Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Clin Cancer Res. 2017 Jan 15;23(2):387-398. doi: 10.1158/1078-0432.CCR-16-0680. Epub 2016 Jul 26.
Recent transcriptomic analyses have identified four distinct molecular subtypes of colorectal cancer with evident clinical relevance. However, the requirement for sufficient quantities of bulk tumor and difficulties in obtaining high-quality genome-wide transcriptome data from formalin-fixed paraffin-embedded tissue are obstacles toward widespread adoption of this taxonomy. Here, we develop an immunohistochemistry-based classifier to validate the prognostic and predictive value of molecular colorectal cancer subtyping in a multicenter study.
Tissue microarrays from 1,076 patients with colorectal cancer from four different cohorts were stained for five markers (CDX2, FRMD6, HTR2B, ZEB1, and KER) by immunohistochemistry and assessed for microsatellite instability. An automated classification system was trained on one cohort using quantitative image analysis or semiquantitative pathologist scoring of the cores as input and applied to three independent clinical cohorts.
This classifier demonstrated 87% concordance with the gold-standard transcriptome-based classification. Application to three validation datasets confirmed the poor prognosis of the mesenchymal-like molecular colorectal cancer subtype. In addition, retrospective analysis demonstrated the benefit of adding cetuximab to bevacizumab and chemotherapy in patients with RAS wild-type metastatic cancers of the canonical epithelial-like subtypes.
This study shows that a practical and robust immunohistochemical assay can be employed to identify molecular colorectal cancer subtypes and uncover subtype-specific therapeutic benefit. Finally, the described tool is available online for rapid classification of colorectal cancer samples, both in the format of an automated image analysis pipeline to score tumor core staining, and as a classifier based on semiquantitative pathology scoring. Clin Cancer Res; 23(2); 387-98. ©2016 AACR.
最近的转录组分析已经确定了具有明显临床相关性的四种不同的结直肠癌分子亚型。然而，这种分类法广泛应用的障碍是需要足够数量的肿瘤组织，以及从福尔马林固定石蜡包埋组织中获得高质量全基因组转录组数据的困难。在这里，我们开发了一种基于免疫组织化学的分类器，以在多中心研究中验证分子结直肠癌亚型的预后和预测价值。
来自四个不同队列的 1076 例结直肠癌患者的组织微阵列用免疫组织化学染色 5 种标志物（CDX2、FRMD6、HTR2B、ZEB1 和 KER），并评估微卫星不稳定性。使用定量图像分析或半定量病理评分作为输入，在一个队列中使用自动化分类系统对核心进行训练，并将其应用于三个独立的临床队列。
该分类器与基于转录组的金标准分类的一致性为 87%。对三个验证数据集的应用证实了间充质样分子结直肠癌亚型的预后不良。此外，回顾性分析表明，在 RAS 野生型转移性经典上皮样亚型患者中，添加西妥昔单抗联合贝伐单抗和化疗具有获益。
本研究表明，可以采用实用且稳健的免疫组织化学检测来鉴定分子结直肠癌亚型，并揭示亚型特异性的治疗获益。最后，所描述的工具可在线用于快速分类结直肠癌样本，包括用于评分肿瘤核心染色的自动图像分析管道格式，以及基于半定量病理学评分的分类器。临床癌症研究；23(2)；387-98. ©2016AACR。
