Cleator Susan J, Powles Trevor J, Dexter Tim, Fulford Laura, Mackay Alan, Smith Ian E, Valgeirsson Haukur, Ashworth Alan, Dowsett Mitch
Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, Fulham Road, SW3 6JB, London, UK.
Breast Cancer Res. 2006;8(3):R32. doi: 10.1186/bcr1506. Epub 2006 Jun 21.
The aim of this study was to examine the effect of the cellular composition of biopsies on the error rates of multigene predictors of response of breast tumours to neoadjuvant adriamycin and cyclophosphamide (AC) chemotherapy.
Core biopsies were taken from primary breast tumours of 43 patients prior to AC, and subsequent clinical response was recorded. Post-chemotherapy (day 21) samples were available for 16 of these samples. Frozen sections of each core were used to estimate the proportion of invasive cancer and other tissue components at three levels. Transcriptional profiling was performed using a cDNA array containing 4,600 elements.
Twenty-three (53%) patients demonstrated a 'good' and 20 (47%) a 'poor' clinical response. The percentage invasive tumour in core biopsies collected from these patients varied markedly. Despite this, agglomerative clustering of sample expression profiles showed that almost all biopsies from the same tumour aggregated as nearest neighbours. SAM (significance analysis of microarrays) regression analysis identified 144 genes which distinguished high- and low-percentage invasive tumour biopsies at a false discovery rate of not more than 5%. The misclassification error of prediction of clinical response using microarray data from pre-treatment biopsies (on leave-one-out cross-validation) was 28%. When prediction was performed on subsets of samples which were more homogeneous in their proportions of malignant and stromal cells, the misclassification error was considerably lower (8%-13%, p < 0.05 on permutation).
The non-tumour content of breast cancer samples has a significant effect on gene expression profiles. Consideration of this factor improves accuracy of response prediction by expression array profiling. Future gene expression array prediction studies should be planned taking this into account.
本研究旨在探讨活检组织的细胞组成对乳腺癌新辅助阿霉素和环磷酰胺（AC）化疗反应多基因预测指标错误率的影响。
在进行AC化疗前，从43例患者的原发性乳腺肿瘤中获取芯针活检样本，并记录随后的临床反应。其中16例样本有化疗后（第21天）的样本。每个芯针的冰冻切片用于在三个层面估计浸润癌和其他组织成分的比例。使用包含4600个元件的cDNA阵列进行转录谱分析。
23例（53%）患者显示“良好”临床反应，20例（47%）显示“不良”临床反应。从这些患者收集的芯针活检中浸润性肿瘤的百分比差异显著。尽管如此，样本表达谱的凝聚性聚类显示，来自同一肿瘤的几乎所有活检样本都聚集为最近邻。SAM（微阵列显著性分析）回归分析确定了144个基因，这些基因在错误发现率不超过5%的情况下区分了高百分比和低百分比浸润性肿瘤活检样本。使用治疗前活检的微阵列数据（留一法交叉验证）预测临床反应的错误分类率为28%。当对恶性和基质细胞比例更均匀的样本子集进行预测时，错误分类率显著降低（8%-13%，置换检验p<0.05）。
乳腺癌样本的非肿瘤成分对基因表达谱有显著影响。考虑这一因素可提高通过表达阵列分析预测反应的准确性。未来的基因表达阵列预测研究应考虑到这一点进行规划。
