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A systematic comparison of copy number alterations in four types of female cancer

View Article: PubMed Central - PubMed

ABSTRACT

Background: Detection and localization of genomic alterations and breakpoints are crucial in cancer research. The purpose of this study was to investigate, in a methodological and biological perspective, different female, hormone-dependent cancers to identify common and diverse DNA aberrations, genes, and pathways.

Methods: In this work, we analyzed tissue samples from patients with breast (n = 112), ovarian (n = 74), endometrial (n = 84), or cervical (n = 76) cancer. To identify genomic aberrations, the Circular Binary Segmentation (CBS) and Piecewise Constant Fitting (PCF) algorithms were used and segmentation thresholds optimized. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was applied to the segmented data to identify significantly altered regions and the associated genes were analyzed by Ingenuity Pathway Analysis (IPA) to detect over-represented pathways and functions within the identified gene sets.

Results and discussion: Analyses of high-resolution copy number alterations in four different female cancer types are presented. For appropriately adjusted segmentation parameters the two segmentation algorithms CBS and PCF performed similarly. We identified one region at 8q24.3 with focal aberrations that was altered at significant frequency across all four cancer types. Considering both, broad regions and focal peaks, three additional regions with gains at significant frequency were revealed at 1p21.1, 8p22, and 13q21.33, respectively. Several of these events involve known cancer-related genes, like PPP2R2A, PSCA, PTP4A3, and PTK2. In the female reproductive system (ovarian, endometrial, and cervix [OEC]), we discovered three common events: copy number gains at 5p15.33 and 15q11.2, further a copy number loss at 8p21.2. Interestingly, as many as 75% of the aberrations (75% amplifications and 86% deletions) identified by GISTIC were specific for just one cancer type and represented distinct molecular pathways.

Conclusions: Our results disclose that some prominent copy number changes are shared in the four examined female, hormone-dependent cancer whereas others are definitive to specific cancer types.

Electronic supplementary material: The online version of this article (doi:10.1186/s12885-016-2899-4) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus

Circular Binary Segmentation (CBS) - and Piecewise Constant Fit (PCF) - segmented data (amplifications). Significant copy number alterations (gains, colored in red) are illustrated in four different cohorts; breast, ovarian, endometrial, and cervical cancers, determined by two different segmentations algorithms PCF and CBS. Both methods allow the trade-off between sensitivity and specificity to be controlled by the user using the significance level for accepting a change point (α) in CBS and the penalty parameter (γ) in PCF. We selected γ = {14, 12, 14, and 16} for the PCF-segmentation and α = {0.02, 0.02, 0.02, and 0.01} for the CBS-segmentation. The statistical significance of the aberrations is displayed as FDR q-values to account for multiple-hypothesis testing (x-axis). Chromosome positions are indicated alongside the y-axis with centromere positions indicated by dotted lines. The significance threshold is allocated by a green line
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Fig1: Circular Binary Segmentation (CBS) - and Piecewise Constant Fit (PCF) - segmented data (amplifications). Significant copy number alterations (gains, colored in red) are illustrated in four different cohorts; breast, ovarian, endometrial, and cervical cancers, determined by two different segmentations algorithms PCF and CBS. Both methods allow the trade-off between sensitivity and specificity to be controlled by the user using the significance level for accepting a change point (α) in CBS and the penalty parameter (γ) in PCF. We selected γ = {14, 12, 14, and 16} for the PCF-segmentation and α = {0.02, 0.02, 0.02, and 0.01} for the CBS-segmentation. The statistical significance of the aberrations is displayed as FDR q-values to account for multiple-hypothesis testing (x-axis). Chromosome positions are indicated alongside the y-axis with centromere positions indicated by dotted lines. The significance threshold is allocated by a green line

Mentions: Accurate detection of chromosomal aberrations is crucial for comparing multiple CNA data sets originating from different platforms and cancer types. The performance of CBS- and PCF-segmented data as input for GISTIC were compared using both simulated and real data from tumor samples from patients with breast (B), ovarian (O), endometrial (E), and cervical (C) cancers, hereafter denoted as BOEC (Additional file 3: Table S2 and Additional file 4: Table S3). For both methods, the threshold for calling copy number gains and losses can be adjusted and must be set appropriately. In most publications, the default values of α and γ are used [29, 30], but as shown here, variations in these parameters may influence the results substantially and the optimal γ and α should be adjusted for every dataset. During the segmentation process, the CBS algorithm illustrated slower processing than PCF. To determine α and γ, we compared the significant regions identified by GISTIC (for details see Additional file 2: Supplementary Methods) for various choices of α and γ and selected the parameter values that maximized the overlap between the GISTIC outputs for the two methods. Detection of amplification events was consistently less dependent on the segmentation procedure than that of deletion events in the different cancers. A large fraction of amplifications (80-86%) and deletions (58–84%) were detected by GISTIC after segmentation by both methods (Additional file 5: Table S4). The significant aberrations fall into two types, focal and broad (as described in Material and Methods). We observed that PCF-segmented data produced a higher number of GISTIC focal peaks (Additional file 6: Figure S1). Based on the adjustment among different arrays, optimal α and γ were selected separately for each data set (Figs. 1 and 2). In each cohort, the numbers of focal events surpassing the significance threshold (green line in Figs. 1 and 2) together with the locations of the peak regions have been identified (Additional file 7: Table S5).Fig. 1


A systematic comparison of copy number alterations in four types of female cancer
Circular Binary Segmentation (CBS) - and Piecewise Constant Fit (PCF) - segmented data (amplifications). Significant copy number alterations (gains, colored in red) are illustrated in four different cohorts; breast, ovarian, endometrial, and cervical cancers, determined by two different segmentations algorithms PCF and CBS. Both methods allow the trade-off between sensitivity and specificity to be controlled by the user using the significance level for accepting a change point (α) in CBS and the penalty parameter (γ) in PCF. We selected γ = {14, 12, 14, and 16} for the PCF-segmentation and α = {0.02, 0.02, 0.02, and 0.01} for the CBS-segmentation. The statistical significance of the aberrations is displayed as FDR q-values to account for multiple-hypothesis testing (x-axis). Chromosome positions are indicated alongside the y-axis with centromere positions indicated by dotted lines. The significance threshold is allocated by a green line
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5120489&req=5

Fig1: Circular Binary Segmentation (CBS) - and Piecewise Constant Fit (PCF) - segmented data (amplifications). Significant copy number alterations (gains, colored in red) are illustrated in four different cohorts; breast, ovarian, endometrial, and cervical cancers, determined by two different segmentations algorithms PCF and CBS. Both methods allow the trade-off between sensitivity and specificity to be controlled by the user using the significance level for accepting a change point (α) in CBS and the penalty parameter (γ) in PCF. We selected γ = {14, 12, 14, and 16} for the PCF-segmentation and α = {0.02, 0.02, 0.02, and 0.01} for the CBS-segmentation. The statistical significance of the aberrations is displayed as FDR q-values to account for multiple-hypothesis testing (x-axis). Chromosome positions are indicated alongside the y-axis with centromere positions indicated by dotted lines. The significance threshold is allocated by a green line
Mentions: Accurate detection of chromosomal aberrations is crucial for comparing multiple CNA data sets originating from different platforms and cancer types. The performance of CBS- and PCF-segmented data as input for GISTIC were compared using both simulated and real data from tumor samples from patients with breast (B), ovarian (O), endometrial (E), and cervical (C) cancers, hereafter denoted as BOEC (Additional file 3: Table S2 and Additional file 4: Table S3). For both methods, the threshold for calling copy number gains and losses can be adjusted and must be set appropriately. In most publications, the default values of α and γ are used [29, 30], but as shown here, variations in these parameters may influence the results substantially and the optimal γ and α should be adjusted for every dataset. During the segmentation process, the CBS algorithm illustrated slower processing than PCF. To determine α and γ, we compared the significant regions identified by GISTIC (for details see Additional file 2: Supplementary Methods) for various choices of α and γ and selected the parameter values that maximized the overlap between the GISTIC outputs for the two methods. Detection of amplification events was consistently less dependent on the segmentation procedure than that of deletion events in the different cancers. A large fraction of amplifications (80-86%) and deletions (58–84%) were detected by GISTIC after segmentation by both methods (Additional file 5: Table S4). The significant aberrations fall into two types, focal and broad (as described in Material and Methods). We observed that PCF-segmented data produced a higher number of GISTIC focal peaks (Additional file 6: Figure S1). Based on the adjustment among different arrays, optimal α and γ were selected separately for each data set (Figs. 1 and 2). In each cohort, the numbers of focal events surpassing the significance threshold (green line in Figs. 1 and 2) together with the locations of the peak regions have been identified (Additional file 7: Table S5).Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Detection and localization of genomic alterations and breakpoints are crucial in cancer research. The purpose of this study was to investigate, in a methodological and biological perspective, different female, hormone-dependent cancers to identify common and diverse DNA aberrations, genes, and pathways.

Methods: In this work, we analyzed tissue samples from patients with breast (n = 112), ovarian (n = 74), endometrial (n = 84), or cervical (n = 76) cancer. To identify genomic aberrations, the Circular Binary Segmentation (CBS) and Piecewise Constant Fitting (PCF) algorithms were used and segmentation thresholds optimized. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was applied to the segmented data to identify significantly altered regions and the associated genes were analyzed by Ingenuity Pathway Analysis (IPA) to detect over-represented pathways and functions within the identified gene sets.

Results and discussion: Analyses of high-resolution copy number alterations in four different female cancer types are presented. For appropriately adjusted segmentation parameters the two segmentation algorithms CBS and PCF performed similarly. We identified one region at 8q24.3 with focal aberrations that was altered at significant frequency across all four cancer types. Considering both, broad regions and focal peaks, three additional regions with gains at significant frequency were revealed at 1p21.1, 8p22, and 13q21.33, respectively. Several of these events involve known cancer-related genes, like PPP2R2A, PSCA, PTP4A3, and PTK2. In the female reproductive system (ovarian, endometrial, and cervix [OEC]), we discovered three common events: copy number gains at 5p15.33 and 15q11.2, further a copy number loss at 8p21.2. Interestingly, as many as 75% of the aberrations (75% amplifications and 86% deletions) identified by GISTIC were specific for just one cancer type and represented distinct molecular pathways.

Conclusions: Our results disclose that some prominent copy number changes are shared in the four examined female, hormone-dependent cancer whereas others are definitive to specific cancer types.

Electronic supplementary material: The online version of this article (doi:10.1186/s12885-016-2899-4) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus