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Mol Oncol. 2015 Aug;9(7):1274-86. doi: 10.1016/j.molonc.2015.03.002. Epub 2015 Mar 20.

Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms.

Author information

1
Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
2
Breakthrough Breast Cancer Research Unit, Department of Research Oncology, Guy's Hospital, King's College London School of Medicine, London, United Kingdom.
3
Division of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
4
Department of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands.
5
Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK.
6
Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands; Department of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands; Department of Medical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands; Netherlands Center for Personalized Cancer Treatment, Utrecht, The Netherlands.
7
Division of Biological Stress Response, Netherlands Cancer Institute, Amsterdam, The Netherlands.
8
The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK.
9
Genomics Core Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands.
10
Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
11
Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK; Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK; Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical, Research Centre, Cambridge University Hospitals NHS, Cambridge, UK.
12
Department of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands; Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.
13
Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands; Division of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands. Electronic address: s.linn@nki.nl.

Abstract

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms.

KEYWORDS:

BRCA1; Breast cancer; Classification; Copy number aberration profiles

PMID:
25825120
PMCID:
PMC5528812
DOI:
10.1016/j.molonc.2015.03.002
[Indexed for MEDLINE]
Free PMC Article

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