A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues.

IF 3.3 2区 生物学 Q1 BIOLOGY Life Science Alliance Pub Date : 2024-05-23 Print Date: 2024-08-01 DOI:10.26508/lsa.202402730
Tim R de Back, Tan Wu, Pascale Jm Schafrat, Sanne Ten Hoorn, Miaomiao Tan, Lingli He, Sander R van Hooff, Jan Koster, Lisanne E Nijman, Geraldine R Vink, Inès J Beumer, Clara C Elbers, Kristiaan J Lenos, Dirkje W Sommeijer, Xin Wang, Louis Vermeulen
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Abstract

Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC) tissues is complicated by RNA degradation upon formalin-fixed paraffin-embedded (FFPE) preservation. Here, we present an FFPE-curated CMS classifier. The CMSFFPE classifier was developed using genes with a high transcript integrity in FFPE-derived RNA. We evaluated the classification accuracy in two FFPE-RNA datasets with matched fresh-frozen (FF) RNA data, and an FF-derived RNA set. An FFPE-RNA application cohort of metastatic CRC patients was established, partly treated with anti-EGFR therapy. Key characteristics per CMS were assessed. Cross-referenced with matched benchmark FF CMS calls, the CMSFFPE classifier strongly improved classification accuracy in two FFPE datasets compared with the original CMSClassifier (63.6% versus 40.9% and 83.3% versus 66.7%, respectively). We recovered CMS-specific recurrence-free survival patterns (CMS4 versus CMS2: hazard ratio 1.75, 95% CI 1.24-2.46). Key molecular and clinical associations of the CMSs were confirmed. In particular, we demonstrated the predictive value of CMS2 and CMS3 for anti-EGFR therapy response (CMS2&3: odds ratio 5.48, 95% CI 1.10-27.27). The CMSFFPE classifier is an optimized FFPE-curated research tool for CMS classification of clinical CRC samples.

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临床结直肠癌组织分子亚型分类共识策略。
大肠癌(CRC)组织的共识分子亚型(CMS)分类因福尔马林固定石蜡包埋(FFPE)保存时的 RNA 降解而变得复杂。在此,我们介绍一种经 FFPE 培养的 CMS 分类器。CMSFFPE 分类器是利用 FFPE 衍生 RNA 中具有高转录本完整性的基因开发的。我们评估了两个FFPE-RNA数据集与匹配的新鲜冷冻(FF)RNA数据以及一个FF衍生RNA集的分类准确性。我们建立了转移性 CRC 患者的 FFPE-RNA 应用队列,其中部分患者接受了抗 EGFR 治疗。评估了每个 CMS 的关键特征。与匹配的基准 FF CMS 调用进行交叉比对,与原始 CMSClassifier 相比,CMSFFPE 分类器大大提高了两个 FFPE 数据集的分类准确性(分别为 63.6% 对 40.9% 和 83.3% 对 66.7%)。我们恢复了 CMS 特异性无复发生存模式(CMS4 与 CMS2:危险比 1.75,95% CI 1.24-2.46)。CMSs的关键分子和临床关联得到了证实。特别是,我们证实了 CMS2 和 CMS3 对抗 EGFR 治疗反应的预测价值(CMS2&3:几率比 5.48,95% CI 1.10-27.27)。CMSFFPE分类器是一种优化的FFPE整理研究工具,用于对临床CRC样本进行CMS分类。
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来源期刊
Life Science Alliance
Life Science Alliance Agricultural and Biological Sciences-Plant Science
CiteScore
5.80
自引率
2.30%
发文量
241
审稿时长
10 weeks
期刊介绍: Life Science Alliance is a global, open-access, editorially independent, and peer-reviewed journal launched by an alliance of EMBO Press, Rockefeller University Press, and Cold Spring Harbor Laboratory Press. Life Science Alliance is committed to rapid, fair, and transparent publication of valuable research from across all areas in the life sciences.
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