在结直肠肿瘤谱系中，具有 DNA 聚合酶 ε (POLE) 突变的微卫星稳定 (MSS) 肿瘤表现出超突变特征，具有与微卫星不稳定 (MSI) 肿瘤相似的免疫治疗反应潜力。然而，由于它们的稀有性和相关的测试成本，通常不会对这些突变进行系统筛查。值得注意的是，理论上 POLE 突变产生的组织病理学表型与 MSI 相似。这种相似性不仅可以促进在 MSI 病理切片上训练的基于变压器的深度学习 (DL) 系统对其进行检测，而且还表明具有 POLE 突变的 MSS 患者有可能获得增强的治疗选择，否则这些治疗选择可能会被忽视。为了利用这一潜力，我们在具有微卫星状态基本事实的大型数据集上训练了深度学习分类器，并随后在三个外部队列中验证了其 MSI 和 POLE 检测的功能。我们的模型仅使用病理图像即可准确识别内部和外部切除队列中的 MSI 状态。值得注意的是，在分类阈值为 0.5 的情况下，外部切除队列中超过 75% 的 POLE 驱动突变患者被接受 MSI 状态训练的 DL 系统标记为“阳性”。在临床环境中，将此 DL 模型部署为初步筛查工具可以促进一次性有效识别结直肠肿瘤中临床相关的 MSI 和 POLE 突变。© 2024。作者。

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as "positive" by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.© 2024. The Author(s).