Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
St Erik Eye Hospital, Stockholm, Sweden.
Mod Pathol. 2016 Apr;29(4):318-29. doi: 10.1038/modpathol.2016.34. Epub 2016 Feb 26.
In the spectrum of breast cancers, categorization according to the four gene expression-based subtypes 'Luminal A,' 'Luminal B,' 'HER2-enriched,' and 'Basal-like' is the method of choice for prognostic and predictive value. As gene expression assays are not yet universally available, routine immunohistochemical stains act as surrogate markers for these subtypes. Thus, congruence of surrogate markers and gene expression tests is of utmost importance. In this study, 3 cohorts of primary breast cancer specimens (total n=436) with up to 28 years of survival data were scored for Ki67, ER, PR, and HER2 status manually and by digital image analysis (DIA). The results were then compared for sensitivity and specificity for the Luminal B subtype, concordance to PAM50 assays in subtype classification and prognostic power. The DIA system used was the Visiopharm Integrator System. DIA outperformed manual scoring in terms of sensitivity and specificity for the Luminal B subtype, widely considered the most challenging distinction in surrogate subclassification, and produced slightly better concordance and Cohen's κ agreement with PAM50 gene expression assays. Manual biomarker scores and DIA essentially matched each other for Cox regression hazard ratios for all-cause mortality. When the Nottingham combined histologic grade (Elston-Ellis) was used as a prognostic surrogate, stronger Spearman's rank-order correlations were produced by DIA. Prognostic value of Ki67 scores in terms of likelihood ratio χ(2) (LR χ(2)) was higher for DIA that also added significantly more prognostic information to the manual scores (LR-Δχ(2)). In conclusion, the system for DIA evaluated here was in most aspects a superior alternative to manual biomarker scoring. It also has the potential to reduce time consumption for pathologists, as many of the steps in the workflow are either automatic or feasible to manage without pathological expertise.
在乳腺癌的分类中，根据四种基于基因表达的亚型——“Luminal A”、“Luminal B”、“HER2 富集”和“基底样”进行分类，是评估预后和预测价值的首选方法。由于基因表达检测尚未普及，常规的免疫组织化学染色可作为这些亚型的替代标志物。因此，替代标志物和基因表达检测的一致性非常重要。在这项研究中，我们对 3 组原发性乳腺癌标本（共 436 例）进行了 Ki67、ER、PR 和 HER2 状态的手动和数字图像分析（DIA）评分，并对这些结果进行了比较，以评估其对 Luminal B 亚型的敏感性和特异性、与 PAM50 检测在亚型分类中的一致性以及预后能力。我们使用的 DIA 系统是 Visiopharm Integrator System。与手动评分相比，DIA 系统在检测 Luminal B 亚型方面具有更高的敏感性和特异性，Luminal B 亚型被广泛认为是替代标志物分类中最具挑战性的区分，与 PAM50 基因表达检测的一致性和 Cohen's κ 也略有提高。对于全因死亡率的 Cox 回归风险比，手动生物标志物评分和 DIA 本质上是相互匹配的。当使用 Nottingham 联合组织学分级（Elston-Ellis）作为预后替代标志物时，DIA 产生了更强的 Spearman 秩相关系数。在考虑到可能性比 χ(2)（LR χ(2)）的情况下，DIA 对 Ki67 评分的预后价值更高，同时也为手动评分增加了更多的预后信息（LR-Δχ(2)）。总之，与手动生物标志物评分相比，这里评估的 DIA 系统在大多数方面都是一个更好的选择。它还有潜力减少病理学家的时间消耗，因为工作流程中的许多步骤要么是自动的，要么可以在没有病理专业知识的情况下管理。
