Open AccessRobust BRCA1‐like classification of copy number profiles of samples repeated across different datasets and platforms
Author(s) -
Schouten Philip C.,
Grigoriadis Anita,
Kuilman Thomas,
Mirza Hasan,
Watkins Johnathan A.,
Cooke Saskia A.,
van Dyk Ewald,
Severson Tesa M.,
Rueda Oscar M.,
Hoogstraat Marlous,
Verhagen Caroline V.M.,
Natrajan Rachael,
Chin Suet-Feung,
Lips Esther H.,
Kruizinga Janneke,
Velds Arno,
Nieuwland Marja,
Kerkhoven Ron M.,
Krijgsman Oscar,
Vens Conchita,
Peeper Daniel,
Nederlof Petra M.,
Caldas Carlos,
Tutt Andrew N.,
Wessels Lodewyk F.,
Linn Sabine C.
Publication year - 2015
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1016/j.molonc.2015.03.002
Subject(s) - comparative genomic hybridization , molecular inversion probe , biology , breast cancer , bacterial artificial chromosome , copy number variation , genetics , computational biology , concordance , copy number analysis , chromosome , cancer , microbiology and biotechnology , genome , gene , genotyping , genotype
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.