Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania.
Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Clin Cancer Res. 2021 Jan 1;27(1):120-130. doi: 10.1158/1078-0432.CCR-20-2403. Epub 2020 Oct 27.
Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cancer and implemented the assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory.
We performed an experiment to build an optimal CMS classifier using a training set of 1,329 samples from 12 studies and validation set of 1,329 samples from 14 studies. We constructed an assay on the basis of NanoString CodeSets for the top 472 genes, and performed analyses on paired flash-frozen (FF)/FFPE samples from 175 colorectal cancers to adapt the classifier to FFPE samples using a subset of genes found to be concordant between FF and FFPE, tested the classifier's reproducibility and repeatability, and validated in a CLIA-certified laboratory. We assessed prognostic significance of CMS in 345 patients pooled across three clinical trials.
The best classifier was weighted support vector machine with high accuracy across platforms and gene lists (>0.95), and the 472-gene model outperforming existing classifiers. We constructed subsets of 99 and 200 genes with high FF/FFPE concordance, and adapted FFPE-based classifier that had strong classification accuracy (>80%) relative to "gold standard" CMS. The classifier was reproducible to sample type and RNA quality, and demonstrated poor prognosis for CMS1-3 and good prognosis for CMS2 in metastatic colorectal cancer ( < 0.001).
We developed and validated a colorectal cancer CMS assay that is ready for use in clinical trials, to assess prognosis in standard-of-care settings and explore as predictor of therapy response.
结直肠癌的共识分子亚型(CMS)有可能重塑结直肠癌的格局。我们开发并验证了一种适用于结直肠癌福尔马林固定石蜡包埋(FFPE)样本的检测方法,并在临床实验室改进修正案(CLIA)认证的实验室中实施了该检测方法。
我们进行了一项实验,使用来自 12 项研究的 1329 个样本的训练集和来自 14 项研究的 1329 个样本的验证集来构建最佳 CMS 分类器。我们基于 Nanostring CodeSets 构建了一个针对前 472 个基因的检测方法,并对 175 例结直肠癌的配对冷冻(FF)/FFPE 样本进行了分析,使用在 FF 和 FFPE 之间具有一致性的基因子集来适应 FFPE 样本,测试了分类器的可重复性和可重复性,并在 CLIA 认证的实验室中进行了验证。我们评估了 CMS 在三个临床试验中 345 例患者中的预后意义。
最佳分类器是在所有平台和基因列表上都具有高精度的加权支持向量机(>0.95),并且 472 个基因模型优于现有分类器。我们构建了具有高 FF/FFPE 一致性的 99 个和 200 个基因子集,并适应了基于 FFPE 的分类器,其分类准确性(>80%)相对于“金标准”CMS 较高。该分类器对样本类型和 RNA 质量具有可重复性,并且在转移性结直肠癌中,CMS1-3 的预后较差,CMS2 的预后较好(<0.001)。
我们开发并验证了一种结直肠癌 CMS 检测方法,可用于临床试验,以评估标准治疗环境中的预后,并探索治疗反应的预测指标。