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

Published version
Peer-reviewed

Type

Article

Change log

Authors

Schouten, Philip C 
Grigoriadis, Anita 
Kuilman, Thomas 
Mirza, Hasan 
Watkins, Johnathan A 

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.

Description

Keywords

BRCA1, Breast cancer, Classification, Copy number aberration profiles, Breast Neoplasms, Cohort Studies, Comparative Genomic Hybridization, DNA Methylation, Datasets as Topic, Female, Gene Dosage, Genes, BRCA1, Humans, Randomized Controlled Trials as Topic

Journal Title

Mol Oncol

Conference Name

Journal ISSN

1574-7891
1878-0261

Volume Title

9

Publisher

Wiley
Sponsorship
Cancer Research UK (unknown)
Department of Health (via National Institute for Health Research (NIHR)) (NF-SI-0515-10090)
Cancer Research UK (CB4140)
Cancer Research UK (unknown)
Cancer Research UK (60098573)
Department of Health (via National Institute for Health Research (NIHR)) (unknown)
European Commission (260791)
Cambridge University Hospitals NHS Foundation Trust (CUH) (RG51913)
Cancer Research Uk (None)
European Commission FP7 Network of Excellence (NoE) (260791)
Cambridge University Hospitals NHS Foundation Trust (CUH) (unknown)
Cancer Research Uk (None)
Academy of Medical Sciences (unknown)
Medical Research Council (MR/M008975/1)
Academy of Medical Sciences (ALI 01/08/14)
Pathological Society of Great Britain & Ireland (CDF 2012/01)
European Commission FP7 Collaborative projects (CP) (258967)
Cancer Research UK (C507/A16278)
European Commission (258967)
Cancer Research UK (20544)
Medical Research Council (MR/P012442/1)
European Commission and European Federation of Pharmaceutical Industries and Associations (EFPIA) FP7 Innovative Medicines Initiative (IMI) (115749)
European Commission (242006)
European Research Council (694620)
Cancer Research UK (A24622)