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18.12.2017 | Preclinical study | Ausgabe 2/2018 Open Access

Breast Cancer Research and Treatment 2/2018

Enrichment of high-grade tumors in breast cancer gene expression studies

Breast Cancer Research and Treatment > Ausgabe 2/2018
M. van Seijen, A. L. Mooyaart, L. Mulder, M. Hoogstraat, C. A. Drukker, C. E. Loo, B. Pouw, G. S. Sonke, J. Wesseling, E. H. Lips
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s10549-017-4622-9) contains supplementary material, which is available to authorized users.
M. van Seijen and A. L. Mooyaart are the shared first authors. J. Wesseling and E. H. Lips are the senior authors



Gene expression (GE) profiling for breast cancer classification and prognostication has become increasingly used in clinical diagnostics. GE profiling requires a reasonable tumor cell percentage and high-quality RNA. As a consequence, a certain amount of samples drop out. If tumor characteristics are different between samples included and excluded from GE profiling, this can lead to bias. Therefore, we assessed whether patient and tumor characteristics differ between tumors suitable or unsuitable for generating GE profiles in breast cancer.


In a consecutive cohort of 738 breast cancer patients who received neoadjuvant chemotherapy at the Netherlands Cancer Institute, GE profiling was performed. We compared tumor characteristics and treatment outcome between patients included and excluded from GE profiling. Results were validated in an independent cohort of 812 patients treated with primary surgery.


GE analysis could be performed in 53% of the samples. Patients with tumor GE profiles more often had high-grade tumors [odds ratio 2.57 (95%CI 1.77–3.72), p < 0.001] and were more often lymph node positive [odds ratio 1.50 (95%CI 1.03–2.19), p = 0.035] compared to the group for which GE profiling was not possible. In the validation cohort, tumors suitable for gene expression analysis were more often high grade.


In our gene expression studies, tumors suitable for GE profiling had more often an unfavorable prognostic profile. Due to selection of samples with a high tumor percentage, we automatically select for tumors with specific features, i.e., tumors with a higher grade and lymph node involvement. It is important to be aware of this phenomenon when performing gene expression analysis in a research or clinical context.

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Supplementary material 1 (PDF 397 kb)
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