Evolutionary dynamics of 1,976 lymphoid malignancies predict clinical outcome
Evolutionary dynamics of 1,976 lymphoid malignancies predict clinical outcome
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DOI：
10.1101/2023.11.10.23298336
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发表时间：
2023-11
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影响因子：
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通讯作者：
C. Gabbutt;M. Duran-Ferrer;H. Grant;D. Mallo;F. Nadeu;J. Househam;N. Villamor;O. Krali
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作者：
C. Gabbutt;M. Duran-Ferrer;H. Grant;D. Mallo;F. Nadeu;J. Househam;N. Villamor;O. Krali
癌症的发展、进展和对治疗的反应是一个进化过程，但在足够的尺度上描述进化动态以具有临床意义仍然具有挑战性。在这里，我们开发了一种名为EVOFLUx的新方法，该方法基于随时间波动的自然DNA甲基化条形码，仅使用大量肿瘤甲基化谱作为输入，即可定量推断进化动力学。我们将EVOFLUx应用于跨越广泛疾病谱系的1976个特征良好的淋巴样癌样本，并显示肿瘤生长速度、恶性肿瘤年龄和上皮化率在不同疾病类型之间以数量级变化。我们测量亚克隆选择仅在大量样本中很少发生，并且偶尔检测到多个独立原发肿瘤的例子。在临床上，我们观察到肿瘤生长速度在侵袭性疾病亚型中较高，在两组慢性淋巴细胞白血病患者中，进化历史是独立的预后因素。使用EVOFLUx对纵向CLL样本进行系统发育分析，在呈现前几十年检测未来里希特转化的种子。我们使用额外的遗传和临床数据对EVOFLUx推论进行正交验证。总的来说，我们展示了广泛可用的低成本大量DNA甲基化数据如何精确测量癌症进化动力学，并为癌症生物学和临床行为提供了新的见解。
Cancer development, progression, and response to treatment are evolutionary processes, but characterising the evolutionary dynamics at sufficient scale to be clinically-meaningful has remained challenging. Here, we develop a new methodology called EVOFLUx, based upon natural DNA methylation barcodes fluctuating over time, that quantitatively infers evolutionary dynamics using only a bulk tumour methylation profile as input. We apply EVOFLUx to 1,976 well-characterised lymphoid cancer samples spanning a broad spectrum of diseases and show that tumour growth rates, malignancy age and epimutation rates vary by orders of magnitude across disease types. We measure that subclonal selection occurs only infrequently within bulk samples and detect occasional examples of multiple independent primary tumours. Clinically, we observe that tumour growth rates are higher in more aggressive disease subtypes, and in two series of chronic lymphocytic leukaemia patients, evolutionary histories are independent prognostic factors. Phylogenetic analyses of longitudinal CLL samples using EVOFLUx detect the seeds of future Richter transformation many decades prior to presentation. We provide orthogonal verification of EVOFLUx inferences using additional genetic and clinical data. Collectively, we show how widely-available, low-cost bulk DNA methylation data precisely measures cancer evolutionary dynamics, and provides new insights into cancer biology and clinical behaviour.