Stage II colon cancer (CC) encompasses a heterogeneous group of patients with diverse survival experiences: 87% to 58% 5-year relative survival rates for stages IIA and IIC, respectively. While stage IIA patients are usually spared the adjuvant chemotherapy, some of them relapse and may benefit from it; thus, their timely identification is crucial. Current gene expression signatures did not specifically target this group nor did they find their place in clinical practice. Since processes at invasion front have also been linked to tumor progression, we hypothesize that aside from bulk tumor features, focusing on the invasion front may provide additional clues for this stratification. A retrospective matched case-control collection of 39 stage IIA microsatellite-stable (MSS) untreated CCs was analyzed to identify prognostic gene expression-based signatures. The endpoint was defined as relapse within 5 years vs. no relapse for at least 6 years. From the same tumors, three different classifiers (bulk tumor, invasion front, and constrained baseline on bulk tumor) were developed and their performance estimated. The baseline classifier, while the weakest, was validated in two independent data sets. The best performing signature was based on invasion front profiles [area under the receiver operating curve (AUC) = 0.931 (0.815–1.0)] and contained genes associated with KRAS pathway activation, apical junction complex, and heme metabolism. Its combination with bulk tumor classifier further improved the accuracy of the predictions.

中文翻译：

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用于选择 IIA 期 MSS 结肠癌高风险患者的侵袭前基因表达特征

II 期结肠癌 (CC) 包含具有不同生存经历的异质患者群体：IIA 期和 IIC 期的 5 年相对生存率分别为 87% 至 58%。虽然 IIA 期患者通常无需接受辅助化疗，但其中一些患者会复发并可能从中受益；因此，及时识别它们至关重要。目前的基因表达特征并没有专门针对这一群体，也没有在临床实践中找到自己的位置。由于侵袭前沿的过程也与肿瘤进展相关，因此我们假设除了大量肿瘤特征之外，关注侵袭前沿可能为这种分层提供更多线索。对 39 个 IIA 期微卫星稳定 (MSS) 未经处理的 CC 的回顾性匹配病例对照集合进行分析，以确定基于预后基因表达的特征。终点定义为 5 年内复发与至少 6 年内无复发。针对相同的肿瘤，开发了三种不同的分类器（块状肿瘤、侵袭前沿和块状肿瘤的约束基线）并评估了它们的性能。基线分类器虽然最弱，但在两个独立的数据集中得到了验证。表现最佳的特征基于侵袭前沿特征[受试者工作曲线下面积 (AUC) = 0.931 (0.815–1.0)]，并包含与 KRAS 通路激活、顶端连接复合物和血红素代谢相关的基因。其与大块肿瘤分类器的结合进一步提高了预测的准确性。