Department of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
Department of Pathology, VU University Medical Centre, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
Breast Cancer Res Treat. 2018 Apr;168(2):327-335. doi: 10.1007/s10549-017-4622-9. Epub 2017 Dec 18.
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.
乳腺癌分类和预后的基因表达（GE）分析已越来越多地应用于临床诊断。GE 分析需要有合理的肿瘤细胞百分比和高质量的 RNA。因此，会有一定数量的样本被排除在外。如果纳入和排除 GE 分析的样本之间的肿瘤特征不同，这可能会导致偏差。因此，我们评估了在乳腺癌中，适合和不适合生成 GE 图谱的肿瘤之间患者和肿瘤特征是否存在差异。
在荷兰癌症研究所接受新辅助化疗的 738 例连续乳腺癌患者队列中进行了 GE 分析。我们比较了纳入和排除 GE 分析的患者之间的肿瘤特征和治疗结果。结果在接受原发性手术治疗的 812 例患者的独立队列中得到了验证。
53%的样本可以进行 GE 分析。具有肿瘤 GE 图谱的患者更常患有高级别肿瘤[优势比 2.57（95%置信区间 1.77-3.72），p<0.001]和更多的淋巴结阳性[优势比 1.50（95%置信区间 1.03-2.19），p=0.035]，与无法进行 GE 分析的组相比。在验证队列中，适合进行基因表达分析的肿瘤更常为高级别。
在我们的基因表达研究中，适合 GE 分析的肿瘤更常具有不良的预后特征。由于选择了具有高肿瘤百分比的样本，我们自动选择了具有特定特征的肿瘤，即具有更高分级和淋巴结受累的肿瘤。在研究或临床环境中进行基因表达分析时，注意到这一现象非常重要。
