MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks
MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks
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DOI：
10.1101/2022.08.21.504710
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发表时间：
2022-08
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影响因子：
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通讯作者：
M. Edrisi;Huw A. Ogilvie;Meng Li;L. Nakhleh
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作者：
M. Edrisi;Huw A. Ogilvie;Meng Li;L. Nakhleh
随着单细胞DNA测序的出现，现在可以推断从单个患者获得的数千个肿瘤细胞的进化历史。这种进化史呈树形，揭示了所研究的特定癌症的进化模式，进而有助于临床诊断、预后和治疗。在这项研究中，我们专注于确定肿瘤细胞的进化模式，从他们推断的进化历史的问题。特别是，我们采用递归神经网络，捕获树结构，肿瘤细胞的进化历史分为四种模式之一-线性，分支，中性和间断。我们训练我们的模型，MoTERNN，使用模拟数据在监督的方式，并将其应用到从单细胞DNA测序数据获得的真实的系统发育树。MoTERNN是用Python实现的，可以在https://github.com/NakhlehLab/MoTERNN上公开获得。
With the advent of single-cell DNA sequencing, it is now possible to infer the evolutionary history of thousands of tumor cells obtained from a single patient. This evolutionary history, which takes the shape of a tree, reveals the mode of evolution of the specific cancer under study and, in turn, helps with clinical diagnosis, prognosis, and therapeutic treatment. In this study we focus on the question of determining the mode of evolution of tumor cells from their inferred evolutionary history. In particular, we employ recursive neural networks that capture tree structures to classify the evolutionary history of tumor cells into one of four modes—linear, branching, neutral, and punctuated. We trained our model, MoTERNN, using simulated data in a supervised fashion and applied it to a real phylogenetic tree obtained from single-cell DNA sequencing data. MoTERNN is implemented in Python and is publicly available at https://github.com/NakhlehLab/MoTERNN.