Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient’s disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.g., ixazomib, lenalidomide, dexamethasone (IRd) vs lenalidomide, dexamethasone (Rd). Using TOURMALINE trial data, we trained and internally validated our model on newly diagnosed myeloma patients (N = 703) and externally validated it on relapsed and refractory myeloma patients (N = 720). Our model achieved superior performance to a risk model based on the multiple myeloma international staging system (ISS) (p < 0.001, Bonferroni corrected) and comparable performance to survival models trained separately on each task, but unable to forecast biomarkers. Our approach outperformed state-of-the-art deep learning models, tailored towards forecasting, on predicting key disease biomarkers (p < 0.001, Bonferroni corrected). Finally, leveraging our model’s capacity to estimate individual-level treatment effects, we found that patients with IgA kappa myeloma appear to benefit the most from IRd. Our study suggests that a holistic assessment of a patient’s myeloma course is possible, potentially serving as the foundation for a personalized decision support system.

多发性骨髓瘤的治疗需要在最大化生存、最小化治疗不良事件和监测疾病进展之间取得平衡。虽然之前的工作提出了针对个体任务的数据驱动模型，但这些方法无法提供患者疾病状态的整体视图，限制了它们协助医生决策的效用。为了解决这一限制，我们开发了一种基于 Transformer 的机器学习模型，该模型联合 (1) 预测无进展生存期 (PFS)、总生存期 (OS) 和不良事件 (AE)，(2) 预测关键疾病生物标志物，以及(3)评估不同治疗策略的效果，例如伊沙佐米、来那度胺、地塞米松(IRd)与来那度胺、地塞米松(Rd)。使用 TOURMALINE 试验数据，我们在新诊断的骨髓瘤患者 ( N = 703) 上训练和内部验证我们的模型，并在复发和难治性骨髓瘤患者 ( N = 720) 上进行外部验证。我们的模型的性能优于基于多发性骨髓瘤国际分期系统 (ISS) 的风险模型（ p < 0.001， Bonferroni 校正），并且与针对每项任务单独训练的生存模型具有相当的性能，但无法预测生物标志物。在预测关键疾病生物标志物方面，我们的方法优于最先进的深度学习模型（ p < 0.001， Bonferroni 校正）。最后，利用我们的模型估计个体水平治疗效果的能力，我们发现 IgA kappa 骨髓瘤患者似乎从 IRd 中受益最多。 我们的研究表明，对患者的骨髓瘤病程进行整体评估是可能的，有可能作为个性化决策支持系统的基础。