Open Access Research article

Histotype-specific copy-number alterations in ovarian cancer

Ruby YunJu Huang12, Geng Bo Chen3, Noriomi Matsumura4, Hung-Cheng Lai5, Seiichi Mori2, Jingjing Li3, Meng Kang Wong2, Ikuo Konishi4, Jean-Paul Thiery26 and Liang Goh378*

Author Affiliations

1 Department of Obstetrics & Gynaecology, National University Hospital, 5 Lower Kent Ridge Road, Singapore, 119074, Singapore

2 Cancer Science Institute, National University of Singapore, Centre for Life Sciences, #02-07 28 Medical Drive, Singapore, 117456, Singapore

3 Cancer & Stem Cell Biology, Duke-National University of Singapore Graduate Medical School, 8 College road, Rm 6-32, Singapore, 169857, Singapore

4 Department of Gynecology and Obstetrics, Kyoto University Graduate Medical School, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan

5 National Defense Medical Center, Taiwan, 114 No.161, Sec. 6, Minquan E. Rd., Neihu Dist, Taipei City, 114, Taiwan

6 Institute of Molecular and Cell Biology, 61 Biopolis Drive, Proteos, Singapore, 138673, Singapore

7 Department of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Drive, Singapore, 169610, Singapore

8 Saw Swee Hock School of Public Health, Yong Loo Lin School of Medicine, National University of Singapore, MD3, 16 Medical Drive, Singapore, 117597, Singapore

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BMC Medical Genomics 2012, 5:47  doi:10.1186/1755-8794-5-47

Published: 18 October 2012

Additional files

Additional file 1: Figure S1:

Data Analysis Workflow. Two pronged approach for individual and merged datasets through selective threshold of GISTIC q-value and concordance in copy number analysis. As some histotypes have lower prevalence, filtering thresholds for individual and merged dataset were set at q<0.25 and q<0.05 respectively to overcome differences in sample size. In addition, any genomic alterations are supported by at least 2 datasets (i.e. concordance criteria). Specifically, the filtering criteria for histotype-specific regions were: (i) q < 0.25 (individual dataset), (ii) q < 0.05 (merged dataset), and (iii) concordance in 2 or more datasets. This resulted in a list of significant gains and loss regions. To identify copy number driver genes that are specific to histotype, copy number segments were mapped to genes and ANOVA was used to identify the differentially altered genes. This resulted in a list of histotype-specific altered genes. Spearman correlation between gene expression and copy number was then used to assess potential driver genes in each individual dataset.

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Additional file 2: Table S1:

Summary of overlapped amplified and deleted genes between histotypes.

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Additional file 3: Figure S2:

Venn diagram of copy number altered genes between the 4 histotypes. Left: amplified genes; Right: deleted genes. Clear cell tumors had the highest number of common altered genes with serous tumors while endometrioid tumors had the lowest number of common altered genes.

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Additional file 4: Table S2:

Summary of datasets used for comparison of commonly altered genes.

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Additional file 5: Table S3:

Summary of comparison with TCGA.

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Additional file 6: Table S4:

Comparison of ERBB2 expression between mucinous and serous tumors.

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Additional file 7: Figure S3:

Principal component analysis of copy number altered gene from the merged datasets. The plot shows that there is minimal copy number alterations difference between the 3 datasets.

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Additional file 8: Table S5:

Summary of anova results on the 3 datasets.

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Additional file 9: Figure S4:

Principal component analysis of copy number altered genes from the merged datasets showing borderline and non-borderline tumors. Borderline tumors were available only in serous and mucinous histotypes. No distinct clustering was observed between borderline and non-borderline tumors for (a) mucinous and (b) serous.

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