Department of Breast Oncology, Moffitt Cancer Center, Tampa, Florida.
Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida.
J Surg Res. 2020 Jan;245:153-162. doi: 10.1016/j.jss.2019.07.021. Epub 2019 Aug 13.
Breast cancer (BC) risk assessment models are statistical estimates based on patient characteristics. We developed a gene expression assay to assess BC risk using benign breast biopsy tissue.
A NanoString-based malignancy risk (MR) gene signature was validated for formalin-fixed paraffin-embedded (FFPE) tissue. It was applied to FFPE benign and BC specimens obtained from women who underwent breast biopsy, some of whom developed BC during follow-up to evaluate diagnostic capability of the MR signature. BC risk was calculated with MR score, Gail risk score, and both tests combined. Logistic regression and receiver operating characteristic curves were used to evaluate these 3 models.
NanoString MR demonstrated concordance between fresh frozen and FFPE malignant samples (r = 0.99). Within the validation set, 563 women with benign breast biopsies from 2007 to 2011 were identified and followed for at least 5 y; 50 women developed BC (affected) within 5 y from biopsy. Three groups were compared: benign tissue from unaffected and affected patients and malignant tissue from affected patients. Kruskal-Wallis test suggested difference between the groups (P = 0.09) with trend in higher predicted MR score for benign tissue from affected patients before development of BC. Neither the MR signature nor Gail risk score were statistically different between affected and unaffected patients; combining both tests demonstrated best predictive value (AUC = 0.71).
FFPE gene expression assays can be used to develop a predictive test for BC. Further investigation of the combined MR signature and Gail Model is required. Our assay was limited by scant cellularity of archived breast tissue.
乳腺癌（BC）风险评估模型是基于患者特征的统计估计。我们开发了一种使用良性乳腺活检组织评估 BC 风险的基因表达检测方法。
验证了基于 NanoString 的恶性风险（MR）基因特征在福尔马林固定石蜡包埋（FFPE）组织中的性能。它被应用于从接受乳腺活检的女性获得的 FFPE 良性和 BC 标本中，其中一些女性在随访期间发生了 BC，以评估 MR 特征的诊断能力。使用 MR 评分、Gail 风险评分和这两种测试的组合来计算 BC 风险。逻辑回归和受试者工作特征曲线用于评估这 3 种模型。
NanoString MR 显示新鲜冷冻和 FFPE 恶性样本之间具有一致性（r=0.99）。在验证组中，从 2007 年到 2011 年，确定了 563 名患有良性乳腺活检的女性，并随访至少 5 年；在活检后 5 年内，有 50 名女性发展为 BC（受影响）。比较了三组：未受影响和受影响患者的良性组织和受影响患者的恶性组织。Kruskal-Wallis 检验表明组间存在差异（P=0.09），在 BC 发生前，受影响患者的良性组织的预测 MR 评分呈上升趋势。MR 特征和 Gail 风险评分在受影响和未受影响的患者之间均无统计学差异；联合两种测试显示出最佳的预测价值（AUC=0.71）。
FFPE 基因表达检测可以用于开发 BC 的预测测试。需要进一步研究联合的 MR 特征和 Gail 模型。我们的检测方法受到存档乳腺组织细胞数量稀少的限制。
