NanoCMSer: a consensus molecular subtype stratification tool for fresh-frozen and paraffin-embedded colorectal cancer samples.

IF 6.6 2区 医学 Q1 Biochemistry, Genetics and Molecular Biology
Arezo Torang, Simone van de Weerd, Veerle Lammers, Sander van Hooff, Inge van den Berg, Saskia van den Bergh, Miriam Koopman, Jan N IJzermans, Jeanine M L Roodhart, Jan Koster, Jan Paul Medema
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Abstract

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).

NanoCMSer:新鲜冷冻和石蜡包埋结直肠癌样本的一致分子亚型分层工具。
结直肠癌(CRC)是癌症相关死亡率的重要因素,强调需要先进的生物标志物来指导治疗。作为国际联盟的一部分,我们之前将crc分为四种公认的分子亚型(CMS1-CMS4),显示出预测结果的希望。为了在常规使用福尔马林固定石蜡包埋(FFPE)样品的环境中促进CMS分类的临床整合,我们开发了NanoCMSer,一个基于纳米字符串的CMS分类器,使用55个基因。NanoCMSer获得了很高的准确率,MATCH队列的新鲜冷冻样本的准确率为95%,CODE队列的FFPE样本的准确率为92%,这是迄今为止报道的FFPE组织的最高准确率。此外,在本研究中编译的23个基于rnaseq的数据集的综合收集中,它显示出96%的准确率,超过了现有模型的性能。仅分类55个基因,CMS预测仍然具有生物学相关性,通过富集分析识别CMS特异性生物学。此外,在比较CMS2/3患者的III期和II期时,我们观察到无复发生存曲线的显著差异。在III期患者中,CMS2 5年后复发率增加21%,CMS3 5年后复发率增加31%,而CMS1和CMS4 5年后复发率的差异不太明显,分别为11%和10%。我们假设NanoCMSer是肿瘤生物学和临床实践中crc亚型的强大工具,可通过NanoCMSer r包(https://github.com/LEXORlab/NanoCMSer)和Shinyapp (https://atorang.shinyapps.io/NanoCMSer)访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Oncology
Molecular Oncology Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
11.80
自引率
1.50%
发文量
203
审稿时长
10 weeks
期刊介绍: Molecular Oncology highlights new discoveries, approaches, and technical developments, in basic, clinical and discovery-driven translational cancer research. It publishes research articles, reviews (by invitation only), and timely science policy articles. The journal is now fully Open Access with all articles published over the past 10 years freely available.
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