Cancer Center Amsterdam, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam, Netherlands.
Amsterdam Gastroenterology Endocrinology Metabolism, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam, Netherlands.
Life Sci Alliance. 2024 May 23;7(8). doi: 10.26508/lsa.202402730. Print 2024 Aug.
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.
结直肠癌（CRC）组织的共识分子亚型（CMS）分类在福尔马林固定石蜡包埋（FFPE）保存时会受到 RNA 降解的影响。在这里，我们提出了一种 FFPE 优化的 CMS 分类器。CMSFFPE 分类器是使用在 FFPE 衍生的 RNA 中具有高转录完整性的基因开发的。我们在具有匹配的新鲜冷冻（FF）RNA 数据和 FF 衍生的 RNA 集的两个 FFPE-RNA 数据集上评估了分类准确性。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 调用交叉引用，CMSFFPE 分类器与原始 CMSClassifier 相比，在两个 FFPE 数据集上大大提高了分类准确性（分别为 63.6%与 40.9%和 83.3%与 66.7%）。我们恢复了 CMS 特异性无复发生存模式（CMS4 与 CMS2：风险比 1.75，95%置信区间 1.24-2.46）。CMS 之间的关键分子和临床关联得到了证实。特别是，我们证明了 CMS2 和 CMS3 对抗 EGFR 治疗反应的预测价值（CMS2&3：比值比 5.48，95%置信区间 1.10-27.27）。CMSFFPE 分类器是用于临床 CRC 样本 CMS 分类的优化 FFPE 优化研究工具。
