Features Of Copy Number Variations Research Articles

Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.

Chromothripsis detection with multiple myeloma patients based on deep graph learning.

Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts' efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data. To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights. The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.

Predicting recurrence and metastasis risk of endometrial carcinoma via prognostic signatures identified from multi-omics data.

ObjectivesEndometrial carcinoma (EC) is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of EC are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of EC. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients.MethodsThe mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), copy number variation (CNV) data and clinical information of patients with EC were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers.ResultsWe achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation.ConclusionWe developed a computational model using omics information, which is able to predicting recurrence and metastasis risk of EC accurately.

CRIA: An Interactive Gene Selection Algorithm for Cancers Prediction Based on Copy Number Variations.

Genomic copy number variations (CNVs) are among the most important structural variations of genes found to be related to the risk of individual cancer and therefore they can be utilized to provide a clue to the research on the formation and progression of cancer. In this paper, an improved computational gene selection algorithm called CRIA (correlation-redundancy and interaction analysis based on gene selection algorithm) is introduced to screen genes that are closely related to cancer from the whole genome based on the value of gene CNVs. The CRIA algorithm mainly consists of two parts. Firstly, the main effect feature is selected out from the original feature set that has the largest correlation with the class label. Secondly, after the analysis involving correlation, redundancy and interaction for each feature in the candidate feature set, we choose the feature that maximizes the value of the custom selection criterion and add it into the selected feature set and then remove it from the candidate feature set in each selection round. Based on the real datasets, CRIA selects the top 200 genes to predict the type of cancer. The experiments' results of our research show that, compared with the state-of-the-art related methods, the CRIA algorithm can extract the key features of CNVs and a better classification performance can be achieved based on them. In addition, the interpretable genes highly related to cancer can be known, which may provide new clues at the genetic level for the treatment of the cancer.

Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization

Integrative analysis of histopathology images and genomic data enables the discovery of potential biomarkers and multimodal association patterns. However, few studies have established effective association models for complex diseases, such as sarcoma, by combining histopathological images with multiple genetic variation data. Here, we present an integrative multiple genomic imaging framework called multi-dimensional constrained joint non-negative matrix factorization (MDJNMF) to identify modules related to lung metastasis of sarcomas based on sample-matched whole-solid image, DNA methylation, and copy number variation features. Three types of feature matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. The correlation between image features and genetic variation features is used as network-regularized constraints to improve the module accuracy. Sparsity and orthogonal constraints are utilized to achieve the modular sparse solution. Multi-level analysis indicates that our method effectively discovers biologically functional modules associated with sarcoma or lung metastasis. The representative module reveals a significant correlation between image features and genetic variation features and excavates potential diagnostic biomarkers. In summary, the proposed method provides new clues for identifying association patterns and biomarkers using multiple types of data sources for other diseases.

Copy Number Signatures Predict Chromothripsis and Poor Clinical Outcome in Newly Diagnosed Multiple Myeloma Patients

Copy Number Signatures Predict Chromothripsis and Poor Clinical Outcome in Newly Diagnosed Multiple Myeloma Patients

The genes CNV features in the tumor cells in patients with ovarian serous adenocarcinoma.

e13641 Background: Serous adenocarcinoma is the most common subtype of ovarian cancer. Early diagnosis of this disease can significantly improve the prognosis. Development of more effective methods for early diagnosis and identification of more reliable markers require understanding the molecular mechanisms underlying the malignant behavior of ovarian epithelial cells. The aim of the study was to analyze the copy number variation (CNV) of genes that regulate apoptosis, DNA repair, cell proliferation, metabolism and estrogen reception in tumor and normal ovarian cells. Methods: For the study, we used tissue sections from FFPE blocks of 65 patients with serous ovarian adenocarcinoma diagnosis. Tumor and normal ovarian epithelial cells were isolated using laser microdissection with non-contact capture (Palm MicroBeam, Carl Zeiss). DNA was extracted from cells using the phenol-chloroform method. CNV of 33 genes (BRAF, KRAS, EGFR, PIK3CA, PTEN, TP53, BRCA1, BRCA2, PRKCI, NOTCH1, AKT1, BAX, CASP7, CASP3, CASP8, CASP9, MDM2, BCL2, CYP1A1, CYP1A1, CYP1A2, CYP1A2, CYP1A2, CYP1A1, CYP1A2, CYP1, ESR2, GPER, STS, SULT1A, SULT1E1, OCT4, SOX2, C-MYC, SOX18, SCNN1A) were determined by Real-Time qPCR method (reference B2M, GAPDH). Statistical analysis was performed using the Mann-Whitney test. For cluster analysis (Hierarchical Clustering, Euclidean distance) scripts in R were used. Results: A statistically significant (p < 0.005) increase in the CNV of PTEN, MDM2, SOX2, CYP1B1, ESR1, and SULT1E1 by 2.0, 2.0, 1.8, 2.5, 3.0 and 2.0 times, respectively, was found, as well as a 2.0-fold decrease in the CNV of CASP3 and CASP8 in tumor cells relatively to normal ones. Cluster analysis allowed us to distinguish 2 groups of serous adenocarcinoma samples that differed in gene CNV (p < 0.005): in group 1 (n = 40), the copy number of the SOX2, MDM2, ESR1, CYP1B1 and SULT1E1 genes was increased and the copy number of the TP53 and BRCA2 genes was reduced, in group 2 (n = 25), copy number of PTEN, PIK3CA, BCL2 was increased and copy number of BAX, CASP3 and CASP8 was lower. Conclusions: CNV analysis revealed the most characteristic markers of ovarian serous adenocarcinoma cells. Based on the differential CNV, 2 molecular subtypes of serous adenocarcinoma were distinguished.

Functional and population genetic features of copy number variations in two dairy cattle populations

BackgroundCopy Number Variations (CNVs) are gain or loss of DNA segments that are known to play a role in shaping a wide range of phenotypes. In this study, we used two dairy cattle populations, Holstein Friesian and Jersey, to discover CNVs using the Illumina BovineHD Genotyping BeadChip aligned to the ARS-UCD1.2 assembly. The discovered CNVs were investigated for their functional impact and their population genetics features.ResultsWe discovered 14,272 autosomal CNVs, which were aggregated into 1755 CNV regions (CNVR) from 451 animals. These CNVRs together cover 2.8% of the bovine autosomes. The assessment of the functional impact of CNVRs showed that rare CNVRs (MAF < 0.01) are more likely to overlap with genes, than common CNVRs (MAF ≥ 0.05). The Population differentiation index (Fst) based on CNVRs revealed multiple highly diverged CNVRs between the two breeds. Some of these CNVRs overlapped with candidate genes such as MGAM and ADAMTS17 genes, which are related to starch digestion and body size, respectively. Lastly, linkage disequilibrium (LD) between CNVRs and BovineHD BeadChip SNPs was generally low, close to 0, although common deletions (MAF ≥ 0.05) showed slightly higher LD (r2 = ~ 0.1 at 10 kb distance) than the rest. Nevertheless, this LD is still lower than SNP-SNP LD (r2 = ~ 0.5 at 10 kb distance).ConclusionsOur analyses showed that CNVRs detected using BovineHD BeadChip arrays are likely to be functional. This finding indicates that CNVs can potentially disrupt the function of genes and thus might alter phenotypes. Also, the population differentiation index revealed two candidate genes, MGAM and ADAMTS17, which hint at adaptive evolution between the two populations. Lastly, low CNVR-SNP LD implies that genetic variation from CNVs might not be fully captured in routine animal genetic evaluation, which relies solely on SNP markers.

Deletion Variants of RABGAP1L, 10q21.3, and C4 Are Associated With the Risk of Systemic Lupus Erythematosus in Korean Women

Several copy number variations (CNVs) have been found to be associated with systemic lupus erythematosus (SLE) through the target gene approach. However, genome-wide features of CNVs and their role in the risk of SLE remain unknown. The aim of this study was to identify SLE-associated CNVs in Korean women. Genome-wide assessments of CNVs were performed in 382 SLE patients and 191 control subjects, using an Illumina HumanHap610 BeadChip genotyping platform. SLE-associated CNV regions that were identified by genome-wide association study (GWAS) were replicated in quantitative polymerase chain reaction (PCR) and deletion-typing PCR analyses in an independent sample set comprising 564 SLE patients and 511 control subjects. Of 144 common CNV regions, 3 deletion-type CNV regions in 1q25.1, 8q23.3, and 10q21.3 were found to be significantly associated with SLE by GWAS analysis. In the independent replication, the CNV regions in 1q25.1 (RABGAP1L) and 10q21.3 were successfully replicated (odds ratio [OR] 1.30, P=0.038 and OR 1.90, P=3.6×10(-5), respectively), and the associations were confirmed again by deletion-typing PCR. The CNV region in the C4 gene, which showed a potential association in the discovery stage, was included in the replication analysis and was found to be significantly associated with the risk of SLE (OR 1.88, P=0.01). Through deletion-typing PCR, the exact sizes and breakpoint sequences of the deletions were defined. Individuals with the deletions in all 3 loci (RABGAP1L, 10q21.3, and C4) had a much higher risk of SLE than did those without any deletions in the 3 loci (OR 5.52, P=3.9×10(-4)). These CNV regions can be useful to identify the pathogenic mechanisms of SLE, and might be used to more accurately predict the risk of SLE by taking into consideration their synergistic effects on disease susceptibility.

A pilot study on collective effects of 22q13.31 deletions on gray matter concentration in schizophrenia.

The association of copy number variation (CNV) with schizophrenia has been reported with evidence of increased frequency of both rare and large CNVs. Yet, little is known about the impact of CNVs in brain structure. In this pilot study, we explored collective effects of all CNVs in each cytogenetic band on the risk of schizophrenia and gray matter variation measured in structural magnetic resonance imaging. With 324 participants’ CNV profiles (151 schizophrenia patients and 173 healthy controls), we first extracted specific CNV features that differ between patients and controls using a two sample t-test, and then tested their associations with gray matter concentration using a linear regression model in a subset of 301 participants. Our data first provided evidence of population structure in CNV features where elevated rare CNV burden in schizophrenia patients was confounded by the levels associated with African American subjects. We considered this ethnic group difference in the following cytoband analyses. Deletions in one cytoband 22q13.31 were observed significantly (p<0.05) more in patients than controls from all samples after controlling ethnicity, and the deletion load was also significantly (p = 1.44×10−4) associated with reduced gray matter concentration of a brain network mainly comprised of the cingulate gyrus and insula. Since 80% deletion carriers were patients, patients with deletions also showed reduced gray matter concentration compared with patients without deletions (p = 3.36×10−4). Our findings indicate that regional CNVs at 22q13.31, no matter the size, may influence the risk of schizophrenia with a remarkably increased mutation rate and with reduced gray matter concentration in the peri-limbic cortex. This proof-of-concept study suggests that the CNVs occurring at some ‘hotspots’ may in fact cause biological downstream effects and larger studies are important for confirming our initial results.

Whole Genome Copy Number Variation Analysis of Chronic Lymphocytic Leukemia (CLL) Cells From Early-Intermediate Stage, High Risk CLL Patients Prior to First Treatment Reveals New Loss of Heterozygosity and Duplication Events in the CLL Genome.

Abstract 1265Poster Board I-287 IntroductionFluorescence in situ hybridization (FISH), in combination with other markers, is used as a prognostic tool for CLL patients at diagnosis. The presence or absence of trisomy 12 and deletions at 13q, 11q, and 17p helps to predict disease progression and to stratify patients for therapeutic decisions. We hypothesized that whole genome single nucleotide polymorphism (SNP)-based copy number variation (CNV) analysis would capture all of the information in current CLL FISH panels and would reveal new CNV features in the CLL genome. Patients and MethodsNineteen early-intermediate clinical stage, untreated CLL patients aged 29 to 77 were determined to be at high risk for disease progression by FISH, IgVH mutation status, ZAP-70, and CD38 prognostic markers. CLL cells and normal cells were separated by magnetic bead selection from patient peripheral blood samples with absolute lymphocyte counts that ranged from 7.4 to 162 × 109/L. CNV analysis was performed on purified genomic DNA from the CLL cells and from normal cells for each patient in order to distinguish acquired CNVs in malignant cells from polymorphic CNVs in the human genome. We used the Illumina human660w-quad beadchip, a SNP-based microarray for whole-genome genotyping and CNV analysis that contains more than 550,000 tag SNPs and approximately 100,000 additional markers that target regions of common CNV. CNV data was analyzed using CNV partition (Illumina Genome Studio software) and PennCNV. Results100% concordance is found between del(13q), del(11q), and del(17p) FISH abnormalities and loss of heterozygosity (LOH) at 13q, 11q, and 17p by CNV analysis. All three patients with trisomy 12 by FISH show copy number(CN)=3 of chromosome 12 by CNV analysis. Of 15 patients with del(13q) by FISH, 12 out of 15 have regions of hemizygous deletion on 13q that vary from ∼830 Kb to ∼38 Mb. The smallest region of LOH is located within 13q14.3. Three out of 15 patients show homozygous deletion within 13q14.3. One of these 3 patients has copy-neutral LOH of the entire 13q arm with an embedded 835 Kb segment of homozygous deletion at 13q14.3. Two patients have large discontinuous segments of LOH on 13q, indicating complex interstitial deletion events. Two out of 5 patients with del(13q) as a sole FISH abnormality show additional CNV events in the CLL genome. One of these patients has copy neutral LOH at 2q33.1-telomere(tel). One other patient with sole del(13q) FISH shows LOH events at 10q23.31-23.33 and at 15q15.1. Five out of six patients with del(11q) by FISH have either 13q LOH (n=4) or chromosome 12 CN=3 (n=1) without any other CNV events detected in the CLL genome. One patient with trisomy 12 and del(11q) by FISH has three additional acquired CNV abnormalities in the CLL genome: LOH at 7p15.2-tel, LOH at 11p13, and CN=3 at 3q24-tel. In contrast to patients with del(11q), del(13q), and trisomy 12, patients with del(17p) by FISH have numerous acquired CNV abnormalities in the CLL genome. These include LOH events at 1p34.3-p34.2, 2q34-q36.3, 3p21.31-tel, 4p13, 4p15.1-tel, 15q11.2-q14 and 15q14-q15.3, 16p13.3-tel, 16p13.11, 16p13.2, 18p11.21-tel, 20p11.21-tel, and 20q13.2-q13.31. CN=3 at 2p12-tel is detected in 2 out of 5 patients with 17p hemizygous deletion. One out of 5 patients with 17p hemizygous deletion shows CN=3 at 10q22.2-tel. One other patient also with 17p hemizygous deletion shows CN=3 at 22q12.2-tel. ConclusionsWhole genome CNV analysis by SNP-based microarrays greatly expands our ability to detect acquired genomic events in CLL cells. These events include hemizygous deletion, homozygous deletion, copy-neutral LOH, and CN=3 duplication. Detection of copy-neutral LOH is not possible by FISH or array comparative genomic hybridization technology. The current study reveals a high number of acquired CNV events in earlier stage, untreated CLL patients with 17p hemizygous deletion. This observation, indicative of genomic instability, is consistent with the known poor prognosis of del(17p) patients. The new somatic CNV abnormalities detected in CLL cells may help to discover additional genes or signaling pathways involved in CLL initiation and progression. In addition, the new CNV markers may be used in larger clinical studies to improve CLL prognosis and patient stratification for therapy. DisclosuresShanafelt:Genentech: Research Funding; Hospira: Membership on an entity's Board of Directors or advisory committees, Research Funding; Polyphenon E International: Research Funding; Celgene: Research Funding; Cephalon: Research Funding; Bayer Health Care Pharmaceuticals: Research Funding. Kay:Genentech, Celgene, Hospira, Polyphenon Pharma, Sanofi-Aventis: Research Funding; Biogenc-Idec, Celgene, Genentech, genmab: Membership on an entity's Board of Directors or advisory committees. Zent:Genentech, Bayer, Genzyme, Novartis: Research Funding.