Copy Number Variation In Cancer Research Articles

CmirC: an integrated database of clustered miRNAs co-localized with copy number variations in cancer

Genomic rearrangements and copy number variations (CNVs) are the major regulators of clustered microRNAs (miRNAs) expression. Several clustered miRNAs are harbored in and around chromosome fragile sites (CFSs) and cancer-associated genomic hotspots. Aberrant expression of such clusters can lead to oncogenic or tumor suppressor activities. Here, we developed CmirC (Clustered miRNAs co-localized with CNVs), a comprehensive database of clustered miRNAs co-localized with CNV regions. The database consists of 481 clustered miRNAs co-localized with CNVs and their expression patterns in 35 cancer types of the TCGA. The portal also provides information on CFSs, miRNA cluster candidates, genomic coordinates, target gene networks, and gene functionality. The web portal is integrated with advanced tools such as JBrowse, NCBI-BLAST, GeneSCF, visNetwork, and NetworkD3 to help the researchers in data analysis, visualization, and browsing. This portal provides a promising avenue for integrated data analytics and offers additional evidence for the complex regulation of clustered miRNAs in cancer. The web portal is freely accessible at http://slsdb.manipal.edu/cmirclust to explore clinically significant miRNAs.

Abstract 2704: Resolving clone-and haplotype-specific copy number variation and DNA methylation in heterogeneous tumors with nanopore sequencing

Abstract Large-scale somatic genomic copy number variations (CNV) accumulate during cancer progression, resulting in a tumor comprised of collections of cells, or clones, with distinct CNV profiles. Untangling intra-tumor heterogeneity and inferring clone- and haplotype-specific CNV profiles is important for cancer research and can help inform treatment. We present a computational workflow to infer clone- and haplotype-specific cancer CNV profiles by processing long nanopore reads obtained with high-throughput bulk sequencing of a tumor and matching normal sample. The workflow first identifies and phases heterozygous germline single nucleotide polymorphisms (SNPs) in the normal sample. Nanopore reads from the tumor sample are then haplotagged with presence/absence of the phased germline SNPs. Both the overall and the haplotype-specific read counts from the tumor are then tallied over fixed-size bins tiled across the reference genome. Finally, we use the state-of-the art HATCHet matrix factorization algorithm to process the total- and allele-specific read counts and get integer clone- and allele-specific copy number profiles, as well as the clonal cellular fractions of the tumor sample. To test the proposed approach, we have nanopore sequenced to 100x average read-depth coverage a COLO829 tumor and a COLO829BL matching normal cell line. Previous NGS-based bulk and single-cell analyses of the COLO829 cell-line have revealed it to be highly aneuploid and heterogeneous. We show that the proposed nanopore-based workflow identifies clone- and haplotype-specific cancer CNV profiles in concordance with previously published results, with regions of heterogeneity in full agreement with earlier bulk and single-cell studies. Inferred CNV profiles and their clonal fractions are further supported by observed allele-frequencies of somatic SNPs. We demonstrate the stability of the obtained results across lower tumor sample sequencing coverage levels, with CNVs remaining consistent down to 40x tumor coverage, putting the proposed approach on par with the industry standard NGS-based experiments. Notably, because of the unique ability of long nanopore reads to retain single-molecule methylation signals, we were further able to identify haplotype-specific differentially methylated regions both within the tumor sample, as well as in a tumor vs normal comparison, thus shedding light on acquisition/loss of DNA modifications during tumor growth. These results show how nanopore sequencing can be used to resolve some of the complexity that characterizes structurally aberrant heterogeneous cancer samples, while also revealing the previously inaccessible dimension of haplotype-specific tumor methylation. Oxford Nanopore Technologies products are not intended for use for health assessment or to diagnose, treat, mitigate, cure, or prevent any disease or condition. Citation Format: Sergey Aganezov, John Beaulaurier, Eoghan Harrington, Sissel Juul. Resolving clone-and haplotype-specific copy number variation and DNA methylation in heterogeneous tumors with nanopore sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2704.

Thousands of high-quality sequencing samples fail to show meaningful correlation between 5S and 45S ribosomal DNA arrays in humans

The ribosomal RNA genes (rDNA) are tandemly arrayed in most eukaryotes and exhibit vast copy number variation. There is growing interest in integrating this variation into genotype–phenotype associations. Here, we explored a possible association of rDNA copy number variation with autism spectrum disorder and found no difference between probands and unaffected siblings. Because short-read sequencing estimates of rDNA copy number are error prone, we sought to validate our 45S estimates. Previous studies reported tightly correlated, concerted copy number variation between the 45S and 5S arrays, which should enable the validation of 45S copy number estimates with pulsed-field gel-verified 5S copy numbers. Here, we show that the previously reported strong concerted copy number variation may be an artifact of variable data quality in the earlier published 1000 Genomes Project sequences. We failed to detect a meaningful correlation between 45S and 5S copy numbers in thousands of samples from the high-coverage Simons Simplex Collection dataset as well as in the recent high-coverage 1000 Genomes Project sequences. Our findings illustrate the challenge of genotyping repetitive DNA regions accurately and call into question the accuracy of recently published studies of rDNA copy number variation in cancer that relied on diverse publicly available resources for sequence data.

Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing

Clear-cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. VHL and PBRM1 are the top two significantly mutated genes in ccRCC specimens, while the genetic mechanism of the VHL/PBRM1-negative ccRCC remains to be elucidated. Here we carried out a comprehensive analysis of single-cell genomic copy number variations (CNVs) in VHL/PBRM1-negative ccRCC. Genomic CNVs were identified at the single-cell level, and the tumor cells showed widespread amplification and deletion across the whole genome. Functional enrichment analysis indicated that the amplified genes are significantly enriched in cancer-related signaling transduction pathways. Besides, receptor protein tyrosine kinase (RTK) genes also showed widespread copy number variations in cancer cells. Our studies indicated that the genomic CNVs in RTK genes and downstream signaling transduction pathways may be involved in VHL/PBRM1-negative ccRCC pathogenesis and progression, and highlighted the role of the comprehensive investigation of genomic CNVs at the single-cell level in both clarifying pathogenic mechanism and identifying potential therapeutic targets in cancers.

Copy Number Analysis of Lung Tumor Progression in Mice

Background & Hypothesis 
 The xeroderma pigmentosum group C (XPC) gene encodes a protein that repairs DNA damage, like that caused by UV light. Its mutation in patients is associated with increased risk of melanoma. In our mouse model, the Xpc gene has a targeted deletion, leading to increased frequency of lung tumors in a mouse strain typically resistant to lung chemical carcinogenesis. We hypothesize that there will be enrichment of specific copy number variations (CNVs) in the genome throughout tumor progression that can distinguish the different stages of carcinogenesis in lung cancer. 
 Project Methods 
 Urethane treatments induced lung tumors in Xpc -/- (ko) mice. Laser capture microscopy was used to isolate tumor, hyperplasia, and normal tissue from lung sections. After DNA extraction, genomic DNA (gDNA) was quantified using Quantifluor assay. Whole genome amplification was completed using Repli-G kit. PCR, copy number analysis, and restriction enzyme digestion were quality control measures for amplified gDNA. Single nucleotide polymorphism (SNP) microarrays will assess CNVs in samples that are successfully amplified. 
 Results 
 DNA was successfully amplified, as confirmed by the Quantifluor assay. PCR, copy number analysis, and restriction enzyme digestion confirmed 3 out of the 4 amplified samples were comparable to matched non-amplified samples, suggesting mouse gDNA was maintained. 
 Potential Impact 
 The mouse model findings can be applied to publically available human genome databases of lung cancer CNVs. Overlapping findings in mice and human CNVs may give insight into novel pathways for effective treatment in lung cancer.

Abstract 4540: Development of well characterized breast, lung, and brain cancer copy number variation reference materials

Abstract Introduction: Copy number variation (CNV), including gene amplification and deletion, can be a key driver of oncogenesis. Pathogenic CNVs are often associated with unfavorable prognosis and drug resistance, therefore detection of these types of genetic changes will be important for personalized treatment. Many tumor profiling workflows can detect CNVs in addition to somatic mutations, but well-characterized reference materials for CNVs are not widely available, which makes it difficult to assess accuracy and sensitivity. CNV content of cell lines can be variable and may change with passages, the genomic background may not be diploid, and germline and somatic variants may not be well characterized. Remnant patient samples often lack characterization and the volumes needed for such characterization and larger validation studies. Therefore, we developed and evaluated Seraseq CNV reference materials to serve as accuracy, precision, and limit-of-detection (LOD) controls for tumor profiling assay development and validation. By providing a consistent source of purified DNA with stable CNV content, these reference materials allow for repeatable assessment of clinical diagnostic assays targeting CNVs. Methods: Seraseq CNV reference materials were prepared by mixing genomic DNA with additional copies of target genes from the well-characterized cell line GM24385. Copies of ERBB2, FGFR3, and MYC were combined with genomic DNA to simulate CNVs found in breast cancer, while sequences for EGFR, MET, and MYCN were used to mimic lung and brain cancer CNVs. Three levels of amplification were targeted: +3, +6, and +12 additional copies. The copy number of each gene was precisely quantified using digital PCR and confirmed on several NGS-based assays internally and externally. Results & Conclusions: Testing revealed CNVs of each target gene were close to the expected copy number. Good correlation was observed between digital PCR data and NGS data generated internally with the Archer VariantPlex Solid Tumor assay. In addition, internal and external NGS data showed correlation and broad assay compatibility. While CNVs observed clinically can be upwards of 50 additional copies and more easily detectable, LOD controls such as those described here are imperative in evaluating assay performance. Collectively, the data demonstrate the utility of biosynthetic Seraseq CNV reference materials, which serve as superior alternatives to DNA from cell lines or patient-derived material. Citation Format: Dana Ruminski Lowe, Jessica Dickens, Catherine Huang, Yves Konigshofer, Bharathi Anekella. Development of well characterized breast, lung, and brain cancer copy number variation reference materials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4540.

Widespread Post-transcriptional Attenuation of Genomic Copy-Number Variation in Cancer.

SummaryCopy-number variations (CNVs) are ubiquitous in cancer and often act as driver events, but the effects of CNVs on the proteome of tumors are poorly understood. Here, we analyze recently published genomics, transcriptomics, and proteomics datasets made available by CPTAC and TCGA consortia on 282 breast, ovarian, and colorectal tumor samples to investigate the impact of CNVs in the proteomes of these cells. We found that CNVs are buffered by post-transcriptional regulation in 23%–33% of proteins that are significantly enriched in protein complex members. Our analyses show that complex subunits are highly co-regulated, and some act as rate-limiting steps of complex assembly, as their depletion induces decreased abundance of other complex members. We identified 48 such rate-limiting interactions and experimentally confirmed our predictions on the interactions of AP3B1 with AP3M1 and GTF2E2 with GTF2E1. This study highlights the importance of post-transcriptional mechanisms in cancer that allow cells to cope with their altered genomes.

Identification of recurrent focal copy number variations and their putative targeted driver genes in ovarian cancer.

BackgroundGenomic regions with recurrent DNA copy number variations (CNVs) are generally believed to encode oncogenes and tumor suppressor genes (TSGs) that drive cancer growth. However, it remains a challenge to delineate the key cancer driver genes from the regions encoding a large number of genes.ResultsIn this study, we developed a new approach to CNV analysis based on spectral decomposition of CNV profiles into focal CNVs and broad CNVs. We performed an analysis of CNV data of 587 serous ovarian cancer samples on multiple platforms. We identified a number of novel focal regions, such as focal gain of ESR1, focal loss of LSAMP, prognostic site at 3q26.2 and losses of sub-telomere regions in multiple chromosomes. Furthermore, we performed network modularity analysis to examine the relationships among genes encoded in the focal CNV regions. Our results also showed that the recurrent focal gains were significantly associated with the known oncogenes and recurrent losses associated with TSGs and the CNVs had a greater effect on the mRNA expression of the driver genes than that of the non-driver genes.ConclusionsOur results demonstrate that spectral decomposition of CNV profiles offers a new way of understanding the role of CNVs in cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1085-7) contains supplementary material, which is available to authorized users.

A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer.

Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. We propose a new graph-based algorithm for identifying recurrent CNVs using the maximal clique detection technique. The algorithm has an optimal solution, which means all maximal cliques can be identified, and guarantees that the identified CNV regions are the most frequent and that the minimal regions have been delineated among tumor samples. The algorithm has successfully been applied to analyze a large cohort of breast cancer samples and identified some breast cancer-associated genes and pathways.

Abstract P4-02-03: Detection of single nucleotide variations and copy number variations in breast cancer tissue and ctDNA samples using single-nucleotide polymorphism-targeted massively multiplexed PCR

Abstract Genomic instability, the hallmark of cancer, presents with a variety of mutation types, most commonly single nucleotide variations (SNVs) and copy number variations (CNVs), which traditionally have required different methods for identification. It has proven challenging to simultaneously achieve sufficient breadth to detect CNVs and depth to detect SNVs on samples of limited input amount. The objective of this study was to validate a new methodology for detection of SNVs and CNVs in a single assay. We used a massively multiplex PCR/NGS approach combining an SNV panel covering 585 point mutation hotspots in breast cancer (Cosmic) and a CNV panel targeting 28,000 SNPs designed to detect copy number at chromosomes 1, 2, 13, 18, 21, and X, and focal regions 4p16, 5p15, 7q11, 15q, 17p, 22q11, and 22q13. We applied these panels to breast cancer cell lines and fresh frozen (FF) breast tumor samples; the presence of CNVs in circulating cell-free tumor DNA (ctDNA) in the plasma of breast cancer patients was also investigated. The CNV assay methodology was validated using genomic DNA isolated from 96 human samples with known karyotype; sensitivity to single region deletions or duplications was 100% (71/71) and specificity was 100% for normal regions in the same samples. Single-molecule sensitivity for the detection of CNVs was established by analyzing isolated single cells. Performance of the mutation assay was demonstrated with the analysis of 5 matched tumor and normal cell lines, with 24 out of 27 SNVs known to be present in these cell lines detected. The 3 undetected SNVs were determined to be a result of assay design failure. Also, multiple somatic CNVs (median: 13) were detected in all 5 tumor cell lines. Analysis of the normal cell lines found no cancer related SNVs or CNVs. In 32 FF tumor samples, 78.1% (25/32) had SNVs detected; of samples with SNVs, 88% (22/25) had SNVs in TP53 or PIK3CA. Of the same 32 FF breast tumor samples, 96.9% (31/32) showed full or partial CNVs in at least 1 and up to 15 regions; of the 31 samples with detected CNVs, 93.5% had a CNV of either 1q or 17p, two of the three most prevalent breast cancer CNVs (the 16q region was not represented in this panel). Overall, a combination of SNV and CNV testing allowed identification of genetic changes in 100% of the breast tumor samples, a significant improvement in diagnostic yield than using SNV detection alone. Of the 12 breast cancer patients with matched tumor tissue and plasma samples, 83.3% (10/12) had CNVs detected in tissue. The CNVs present in each primary tumor sample were identified in corresponding plasma ctDNA samples (1 stage IIa, 7 stage IIb, and 2 stage III). The ctDNA fractions in these samples ranged from 0.58 to 4.33%; detection required as few as 86 heterozygous SNPs per CNV. Analysis of ctDNA for cancer-associated mutations may allow earlier, safer and more accurate profiling and monitoring of breast cancer. Thus, this targeted PCR approach offers the promise of an assay able to detect both cancer-associated SNVs and CNVs in the same sample with good sensitivity and specificity, and improved detection rates compared to assays that only detect SNVs. Citation Format: Robert J Pelham, Bernhard G Zimmermann, Eser Kirkizlar, Ryan K Swenerton, Bin Hoang, Onur Sakarya, Joshua E Babiarz, Nicholas Wayham, Tudor Constantin, Styrmir Sigurjonsson, Matthew Rabinowitz, Matthew Hill. Detection of single nucleotide variations and copy number variations in breast cancer tissue and ctDNA samples using single-nucleotide polymorphism-targeted massively multiplexed PCR [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-02-03.

Improving Detection of Driver Genes: Power-Law Null Model of Copy Number Variation in Cancer.

In this paper, we study Copy Number Variation (CNV) data. The underlying process generating CNV segments is generally assumed to be memory-less, giving rise to an exponential distribution of segment lengths. In this paper, we provide evidence from cancer patient data, which suggests that this generative model is too simplistic, and that segment lengths follow a power-law distribution instead. We conjecture a simple preferential attachment generative model that provides the basis for the observed power-law distribution. We then show how an existing statistical method for detecting cancer driver genes can be improved by incorporating the power-law distribution in the null model.

Abnormal dosage of ultraconserved elements is highly disfavored in healthy cells but not cancer cells.

Ultraconserved elements (UCEs) are strongly depleted from segmental duplications and copy number variations (CNVs) in the human genome, suggesting that deletion or duplication of a UCE can be deleterious to the mammalian cell. Here we address the process by which CNVs become depleted of UCEs. We begin by showing that depletion for UCEs characterizes the most recent large-scale human CNV datasets and then find that even newly formed de novo CNVs, which have passed through meiosis at most once, are significantly depleted for UCEs. In striking contrast, CNVs arising specifically in cancer cells are, as a rule, not depleted for UCEs and can even become significantly enriched. This observation raises the possibility that CNVs that arise somatically and are relatively newly formed are less likely to have established a CNV profile that is depleted for UCEs. Alternatively, lack of depletion for UCEs from cancer CNVs may reflect the diseased state. In support of this latter explanation, somatic CNVs that are not associated with disease are depleted for UCEs. Finally, we show that it is possible to observe the CNVs of induced pluripotent stem (iPS) cells become depleted of UCEs over time, suggesting that depletion may be established through selection against UCE-disrupting CNVs without the requirement for meiotic divisions.

Haploinsufficiency of an RB-E2F1-Condensin II complex leads to aberrant replication and aneuploidy.

Genome instability is a characteristic of malignant cells; however, evidence for its contribution to tumorigenesis has been enigmatic. In this study, we demonstrate that the retinoblastoma protein, E2F1, and Condensin II localize to discrete genomic locations including major satellite repeats at pericentromeres. In the absence of this complex, aberrant replication ensues followed by defective chromosome segregation in mitosis. Surprisingly, loss of even one copy of the retinoblastoma gene reduced recruitment of Condensin II to pericentromeres and caused this phenotype. Using cancer genome data and gene-targeted mice, we demonstrate that mutation of one copy of RB1 is associated with chromosome copy-number variation in cancer. Our study connects DNA replication and chromosome structure defects with aneuploidy through a dosage-sensitive complex at pericentromeric repeats. Genome instability is inherent to most cancers and is the basis for selective killing of cancer cells by genotoxic therapeutics. In this report, we demonstrate that instability can be caused by loss of a single allele of the retinoblastoma gene that prevents proper replication and condensation of pericentromeric chromosomal regions, leading to elevated levels of aneuploidy in cancer.

Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays.

Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.

Combined analysis with copy number variation identifies risk loci in lung cancer.

Background. Lung cancer is the most important cause of cancer mortality worldwide, but the underlying mechanisms of this disease are not fully understood. Copy number variations (CNVs) are promising genetic variations to study because of their potential effects on cancer. Methodology/Principal Findings. Here we conducted a pilot study in which we systematically analyzed the association of CNVs in two lung cancer datasets: the Environment And Genetics in Lung cancer Etiology (EAGLE) and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial datasets. We used a preestablished association method to test the datasets separately and conducted a combined analysis to test the association accordance between the two datasets. Finally, we identified 167 risk SNP loci and 22 CNVs associated with lung cancer and linked them with recombination hotspots. Functional annotation and biological relevance analyses implied that some of our predicted risk loci were supported by other studies and might be potential candidate loci for lung cancer studies. Conclusions/Significance. Our results further emphasized the importance of copy number variations in cancer and might be a valuable complement to current genome-wide association studies on cancer.

Copy number variations and cancer susceptibility

DNA copy number variations (CNVs) comprise a recently discovered element of genetic variation that affects a greater cumulative fraction of the genome than single-nucleotide polymorphisms (SNPs). This review discusses current understanding of the characteristics of CNVs in the human genome and explores the emerging discoveries of both constitutional and somatic CNVs in an ever-expanding variety of human cancers. The advent of high-resolution SNP arrays has made it possible to identify CNVs. Characterization of widespread constitutional CNVs offers insight into their role in disease susceptibility, whereas somatic CNVs identify regions of the genome involved in disease phenotype. The role of CNVs in cancer has only emerged in the last 2 years, with constitutional CNVs originally being observed in the Li-Fraumeni cancer susceptibility syndrome, and more recently in neuroblastoma. It is not yet known how common or how functionally relevant CNVs will be to the process of carcinogenesis. Nonetheless, the inherent instability and structural variability that characterize cancer cell genomes make this form of genetic variation particularly intriguing to the study of cancer.

Patterns of copy number variation in cancer

Patterns of copy number variation in cancer

Large scale copy number variation (CNV) at 14q12 is associated with the presence of genomic abnormalities in neoplasia

BackgroundAdvances made in the area of microarray comparative genomic hybridization (aCGH) have enabled the interrogation of the entire genome at a previously unattainable resolution. This has lead to the discovery of a novel class of alternative entities called large-scale copy number variations (CNVs). These CNVs are often found in regions of closely linked sequence homology called duplicons that are thought to facilitate genomic rearrangements in some classes of neoplasia. Recently, it was proposed that duplicons located near the recurrent translocation break points on chromosomes 9 and 22 in chronic myeloid leukemia (CML) may facilitate this tumor-specific translocation. Furthermore, ~15–20% of CML patients also carry a microdeletion on the derivative 9 chromosome (der(9)) and these patients have a poor prognosis. It has been hypothesised that der(9) deletion patients have increased levels of chromosomal instability.ResultsIn this study aCGH was performed and identified a CNV (RP11-125A5, hereafter called CNV14q12) that was present as a genomic gain or loss in 10% of control DNA samples derived from cytogenetically normal individuals. CNV14q12 was the same clone identified by Iafrate et al. as a CNV. Real-time polymerase chain reaction (Q-PCR) was used to determine the relative frequency of this CNV in DNA from a series of 16 CML patients (both with and without a der(9) deletion) together with DNA derived from 36 paediatric solid tumors in comparison to the incidence of CNV in control DNA. CNV14q12 was present in ~50% of both tumor and CML DNA, but was found in 72% of CML bearing a der(9) microdeletion. Chi square analysis found a statistically significant difference (p ≤ 0.001) between the incidence of this CNV in cancer and normal DNA and a slightly increased incidence in CML with deletions in comparison to those CML without a detectable deletion.ConclusionThe increased incidence of CNV14q12 in tumor samples suggests that either acquired or inherited genomic variation of this new class of variation may be associated with onset or progression of neoplasia.