The use of Hypomethylating agents combined with Venetoclax (VH) for the treatment of Acute Myeloid Leukemia (AML) has greatly improved outcomes in recent years. However not all patients benefit from the VH regimen and a way to rationally select between VH and Conventional Chemotherapy (CC) for individual AML patients is needed. Here, we developed a proteomic-based triaging strategy using Reverse-phase Protein Arrays (RPPA) to optimize therapy selection. We evaluated the expression of 411 proteins in 810 newly diagnosed adult AML patients, identifying 109 prognostic proteins, that divided into five patient expression profiles, which are useful for optimizing therapy selection. Furthermore, using machine learning algorithms, we determined a set of 14 proteins, among those 109, that were able to accurately recommend therapy, making it feasible for clinical application. Next, we identified a group of patients who did not benefit from either VH or CC and proposed target-based approaches to improve outcomes. Finally, we calculated that the clinical use of our proteomic strategy would have led to a change in therapy for 30% of patients, resulting in a 43% improvement in OS, resulting in around 2600 more cures from AML per year in the United States.

近年来，使用去甲基化药物与维奈托克（VH）联合治疗急性髓系白血病（AML）已大大改善了预后。然而，并非所有患者都能从 VH 方案中受益，因此需要一种针对个体 AML 患者在 VH 和传统化疗 (CC) 之间合理选择的方法。在这里，我们开发了一种基于蛋白质组学的分类策略，使用反相蛋白阵列 (RPPA) 来优化治疗选择。我们评估了 810 名新诊断的成人 AML 患者中 411 种蛋白的表达，鉴定了 109 种预后蛋白，将其分为 5 种患者表达谱，这对于优化治疗选择非常有用。此外，使用机器学习算法，我们确定了这 109 种蛋白质中的一组 14 种蛋白质，能够准确推荐治疗，使其适合临床应用。接下来，我们确定了一组未从 VH 或 CC 中受益的患者，并提出了基于目标的方法来改善结果。最后，我们计算出，我们的蛋白质组学策略的临床应用将导致 30% 患者的治疗方法发生改变，从而使 OS 改善 43%，从而使美国每年 AML 治愈人数增加约 2600 例。