Azacitidine is an approved therapy for higher-risk myelodysplastic syndrome (MDS). However, only 30–40% patients respond to azacitidine, and the responses may take up to six cycles to become evident. Delayed responses and the myelosuppressive effects of azacitidine make it challenging to predict which patients will benefit. This is further compounded by a lack of uniform prognostic tools to identify patients at risk of early treatment failure. Hence, we performed a retrospective analysis of 273 consecutive azacytidine-treated patients. The median overall survival was 16.25 months with only 9% alive at 5 years. By using pre-treatment variables incorporated into a random forest machine learning model, we successfully identified those patients unlikely to benefit from azacytidine upfront (7.99 vs. 22.8 months,p< 0.0001). This model also identified those who required significantly more hospitalizations and transfusion support. Notably, it accurately predicted survival outcomes, outperforming the existing prognostic scoring system. By integrating somatic mutations, we further refined the model and identified three distinct risk groups with significant differences in survival (5.6 vs. 10.5 vs. 43.5 months,p< 0.0001). These real-world findings emphasize the urgent need for personalized prediction tools tailored to hypomethylating agents, reducing unnecessary complications and resource utilization in MDS treatment.
阿扎胞苷是获批用于治疗高风险骨髓增生异常综合征（MDS）的方法。然而，仅30-40%的患者对阿扎胞苷产生应答，且应答可能需长达六个周期才显现。延迟应答及阿扎胞苷的骨髓抑制效应使得预测哪些患者将获益具有挑战性。缺乏统一的预后工具以识别早期治疗失败风险的患者，进一步加剧了这一难题。因此，我们对连续接受阿扎胞苷治疗的273例患者进行了回顾性分析。中位总生存期为16.25个月，仅9%的患者存活至5年。通过将治疗前变量纳入随机森林机器学习模型，我们成功识别出那些可能无法从阿扎胞苷初始治疗中获益的患者（7.99个月 vs. 22.8个月，p<0.0001）。该模型还识别出需要显著更多住院和输血支持的患者。值得注意的是，其准确预测了生存结局，表现优于现有预后评分系统。通过整合体细胞突变，我们进一步优化模型，识别出三个生存期差异显著的风险组（5.6个月 vs. 10.5个月 vs. 43.5个月，p<0.0001）。这些真实世界研究结果强调，亟需开发针对去甲基化药物的个性化预测工具，以降低MDS治疗中不必要的并发症和资源消耗。
肿瘤(癌症)患者之家