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.
Supplementary material 1 (PDF 397 kb)10549_2017_4622_MOESM1_ESM.pdf
Ward S, Scope A, Rafia R, Pandor A, Harnan S, Evans P, Wyld L (2013) Gene expression profiling and expanded immunohistochemistry tests to guide the use of adjuvant chemotherapy in breast cancer management: a systematic review and cost-effectiveness analysis. Health Technol Assess 17(44):1–302. https://doi.org/10.3310/hta17440CrossRefPubMedPubMedCentral
Mamounas EP, Tang G, Fisher B, Paik S, Shak S, Costantino JP, Watson D, Geyer CE Jr, Wickerham DL, Wolmark N (2010) Association between the 21-gene recurrence score assay and risk of locoregional recurrence in node-negative, estrogen receptor-positive breast cancer: results from NSABP B-14 and NSABP B-20. J Clin Oncol 28(10):1677–1683. https://doi.org/10.1200/JCO.2009.23.7610CrossRefPubMedPubMedCentral
van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536. https://doi.org/10.1038/415530aCrossRef
Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron J, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27(8):1160–1167. https://doi.org/10.1200/JCO.2008.18.1370CrossRefPubMedPubMedCentral
Bass BP, Engel KB, Greytak SR, Moore HM (2014) A review of preanalytical factors affecting molecular, protein, and morphological analysis of formalin-fixed, paraffin-embedded (FFPE) tissue: how well do you know your FFPE specimen? Arch Pathol Lab Med 138(11):1520–1530. https://doi.org/10.5858/arpa.2013-0691-RACrossRefPubMed
Lips EH, Mulder L, de Ronde JJ, Mandjes IA, Koolen BB, Wessels LF, Rodenhuis S, Wesseling J (2013) Breast cancer subtyping by immunohistochemistry and histological grade outperforms breast cancer intrinsic subtypes in predicting neoadjuvant chemotherapy response. Breast Cancer Res Treat 140(1):63–71. https://doi.org/10.1007/s10549-013-2620-0CrossRefPubMedPubMedCentral
Lips EH, Mulder L, de Ronde JJ, Mandjes IAM, Vincent A, Vrancken Peeters MTFD, Nederlof PM, Wesseling J, Rodenhuis S (2012) Neoadjuvant chemotherapy in ER + HER2 − breast cancer: response prediction based on immunohistochemical and molecular characteristics. Breast Cancer Res Treat 131(3):827–836. https://doi.org/10.1007/s10549-011-1488-0CrossRefPubMed
Morris EA, Comstock CE, Lee CH (2013) ACR BI-RADS ® Magnetic resonance imaging. ACR BI-RADS ® Atlas, Breast imaging reporting and data system. American College of Radiology, Reston, VA
Bueno-de-Mesquita JM, van Harten WH, Retel VP, van’t Veer LJ, van Dam FS, Karsenberg K, Douma KF, van Tinteren H, Peterse JL, Wesseling J, Wu TS (2007) Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet oncol. 8(12):1079–1087 CrossRefPubMed
Altman DG, McShane LM, Sauerbrei W, Taube SE (2012) Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. PLoS medicine 9(5):e1001216. https://doi.org/10.1371/journal.pmed.1001216CrossRefPubMedPubMedCentral
de Cremoux P, Valet F, Gentien D, Lehmann-Che J, Scott V, Tran-Perennou C, Barbaroux C, Servant N, Vacher S, Sigal-Zafrani B, Mathieu MC, Bertheau P, Guinebretiere JM, Asselain B, Marty M, Spyratos F (2011) Importance of pre-analytical steps for transcriptome and RT-qPCR analyses in the context of the phase II randomised multicentre trial REMAGUS02 of neoadjuvant chemotherapy in breast cancer patients. BMC Cancer 11:215. https://doi.org/10.1186/1471-2407-11-215CrossRefPubMedPubMedCentral
Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A, Glas AM, Bogaerts J, Cardoso F, Piccart-Gebhart MJ, Rutgers ET, Van’t Veer LJ (2009) The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat 116(2):295–302. https://doi.org/10.1007/s10549-008-0130-2CrossRefPubMed
Goetz MP, Suman VJ, Ingle JN, Nibbe AM, Visscher DW, Reynolds CA, Lingle WL, Erlander M, Ma XJ, Sgroi DC, Perez EA, Couch FJ (2006) A two-gene expression ratio of homeobox 13 and interleukin-17B receptor for prediction of recurrence and survival in women receiving adjuvant tamoxifen. Clin Cancer Res 12(7 Pt 1):2080–2087. https://doi.org/10.1158/1078-0432.ccr-05-1263CrossRefPubMed
Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J, Costantino JP, Geyer CE Jr, Wickerham DL, Wolmark N (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24(23):3726–3734. https://doi.org/10.1200/JCO.2005.04.7985CrossRefPubMed
- Enrichment of high-grade tumors in breast cancer gene expression studies
M. van Seijen
A. L. Mooyaart
C. A. Drukker
C. E. Loo
G. S. Sonke
E. H. Lips
- Springer US
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