroups with different clinical features. Data on the clinical features, genetic etiologies, audiological profiles, and outcomes were collected from 350 patients who underwent NGS-based genetic examinations for idiopathic bilateral sensorineural hearing impairment between 2020 and 2022. The overall diagnostic yield was 52%, with slight differences in genetic etiology between patients with different degrees of hearing impairment and ages of onset. No significant difference was found in the diagnostic yields between the two panels, regardless of clinical features, except for a lower detection rate of the 30-gene panel in the late-onset group. For patients with negative genetic results, where the causative variant is undetectable on current NGS-based methods, part of the negative results may be due to genes not covered by the panel or yet to be identified. In such cases, the hearing prognosis varies and may decline over time, necessitating appropriate follow-up and consultation. In conclusion, genetic etiologies can serve as references for refining targeted NGS panels with satisfactory diagnostic performance.

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.

Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning

Exome sequencing is widely used in genetic studies of human diseases and clinical genetic diagnosis. Accurate detection of copy number variants (CNVs) is important to fully utilize exome sequencing data. However, exome data are noisy. None of the existing methods alone can achieve both high precision and recall rate. A common practice is to perform heuristic filtration followed by manual inspection of read depth of putative CNVs. This approach does not scale in large studies. To address this issue, we developed a transfer learning method, CNV-espresso, for in silico confirming rare CNVs from exome sequencing data. CNV-espresso encodes candidate CNVs from exome data as images and uses pretrained convolutional neural network models to classify copy number states. We trained CNV-espresso using an offspring-parents trio exome sequencing dataset, with inherited CNVs as positives and CNVs with Mendelian errors as negatives. We evaluated the performance using additional samples that have both exome and whole-genome sequencing (WGS) data. Assuming the CNVs detected from WGS data as a proxy of ground truth, CNV-espresso significantly improves precision while keeping recall almost intact, especially for CNVs that span a small number of exons. CNV-espresso can effectively replace manual inspection of CNVs in large-scale exome sequencing studies.

A phase II study of buparlisib in relapsed or refractory thymomas

PURPOSE

To investigate the efficacy and safety of buparlisib, an oral pan-PI3K inhibitor, in relapsed or refractory thymomas.

METHODS

This was a single center, single arm, open label phase II trial of buparlisib in patients with recurrent thymoma who have progressed after at least one prior line of treatment. The primary endpoint was objective response rate (complete response [CR] + partial response [PR]). Secondary endpoints included toxicity; progression free survival (PFS); overall survival (OS); disease control rate (DCR), i.e., the percentage of patients who achieve either PR or CR or stable disease [SD] for at least 4 months.

RESULTS

Between 10/13/2014 and 1/18/2017, 14 patients with stage IV disease were enrolled. Median age was 58y (23-74). 71% were females and 71% white. All patients had WHO B2 (29%) or B3 (71%) thymoma. Patients received buparlisib for a median of 4.5m (2-33). At a median follow up of 16.6m (2.4-31.3), onr patients (7%) achieved a PR. DCR was 50%. Median PFS was 11.1m (95% CI 2.9 - 18.8). Median OS, updated as of March, 2021 was 22.5m (10.7-31.3). Most common grade 3-4 adverse events related to buparlisib were dyspnea (21%), rash (14%), elevated transaminases (14%), cough (7%), pneumonitis (7%), anxiety (7%), fatigue (7%) and hyperglycemia (7%). Reasons for treatment discontinuation included progression of disease (n= 5), rash (n=4), pulmonary toxicity (n=3), sinusitis (n=1), and disseminated toxoplasmosis plus autoimmune cholangitis (n=1). As of 3/2021, 8 patients have died, 7 due to disease progression and 1 due to central nervous system toxoplasmosis and autoimmune cholangitis.

CONCLUSION

Buparlisib showed modest activity in patients with relapsed or refractory thymomas. Further investigation of PI3K pathway targeted therapy in thymoma is warranted. (clinicaltrials.gov ID: NCT02220855).

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov, identifier (NCT02220855).

Initial Phase I Safety Study of Gedatolisib plus Cofetuzumab Pelidotin for Patients with Metastatic Triple-Negative Breast Cancer

PURPOSE

The PI3K pathway is dysregulated in the majority of triple-negative breast cancers (TNBC), yet single-agent inhibition of PI3K has been ineffective in TNBC. PI3K inhibition leads to an immediate compensatory upregulation of the Wnt pathway. Dual targeting of both pathways is highly synergistic against TNBC models in vitro and in vivo. We initiated a phase I clinical trial combining gedatolisib, a pan-class I isoform PI3K/mTOR inhibitor, and cofetuzumab pelidotin, an antibody-drug conjugate against the cell-surface PTK7 protein (Wnt pathway coreceptor) with an auristatin payload.

PATIENTS AND METHODS

Participants (pt) had metastatic TNBC or estrogen receptor (ER) low (ER and PgR < 5%, HER2-negative) breast cancer, and had received at least one prior chemotherapy for advanced disease. The primary objective was safety. Secondary endpoints included overall response rate (ORR), clinical benefit at 18 weeks (CB18), progression-free survival (PFS), and correlative analyses.

RESULTS

A total of 18 pts were enrolled in three dose cohorts: gedatolisib 110 mg weekly + cofetuzumab pelidotin 1.4 mg/kg every 3 weeks (n = 4), 180 mg + 1.4 mg/kg (n = 3), and 180 mg + 2.8 mg/kg (n = 11). Nausea, anorexia, fatigue, and mucositis were common but rarely reached ≥grade 3 severity. Myelosuppression was uncommon. ORR was 16.7% (3/18). An additional 3 pts had stable disease (of these 2 had stable disease for >18 weeks); CB18 was 27.8%. Median PFS was 2.0 months (95% confidence interval for PFS: 1.2-6.2). Pts with clinical benefit were enriched with genomic alterations in the PI3K and PTK7 pathways.

CONCLUSIONS

The combination of gedatolisib + cofetuzumab pelidotin was well tolerated and demonstrated promising clinical activity. Further investigation of this drug combination in metastatic TNBC is warranted.

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.

CNVind: an open source cloud-based pipeline for rare CNVs detection in whole exome sequencing data based on the depth of coverage

Background

A typical Copy Number Variations (CNVs) detection process based on the depth of coverage in the Whole Exome Sequencing (WES) data consists of several steps: (I) calculating the depth of coverage in sequencing regions, (II) quality control, (III) normalizing the depth of coverage, (IV) calling CNVs. Previous tools performed one normalization process for each chromosome—all the coverage depths in the sequencing regions from a given chromosome were normalized in a single run.

Methods

Herein, we present the new CNVind tool for calling CNVs, where the normalization process is conducted separately for each of the sequencing regions. The total number of normalizations is equal to the number of sequencing regions in the investigated dataset. For example, when analyzing a dataset composed of n sequencing regions, CNVind performs n independent depth of coverage normalizations. Before each normalization, the application selects the k most correlated sequencing regions with the depth of coverage Pearson’s Correlation as distance metric. Then, the resulting subgroup of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k+1$$\end{document}k+1 sequencing regions is normalized, the results of all n independent normalizations are combined; finally, the segmentation and CNV calling process is performed on the resultant dataset.

Results and conclusions

We used WES data from the 1000 Genomes project to evaluate the impact of independent normalization on CNV calling performance and compared the results with state-of-the-art tools: CODEX and exomeCopy. The results proved that independent normalization allows to improve the rare CNVs detection specificity significantly. For example, for the investigated dataset, we reduced the number of FP calls from over 15,000 to around 5000 while maintaining a constant number of TP calls equal to about 150 CNVs. However, independent normalization of each sequencing region is a computationally expensive process, therefore our pipeline is customized and can be easily run in the cloud computing environment, on the computer cluster, or the single CPU server. To our knowledge, the presented application is the first attempt to implement an innovative approach to independent normalization of the depth of WES data coverage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04617-x.

CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications

CoverageMaster (CoM) is a copy number variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome [whole exome sequencing (WES)] and genome [whole genome sequencing (WGS)] data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model. CoM processes WES and WGS data at nucleotide scale resolution and accurately detects and visualizes full size range CNVs, including single or partial exon deletions and duplications. The results obtained with this approach support the possibility for coverage-based CNV callers to replace probe-based methods such as array comparative genomic hybridization and multiplex ligation-dependent probe amplification in the near future.

DECoN: A Detection and Visualization Tool for Exonic Copy Number Variants

Detection of copy number variants from targeted sequencing, including whole-exome sequencing, can be particularly difficult since the break points of the CNV are not always captured. Here we describe DECoN, a software tool which uses changes in read depth to identify CNVs that affect whole exons. It is optimized for clinical use and allows for interactive visualization of CNVs identified.

Copy Number Variation Detection by Single-Cell DNA Sequencing with SCOPE

Whole-genome single-cell DNA sequencing (scDNA-seq) enables the characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we describe SCOPE, a normalization and copy number estimation method for scDNA-seq data. We give an overview of the methodology and illustrate SCOPE with step-by-step demonstrations.

The association of complex genetic background with the prognosis of acute leukemia with ambiguous lineage

Acute leukemia with ambiguous lineage (ALAL) is a rare and highly aggressive malignancy with limited molecular characterization and therapeutic recommendations. In this study, we retrospectively analyzed 1635 acute leukemia cases in our center from January 2012 to June 2018. The diagnose of ALAL was based on either EGIL or 2016 WHO criteria, a total of 39 patients were included. Four patients diagnosed as acute undifferentiated leukemia (AUL) by both classification systems. Among the patients underwent high-throughput sequencing, 89.5% were detected at least one mutation and the median number of gene mutation was 3 (0–8) per sample. The most frequently mutated genes were NRAS (4, 21%), CEBPA (4, 21%), JAK3 (3, 16%), RUNX1 (3, 16%). The mutations detected in mixed-phenotype acute leukemia (MPAL) enriched in genes related to genomic stability and transcriptional regulation; while AUL cases frequently mutated in genes involved in signaling pathway. The survival analysis strongly suggested that mutation burden may play important roles to predict the clinical outcomes of ALAL. In addition, the patients excluded by WHO criteria had even worse clinical outcome than those included. The association of the genetic complexity of blast cells with the clinical outcomes and rationality of the diagnostic criteria of WHO system need to be evaluated by more large-scale prospective clinical studies.

Interleukin-7 receptor α mutational activation can initiate precursor B-cell acute lymphoblastic leukemia

Interleukin-7 receptor α (encoded by IL7R) is essential for lymphoid development. Whether acute lymphoblastic leukemia (ALL)-related IL7R gain-of-function mutations can trigger leukemogenesis remains unclear. Here, we demonstrate that lymphoid-restricted mutant IL7R, expressed at physiological levels in conditional knock-in mice, establishes a pre-leukemic stage in which B-cell precursors display self-renewal ability, initiating leukemia resembling PAX5 P80R or Ph-like human B-ALL. Full transformation associates with transcriptional upregulation of oncogenes such as Myc or Bcl2, downregulation of tumor suppressors such as Ikzf1 or Arid2, and major IL-7R signaling upregulation (involving JAK/STAT5 and PI3K/mTOR), required for leukemia cell viability. Accordingly, maximal signaling drives full penetrance and early leukemia onset in homozygous IL7R mutant animals. Notably, we identify 2 transcriptional subgroups in mouse and human Ph-like ALL, and show that dactolisib and sphingosine-kinase inhibitors are potential treatment avenues for IL-7R-related cases. Our model, a resource to explore the pathophysiology and therapeutic vulnerabilities of B-ALL, demonstrates that IL7R can initiate this malignancy.

A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data

Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard-SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.

High-content single-cell combinatorial indexing

Single-cell combinatorial indexing (sci) with transposase-based library construction increases the throughput of single-cell genomics assays but produces sparse coverage in terms of usable reads per cell. We develop symmetrical strand sci ('s3'), a uracil-based adapter switching approach that improves the rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-assay for transposase-accessible chromatin, s3-ATAC) in human cortical and mouse whole-brain tissues, with mouse datasets demonstrating a six- to 13-fold improvement in usable reads per cell compared with other available methods. Application of s3 to single-cell whole-genome sequencing (s3-WGS) and to whole-genome plus chromatin conformation (s3-GCC) yields 148- and 14.8-fold improvements, respectively, in usable reads per cell compared with sci-DNA-sequencing and sci-HiC. We show that s3-WGS and s3-GCC resolve subclonal genomic alterations in patient-derived pancreatic cancer cell lines. We expect that the s3 platform will be compatible with other transposase-based techniques, including sci-MET or CUT&Tag.

isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data

Background

Accurate copy number variant (CNV) detection is especially challenging for both targeted sequencing (TS) and whole‐exome sequencing (WES) data. To maximize the performance, the parameters of the CNV calling algorithms should be optimized for each specific dataset. This requires obtaining validated CNV information using either multiplex ligation-dependent probe amplification (MLPA) or array comparative genomic hybridization (aCGH). They are gold standard but time-consuming and costly approaches.

Results

We present isoCNV which optimizes the parameters of DECoN algorithm using only NGS data. The parameter optimization process is performed using an in silico CNV validated dataset obtained from the overlapping calls of three algorithms: CNVkit, panelcn.MOPS and DECoN. We evaluated the performance of our tool and showed that increases the sensitivity in both TS and WES real datasets.

Conclusions

isoCNV provides an easy-to-use pipeline to optimize DECoN that allows the detection of analysis-ready CNV from a set of DNA alignments obtained under the same conditions. It increases the sensitivity of DECoN without the need for orthogonal methods. isoCNV is available at https://gitlab.com/sequentiateampublic/isocnv.

CCNE1 copy number is a biomarker for response to combination WEE1-ATR inhibition in ovarian and endometrial cancer models

CCNE1-amplified ovarian cancers (OVCAs) and endometrial cancers (EMCAs) are associated with platinum resistance and poor survival, representing a clinically unmet need. We hypothesized that dysregulated cell-cycle progression promoted by CCNE1 overexpression would lead to increased sensitivity to low-dose WEE1 inhibition and ataxia telangiectasia and Rad3-related (ATR) inhibition (WEE1i-ATRi), thereby optimizing efficacy and tolerability. The addition of ATRi to WEE1i is required to block feedback activation of ATR signaling mediated by WEE1i. Low-dose WEE1i-ATRi synergistically decreases viability and colony formation and increases replication fork collapse and double-strand breaks (DSBs) in a CCNE1 copy number (CN)-dependent manner. Only upon CCNE1 induction does WEE1i perturb DNA synthesis at S-phase entry, and addition of ATRi increases DSBs during DNA synthesis. Inherent resistance to WEE1i is overcome with WEE1i-ATRi, with notable durable tumor regressions and improved survival in patient-derived xenograft (PDX) models in a CCNE1-level-dependent manner. These studies demonstrate that CCNE1 CN is a clinically tractable biomarker predicting responsiveness to low-dose WEE1i-ATRi for aggressive subsets of OVCAs/EMCAs.

SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing

Whole-genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy-number profiles at the cellular level. We propose SCOPE, a normalization and copy-number estimation method for the noisy scDNA-seq data. SCOPE's main features include the following: (1) a Poisson latent factor model for normalization, which borrows information across cells and regions to estimate bias, using in silico identified negative control cells; (2) an expectation-maximization algorithm embedded in the normalization step, which accounts for the aberrant copy-number changes and allows direct ploidy estimation without the need for post hoc adjustment; and (3) a cross-sample segmentation procedure to identify breakpoints that are shared across cells with the same genetic background. We evaluate SCOPE on a diverse set of scDNA-seq data in cancer genomics and show that SCOPE offers accurate copy-number estimates and successfully reconstructs subclonal structure. A record of this paper's transparent peer review process is included in the Supplemental Information.

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.

Whole-Exome Sequencing of Radiation-Induced Thymic Lymphoma in Mouse Models Identifies Notch1 Activation as a Driver of p53 Wild-Type Lymphoma

Mouse models of radiation-induced thymic lymphoma are widely used to study the development of radiation-induced blood cancers and to gain insights into the biology of human T-cell lymphoblastic leukemia/lymphoma. Here we aimed to identify key oncogenic drivers for the development of radiation-induced thymic lymphoma by performing whole-exome sequencing using tumors and paired normal tissues from mice with and without irradiation. Thymic lymphomas from irradiated wild-type (WT), p53^+/-, and Kras^LA1 mice were not observed to harbor significantly higher numbers of nonsynonymous somatic mutations compared with thymic lymphomas from unirradiated p53^-/- mice. However, distinct patterns of recurrent mutations arose in genes that control the Notch1 signaling pathway based on the mutational status of p53. Preferential activation of Notch1 signaling in p53 WT lymphomas was also observed at the RNA and protein level. Reporter mice for activation of Notch1 signaling revealed that total-body irradiation (TBI) enriched Notch1^hi CD44⁺ thymocytes that could propagate in vivo after thymocyte transplantation. Mechanistically, genetic inhibition of Notch1 signaling in immature thymocytes prevented formation of radiation-induced thymic lymphoma in p53 WT mice. Taken together, these results demonstrate a critical role of activated Notch1 signaling in driving multistep carcinogenesis of thymic lymphoma following TBI in p53 WT mice. SIGNIFICANCE: These findings reveal the mutational landscape and key drivers in murine radiation-induced thymic lymphoma, a classic animal model that has been used to study radiation carcinogenesis for over 70 years.

Human-chimpanzee fused cells reveal cis-regulatory divergence underlying skeletal evolution

Gene regulatory divergence is thought to play a central role in determining human-specific traits. However, our ability to link divergent regulation to divergent phenotypes is limited. Here, we utilized human-chimpanzee hybrid induced pluripotent stem cells to study gene expression separating these species. The tetraploid hybrid cells allowed us to separate cis- from trans-regulatory effects, and to control for non-genetic confounding factors. We differentiated these cells into cranial neural crest cells (CNCCs), the primary cell type giving rise to the face. We discovered evidence of lineage-specific selection on the hedgehog signaling pathway, including a human-specific 6-fold down-regulation of EVC2 (LIMBIN), a key hedgehog gene. Inducing a similar down-regulation of EVC2 substantially reduced hedgehog signaling output. Mice and humans lacking functional EVC2 show striking phenotypic parallels to human-chimpanzee craniofacial differences, suggesting that the regulatory divergence of hedgehog signaling may have contributed to the unique craniofacial morphology of humans.

Molecular characterization of lung squamous cell carcinoma tumors reveals therapeutically relevant alterations

INTRODUCTION

Unlike lung adenocarcinoma patients, there is no FDA-approved targeted-therapy likely to benefit lung squamous cell carcinoma patients.

MATERIALS AND METHODS

We performed survival analyses of lung squamous cell carcinoma patients harboring therapeutically relevant alterations identified by whole exome sequencing and mass spectrometry-based validation across 430 lung squamous tumors.

RESULTS

We report a mean of 11.6 mutations/Mb with a characteristic smoking signature along with mutations in TP53 (65%), CDKN2A (20%), NFE2L2 (20%), FAT1 (15%), KMT2C (15%), LRP1B (15%), FGFR1 (14%), PTEN (10%) and PREX2 (5%) among lung squamous cell carcinoma patients of Indian descent. In addition, therapeutically relevant EGFR mutations occur in 5.8% patients, significantly higher than as reported among Caucasians. In overall, our data suggests 13.5% lung squamous patients harboring druggable mutations have lower median overall survival, and 19% patients with a mutation in at least one gene, known to be associated with cancer, result in significantly shorter median overall survival compared to those without mutations.

CONCLUSIONS

We present the first comprehensive landscape of genetic alterations underlying Indian lung squamous cell carcinoma patients and identify EGFR, PIK3CA, KRAS and FGFR1 as potentially important therapeutic and prognostic target.

Prioritizing variants of uncertain significance for reclassification using a rule-based algorithm in inherited retinal dystrophies

Inherited retinal dystrophies (IRD) are a highly heterogeneous group of rare diseases with a molecular diagnostic rate of >50%. Reclassification of variants of uncertain significance (VUS) poses a challenge for IRD diagnosis. We collected 668 IRD cases analyzed by our geneticists using two different clinical exome-sequencing tests. We identified 114 unsolved cases pending reclassification of 125 VUS and studied their genomic, functional, and laboratory-specific features, comparing them to pathogenic and likely pathogenic variants from the same cohort (N = 390). While the clinical exome used did not show differences in diagnostic rate, the more IRD-experienced geneticist reported more VUS (p = 4.07e-04). Significantly fewer VUS were reported in recessive cases (p = 2.14e-04) compared to other inheritance patterns, and of all the genes analyzed, ABCA4 and IMPG2 had the lowest and highest VUS frequencies, respectively (p = 3.89e-04, p = 6.93e-03). Moreover, few frameshift and stop-gain variants were found to be informed VUS (p = 6.73e-08 and p = 2.93e-06). Last, we applied five pathogenicity predictors and found there is a significant proof of deleteriousness when all score for pathogenicity in missense variants. Altogether, these results provided input for a set of rules that correctly reclassified ~70% of VUS as pathogenic in validation datasets. Disease- and setting-specific features influence VUS reporting. Comparison with pathogenic and likely pathogenic variants can prioritize VUS more likely to be reclassified as causal.

Transcriptome-directed analysis for Mendelian disease diagnosis overcomes limitations of conventional genomic testing

BACKGROUNDTranscriptome sequencing (RNA-seq) improves diagnostic rates in individuals with suspected Mendelian conditions to varying degrees, primarily by directing the prioritization of candidate DNA variants identified on exome or genome sequencing (ES/GS). Here we implemented an RNA-seq-guided method to diagnose individuals across a wide range of ages and clinical phenotypes.METHODSOne hundred fifteen undiagnosed adult and pediatric patients with diverse phenotypes and 67 family members (182 total individuals) underwent RNA-seq from whole blood and skin fibroblasts at the Baylor College of Medicine (BCM) Undiagnosed Diseases Network clinical site from 2014 to 2020. We implemented a workflow to detect outliers in gene expression and splicing for cases that remained undiagnosed despite standard genomic and transcriptomic analysis.RESULTSThe transcriptome-directed approach resulted in a diagnostic rate of 12% across the entire cohort, or 17% after excluding cases solved on ES/GS alone. Newly diagnosed conditions included Koolen-de Vries syndrome (KANSL1), Renpenning syndrome (PQBP1), TBCK-associated encephalopathy, NSD2- and CLTC-related intellectual disability, and others, all with negative conventional genomic testing, including ES and chromosomal microarray (CMA). Skin fibroblasts exhibited higher and more consistent expression of clinically relevant genes than whole blood. In solved cases with RNA-seq from both tissues, the causative defect was missed in blood in half the cases but none from fibroblasts.CONCLUSIONSFor our cohort of undiagnosed individuals with suspected Mendelian conditions, transcriptome-directed genomic analysis facilitated diagnoses, primarily through the identification of variants missed on ES and CMA.TRIAL REGISTRATIONNot applicable.FUNDINGNIH Common Fund, BCM Intellectual and Developmental Disabilities Research Center, Eunice Kennedy Shriver National Institute of Child Health & Human Development.

Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer

Simple Summary

The identification of germline copy number variants (CNVs) by targeted nextgeneration sequencing frequently relies on in silico prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools in 17 cancer predisposition genes in a large series of 4208 female index patients with familial breast and/or ovarian cancer. We identified 77 CNVs in 76 out of 4208 patients; six CNVs were missed by at least one of the prediction tools. Experimental verification of in silico predicted CNVs is required due to high frequencies of false positive predictions. For female index patients with familial breast and/or ovarian cancer, CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further cancer predisposition genes.

Abstract

The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than BRCA1/2, mostly in ATM, CHEK2, and RAD51C and less frequently in BARD1, MLH1, MSH2, PALB2, PMS2, RAD51D, and TP53. The Sophia Genetics DDM software showed the highest sensitivity; six CNVs were missed by at least one of the non-commercial tools. The positive predictive values ranged from 5.9% (74/1249) for panelcn.MOPS to 79.1% (72/91) for ExomeDepth. Verification of in silico predicted CNVs is required due to high frequencies of false positive predictions, particularly affecting target regions at the extremes of the GC content or target length distributions. CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further BC/OC predisposition genes.

Adaptation and selection shape clonal evolution of tumors during residual disease and recurrence

The survival and recurrence of residual tumor cells following therapy constitutes one of the biggest obstacles to obtaining cures in breast cancer, but it remains unclear how the clonal composition of tumors changes during relapse. We use cellular barcoding to monitor clonal dynamics during tumor recurrence in vivo. We find that clonal diversity decreases during tumor regression, residual disease, and recurrence. The recurrence of dormant residual cells follows several distinct routes. Approximately half of the recurrent tumors exhibit clonal dominance with a small number of subclones comprising the vast majority of the tumor; these clonal recurrences are frequently dependent upon Met gene amplification. A second group of recurrent tumors comprises thousands of subclones, has a clonal architecture similar to primary tumors, and is dependent upon the Jak/Stat pathway. Thus the regrowth of dormant tumors proceeds via multiple routes, producing recurrent tumors with distinct clonal composition, genetic alterations, and drug sensitivities.

The cellular composition of recurrent tumors can provide insight into resistance to therapy and inform on second line therapies. Here, using a genetically modified mouse, the authors perform barcoding experiments of the primary tumors to allow them to study the clonal dynamics of tumor recurrence.

Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines

Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm's robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal $P$-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.

Evaluation of CNV detection tools for NGS panel data in genetic diagnostics

Although germline copy-number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. Five tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth, and CODEX2) were tested against four genetic diagnostics datasets (two in-house and two external) for a total of 495 samples with 231 single and multi-exon validated CNVs. The evaluation was performed using the default and sensitivity-optimized parameters. Results showed that most tools were highly sensitive and specific, but the performance was dataset dependant. When evaluating them in our diagnostics scenario, DECoN and panelcn.MOPS detected all CNVs with the exception of one mosaic CNV missed by DECoN. However, DECoN outperformed panelcn.MOPS specificity achieving values greater than 0.90 when using the optimized parameters. In our in-house datasets, DECoN and panelcn.MOPS showed the highest performance for CNV screening before orthogonal confirmation. Benchmarking and optimization code is freely available at https://github.com/TranslationalBioinformaticsIGTP/CNVbenchmarkeR .

SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes

Copy number variants are duplications and deletions of the genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications. Simulated datasets containing copy number variants allow comprehensive comparisons of the operating characteristics of existing and novel copy number variant detection methods. Several software applications have been developed to simulate copy number variants and other structural variants in whole-genome sequencing data. However, none of the applications reliably simulate copy number variants in whole-exome sequencing data. We have developed and tested Simulator of Exome Copy Number Variants (SECNVs), a fast, robust and customizable software application for simulating copy number variants and whole-exome sequences from a reference genome. SECNVs is easy to install, implements a wide range of commands to customize simulations, can output multiple samples at once, and incorporates a pipeline to output rearranged genomes, short reads and BAM files in a single command. Variants generated by SECNVs are detected with high sensitivity and precision by tools commonly used to detect copy number variants. SECNVs is publicly available at https://github.com/YJulyXing/SECNVs.

The pathogenicity of SLC38A8 in five families with foveal hypoplasia and congenital nystagmus

PURPOSE

A recently described subtype of foveal hypoplasia with congenital nystagmus and optic-nerve-decussation defects was found to be associated with mutations in the SLC38A8 gene. The aim of this study is to advance the clinical and molecular knowledge of SLC38A8 gene mutations.

METHODS

Five Israeli families with congenital foveal hypoplasia were studied, two of Karait Jewish origins and three of Indian Jewish origins. Subjects underwent a comprehensive ophthalmic examination including retinal photography and ocular coherence tomography. Molecular analysis including whole exome sequencing and screening of the SLC38A8 gene for specific disease-causing variants was performed.

RESULTS

Eight affected individuals were identified, all had congenital nystagmus and all but one had hypoplastic foveal pits. Anterior segment dysgenesis was observed in only one patient, one had evidence of developmental delay and another displayed early age-related macular degeneration (AMD). Molecular analysis revealed a recently described homozygous mutation, c.95T > G; p.Ile32Ser, in two families of Jewish Indian descent, and the same mutation in two families of Karaite Jewish descent. In a patient with only one pathogenic mutation (c.95T > G; p.Ile32Ser), a possible partial clinical expression of the disorder was seen. One patient of Jewish Indian descent was found to be compound heterozygous for c.95T > G; p.Ile32Ser and a novel mutation c.490_491delCT; p.L164Vfs*41.

CONCLUSIONS

In five unrelated families with congenital nystagmus and foveal hypoplasia, mutations in the SLC38A8 gene were identified. Possible partial expression in a heterozygous patient was observed and novel potential disease-related phenotypes were identified including early-onset AMD and developmental delay. A novel mutation was also identified and a similar mutation in both Indian and Karaite Jewish ethnicities could be suggestive for common ancestry.

A new case of SMABF2 diagnosed in stillbirth expands the prenatal presentation and mutational spectrum of ASCC1

Spinal muscular atrophy with congenital bone fractures 2 (SMABF2) is a rare autosomal recessive neuromuscular disorder characterized by arthrogryposis multiplex congenita and prenatal fractures of the long bones, with poor prognosis. The most affected patients present with biallelic loss-of-function nucleotide variants in ASCC1 gene, coding a subunit of the transcriptional coactivator ASC-1 complex, although the exact pathogenesis is yet unknown. This work describes the first case of SMABF2 in a stillbirth with documented evolution of the disease in the prenatal period. A microdeletion copy number variant (CNV) of about 64 Kb, involving four exons of ASCC1, was firstly detected by microarray analysis, requested for arthrogryposis and hydrops. Subsequent exome analysis disclosed a nucleotide variant of the same gene [c.1027C>T; (p. Arg343*)], resulting in the introduction of a premature termination codon. This stillbirth represents the first case of ASCC1 compound heterozygosity, due to an exonic microdeletion and a nucleotide variant, expanding the mutational spectrum of this gene. It also provides further evidence that exonic CNVs are an underestimated cause of disease-alleles and that the integrated use of the last generation genetic analysis tools, together with careful clinical evaluations, are fundamental for the characterization of rare diseases even in the prenatal setting.

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use.

Mutational landscape in genetically engineered, carcinogen-induced, and radiation-induced mouse sarcoma

Cancer development is influenced by hereditary mutations, somatic mutations due to random errors in DNA replication, or external factors. It remains unclear how distinct cell-intrinsic and -extrinsic factors impact oncogenesis within the same tissue type. We investigated murine soft tissue sarcomas generated by oncogenic alterations (KrasG12D activation and p53 deletion), carcinogens (3-methylcholanthrene [MCA] or ionizing radiation), and in a novel model combining both factors (MCA plus p53 deletion). Whole-exome sequencing demonstrated distinct mutational signatures in individual sarcoma cohorts. MCA-induced sarcomas exhibited high mutational burden and predominantly G-to-T transversions, while radiation-induced sarcomas exhibited low mutational burden and a distinct genetic signature characterized by C-to-T transitions. The indel to substitution ratio and amount of gene copy number variations were high for radiation-induced sarcomas. MCA-induced tumors generated on a p53-deficient background showed the highest genomic instability. MCA-induced sarcomas harbored mutations in putative cancer-driver genes that regulate MAPK signaling (Kras and Nf1) and the Hippo pathway (Fat1 and Fat4). In contrast, radiation-induced sarcomas and KrasG12Dp53-/- sarcomas did not harbor recurrent oncogenic mutations, rather they exhibited amplifications of specific oncogenes: Kras and Myc in KrasG12Dp53-/- sarcomas, and Met and Yap1 for radiation-induced sarcomas. These results reveal that different initiating events drive oncogenesis through distinct mechanisms.