Genetic heterogeneity and co-occurring driver mutations impact clinical outcomes in blood cancers, but predicting the emergent effect of co-occurring mutations that impact multiple complex and interacting signalling networks is challenging. Here, we used mathematical models to predict the impact of co-occurring mutations on cellular signalling and cell fates in diffuse large B cell lymphoma and multiple myeloma. Simulations predicted adverse impact on clinical prognosis when combinations of mutations induced both anti-apoptotic (AA) and pro-proliferative (PP) signalling. We integrated patient-specific mutational profiles into personalised lymphoma models, and identified patients characterised by simultaneous upregulation of anti-apoptotic and pro-proliferative (AAPP) signalling in all genomic and cell-of-origin classifications (8-25% of patients). In a discovery cohort and two validation cohorts, patients with upregulation of neither, one (AA or PP), or both (AAPP) signalling states had good, intermediate and poor prognosis respectively. Combining AAPP signalling with genetic or clinical prognostic predictors reliably stratified patients into striking prognostic categories. AAPP patients in poor prognosis genetic clusters had 7.8 months median overall survival, while patients lacking both features had 90% overall survival at 120 months in a validation cohort. Personalised computational models enable identification of novel risk-stratified patient subgroups, providing a valuable tool for future risk-adapted clinical trials.
遗传异质性及共现驱动突变会影响血液癌症的临床结局，但预测那些影响多个复杂相互作用信号网络的共现突变所引发的级联效应具有挑战性。本研究通过数学模型预测弥漫性大B细胞淋巴瘤和多发性骨髓瘤中共现突变对细胞信号传导及细胞命运的影响。模拟结果表明，当突变组合同时诱发抗凋亡（AA）和促增殖（PP）信号时，会对临床预后产生不利影响。我们将患者特异性突变谱整合到个性化淋巴瘤模型中，发现所有基因组学和细胞起源分类中均存在抗凋亡与促增殖信号同时上调（AAPP）的患者亚群（占患者总数的8-25%）。在一个探索队列和两个验证队列中，信号状态无上调、单类上调（AA或PP）及双类上调（AAPP）的患者分别对应良好、中等和不良预后。将AAPP信号状态与遗传或临床预后预测因子结合，能够可靠地将患者划分为具有显著差异的预后分组。在验证队列中，处于不良预后遗传集群的AAPP患者中位总生存期仅为7.8个月，而同时不具备两种信号特征的患者120个月总生存率达到90%。个性化计算模型能够识别新的风险分层患者亚群，为未来风险适应性临床试验提供了重要工具。
肿瘤(癌症)患者之家