SeqCNV: a novel method for identification of copy number variations in targeted next-generation sequencing data

Detection of copy number variations based on a local distance using next-generation sequencing data

As one of the main types of structural variation in the human genome, copy number variation (CNV) plays an important role in the occurrence and development of human cancers. Next-generation sequencing (NGS) technology can provide base-level resolution, which provides favorable conditions for the accurate detection of CNVs. However, it is still a very challenging task to accurately detect CNVs from cancer samples with different purity and low sequencing coverage. Local distance-based CNV detection (LDCNV), an innovative computational approach to predict CNVs using NGS data, is proposed in this work. LDCNV calculates the average distance between each read depth (RD) and its k nearest neighbors (KNNs) to define the distance of KNNs of each RD, and the average distance between the KNNs for each RD to define their internal distance. Based on the above definitions, a local distance score is constructed using the ratio between the distance of KNNs and the internal distance of KNNs for each RD. The local distance scores are used to fit a normal distribution to evaluate the significance level of each RDS, and then use the hypothesis test method to predict the CNVs. The performance of the proposed method is verified with simulated and real data and compared with several popular methods. The experimental results show that the proposed method is superior to various other techniques. Therefore, the proposed method can be helpful for cancer diagnosis and targeted drug development.

Clinical and Molecular Features of a Chinese Cohort With Syndromic and Nonsyndromic Retinal Dystrophies Related to the CEP290 Gene

PURPOSE

To reveal the clinical and genetic features of 54 Chinese pedigrees with syndromic or nonsyndromic retinal dystrophies related to CEP290 and to explore the genotype-phenotype correlation.

DESIGN

Retrospective cohort study.

METHODS

Patients diagnosed with nonsyndromic inherited retinal dystrophy (IRD) or syndromic ciliopathy (SCP) were enrolled. We identified 61 patients from 54 families carrying biallelic pathogenic CEP290 variants using next-generation sequencing, Sanger sequencing, and co-segregation validation. Genotype-phenotype correlation was evaluated.

RESULTS

This study included 37 IRD patients from 32 families and 24 patients with SCP from 22 pedigrees. Four retinal dystrophy phenotypes were confirmed: Leber congenital amaurosis (LCA, 46/61), early-onset severe retinal dystrophy (EOSRD, 4/61), retinitis pigmentosa (RP, 10/61), and cone-rod dystrophy (CORD, 1/61). The SCP phenotypes included Joubert syndrome (JS) (23/24) and Bardet-Biedl syndrome (BBS) (1/24). We detected 73 different CEP290 variants, of which 33 (45.2%) were not previously reported. Two novel copy number variations (CNVs) and 1 novel pathogenic synonymous change were identified. The most recurrent alterations in the IRD and SCP were p.Q123* (6/64, 9.4%) and p.I556Ffs*17 (10/44, 22.7%), respectively. IRD patients carried more stop-gain alleles (25/64, 39.1%), whereas SCP patients carried more frameshift alleles (23/44, 52.3%).

CONCLUSIONS

LCA was the most common retinal dystrophy phenotype, and JS was the most prevalent syndrome in CEP290 patients; RP/CORD and BBS may be present in early adulthood. The hot spot variants and distribution of genotypes were distinct between IRD and SCP. Our study expands the CEP290 variant spectrum and enhances the current knowledge of CEP290 heterogeneity.

Evaluation of the Oncomine Comprehensive Assay v3 panel for the detection of 1p/19q codeletion in oligodendroglial tumours

AIMS

Accurate assessment of 1p/19q codeletion status in diffuse gliomas is of paramount importance for diagnostic, prognostic and predictive purposes. While targeted next generation sequencing (NGS) has been widely implemented for glioma molecular profiling, its role in detecting structural chromosomal variants is less well established, requiring supplementary informatic tools for robust detection. Herein, we evaluated a commercially available amplicon-based targeted NGS panel (Oncomine Comprehensive Assay v3) for the detection of 1p/19q losses in glioma tissues using an Ion Torrent platform and the standard built-in NGS data analysis pipeline solely.

METHODS

Using as little as 20 ng of DNA from formalin-fixed paraffin-embedded tissues, we analysed 25 previously characterised gliomas for multi-locus copy number losses (CNLs) on 1p and 19q, including 11 oligodendrogliomas (ODG) and 14 non-oligodendroglial (non-ODG) controls. Fluorescence in-situ hybridisation (FISH) was used as a reference standard.

RESULTS

The software confidently detected combined contiguous 1p/19q CNLs in 11/11 ODGs (100% sensitivity), using a copy number cut-off of ≤1.5 and a minimum of 10 amplicons covering the regions. Only partial non-specific losses were identified in non-ODGs (100% specificity). Copy number averages of ODG and non-ODG groups were significantly different (p<0.001). NGS was concordant with FISH and was superior to it in distinguishing partial from contiguous losses indicative of whole-arm chromosomal deletion.

CONCLUSIONS

This commercial NGS panel, along with the standard Ion Torrent algorithm, accurately detected 1p/19q losses in ODG samples, obviating the need for specialised custom-made informatic analyses. This can easily be incorporated into routine glioma workflow as an alternative to FISH.

A shortest path-based approach for copy number variation detection from next-generation sequencing data

Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest in the fields of oncogene discovery, clinical decision-making, bioinformatics, and drug discovery. The advent of next-generation sequencing data makes CNV detection possible, and a large number of CNV detection tools are based on next-generation sequencing data. Due to the complexity (e.g., bias, noise, alignment errors) of next-generation sequencing data and CNV structures, the accuracy of existing methods in detecting CNVs remains low. In this work, we design a new CNV detection approach, called shortest path-based Copy number variation (SPCNV), to improve the detection accuracy of CNVs. SPCNV calculates the k nearest neighbors of each read depth and defines the shortest path, shortest path relation, and shortest path cost sets based on which further calculates the mean shortest path cost of each read depth and its k nearest neighbors. We utilize the ratio between the mean shortest path cost for each read depth and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score formula that is able to determine a score for each read depth. Based on the score profile, a boxplot is then applied to predict CNVs. The performance of the proposed method is verified by simulation data experiments and compared against several popular methods of the same type. Experimental results show that the proposed method achieves the best balance between recall and precision in each set of simulated samples. To further verify the performance of the proposed method in real application scenarios, we then select real sample data from the 1,000 Genomes Project to conduct experiments. The proposed method achieves the best F1-scores in almost all samples. Therefore, the proposed method can be used as a more reliable tool for the routine detection of CNVs.

CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank

Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.

WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering

Copy number variation (CNV) detection based on second-generation sequencing technology is the basis of much gene research, but the read depth is affected by mapping errors, repeated reads, and GC bias. The existing methods have low sensitivity to variation regions with a short length and small variation range. Therefore, it is necessary to improve the sensitivity of algorithms to short-variation fragments. This study proposes a new CNV-detection method named WAVECNV to solve this issue. The algorithm uses wavelet clustering to process the read depth and determine the normal cluster and abnormal cluster according to the size of the cluster. Then, according to the distance between genome bins and normal clusters, the outlier of each genome bin is evaluated. Finally, a statistical model is established, and the p-value test is used for calling CNVs. Through this method, the information of the short variation region is retained. WAVECNV was tested and compared with peer methods in terms of simulated data and real cancer-sequencing data. The results show that the sensitivity of WAVECNV is better than the existing methods. It also has high precision in data with low purity and coverage. In real data experiments, WAVECNV can detect more cancer genes than existing methods. Therefore, this method can be regarded as a conventional method in the field of genomic mutation analysis of cancer samples.

Rapid Molecular Diagnosis of Genetically Inherited Neuromuscular Disorders Using Next-Generation Sequencing Technologies

Neuromuscular diseases are genetically highly heterogeneous, and differential diagnosis can be challenging. Over a 3-year period, we prospectively analyzed 268 pediatric and adult patients with a suspected diagnosis of inherited neuromuscular disorder (INMD) using comprehensive gene-panel analysis and next-generation sequencing. The rate of diagnosis increased exponentially with the addition of genes to successive versions of the INMD panel, from 31% for the first iteration (278 genes) to 40% for the last (324 genes). The global mean diagnostic rate was 36% (97/268 patients), with a diagnostic turnaround time of 4–6 weeks. Most diagnoses corresponded to muscular dystrophies/myopathies (68.37%) and peripheral nerve diseases (22.45%). The most common causative genes, TTN, RYR1, and ANO5, accounted for almost 30% of the diagnosed cases. Finally, we evaluated the utility of the differential diagnosis tool Phenomizer, which established a correlation between the phenotype and molecular findings in 21% of the diagnosed patients. In summary, comprehensive gene-panel analysis of all genes implicated in neuromuscular diseases facilitates a rapid diagnosis and provides a high diagnostic yield.

Genetic analysis using targeted exome sequencing of 53 Vietnamese children with developmental and epileptic encephalopathies

Developmental and epileptic encephalopathies (DEE) refers to a group of rare and severe neurodevelopmental disorders where genetic etiologies can play a major role. This study aimed to elucidate the genetic etiologies of a cohort of 53 Vietnamese patients with DEE. All patients were classified into known electroclinical syndromes where possible. Exome sequencing (ES) followed by a targeted analysis on 294 DEE-related genes was then performed. Patients with identified causative variants were followed for 6 months to determine the impact of genetic testing on their treatment. The diagnostic yield was 38.0% (20/53), which was significantly higher in the earlier onset group (<12 months) than in the later onset group (≥12 months). The 19 identified variants belonged to 11 genes with various cellular functions. Genes encoding ion channels especially sodium voltage-gated channel were the most frequently involved. Most variants were missense variants and located in key protein functional domains. Four variants were novel and four had been reported previously but in different phenotypes. Within 6 months of further follow-up, treatment changes were applied for six patients based on the identified disease-causing variants, with five patients showing a positive impact. This is the first study in Vietnam to analyze the genetics of DEE. This study confirms the strong involvement of genetic etiologies in DEE, especially early onset DEE. The study also contributes to clarify the genotype-phenotype correlations of DEE and highlights the efficacy of targeted ES in the diagnosis and treatment of DEE.

Ocular manifestations of Chinese patients with copy number variants in the NDP gene

PURPOSE

Familial exudative vitreoretinopathy (FEVR) and Norrie disease (ND) are genetic disorders that can be caused by mutations in the NDP gene and affect retinal vasculature growth and development. This study aimed to describe the copy number variations (CNVs) in the NDP gene in Chinese FEVR families and the associated phenotypes.

METHODS

This study recruited 651 FEVR families. SeqCNV was used to analyze the CNVs in the families without mutations in known FEVR-associated genes. Multiplex ligation-dependent probe amplification and semiquantitative multiplex PCR were performed to verify the NDP CNVs. The probands and family members underwent complete ocular examinations.

RESULTS

NDP CNVs were identified in four patients from three unrelated families, accounting for 15% of the patients with NDP mutations and 0.46% of the entire FEVR cohort. Exon 2 deletions were detected in two families, and whole gene deletion was identified in one family. The affected individuals were born blind with total retinal detachment.

CONCLUSIONS

The findings confirm that CNVs are a common NDP mutation type. The CNV-associated phenotype is congenital blindness with total retinal detachment. Antenatal genetic analyses and fetal ultrasound can facilitate early diagnosis and interventions in patients with NDP mutations.

CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data

Copy number variation (CNV), is defined as repetitions or deletions of genomic segments of 1 Kb to 5 Mb, and is a major trigger for human disease. The high-throughput and low-cost characteristics of next-generation sequencing technology provide the possibility of the detection of CNVs in the whole genome, and also greatly improve the clinical practicability of next-generation sequencing (NGS) testing. However, current methods for the detection of CNVs are easily affected by sequencing and mapping errors, and uneven distribution of reads. In this paper, we propose an improved approach, CNV-MEANN, for the detection of CNVs, involving changing the structure of the neural network used in the MFCNV method. This method has three differences relative to the MFCNV method: (1) it utilizes a new feature, mapping quality, to replace two features in MFCNV, (2) it considers the influence of the loss categories of CNV on disease prediction, and refines the output structure, and (3) it uses a mind evolutionary algorithm to optimize the backpropagation (neural network) neural network model, and calculates individual scores for each genome bin to predict CNVs. Using both simulated and real datasets, we tested the performance of CNV-MEANN and compared its performance with those of seven widely used CNV detection methods. Experimental results demonstrated that the CNV-MEANN approach outperformed other methods with respect to sensitivity, precision, and F1-score. The proposed method was able to detect many CNVs that other approaches could not, and it reduced the boundary bias. CNV-MEANN is expected to be an effective method for the analysis of changes in CNVs in the genome.

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.

ERINS: Novel Sequence Insertion Detection by Constructing an Extended Reference

Next generation sequencing technology has led to the development of methods for the detection of novel sequence insertions (nsINS). Multiple signatures from short reads are usually extracted to improve nsINS detection performance. However, characterization of nsINSs larger than the mean insert size is still challenging. This article presents a new method, ERINS, to detect nsINS contents and genotypes of full spectrum range size. It integrates the features of structural variations and mapping states of split reads to find nsINS breakpoints, and then adopts a left-most mapping strategy to infer nsINS content by iteratively extending the standard reference at each breakpoint. Finally, it realigns all reads to the extended reference and infers nsINS genotypes through statistical testing on read counts. We test and validate the performance of ERINS on simulation and real sequencing datasets. The simulation experimental results demonstrate that it outperforms several peer methods with respect to sensitivity and precision. The real data application indicates that ERINS obtains high consistent results with those of previously reported and detects nsINSs over 200 base pairs that many other methods fail. In conclusion, ERINS can be used as a supplement to existing tools and will become a routine approach for characterizing nsINSs.

Co-mutation pattern, clonal hierarchy, and clone size concur to determine disease phenotype of SRSF2P95-mutated neoplasms

Somatic mutations in splicing factor genes frequently occur in myeloid neoplasms. While SF3B1 mutations are associated with myelodysplastic syndromes (MDS) with ring sideroblasts, SRSF2^P95 mutations are found in different disease categories, including MDS, myeloproliferative neoplasms (MPN), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), and acute myeloid leukemia (AML). To identify molecular determinants of this phenotypic heterogeneity, we explored molecular and clinical features of a prospective cohort of 279 SRSF2^P95-mutated cases selected from a population of 2663 patients with myeloid neoplasms. Median number of somatic mutations per subject was 3. Multivariate regression analysis showed associations between co-mutated genes and clinical phenotype, including JAK2 or MPL with myelofibrosis (OR = 26.9); TET2 with monocytosis (OR = 5.2); RAS-pathway genes with leukocytosis (OR = 5.1); and STAG2, RUNX1, or IDH1/2 with blast phenotype (MDS or AML) (OR = 3.4, 1.9, and 2.1, respectively). Within patients with SRSF2-JAK2 co-mutation, JAK2 dominance was invariably associated with clinical feature of MPN, whereas SRSF2 mutation was dominant in MDS/MPN. Within patients with SRSF2-TET2 co-mutation, clinical expressivity of monocytosis was positively associated with co-mutated clone size. This study provides evidence that co-mutation pattern, clone size, and hierarchy concur to determine clinical phenotype, tracing relevant genotype-phenotype associations across disease entities and giving insight on unaccountable clinical heterogeneity within current WHO classification categories.

A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey's fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.

Whole-Gene Deletions of FZD4 Cause Familial Exudative Vitreoretinopathy

Familial exudative vitreoretinopathy (FEVR) is an inherited disorder characterized by abnormalities in the retinal vasculature. The FZD4 gene is associated with FEVR, but the prevalence and impact of FZD4 copy number variation (CNV) on FEVR patients are unknown. The aim of this study was to better understand the genetic features and clinical manifestations of patients with FZD4 CNVs. A total of 651 FEVR families were recruited. Families negative for mutations in FEVR-associated genes were selected for CNV analysis using SeqCNV. Semiquantitative multiplex polymerase chain reaction and multiplex ligation-dependent probe amplification were conducted to verify the CNVs. Four probands were found to carry whole-gene deletions of FZD4, accounting for 5% (4/80) of probands with FZD4 mutations and 0.6% (4/651) of all FEVR probands. The four probands exhibited similar phenotypes of unilateral retinal folds. FEVR in probands with CNVs was not more severe than in probands with FZD4 missense mutations (p = 1.000). Although this is the first report of FZD4 CNVs and the associated phenotypes, the interpretation of FZD4 CNVs should be emphasized when analyzing the next-generation sequencing data of FEVR patients because of their high prevalence.

HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data

Copy number variation (CNV) is a genomic mutation that plays an important role in tumor evolution and tumor genesis. Accurate detection of CNVs from next-generation sequencing (NGS) data is still a challenging task due to artifacts such as uneven mapped reads and unbalanced amplitudes of gains and losses. This study proposes a new approach called HBOS-CNV to detect CNVs from NGS data. The central point of HBOS-CNV is that it uses a new statistic, the histogram-based outlier score (HBOS), to evaluate the fluctuation of genome bins to determine those of changed copy numbers. In comparison with existing statistics in the evaluation of CNVs, HBOS is a non-linearly transformed value from the observed read depth (RD) value of each genome bin, having the potential ability to relieve the effects resulted from the above artifacts. In the calculation of HBOS values, a dynamic width histogram is utilized to depict the density of bins on the genome being analyzed, which can reduce the effects of noises partially contributed by mapping and sequencing errors. The evaluation of genome bins using such a new statistic can lead to less extremely significant CNVs having a high probability of detection. We evaluated this method using a large number of simulation datasets and compared it with four existing methods (CNVnator, CNV-IFTV, CNV-LOF, and iCopyDav). The results demonstrated that our proposed method outperforms the others in terms of sensitivity, precision, and F1-measure. Furthermore, we applied the proposed method to a set of real sequencing samples from the 1000 Genomes Project and determined a number of CNVs with biological meanings. Thus, the proposed method can be regarded as a routine approach in the field of genome mutation analysis for cancer samples.

Molecular Diagnostics of Non-Hodgkin Lymphoma

Non-Hodgkin lymphoma encompasses a diverse group of B-cell and T-cell neoplasms. Current classification is based on clinical information, histologic assessment, immunophenotypic characteristics, and molecular alterations. A wide range of genetic alterations, including large chromosomal structural rearrangements, aneuploidies, point mutations, and copy number alterations, have been reported across all types of lymphomas. Many of these are now incorporated into the World Health Organization-defined criteria for the diagnostic evaluation of patients with lymphoid proliferations and, therefore, their accurate identification is paramount for diagnosis, subclassification, and selection of treatment. In addition to their value in the diagnostic setting, many alterations that are not routinely evaluated in standard clinical practice may still define specific disease entities as they have important implications in risk stratification, as well as roles in emerging alternate therapies and disease monitoring. Because of the complexity and range of alterations, their accurate and sensitive assessment requires a careful selection of technology. Here, we discuss the most commonly used molecular techniques in current clinical practice and highlight some of the benefits and pitfalls based on the type of alteration.

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 Density Peak-Based Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Copy number variation (CNV) is a common type of structural variations in human genome and confers biological meanings to human complex diseases. Detection of CNVs is an important step for a systematic analysis of CNVs in medical research of complex diseases. The recent development of next-generation sequencing (NGS) platforms provides unprecedented opportunities for the detection of CNVs at a base-level resolution. However, due to the intrinsic characteristics behind NGS data, accurate detection of CNVs is still a challenging task. In this article, we propose a new density peak-based method, called dpCNV, for the detection of CNVs from NGS data. The algorithm of dpCNV is designed based on density peak clustering algorithm. It extracts two features, i.e., local density and minimum distance, from sequencing read depth (RD) profile and generates a two-dimensional data. Based on the generated data, a two-dimensional null distribution is constructed to test the significance of each genome bin and then the significant genome bins are declared as CNVs. We test the performance of the dpCNV method on a number of simulated datasets and make comparison with several existing methods. The experimental results demonstrate that our proposed method outperforms others in terms of sensitivity and F1-score. We further apply it to a set of real sequencing samples and the results demonstrate the validity of dpCNV. Therefore, we expect that dpCNV can be used as a supplementary to existing methods and may become a routine tool in the field of genome mutation analysis.

Clinical Applications of Chromosomal Microarray Testing in Myeloid Malignancies

PURPOSE OF REVIEW

Knowledge of both somatic mutations and copy number aberrations are important for the understanding of cancer pathogenesis and management of myeloid neoplasms. The currently available standard of care technologies for copy number assessment such as conventional karyotype and FISH are either limited by low resolution or restriction to targeted assessment.

RECENT FINDINGS

Chromosomal microarray (CMA) is effective in characterization of chromosomal and gene aberrations of diagnostic, prognostic, and therapeutic significance at a higher resolution than conventional karyotyping. These results are complementary to NGS mutation studies. Copy-neutral loss of heterozygosity (CN-LOH), which is prognostic in AML, is currently only identified by CMA. Yet, despite the widespread availability, CMA testing is not routinely performed in diagnostic laboratories due to lack of knowledge on best-testing practices for clinical work-up of myeloid neoplasms. In this review, we provide an overview of the clinical significance of CMA in acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), and myelodysplastic/myeloproliferative neoplasms (MDS/MPN). We will also elaborate the specific clinical scenarios where CMA can provide additional information essential for management and could potentially alter treatment. Chromosomal microarray (CMA) is an effective technology for characterizing chromosomal copy number changes and copy-neutral loss of heterozygosity of diagnostic, prognostic, and therapeutic significance at a high resolution in myeloid malignancies.

RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data

Copy number variations (CNVs) are significant causes of many human cancers and genetic diseases. The detection of CNVs has become a common method by which to analyze human diseases using next-generation sequencing (NGS) data. However, effective detection of insignificant CNVs is still a challenging task. In this study, we propose a new detection method, RKDOSCNV, to meet the need. RKDOSCNV uses kernel density estimation method to evaluate the local kernel density distribution of each read depth segment (RDS) based on an expanded nearest neighbor (k-nearest neighbors, reverse nearest neighbors, and shared nearest neighbors of each RDS) data set, and assigns a relative kernel density outlier score (RKDOS) for each RDS. According to the RKDOS profile, RKDOSCNV predicts the candidate CNVs by choosing a reasonable threshold, which it uses split read approach to correct the boundaries of candidate CNVs. The performance of RKDOSCNV is assessed by comparing it with several current popular methods via experiments with simulated and real data at different tumor purity levels. The experimental results verify that the performance of RKDOSCNV is superior to that of several other methods. In summary, RKDOSCNV is a simple and effective method for the detection of CNVs from whole genome sequencing (WGS) data, especially for samples with low tumor purity.

REDBot: Natural language process methods for clinical copy number variation reporting in prenatal and products of conception diagnosis

Background

Current copy number variation (CNV) identification methods have rapidly become mature. However, the postdetection processes such as variant interpretation or reporting are inefficient. To overcome this situation, we developed REDBot as an automated software package for accurate and direct generation of clinical diagnostic reports for prenatal and products of conception (POC) samples.

Methods

We applied natural language process (NLP) methods for analyzing 30,235 in‐house historical clinical reports through active learning, and then, developed clinical knowledge bases, evidence‐based interpretation methods and reporting criteria to support the whole postdetection pipeline.

Results

Of the 30,235 reports, we obtained 37,175 CNV‐paragraph pairs. For these pairs, the active learning approaches achieved a 0.9466 average F1‐score in sentence classification. The overall accuracy for variant classification was 95.7%, 95.2%, and 100.0% in retrospective, prospective, and clinical utility experiments, respectively.

Conclusion

By integrating NLP methods in CNVs postdetection pipeline, REDBot is a robust and rapid tool with clinical utility for prenatal and POC diagnosis.

USH2A variants in Chinese patients with Usher syndrome type II and non-syndromic retinitis pigmentosa

Aims

To reveal the Usher syndrome type IIA (USH2A) gene variant profile in a large cohort of Chinese patients with non-syndromic retinitis pigmentosa (RP) or Usher syndrome type II (USH2) and to explore the genotype–phenotype correlation.

Methods

Targeted exome capture plus next-generation sequencing confirmed that 284 patients from 260 unrelated Chinese families carried USH2A disease-associated variants. Both personal medical history and family histories were reviewed. Ocular examinations were performed and audiograms were recorded if hearing loss was suspected. The genotype–phenotype correlation was evaluated by statistical analyses.

Results

A total of 230 variants in the USH2A gene were identified, of which 90 (39.13%) were novel. The most common variants in the RP and USH2 probands were p.Cys934Trp and p.Tyr2854_2894del, respectively, and 26.42% and 63.64% of the alleles in the RP and USH2 groups were truncating, respectively. Patients harbouring biallelic truncating variants had a younger age at the initial clinical visit and symptom onset than patients with missense variants; furthermore, the patients with USH2 had a younger age at the initial clinical visit and nyctalopia onset compared with the patients with RP (p<0.001). For the patients with USH2, the age of nyctalopia onset was positively correlated with that of hearing loss (p<0.05, r=0.219). In addition, three pseudo-dominant pedigrees were identified carrying biallelic USH2A variants.

Conclusions

This study enrolled the largest cohort of Chinese patients with USH2A and identified the most prevalent USH2A variants in USH2 and RP. We found that the patients with USH2 had more truncating variants and experienced an earlier decline in visual function. The findings enhance the current knowledge of USH2A heterogeneity and provide valuable information for future therapies.

CONDEL: Detecting Copy Number Variation and Genotyping Deletion Zygosity from Single Tumor Samples Using Sequence Data

Characterizing copy number variations (CNVs) from sequenced genomes is a both feasible and cost-effective way to search for driver genes in cancer diagnosis. A number of existing algorithms for CNV detection only explored part of the features underlying sequence data and copy number structures, resulting in limited performance. Here, we describe CONDEL, a method for detecting CNVs from single tumor samples using high-throughput sequence data. CONDEL utilizes a novel statistic in combination with a peel-off scheme to assess the statistical significance of genome bins, and adopts a Bayesian approach to infer copy number gains, losses, and deletion zygosity based on statistical mixture models. We compare CONDEL to six peer methods on a large number of simulation datasets, showing improved performance in terms of true positive and false positive rates, and further validate CONDEL on three real datasets derived from the 1000 Genomes Project and the EGA archive. CONDEL obtained higher consistent results in comparison with other three single sample-based methods, and exclusively identified a number of CNVs that were previously associated with cancers. We conclude that CONDEL is a powerful tool for detecting copy number variations on single tumor samples even if these are sequenced at low-coverage.

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Copy number variation (CNV) is a very important phenomenon in tumor genomes and plays a significant role in tumor genesis. Accurate detection of CNVs has become a routine and necessary procedure for a deep investigation of tumor cells and diagnosis of tumor patients. Next-generation sequencing (NGS) technique has provided a wealth of data for the detection of CNVs at base-pair resolution. However, such task is usually influenced by a number of factors, including GC-content bias, sequencing errors, and correlations among adjacent positions within CNVs. Although many existing methods have dealt with some of these artifacts by designing their own strategies, there is still a lack of comprehensive consideration of all the factors. In this paper, we propose a new method, MFCNV, for an accurate detection of CNVs from NGS data. Compared with existing methods, the characteristics of the proposed method include the following: (1) it makes a full consideration of the intrinsic correlations among adjacent positions in the genome to be analyzed, (2) it calculates read depth, GC-content bias, base quality, and correlation value for each genome bin and combines them as multiple features for the evaluation of genome bins, and (3) it addresses the joint effect among the factors via training a neural network algorithm for the prediction of CNVs. We test the performance of the MFCNV method by using simulation and real sequencing data and make comparisons with several peer methods. The results demonstrate that our method is superior to other methods in terms of sensitivity, precision, and F1-score and can detect many CNVs that other methods have not discovered. MFCNV is expected to be a complementary tool in the analysis of mutations in tumor genomes and can be extended to be applied to the analysis of single-cell sequencing data.

Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Inference of absolute copy numbers in tumor genomes is one of the key points in the study of tumor genesis. However, the mixture of tumor and normal cells poses a big challenge to this task. Accurate estimation of tumor purity (i.e., the fraction of tumor cells) is a necessary step to solve this problem. In this paper, we propose a new approach, AITAC, to accurately infer tumor purity and absolute copy numbers in a tumor sample by using high-throughput sequencing (HTS) data. In contrast to many existing algorithms for estimating tumor purity, which usually rely on pre-detected mutation genotypes (heterogeneity and homogeneity), AITAC just requires read depths (RDs) observed at the regions with copy number losses. AITAC creates a non-linear model to correlate tumor purity, observed and expected RDs. It adopts an exhaustive search strategy to scan tumor purity in a wide range, and chooses the tumor purity that minimizes the deviation between observed RDs and expected ones as the optimal solution. We apply the proposed approach to both simulation and real sequencing data sets and demonstrate its performance by comparing with two classical approaches. AITAC is freely available at https://github.com/BDanalysis/aitac and can be expected to become a useful approach for researchers to analyze copy numbers in cancer genome.

Scaling New Heights in the Genetic Diagnosis of Inherited Retinal Dystrophies

During the last 20 years, our group has focused on identifying the genes and mutations causative of inherited retinal dystrophies (IRDs). By applying massive sequencing approaches (NGS) in more than 500 familial and sporadic cases, we attained high diagnostic efficiency (85%) with a custom target gene panel and over 75% using whole exome sequencing (WES). Close to 40% of pathogenic alleles are novel mutations, which demand specific in silico tests and in vitro assays. Notably, missense variants are by far the most common type of mutation identified (around 40%), with small in-frame indels being less frequent (2%). To fill the gap of unsolved cases, when no candidate gene or only a single pathogenic allele has been identified, additional scientific and technical issues remain to be addressed. Reliable detection of genomic rearrangements and copy number variants (partial or complete), deep intronic mutations, variants that cause aberrant splicing events in retina-specific transcripts, functional assessment of hypomorphic missense alleles, mutations in regulatory sequences, the contribution of modifier genes to the IRD phenotype, and detection of low heteroplasmy mtDNA mutations are among the new challenges to be met.

Applications and analysis of targeted genomic sequencing in cancer studies

Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.

Detection of fetal trisomy and single gene disease by massively parallel sequencing of extracellular vesicle DNA in maternal plasma: a proof-of-concept validation

BACKGROUND

During human pregnancy, placental trophectoderm cells release extracellular vesicles (EVs) into maternal circulation. Trophoblasts also give rise to cell-free DNA (cfDNA) in maternal blood, and has been used for noninvasive prenatal screening for chromosomal aneuploidy. We intended to prove the existence of DNA in the EVs (evDNA) of maternal blood, and compared evDNA with plasma cfDNA in terms of genome distribution, fragment length, and the possibility of detecting genetic diseases.

METHODS

Maternal blood from 20 euploid pregnancies, 9 T21 pregnancies, 3 T18 pregnancies, 1 T13 pregnancy, and 2 pregnancies with FGFR3 mutations were obtained. EVs were separated from maternal plasma, and confirmed by transmission electronic microscopy (TEM), western blotting, and flow cytometry (FACS). evDNA was extracted and its fetal origin was confirmed by quantitative PCR (qPCR). Pair-end (PE) whole genome sequencing was performed to characterize evDNA, and the results were compared with that of cfDNA. The fetal risk of aneuploidy and monogenic diseases was analyzed using the evDNA sequencing data.

RESULTS

EVs separated from maternal plasma were confirmed with morphology by TEM, and protein markers of CD9, CD63, CD81 as well as the placental specific protein placental alkaline phosphatase (PLAP) were confirmed by western blotting or flow cytometry. EvDNA could be successfully extracted for qPCR and sequencing from the plasma EVs. Sequencing data showed that evDNA span on all 23 pairs of chromosomes and mitochondria, sharing a similar distribution pattern and higher GC content comparing with cfDNA. EvDNA showed shorter fragments yet lower fetal fraction than cfDNA. EvDNA could be used to correctly determine fetal gender, trisomies, and de novo FGFR3 mutations.

CONCLUSIONS

We proved that fetal DNA could be detected in EVs separated from maternal plasma. EvDNA shared some similar features to plasma cfDNA, and could potentially be used to detect genetic diseases in fetus.

Identification of a novel LDLR disease-causing variant using capture-based next-generation sequencing screening of familial hypercholesterolemia patients in Taiwan

BACKGROUND AND AIMS

Familial hypercholesterolemia (FH) is an autosomal dominant disorder with paramount health impacts. However, less than 1% FH patients in Taiwan were formally diagnosed, partly due to the lack of reliable cost-effective genetic testing. We aimed at using a next-generation sequencing (NGS) platform as the clinical genetic testing method for FH.

METHODS

We designed probes to capture the whole LDLR gene and all coding sequences of APOB and PCSK9, and then sequenced with Illumina MiSeq platform (2 × 300 bps). The entire pipeline was tested on 13 DNA samples with known causative variants (including 3 large duplications and 2 large deletions). Then we enrolled a new cohort of 28 unrelated FH patients with Dutch Lipid Clinic Network score ≥5. Relatives were included in the cascade screening.

RESULTS

From the 13 DNA samples, we correctly identify all the variants, including big duplications and deletions. From the new cohort, we identified the causative variants in 21 of the 28 unrelated probands; five of them carrying a novel splice site variant c.1186+2T>G in LDLR. Among the family members, the concentration of LDL cholesterol was 7.82 ± 2.13 mmol/l in LDLR c.1186+2T>G carrier group (n = 26), and was significantly higher than 3.18 ± 1.36 mmol/l in the non-carrier group (n = 25).

CONCLUSIONS

This is the first capture-based NGS testing for FH to cover the whole LDLR genomic region, and therefore making reliable structural variation detection. This panel can comprehensively detect disease-causing variants in LDLR, APOB, and PCSK9 for FH patients.

High-throughput genotyping of a full voltage-gated sodium channel gene via genomic DNA using target capture sequencing and analytical pipeline MoNaS to discover novel insecticide resistance mutations

In insects, the voltage-gated sodium channel (VGSC) is the primary target site of pyrethroid insecticides. Various amino acid substitutions in the VGSC protein, which are selected under insecticide pressure, are known to confer insecticide resistance. In the genome, the VGSC gene consists of more than 30 exons sparsely distributed across a large genomic region, which often exceeds 100 kbp. Due to this complex genomic structure, it is often challenging to genotype full coding nucleotide sequences (CDSs) of VGSC from individual genomic DNA (gDNA). In this study, we designed biotinylated oligonucleotide probes from CDSs of VGSC of Asian tiger mosquito, Aedes albopictus. The probe set effectively concentrated (>80,000-fold) all targeted regions of gene VGSC from pooled barcoded Illumina libraries each constructed from individual A. albopictus gDNAs. The probe set also captured all orthologous VGSC CDSs, except some tiny exons, from the gDNA of other Culicinae mosquitos, A. aegypti and Culex pipiens complex, with comparable efficiency as a result of the high nucleotide-level conservation of VGSC. To improve efficiency of the downstream bioinformatic process, we developed an automated pipeline-MoNaS (Mosquito Na+ channel mutation Search)-which calls amino acid substitutions in the VGSC from NGS reads and compares those to known resistance mutations. The proposed method and our bioinformatic tool should facilitate the discovery of novel amino acid variants conferring insecticide resistance on VGSC and population genetic studies on resistance alleles (with respect to the origin, selection, and migration etc.) in both clinically and agriculturally important insect pests.

Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer

Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor.

A Review of the Genomic Analysis of Children Presenting with Developmental Delay/Intellectual Disability and Associated Dysmorphic Features

This review describes the clinical criteria of developmental delay (DD)/intellectual disability (ID) as well as the various techniques that are currently implemented to diagnose neurodevelopmental disorders that typically present with associated dysmorphic features such as Angelman syndrome, Prader-Willi syndrome, and DiGeorge syndrome. These analyses include various forms of chromosomal microarray (CMA), which have proven to be superior to previously implemented techniques such as G-banded karyotyping and fluorescent in situ hybridization (FISH) analysis, as well as whole exome sequencing (WES), which is implemented as a secondary examination when CMA analysis is unrevealing. The clinical significance of identified variants and how it relates to facilitating the management of specific genetic disorders such as the above mentioned is also discussed. In addition, the importance of genomic databases and bioinformatics technologies as they relate to variant classification is also considered. Essentially, the discovery of pathogenic variants allows for enhanced management of a patient’s clinical phenotype, whereas the identification of variants of uncertain significance (VUS) has proven to have an increase in the number of associated conflicts as they typically generate more ambiguity in regard to the clinical manifestations present within the child. As a result, additional procedures need to be implemented to mitigate the issues that surround their identification. The concluding remarks are in regard to both the ethical and legal considerations of genetic testing as they relate to informed consent, testing of minors, how to handle secondary findings, as well as the anticipated future direction of genomic analysis.

TargetClone: A multi-sample approach for reconstructing subclonal evolution of tumors

Most tumors are composed of a heterogeneous population of subclones. A more detailed insight into the subclonal evolution of these tumors can be helpful to study progression and treatment response. Problematically, tumor samples are typically very heterogeneous, making deconvolving individual tumor subclones a major challenge. To overcome this limitation, reducing heterogeneity, such as by means of microdissections, coupled with targeted sequencing, is a viable approach. However, computational methods that enable reconstruction of the evolutionary relationships require unbiased read depth measurements, which are commonly challenging to obtain in this setting. We introduce TargetClone, a novel method to reconstruct the subclonal evolution tree of tumors from single-nucleotide polymorphism allele frequency and somatic single-nucleotide variant measurements. Furthermore, our method infers copy numbers, alleles and the fraction of the tumor component in each sample. TargetClone was specifically designed for targeted sequencing data obtained from microdissected samples. We demonstrate that our method obtains low error rates on simulated data. Additionally, we show that our method is able to reconstruct expected trees in a testicular germ cell cancer and ovarian cancer dataset. The TargetClone package including tree visualization is written in Python and is publicly available at https://github.com/UMCUGenetics/targetclone.

An Extended Targeted Copy Number Variation Detection Array Including 187 Genes for the Diagnostics of Neuromuscular Disorders

BACKGROUND

Our previous array, the Comparative Genomic Hybridisation design (CGH-array) for nemaline myopathy (NM), named the NM-CGH array, revealed pathogenic copy number variation (CNV) in the genes for nebulin (NEB) and tropomyosin 3 (TPM3), as well as recurrent CNVs in the segmental duplication (SD), i.e. triplicate, region of NEB (TRI, exons 82-89, 90-97, 98-105). In the light of this knowledge, we have designed and validated an extended CGH array, which includes a selection of 187 genes known to cause neuromuscular disorders (NMDs).

OBJECTIVE

Our aim was to develop a reliable method for CNV detection in genes related to neuromuscular disorders for routine mutation detection and analysis, as a much-needed complement to sequencing methods.

METHODS

We have developed a novel custom-made 4×180 k CGH array for the diagnostics of NMDs. It includes the same tiled ultra-high density coverage of the 12 known or putative NM genes as our 8×60 k NM-CGH-array but also comprises a selection of 175 additional genes associated with NMDs, including titin (TTN), at a high to very high coverage. The genes were divided into three coverage groups according to known and potential pathogenicity in neuromuscular disorders.

RESULTS

The array detected known and putative CNVs in all three gene coverage groups, including the repetitive regions of NEB and TTN.

CONCLUSIONS

The targeted neuromuscular disorder 4×180 k array-CGH (NMD-CGH-array v1.0) design allows CNV detection for a broader spectrum of neuromuscular disorders at a high resolution.

Copy number aberrations from Affymetrix SNP 6.0 genotyping data-how accurate are commonly used prediction approaches?

Copy number aberrations (CNAs) are known to strongly affect oncogenes and tumour suppressor genes. Given the critical role CNAs play in cancer research, it is essential to accurately identify CNAs from tumour genomes. One particular challenge in finding CNAs is the effect of confounding variables. To address this issue, we assessed how commonly used CNA identification algorithms perform on SNP 6.0 genotyping data in the presence of confounding variables. We simulated realistic synthetic data with varying levels of three confounding variables-the tumour purity, the length of a copy number region and the CNA burden (the percentage of CNAs present in a profiled genome)-and evaluated the performance of OncoSNP, ASCAT, GenoCNA, GISTIC and CGHcall. Furthermore, we implemented and assessed CGHcall*, an adjusted version of CGHcall accounting for high CNA burden. Our analysis on synthetic data indicates that tumour purity and the CNA burden strongly influence the performance of all the algorithms. No algorithm can correctly find lost and gained genomic regions across all tumour purities. The length of CNA regions influenced the performance of ASCAT, CGHcall and GISTIC. OncoSNP, GenoCNA and CGHcall* showed little sensitivity. Overall, CGHcall* and OncoSNP showed reasonable performance, particularly in samples with high tumour purity. Our analysis on the HapMap data revealed a good overlap between CGHcall, CGHcall* and GenoCNA results and experimentally validated data. Our exploratory analysis on the TCGA HNSCC data revealed plausible results of CGHcall, CGHcall* and GISTIC in consensus HNSCC CNA regions. Code is available at https://github.com/adspit/PASCAL.

Prevalence and properties of intragenic copy-number variation in Mendelian disease genes

Purpose

We investigated the frequencies and characteristics of intragenic copy-number variants (CNVs) in a deep sampling of disease genes associated with monogenic disorders.

Methods

Subsets of 1507 genes were tested using next-generation sequencing to simultaneously detect sequence variants and CNVs in >143,000 individuals referred for genetic testing. We analyzed CNVs in gene panels for hereditary cancer syndromes and cardiovascular, neurological, or pediatric disorders.

Results

Our analysis identified 2844 intragenic CNVs in 384 clinically tested genes. CNVs were observed in 1.9% of the entire cohort but in a disproportionately high fraction (9.8%) of individuals with a clinically significant result. CNVs accounted for 4.7–35% of pathogenic variants, depending on clinical specialty. Distinct patterns existed among CNVs in terms of copy number, location, exons affected, clinical classification, and genes affected. Separately, analysis of de-identified data for 599 genes unrelated to the clinical phenotype yielded 4054 CNVs. Most of these CNVs were novel rare events, present as duplications, and enriched in genes associated with recessive disorders or lacking loss-of-function mutational mechanisms.

Conclusion

Universal intragenic CNV analysis adds substantial clinical sensitivity to genetic testing. Clinically relevant CNVs have distinct properties that distinguish them from CNVs contributing to normal variation in human disease genes.

Resistance Mechanisms to Targeted Therapies in ROS1+ and ALK+ Non-small Cell Lung Cancer

Purpose: Despite initial benefit from tyrosine kinase inhibitors (TKIs), patients with advanced non-small cell lung cancer (NSCLC) harboring ALK (ALK⁺) and ROS1 (ROS1⁺) gene fusions ultimately progress. Here, we report on the potential resistance mechanisms in a series of patients with ALK⁺ and ROS1⁺ NSCLC progressing on different types and/or lines of ROS1/ALK-targeted therapy.Experimental Design: We used a combination of next-generation sequencing (NGS), multiplex mutation assay, direct DNA sequencing, RT-PCR, and FISH to identify fusion variants/partners and copy-number gain (CNG), kinase domain mutations (KDM), and copy-number variations (CNVs) in other cancer-related genes. We performed testing on 12 ROS1⁺ and 43 ALK⁺ patients.Results: One of 12 ROS1⁺ (8%) and 15 of 43 (35%) ALK ⁺ patients harbored KDM. In the ROS1⁺ cohort, we identified KIT and β-catenin mutations and HER2-mediated bypass signaling as non-ROS1-dominant resistance mechanisms. In the ALK⁺ cohort, we identified a novel NRG1 gene fusion, a RET fusion, 2 EGFR, and 3 KRAS mutations, as well as mutations in IDH1, RIT1, NOTCH, and NF1 In addition, we identified CNV in multiple proto-oncogenes genes including PDGFRA, KIT, KDR, GNAS, K/HRAS, RET, NTRK1, MAP2K1, and others.Conclusions: We identified a putative TKI resistance mechanism in six of 12 (50%) ROS1 ⁺ patients and 37 of 43 (86%) ALK⁺ patients. Our data suggest that a focus on KDMs will miss most resistance mechanisms; broader gene testing strategies and functional validation is warranted to devise new therapeutic strategies for drug resistance. Clin Cancer Res; 24(14); 3334-47. ©2018 AACR.

Neurodevelopmental Genetic Diseases Associated With Microdeletions and Microduplications of Chromosome 17p13.3

Chromosome 17p13.3 is a region of genomic instability that is linked to different rare neurodevelopmental genetic diseases, depending on whether a deletion or duplication of the region has occurred. Chromosome microdeletions within 17p13.3 can result in either isolated lissencephaly sequence (ILS) or Miller-Dieker syndrome (MDS). Both conditions are associated with a smooth cerebral cortex, or lissencephaly, which leads to developmental delay, intellectual disability, and seizures. However, patients with MDS have larger deletions than patients with ILS, resulting in additional symptoms such as poor muscle tone, congenital anomalies, abnormal spasticity, and craniofacial dysmorphisms. In contrast to microdeletions in 17p13.3, recent studies have attracted considerable attention to a condition known as a 17p13.3 microduplication syndrome. Depending on the genes involved in their microduplication, patients with 17p13.3 microduplication syndrome may be categorized into either class I or class II. Individuals in class I have microduplications of the YWHAE gene encoding 14-3-3ε, as well as other genes in the region. However, the PAFAH1B1 gene encoding LIS1 is never duplicated in these patients. Class I microduplications generally result in learning disabilities, autism, and developmental delays, among other disorders. Individuals in class II always have microduplications of the PAFAH1B1 gene, which may include YWHAE and other genetic microduplications. Class II microduplications generally result in smaller body size, developmental delays, microcephaly, and other brain malformations. Here, we review the phenotypes associated with copy number variations (CNVs) of chromosome 17p13.3 and detail their developmental connection to particular microdeletions or microduplications. We also focus on existing single and double knockout mouse models that have been used to study human phenotypes, since the highly limited number of patients makes a study of these conditions difficult in humans. These models are also crucial for the study of brain development at a mechanistic level since this cannot be accomplished in humans. Finally, we emphasize the usefulness of the CRISPR/Cas9 system and next generation sequencing in the study of neurodevelopmental diseases.

Genetics of cerebellar disorders

The approach to identifying a genetic cause in patients with cerebellar disorders relies on history, examination, consultation, and testing, combined with specialized expertise because they are rare and genetically diverse. Cerebellar disorders can be caused by a variety of DNA alterations including single-nucleotide changes, small insertions or deletions, larger copy number variants, and nucleotide repeat expansions, exhibiting autosomal-recessive, autosomal-dominant (inherited and de novo), X-linked, and mitochondrial modes of inheritance. Imaging findings and a variety of neurologic and nonneurologic clinical features can help direct genetic testing and choose the most appropriate strategy. Clinical and genetic diagnoses are complementary, each providing distinct information for the care of the patient. In this chapter, we provide an overview of inheritance modes for different cerebellar disorders and the variety of genetic testing and tools that are currently available to reach a genetic diagnosis, including conventional and next-generation sequencing, classic, molecular and virtual cytogenetics, testing for repeat expansions, and other techniques. Practical examples are presented in both the text and accompanying vignettes.