Torang Arezo, van de Weerd Simone, Lammers Veerle, van Hooff Sander, van den Berg Inge, van den Bergh Saskia, Koopman Miriam, IJzermans Jan N, Roodhart Jeanine M L, Koster Jan, Medema Jan Paul
Amsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.
Oncode Institute, Amsterdam UMC, University of Amsterdam, The Netherlands.
Mol Oncol. 2025 May;19(5):1332-1346. doi: 10.1002/1878-0261.13781. Epub 2024 Dec 25.
Colorectal cancer (CRC) is a significant contributor to cancer-related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1-CMS4), showing promise for outcome prediction. To facilitate clinical integration of CMS classification in settings where formalin-fixed paraffin-embedded (FFPE) samples are routinely used, we developed NanoCMSer, a NanoString-based CMS classifier using 55 genes. NanoCMSer achieved high accuracy rates, with 95% for fresh-frozen samples from the MATCH cohort and 92% for FFPE samples from the CODE cohort, marking the highest reported accuracy for FFPE tissues to date. Additionally, it demonstrated 96% accuracy across a comprehensive collection of 23 RNAseq-based datasets, compiled in this study, surpassing the performance of existing models. Classifying with only 55 genes, the CMS predictions were still biologically relevant, recognizing CMS-specific biology upon enrichment analysis. Additionally, we observed substantial differences in recurrence-free survival curves when comparing CMS2/3 patients in stage III versus II. Probability of recurrence after 5 years increased by 21% in CMS2 and 31% in CMS3 for patients in stage III, whereas this difference was less pronounced for CMS1 and CMS4, with 11% and 10%, respectively. We posit NanoCMSer as a robust tool for subtyping CRCs for both tumor biology and clinical practice, accessible via nanocmser r package (https://github.com/LEXORlab/NanoCMSer) and Shinyapp (https://atorang.shinyapps.io/NanoCMSer).
结直肠癌（CRC）是癌症相关死亡的一个重要因素，这凸显了需要先进的生物标志物来指导治疗。作为一个国际联盟的一部分，我们之前将结直肠癌分为四种共识分子亚型（CMS1 - CMS4），显示出在预后预测方面的前景。为了便于在常规使用福尔马林固定石蜡包埋（FFPE）样本的情况下将CMS分类临床应用，我们开发了NanoCMSer，一种基于NanoString技术的使用55个基因的CMS分类器。NanoCMSer实现了高准确率，MATCH队列的新鲜冷冻样本准确率为95%，CODE队列的FFPE样本准确率为92%，这是迄今为止报道的FFPE组织的最高准确率。此外，在本研究汇编的23个基于RNAseq的数据集的综合集合中，它的准确率达到了96%，超过了现有模型的性能。仅用55个基因进行分类，CMS预测在生物学上仍然具有相关性，富集分析能够识别CMS特异性生物学特征。此外，我们观察到在比较II期和III期的CMS2/3患者时，无复发生存曲线存在显著差异。III期患者中，CMS2的5年复发概率增加了21%，CMS3增加了31%，而CMS1和CMS4的差异不太明显，分别为11%和10%。我们认为NanoCMSer是一种强大的工具，可用于结直肠癌的亚型分类，适用于肿瘤生物学研究和临床实践，可通过nanocmser R包（https://github.com/LEXORlab/NanoCMSer）和Shinyapp（https://atorang.shinyapps.io/NanoCMSer）获取。
