通过量化基因测序数据中的促增殖和抗凋亡特征,建立预测 B 细胞淋巴瘤预后的患者特异性计算模型

IF 12.9 1区 医学 Q1 HEMATOLOGY
Richard Norris, John Jones, Erika Mancini, Timothy Chevassut, Fabio A. Simoes, Chris Pepper, Andrea Pepper, Simon Mitchell
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引用次数: 0

摘要

基因异质性和共生驱动突变会影响血癌的临床结果,但预测影响多个复杂且相互作用的信号网络的共生突变的突发性效应却很有挑战性。在这里,我们利用数学模型预测了共生突变对弥漫大B细胞淋巴瘤和多发性骨髓瘤的细胞信号传导和细胞命运的影响。模拟预测了当突变组合同时诱导抗凋亡(AA)和促增殖(PP)信号时对临床预后的不利影响。我们将患者特异性突变特征整合到个性化淋巴瘤模型中,并确定了在所有基因组和原发细胞分类中同时上调抗凋亡和促增殖(AAPP)信号的患者(占患者总数的 8-25%)。在一个发现队列和两个验证队列中,信号状态均不上调、一种(AA 或 PP)上调或两种(AAPP)均上调的患者预后分别为良好、中等和不良。将 AAPP 信号与遗传或临床预后预测因素相结合,可以可靠地将患者分为显著的预后类别。预后不良基因群中的AAPP患者的中位总生存期为7.8个月,而缺乏这两种特征的患者在验证队列中120个月的总生存期为90%。个性化计算模型能够识别新的风险分层患者亚群,为未来的风险适应性临床试验提供了宝贵的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data

Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data

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.

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来源期刊
CiteScore
16.70
自引率
2.30%
发文量
153
审稿时长
>12 weeks
期刊介绍: Blood Cancer Journal is dedicated to publishing high-quality articles related to hematologic malignancies and related disorders. The journal welcomes submissions of original research, reviews, guidelines, and letters that are deemed to have a significant impact in the field. While the journal covers a wide range of topics, it particularly focuses on areas such as: Preclinical studies of new compounds, especially those that provide mechanistic insights Clinical trials and observations Reviews related to new drugs and current management of hematologic malignancies Novel observations related to new mutations, molecular pathways, and tumor genomics Blood Cancer Journal offers a forum for expedited publication of novel observations regarding new mutations or altered pathways.
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