Segmental Copy Number Variation Research Articles

Genome-wide association study of copy number variations with shank traits in a F2 crossbred chicken population.

Copy number variations (CNVs) are large-scale changes in the DNA sequence that can affect the genetic structure and phenotype of an organism. The purpose of this study was to investigate the existing CNVs and their associations with the shank diameter (ShD) and shank length (ShL) traits using data from an F2 crossbred chicken population. To carry out the study, 312 chickens were genotyped using the Illumina 60k SNP Beadchip. The shank traits of the birds were measured from day 1 to 12 weeks of age. penncnv and cnvruler tools were used to find copy numbers and regions with copy number changes (CNVR), respectively. The CNVRanger package was used to perform a genome-wide association study between shank traits and CNVs. Gene ontology research in CNVRs was carried out using the david database. In this investigation, 966 CNVs and 606 regions with copy number changes were discovered. The copy number states and variations were randomly distributed along the length of the autosomal chromosomes. Weeks 1-4, 9 and 12 of growth revealed a significant association of copy number variations with shank traits, false discovery rate (FDR-corrected p-value < 0.01), and the majority of CNVs that were statistically significant were found on chromosomes 1-3. These CNV segments are nearby genes such as KCNJ12, FGF6 and MYF5, which are fundamental to growth and development. In addition, gene set analyses revealed terms related to muscle physiology, regulation of cellular processes and potassium channels.

Abstract A034: Development of a novel class of genomic biomarkers through mathematical modeling of cancer genome contiguity

Abstract Introduction: Chromosomal instability (CIN) is a hallmark of cancer aggressiveness. However, in lung adenocarcinoma (LUAD) the most common methods for measuring genomic instability, such as the weighted Genome Instability Index (wGII), are not reliable predictors of patient outcomes. The wGII measures the portion of the genome altered by copy number variation (CNV). The wGII quantifies aneuploidy but ignores other properties of CNVs such as their length distribution. We hypothesized that quantification of genome contiguity may overcome the limitations of wGII and help identify novel prognostic biomarkers. Method: We develop a novel measure of CIN based on mathematical modeling of genome breakage patterns. Utilizing segmented CNV profiles we infer two properties of the genome breakage process – its intensity and dispersion. Intensity is proportional to the number of genome breakpoints and is estimated by the total number of CNV segments. Dispersion quantifies spatial dependence of breakpoints over a wide range of genomic scales. It is computed by Ripley’s K-function which compares the distribution of breakpoints with a random Poisson point process. Jointly, intensity and dispersion, which we term the weighted Genome Contiguity Index (wGCI), estimate the contiguity of the genome. We contrast the wGCI with a heuristic score based on quantiles of segment lengths. Results: We sought to evaluate the association between the wGCI and patients’ survival in a proteogenomic cohort of 215 primary LUAD tumors profiled using whole-genome sequencing (WGS). In support of our hypothesis, we observed that patients had significantly better outcomes if their tumor genomes had longer segments compared to those with a high degree of fragmentation (P=0.014). Utilizing the wGCI, we found that tumor genomes with dispersed breakage (and low contiguity) were associated with worst outcomes (P=0.01). To identify mechanisms associated with dispersed breakage patterns we compared genomic features of the two groups. We found that LUAD tumors with highly fragmented genomes have a higher rate of TERT amplifications (P=0.005) and increased TERT RNA expression (P=2e-6). These tumors are also enriched for TP53 mutations (P=2e-16) and MYC amplifications (P=0.01). Given the cost and practical challenges of clinical WGS we leveraged mass-spectrometry proteomics to identify protein biomarkers. We found IGF2BP3 to be significantly upregulated (P=0.0006) in highly fragmented tumors and to be associated with worse outcomes (P=0.011). We use immunohistochemistry (IHC) to develop and validate IGF2BP3 as a prognostic biomarker in independent lung cancer cohorts. Conclusion: We introduced a novel quantitative measure of CIN that uniquely captures the impact of chromosome breakage on LUAD patients' clinical outcomes. Further, we identified upregulation of IGF2BP3 as a proxy to identify fragmented genomes using IHC. Our work underscores the value of mathematical modeling of genome breakage patterns to develop complex genomic biomarkers. Citation Format: Noshad Hosseini, Rahul Mannan, Alexey Nesvizhskii, Saravana Dhanasekaran, Marcin Cieslik. Development of a novel class of genomic biomarkers through mathematical modeling of cancer genome contiguity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr A034.

Genetic Association Analysis of Copy Number Variations for Meat Quality in Beef Cattle.

Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.

GeneToCN: an alignment-free method for gene copy number estimation directly from next-generation sequencing reads

Genomes exhibit large regions with segmental copy number variation, many of which include entire genes and are multiallelic. We have developed a computational method GeneToCN that counts the frequencies of gene-specific k-mers in FASTQ files and uses this information to infer copy number of the gene. We validated the copy number predictions for amylase genes (AMY1, AMY2A, AMY2B) using experimental data from digital droplet PCR (ddPCR) on 39 individuals and observed a strong correlation (R = 0.99) between GeneToCN predictions and experimentally determined copy numbers. An additional validation on FCGR3 genes showed a higher concordance for FCGR3A compared to two other methods, but reduced accuracy for FCGR3B. We further tested the method on three different genomic regions (SMN, NPY4R, and LPA Kringle IV-2 domain). Predicted copy number distributions of these genes in a set of 500 individuals from the Estonian Biobank were in good agreement with the previously published studies. In addition, we investigated the possibility to use GeneToCN on sequencing data generated by different technologies by comparing copy number predictions from Illumina, PacBio, and Oxford Nanopore data of the same sample. Despite the differences in variability of k-mer frequencies, all three sequencing technologies give similar predictions with GeneToCN.

Comparative analyses of copy number variations between swamp buffaloes and river buffaloes.

As an important source of genomic variation, copy number variation (CNV) contributes to environmental adaptation in worldwide buffaloes. Despite this importance, CNV divergence between swamp buffaloes and river buffaloes has not been studied previously. Here, we report 21 152 CNV regions (CNVRs) in 141 buffaloes of 20 breeds detected through multiple CNV calling strategies. Only 248 CNVRs were shared between river buffalo and swamp buffalo, reflecting great variation of CNVRs between the two subspecies. Population structure analysis based on CNVs successfully separated the two buffalo subspecies. We further assessed CNV divergence by calculating FST for genome-wide CNVs. Totally, we identified 110 significantly divergent CNV segments and 44 putatively selected genes between river buffaloes and swamp buffaloes. In particular, LALBA, a key gene controlling milk production in cattle, presented a highly differentiated CNV in the promoter region, which makes it a strong functional candidate gene for differences between swamp buffaloes and river buffaloes in traits related to milk production. Our study provides useful information of CNVs in buffaloes, which may help explain the genetic differences between the two subspecies.

Concordance between single-nucleotide polymorphism–based genomic instability assays and a next-generation sequencing–based homologous recombination deficiency test

Background:We evaluated the performance of single-nucleotide polymorphism (SNP) genotyping arrays OncoScan (Thermo Fisher Scientific, San Diego, CA) and Infinium CytoSNP-850K (CytoSNP; Illumina, Waltham, MA) for assessing homologous recombination deficiency (HRD) genomic instability.Methods:DNA (pretreatment samples) across 20 tumor types was evaluated with OncoScan, CytoSNP, and the clinically validated HRD test. Copy number variation (CNV) and loss of heterozygosity (LOH) analyses were performed with ASCATv2.5.1. Aggregate HRD genomic metrics included LOH, telomeric-allelic imbalance number (TAI), and large-scale state transition (LST). Associations between BRCA mutation (BRCAm) status and the clinically validated HRD test metric (dichotomized at a clinical cut-off) were evaluated using area under the receiver operating characteristic (AUROC); Spearman ρ was calculated for continuous metrics. CNV segmentation and HRD genomic metrics were calculated (n = 120, n = 106, and n = 126 for OncoScan, CytoSNP and clinically validated HRD test, respectively).Results:When assessed by SNP arrays, the genomic metric demonstrated good association with BRCAm (AUROC of HRD: OncoScan, 0.87; CytoSNP, 0.75) and the clinically validated test (cut-off, 42; AUROC of HRD: OncoScan, 0.92; CytoSNP, 0.91). The genomic metrics demonstrated good correlation with the clinically validated aggregate HRD test metric (ρ: OncoScan, 0.82; CytoSNP, 0.81) and for each component (ρ: OncoScan, 0.68 [LOH], 0.76 [TAI], and 0.78 [LST]; CytoSNP, 0.59 [LOH], 0.77 [TAI], and 0.82 [LST]). HRD assessed by SNP genotyping arrays and the clinically validated test showed good correlation.Conclusion:OncoScan and CytoSNP may potentially identify most HRD-positive tumors with appropriate clinically relevant cut-offs.

Copy Number Variation (CNV): A New Genomic Insight in Horses.

Simple SummaryThis study aimed to contribute to our knowledge of CNVs, a type of genomic marker in equines, by producing, for the first time, a fine-scale characterization of the CNV regions (CNVRs) in the Pura Raza Española horse breed. We found not only the existence of a unique pattern of genomic regions enriched in CNVs in the PRE in comparison with the data available from other breeds but also the incidence of CNVs across the entire genome. Since these regions could affect the structure and dose of the genes involved, we also performed a gene ontology analysis which revealed that most of the genes overlapping in CNVRs were related to the olfactory pathways and immune response.Copy number variations (CNVs) are a new-fangled source of genetic variation that can explain changes in the phenotypes in complex traits and diseases. In recent years, their study has increased in many livestock populations. However, the study and characterization of CNVs in equines is still very limited. Our study aimed to investigate the distribution pattern of CNVs, characterize CNV regions (CNVRs), and identify the biological pathways affected by CNVRs in the Pura Raza Española (PRE) breed. To achieve this, we analyzed high-density SNP genotyping data (670,804 markers) from a large cohort of 654 PRE horses. In total, we identified 19,902 CNV segments and 1007 CNV regions in the whole population. The length of the CNVs ranged from 1.024 kb to 4.55 Mb, while the percentage of the genome covered by CNVs was 4.4%. Interestingly, duplications were more abundant than deletions and mixed CNVRs. In addition, the distribution of CNVs across the chromosomes was not uniform, with ECA12 being the chromosome with the largest percentage of its genome covered (19.2%), while the highest numbers of CNVs were found in ECA20, ECA12, and ECA1. Our results showed that 71.4% of CNVRs contained genes involved in olfactory transduction, olfactory receptor activity, and immune response. Finally, 39.1% of the CNVs detected in our study were unique when compared with CNVRs identified in previous studies. To the best of our knowledge, this is the first attempt to reveal and characterize the CNV landscape in PRE horses, and it contributes to our knowledge of CNVs in equines, thus facilitating the understanding of genetic and phenotypic variations in the species. However, further research is still needed to confirm if the CNVs observed in the PRE are also linked to variations in the specific phenotypical differences in the breed.

CNV detection and their association with growth, efficiency and carcass traits in Santa Inês sheep.

Copy number variations (CNV) are an important source of genetic variation. CNV has been increasingly studied and frequently associated with diseases and productive traits in livestock animals. However, CNV-based genome-wide association studies (GWAS) in Santa Inês sheep, one of the principal sheep breeds in Brazil, have not yet been reported. Thus, the aim of this study was to investigate the association between CNV and growth, efficiency and carcass traits in sheep. The Illumina OvineSNP50 BeadChip array was used to detect CNV in 491 Santa Inês individuals. Then, CNV-based GWAS was performed with a linear mixed model approach considering a genomic relationship matrix, for ten traits: (1) growth: body weight at three (W3) and six (W6) months of age; (2) efficiency: residual feed intake (RFI) and feed efficiency (FE) and (3) carcass: external carcass length (ECL), leg length (LL), carcass yield (CY), commercial cuts weight (CCW), loin eye area (LEA) and subcutaneous fat thickness (SFT). We identified 1,167 autosomal CNV in 438 sheep, with 294 non-redundant CNV, ranging from 21.8 to 861.9kb, merged into 216 distinct copy number variation regions (CNVRs). One significant CNV segment (pFDR -value<0.05) in OAR3 was associated with CY, while another significant CNV in OAR6 was associated with RFI. Additionally, another 5 CNV segments were considered relevant for investigation in the future studies. The significant segments overlapped 4 QTLs and spanned 8 genes, including the SPAST,TGFA and ADGRL3 genes, involved in cell differentiation and energy metabolism. Therefore, the results of the present study increase knowledge about CNV in sheep, their possible impacts on productive traits, and provide information for future investigations, being especially useful for those interested in structural variations in the sheep genome.

Comparative analyses of copy number variations between Bos taurus and Bos indicus

BackgroundBos taurus and Bos indicus are two main sub-species of cattle. However, the differential copy number variations (CNVs) between them are not yet well studied.ResultsBased on the new high-quality cattle reference genome ARS-UCD1.2, we identified 13,234 non-redundant CNV regions (CNVRs) from 73 animals of 10 cattle breeds (4 Bos taurus and 6 Bos indicus), by integrating three detection strategies. While 6990 CNVRs (52.82%) were shared by Bos taurus and Bos indicus, large CNV differences were discovered between them and these differences could be used to successfully separate animals into two subspecies. We found that 2212 and 538 genes uniquely overlapped with either indicine-specific CNVRs and or taurine-specific CNVRs, respectively. Based on FST, we detected 16 candidate lineage-differential CNV segments (top 0.1%) under selection, which overlapped with eight genes (CTNNA1, ENSBTAG00000004415, PKN2, BMPER, PDE1C, DNAJC18, MUSK, and PLCXD3). Moreover, we obtained 1.74 Mbp indicine-specific sequences, which could only be mapped on the Bos indicus reference genome UOA_Brahman_1. We found these sequences and their associated genes were related to heat resistance, lipid and ATP metabolic process, and muscle development under selection. We further analyzed and validated the top significant lineage-differential CNV. This CNV overlapped genes related to muscle cell differentiation, which might be generated from a retropseudogene of CTH but was deleted along Bos indicus lineage.ConclusionsThis study presents a genome wide CNV comparison between Bos taurus and Bos indicus. It supplied essential genome diversity information for understanding of adaptation and phenotype differences between the Bos taurus and Bos indicus populations.

Copy Number Variation Detection Using Total Variation.

Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.

4. VALIDATION STUDY OF A NGS-BASED PGT-A WORKFLOW FOR MOSAICISM AND SEGMENTAL COPY NUMBER VARIATION

Introduction Recently, with the introduction of Next Generation Sequencing (NGS), preimplantation genetics test for aneuploidy (PGT-A) has been greatly advanced. While copy number variation (CNV) calling of full chromosome aberrations has been well developed by many NGS-based PGT-A workflows, the calling algorithms for mosaicism and segmental CNVs are still not well established. In this study, we assess the specificity and accuracy of a newly developing PGT-A workflow for calling mosaicism and segmental CNV. Material & methods Thirteen Coriell cell lines with known karyotypes were analyzed in this study. To test the accuracy of mosaic calling, 5 cells were precisely picked under dissection microscope and processed using the protocol for the Ion ReproSeq™ PGS Kits for the Ion GeneStudio S5™ System (Thermo Fisher Scientific). Mosaic samples with mosaicism of 0%, 20%, 40%, 60%, 80% and 100% were generated by combining defined number of cells from two cell lines. For evaluating full and segmental chromosome CNV calling, 4-5 cells of each cell lines were sampled. The sequence data was analyzed using decimal ploidy value copy number calling (mosaic) algorithm in Ion Reporter v5.10. This algorithm was combined in a workflow with either default (2Mb), 1Mb or 0.5Mb tile size reference baseline. In total, 144 samples were analyzed with 4 replications for each mosaic sample and 3 replications for all the other samples. Results To evaluate specificity and sensitivity of mosaic calling, two sets of mosaic samples were generated by mixing (i) 46,XY with 47,XX,+21, and (ii) 46,XY,del(4p) with 47,XY,+13 cell lines. Mosaic profiles of Chr13 closely matched expected values at all tested combinations, and in contrast, those of Chr21 showed expected mosaic values at 60% and 80% but not 20% and 40% groups. Meanwhile, mosaic calling workflow with 0.5Mb baseline could not detect 20% segmental mosaic loss of del(4p)27Mb, and displayed 9%, 15% and 17 % overestimation of mosaicism at 40%, 60% and 80% groups, respectively. By choosing different tile size reference baselines while keeping other parameters at default values, we tested the ability of the workflow to detect full and segmental chromosome CNV. With default baseline, trisomy of Chr13, Chr18, Chr21 and ChrX, monosomy of Chr21, dup(3q)99.1Mb, del(5p)32Mb, del(4p)27Mb and dup(6p)20.97Mb could be correctly called. With 0.5Mb baseline, dup(22q)14.75Mb and dup(9p)11Mb but not dup(16p) 3.6Mb, del(5q) 2.3Mb, dup(6p) 2.19Mb, del(9p) 1.81Mb and del(14q) 1.18 Mb, could be consistently reported with expected size and breakpoints. Of note, the workflow with 0.5Mb baseline identified some unexpected segmental CNV events of various sizes and locations, such as dup(1q)4.6Mb, del(15q)2.6Mb, del(4p)1.4Mb, del(17q)1.4Mb, dup(2q)0.9Mb, del(22q)0.8Mb and del(15q)0.7Mb. Conclusion Accuracy and sensitivity of both mosaic calling and segmental CNV callings may vary among different chromosomes, different sizes and regions of the chromosomes. Segmental mosaic calling is less sensitive and accurate than full chromosome mosaic calling. Choosing higher resolution baseline could enhance sensitivity of small segmental CNV calling but reduce specificity. Further modification and optimization of the calling algorithm for mosaicism and small segmental CNV events are needed. This study provides useful information for establishing NGS-based PGT-A guidelines.

Genome reorganization in different cancer types: detection of cancer specific breakpoint regions

BackgroundTumorigenesis is a multi-step process which is accompanied by substantial changes in genome organization. The development of these changes is not only a random process, but rather comprise specific DNA regions that are prone to the reorganization process.ResultsWe have analyzed previously published SNP arrays from three different cancer types (pancreatic adenocarcinoma, breast cancer and metastatic melanoma) and from non-malignant control samples. We calculated segmental copy number variations as well as breakpoint regions. Some of these regions were not randomly involved in genome reorganization since we detected fifteen of them in at least 20% of all tumor samples and one region on chromosome 9 where 43% of tumors have a breakpoint. Further, the top-15 breakpoint regions show an association to known fragile sites. The relevance of these common breakpoint regions was further confirmed by analyzing SNP arrays from 917 cancer cell lines.ConclusionOur analyses suggest that genome reorganization is common in tumorigenesis and that some breakpoint regions can be found across all cancer types, while others exclusively occur in specific entities.

Noise cancellation using total variation for copy number variation detection

BackgroundDue to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data’s noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm.ResultsTo investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments’ breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates.ConclusionsIn this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.

Tailoring resolution for PGT-A and PGT-SR by modifying NGS sample throughput

Unlike traditional DNA analysis platforms used for Preimplantation Genetic Testing (PGT), Next Generation Sequencing (NGS) platforms provide a highly flexible and customizable workflow for the detection of segmental copy number variations (CNVs) offering dynamic resolution. However, greater resolution is achieved at the expense of sample throughput. Herein, we aim to define the level of resolution that can be achieved with RHS’ PG-Seq with different NGS throughput. PG-Seq NGS files representing different sample throughput scenarios and containing 100Kb to 5Mb CNVs were created in silico then analysed with PG-Seq software. NGS files reflecting the expected total read count per sample from a single Illumina MiSeq run with throughput from 1-96 samples were created in silico from PG-Seq NGS files. Random sampling was used to create files with total read counts of 250,000 (96 samples), 500,000 (48 samples recommended for PG-Seq), 1 million (24 samples), 3 million (8 samples), 12 million (2 samples) and 24 million (1 sample). Artificial CNVs, comprising gains and losses ranging from 100Kb to 5Mb were added across chromosomes 1, 5, 15, 17, 19, and 22. PG-Seq software was used to call CNVs. Detection of CNVs of each size are detailed in the table below for each corresponding total NGS read count. High resolution CNV calling, for the detection of germline or de novo 100Kb segmental CNVs, required > 12 million reads, or no more than 2 samples per MiSeq run. A standard 48 sample PG-Seq run reliably detected segmental CNVs of 5Mb and should theoretically be able to detect de novo CNVs of this size. Using a higher throughput of 96 samples per run, reliable detection of CNVs should be possible down to 10Mb; while germline segmental CNVs should be detected at this low resolution, there is a risk of false positives if calling de novo segmental CNVs. In silico analysis of sequencing data highlights the technical limitations and financial implications for the detection of small segmental CNVs by NGS. If greater resolution is required, throughput needs to be reduced. Decisions regarding increased resolution and the need to validate workflows using cell line or clinical samples would best be made on a case-by-case basis, should the need arise.Tabled 1Percentage of CNVs of different size detected for each read count scenario.CNV detection % (number detected/expected)250,000 reads (96 samples)500,000 reads (48 samples)1 million reads (24 samples)3 million reads (8 samples)12 million reads (2 samples)24 million reads (1 sample)Size of CNV100Kb0 (0/180)0 (0/180)0 (0/180)0 (0/180)51.95 (93/180)90 (162/180)500Kb0 (0/200)0 (0/200)3.5 (7/200)76.5 (153/200)100 (200/2)100 (200/200)1Mb0 (0/210)1.9 (4/209)64.13 (131/204)100 (210/210)100 (210/210)100 (210/210)2Mb4.76 (10/210)49.67 (100/202)97.58 (203/208)100 (210/210)100 (210/210)100 (210/210)5Mb73.99 (134/181)99.53 (201/202)100 (220/220)100 (220/220)100 (220/220)100 (220/220) Open table in a new tab

Genomic DNA Methylation-Derived Algorithm Enables Accurate Detection of Malignant Prostate Tissues.

The current methodology involving diagnosis of prostate cancer (PCa) relies on the pathology examination of prostate needle biopsies, a method with high false negative rates partly due to temporospatial, molecular, and morphological heterogeneity of prostate adenocarcinoma. It is postulated that molecular markers have a potential to assign diagnosis to a considerable portion of undetected prostate tumors. This study examines the genome-wide DNA methylation changes in PCa in search of genomic markers for the development of a diagnostic algorithm for PCa screening. Archival PCa and normal tissues were assessed using genomic DNA methylation arrays. Differentially methylated sites and regions (DMRs) were used for functional assessment, gene-set enrichment and protein interaction analyses, and examination of transcription factor-binding patterns. Raw signal intensity data were used for identification of recurrent copy number variations (CNVs). Non-redundant fully differentiating cytosine-phosphate-guanine sites (CpGs), which did not overlap CNV segments, were used in an L1 regularized logistic regression model (LASSO) to train a classification algorithm. Validation of this algorithm was performed using a large external cohort of benign and tumor prostate arrays. Approximately 6,000 probes and 600 genomic regions showed significant DNA methylation changes, primarily involving hypermethylation. Gene-set enrichment and protein interaction analyses found an overrepresentation of genes related to cell communications, neurogenesis, and proliferation. Motif enrichment analysis demonstrated enrichment of tumor suppressor-binding sites nearby DMRs. Several of these regions were also found to contain copy number amplifications. Using four non-redundant fully differentiating CpGs, we trained a classification model with 100% accuracy in discriminating tumors from benign samples. Validation of this algorithm using an external cohort of 234 tumors and 92 benign samples yielded 96% sensitivity and 98% specificity. The model was found to be highly sensitive to detect metastatic lesions in bone, lymph node, and soft tissue, while being specific enough to differentiate the benign hyperplasia of prostate from tumor. A considerable component of PCa DNA methylation profile represent driver events potentially established/maintained by disruption of tumor suppressor activity. As few as four CpGs from this profile can be used for screening of PCa.

Statistical Detection of Genome Differences Based on CNV Segments.

Population analysis using copy number variation (CNV) is far more complex than analysis using SNPs because of the diverse copy number and inconsistent boundaries of CNVs in different individuals that causes changes in frequency. Multiple studies have reported CNV regions associated with diseases or body traits based on a CNV segmentation strategy that condenses calls from multiple different sources into a genotype state. Here, we provide a guideline of how to generate CNV segments from known CNV results, and how to detect genome differences based on CNV segments.

Characterization of Copy Number Variation's Potential Role in Marek's Disease.

Marek’s Disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. Chicken Lines 63 and 72, as well as their recombinant congenic strains (RCS) with varied susceptibility to MD, are ideal models to study the complex mechanisms of genetic resistance to MD. In this study, we investigated copy number variation (CNV) in these inbred chicken lines using the Affymetrix Axiom HD 600 K SNP genotyping array. We detected 393 CNV segments across all ten chicken lines, of which 12 CNVs were specifically identified in Line 72. We then assessed genetic structure based on CNV and observed markedly different patterns. Finally, we validated two deletion events in Line 72 and correlated them with genes expression using qPCR and RNA-seq, respectively. Our combined results indicated that these two CNV deletions were likely to contribute to MD susceptibility.

AB015. The relationship between copy number variations and high myopia in Chinese: a case-control study

: As a complex disease, myopia is the most common eye disease worldwide. Many myopia susceptibility genes or variants have been successfully identified in the past years by genome-wide genetic association studies (GWAS), which focus mainly on the single-nucleotide polymorphisms. Little attention has been paid to examine the role of copy number variations (CNVs) in refractive error and myopia. This study adopted a systematic strategy to investigate the role of CNVs in high myopia. In the discovery phase, a pilot GWAS suggests putative CNVs for follow-up. Multiplex ligation-dependent probe amplification was then used to quantify the copy number of 89 CNV segments in 737 case-control samples in the second phase and then 24 top-ranking CNVs in a second group of 1,029 case-control samples in the final validation phase. This validation phase identified 22 significant CNVs. Further work is needed to examine the role of these few CNVs in myopia development.

Alteration of Chromatin Modifiers and Misregulation of Transcription Factors Define the Genomic Profile of Infant AML

AML is a malignancy of hematopoietic progenitor cells characterized by differentiation arrest leading to hematopoietic insufficiency secondary to the accumulation of hematopoietic progenitors. While much is known about the karyotypic and immunophenotypic makeup of AML, comprehensive genomic data is still emerging. The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) AML project, a cooperative effort between the National Cancer Institute (NCI) and the Children's Oncology Group (COG), has sought to deepen the knowledge-base of genomic alterations that are present in childhood cancers.Sequence data from 918 children with AML was available, including 227 whole genome, 145 transcriptome, and 691 targeted exome capture. We identified 164 patients diagnosed with AML before the age of 2 years, which contributed to our study. From these data, the presence of fusions, somatic SNVs, and copy number alterations (for those with whole genome data) were assessed and evaluated in a pathway-centric analysis (TargetMine, http://targetmine.mizuguchilab.org). The OncoPrinter function (www.cbioportal.org) was used to visualize the mutations.Of the 164 patients <2 years of age, all but 3 (98%) had at least one detectable genomic alteration. In contrast to older patients, structural alterations were the predominant variants in this age group with 142 patients (87%) harboring translocations. The most prevalent fusion involved KMT2A (MLL) with 14 different partners in 45%, NUP98 fusions with 6 different fusion partners in 8%, and CBFA2T3-GLIS2 fusions in 7%. Other notable fusions include CBFB-MYH11 (6%), RUNX1 with 3 fusion partners (3%), RBM15-MKL1 (3%), and translocations involving ETV6 and KAT6A (2% each). In addition to fusions, copy number variations (CNVs) including deletions, duplications and copy-neutral LOH were observed in 33 patients (20%). Regions of involvement of these CNVs ranged from segmental intragenic CNVs to larger chromosomal gains, trisomy 19 or 21 among the most frequent (7% and 6%, respectively). Intragenic exon 8 and 9 deletion of the CBLgene was the most common intragenic abnormality observed in 5 patients (3%).Sequence variants including Single Nucleotide variants (SNVs) as well as insertions/deletions were detected in 102 patients (62%). Activating mutations of the RAS/MAPK signaling pathway were the predominant variants with prevalence of 46% (NRAS (18%), KRAS (14%), FLT3 (7%), KIT (3%), CBL and PTPN11 (5% each). Overall, 75 patients had a concomitant structural alteration and a RAS/MAPK variant, suggesting cooperation between these two class of variants. Additional sequence variants included mutations in WT1 (3%), CREBBP (2%), TET2 (2%), and GATA1 (2%).Known fusions involving KMT2A were seen in 76 patients (46%). Where genomic data were available, the site of breakpoint varied significantly within KMT2A (16 different breakpoints) ranging from 5’ ATH motif to the 3’ SET domain with 6/16 fusions clustering in the ring finger domain of the gene. KMT2A-SEPT6 fusion was seen in 6 patients (8%) and was uniquely detected in those <2 years of age (P<0.0001). In those with KMT2A fusions, additional CNVs involving KMT2Awere seen in 3 patients. Concomitant mutations in RAS activating signaling pathway were seen in 43 cases (57%).To determine whether these alterations were enriched in any functional pathways, genes involved in fusions/SNVs were uploaded in the TargetMine data warehouse and analyzed for pathway enrichment. Strikingly, 67% of all known fusions in this cohort contained a gene from the Chromatin Modifier classification including CREBBP, EP300, KAT6A, KDM5A, KMT2A, and NCOA2 and an additional 15% of fusion partners could be categorized as genes involved in transcriptional regulation (CBFB, ETV6, RUNX1, and PDGFRB). Likewise, 75% of all SNVs were enriched in pathways involving transcriptional regulation by TP53, (CREBBP, FLT3, GATA1, KIT, KMT2A, KRAS, NRAS, and WT1).Although sequence variants are the most common variants in older AML, structural alterations dominate the genomic landscape in children with AML <2 years of age with a prevalence of >90% (translocation or CNV). Cooperating mutations of the RAS/MAPK pathway appear to be the dominant variants that in cooperation with the chromatin modifying alterations might contribute to the leukemic initiation and progression. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Abstract 131: Allelic imbalance analysis of uterine carcinosarcoma: An inquiry into the dual nature of the neoplasm

Abstract Uterine carcinosarcoma (UCS), also known as Malignant Mixed Mullerian Tumor, is a neoplasm of the female genital tract that shows histological features of both carcinoma and sarcoma. Some studies have indicated that UCS arises from sarcomatous differentiation of high-grade carcinoma while others have confirmed a bi-clonal nature of the tumor. Given these differences, further investigation in the origin of this tumor with newer approaches and technologies is warranted. In this study we determined the allelic imbalance (AI) profiles of the two components of UCS and compared the AI events that were shared by, or differed between, them. Samples were obtained from 10 patients diagnosed with UCS and were preserved in formalin fixed paraffin embedded (FFPE) blocks. The two components (carcinoma &amp; sarcoma) were identified and micro-dissected, and whole-genome DNA micro-arrays were applied to the isolated DNA. We then applied a sensitive computational method that combines haplotype information with SNP array data to identify somatic segmental copy number variations and copy-neutral loss of heterozygosity (cnLOH). After we obtained the AI events we filtered small events (&amp;lt;2 MB) to rule out germline duplications. We then obtained events that are common to both components by using a criterion of 80% reciprocal overlap. All such events were classified as a gain, loss or cnLOH. We found that, on an average, 80% of the events found in the carcinoma component are common with the sarcoma component. Conversely, only 64% of events seen in the sarcoma component are found in the carcinoma. This may be in part due to the fact that the sarcoma component usually showed greater number of events and most of these events were larger than the corresponding carcinoma component. Also the sarcoma component showed an increased degree of allelic imbalance than the counterpart. We then looked into particular regions of known tumor suppressor and oncogenes and identified loss in the TP53 gene region (60% of tumors), gains in PIK3CA and PTEN gene regions (40% of tumors), among other events. Our analysis showed that the sarcomatous component shared major portions of the AI profile with the carcinomatous component, but also showed additional events and a greater degree of AI. One explanation for this observation can be that the sarcomatous component retains the AI profile of the carcinomatous component and also gathers additional imbalance. This suggests directionality to the development of UCS: that it arises by sarcomatous differentiation of already existing carcinoma of the uterus. We are pursuing additional assessments of point mutations and expression profiles to complement our existing analyses. Citation Format: Aditya S. Deshpande, Zachary Weber, Raed Sulaiman, Natasha Flier, Cheryl Ageton, Mary Fagerness, Joseph Sulaiman, Gareth E. Davies, David Starks, Luis Rojas-Espaillat, Paul A. Scheet, Erik Ehli. Allelic imbalance analysis of uterine carcinosarcoma: An inquiry into the dual nature of the neoplasm. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 131.