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

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.

Figure 1

Average copy number profiles compared between two technologies. Comparison of average of all samples based on their segmented copy number profiles showing the genomic position on the x‐axis and the average log2 ratio on the y‐axis. Original profiles are plotted in black and mapped profiles in red. A) BAC vs. MIP segmented; B: BAC vs. NG135 segmented; C: BAC vs NG720 segmented; D: BAC vs. NGS segmented; E: BAC3K vs BAC32K segmented; F: NG135 vs. SNP6 segmented; G: CopywriteR vs BAC segmented; H: CopywriteR vs NG135 segmented.

Figure 1

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Figure 2

Classification of all patients in the cohort. Classification of 210 patients with copy number data from 6 different copy number profiling platforms. Classification is colored as blue (non‐BRCA1‐like) and orange (BRCA1‐like). The Differential classification row indicates whether differential classification occurred within a patient: green (no), red (yes). The mean BRCA1‐like score is a gradient from blue (0) to yellow (1) indicating the probability of being BRCA1‐like (score = 1). The max diff BRCA1‐like score indicates the largest difference in probability between the classifications in one patient which ranges from 0 (no difference) to 1 (probability change of 1). These two scores can be used to identify clearly discordant samples (max diff → 1) or unconvincingly assigned profiles (mean score around 0.5, max diff small). ER, PR and HER2 status, BRCA1 mutation status and BRCA1 promoter hypermethylation status (negative or positive). Missing values are white.

Figure 3

Strength of classification Density plots of the strength and variation of classification within one patient for concordantly and discordantly classified patients. A) density plot indicating the strength of assignment to the BRCA1‐like or non‐BRCA1‐like of all 243 patients. The strength of assignment is calculated as the mean absolute difference between one patient's profiles and the BRCA1‐like and non‐BRCA1‐like class average profile. Zero indicates that the profile is equally close to the BRCA1‐like as the non‐BRCA1‐like average. The higher the value the stronger its association with a particular class. B) Density plot of the standard deviation of the strength of assignment, indicating the association of the profiles from a patient with a particular class. In black are the patients for which all copy number profiles classified the same class, in red the patients that have different class assignment across technologies.