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MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks

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Comparative Genomics (RECOMB-CG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13883))

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Abstract

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.

This study was supported in part by the National Science Foundation, grants IIS-1812822 and IIS-2106837 (L.N.).

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Notes

  1. 1.

    A clone consists of a group of cells with similar genotypes.

  2. 2.

    Sometimes the acronym RvNN is used to distinguish it from recurrent neural networks.

  3. 3.

    Hereafter, we use embedding instead of vector embedding.

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Authors and Affiliations

  1. Department of Computer Science, Rice University, Houston, TX, USA

    Mohammadamin Edrisi, Huw A. Ogilvie & Luay Nakhleh

  2. Department of Statistics, Rice University, Houston, TX, USA

    Meng Li

Authors
  1. Mohammadamin Edrisi
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  2. Huw A. Ogilvie
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  3. Meng Li
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  4. Luay Nakhleh
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Corresponding authors

Correspondence to Mohammadamin Edrisi or Luay Nakhleh .

Editor information

Editors and Affiliations

  1. Freie Universität Berlin, Berlin, Germany

    Katharina Jahn

  2. Comenius University, Bratislava, Slovakia

    Tomáš Vinař

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Edrisi, M., Ogilvie, H.A., Li, M., Nakhleh, L. (2023). MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks. In: Jahn, K., Vinař, T. (eds) Comparative Genomics. RECOMB-CG 2023. Lecture Notes in Computer Science(), vol 13883. Springer, Cham. https://doi.org/10.1007/978-3-031-36911-7_15

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  • DOI: https://doi.org/10.1007/978-3-031-36911-7_15

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  • Online ISBN: 978-3-031-36911-7

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