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Updated: Jun 23, 2026
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
High-resolution analysis of copy number alterations and associated expression changes in ovarian tumors
Peter M Haverty1, Lawrence S Hon, Joshua S Kaminker
1Department of Bioinformatics, Genentech, Inc, South San Francisco, CA, USA. phaverty@gene.com
Background:
DNA copy number alterations are frequently observed in ovarian cancer, but it remains a challenge to identify the most relevant alterations and the specific causal genes in those regions.
Methods:
We obtained high-resolution 500K SNP array data for 52 ovarian tumors and identified the most statistically significant minimal genomic regions with the most prevalent and highest-level copy number alterations (recurrent CNAs). Within a region of recurrent CNA, comparison of expression levels in tumors with a given CNA to tumors lacking that CNA and to whole normal ovary samples was used to select genes with CNA-specific expression patterns. A public expression array data set of laser capture micro-dissected (LCM) non-malignant fallopian tube epithelia and LCM ovarian serous adenocarcinoma was used to evaluate the effect of cell-type mixture biases.
Results:
Fourteen recurrent deletions were detected on chromosomes 4, 6, 9, 12, 13, 15, 16, 17, 18, 22 and most prevalently on X and 8. Copy number and expression data suggest several apoptosis mediators as candidate drivers of the 8p deletions. Sixteen recurrent gains were identified on chromosomes 1, 2, 3, 5, 8, 10, 12, 15, 17, 19, and 20, with the most prevalent gains localized to 8q and 3q. Within the 8q amplicon, PVT1, but not MYC, was strongly over-expressed relative to tumors lacking this CNA and showed over-expression relative to normal ovary. Likewise, the cell polarity regulators PRKCI and ECT2 were identified as putative drivers of two distinct amplicons on 3q. Co-occurrence analyses suggested potential synergistic or antagonistic relationships between recurrent CNAs. Genes within regions of recurrent CNA showed an enrichment of Cancer Census genes, particularly when filtered for CNA-specific expression.
Conclusion:
These analyses provide detailed views of ovarian cancer genomic changes and highlight the benefits of using multiple reference sample types for the evaluation of CNA-specific expression changes.
Insights
This study identifies key DNA copy number alterations (CNAs) in ovarian cancer, pinpointing specific genes like PVT1 and polarity regulators as potential drivers. The findings offer insights into ovarian cancer genomics and CNA-specific expression changes.
Area of Science:
- Genomics
- Cancer Biology
- Molecular Oncology
Background:
- DNA copy number alterations (CNAs) are common in ovarian cancer.
- Identifying driver genes within CNA regions remains a significant challenge.
Purpose of the Study:
- To identify recurrent CNAs and associated driver genes in ovarian cancer.
- To investigate the relationship between CNAs and gene expression patterns.
- To evaluate the impact of different reference sample types on CNA analysis.
Main Methods:
- Utilized high-resolution 500K SNP array data from 52 ovarian tumors.
- Identified recurrent CNAs and selected genes with CNA-specific expression patterns by comparing tumor and normal ovary samples.
- Employed public expression data from laser capture micro-dissected (LCM) tissues to assess cell-type mixture biases.
Main Results:
- Detected 14 recurrent deletions and 16 recurrent gains across multiple chromosomes, with prevalent alterations on chromosomes 8 and 3.
- Identified apoptosis mediators as candidate drivers for 8p deletions and PVT1, PRKCI, and ECT2 as drivers for 8q and 3q amplicons.
- Observed co-occurrence patterns suggesting synergistic or antagonistic CNA relationships and enrichment of Cancer Census genes in CNA regions.
Conclusions:
- Provides a detailed genomic landscape of ovarian cancer, highlighting recurrent CNAs and potential driver genes.
- Demonstrates the value of integrating copy number and expression data with multiple reference samples for accurate CNA-specific expression analysis.
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