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Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Nature Computational Science volume 3, pages 301–313 (2023)Cite this article

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Abstract

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

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Fig. 1: Regulation of non-genetic heterogeneity and its outcomes can be recapitulated using mathematical models.
Fig. 2: Evolution of mechanistic mathematical models for unraveling non-genetic heterogeneity in cancer.
Fig. 3: Interplay between computational, mathematical and experimental frameworks is crucial to develop a multiscale understanding of cancer systems.

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Acknowledgements

We thank the members of the Goyal and Jolly labs for helpful discussions. We thank K. Kiani and I. Mellis for their critical reading of the manuscript. The Goyal lab thanks R. Valadka at Northwestern University for his prompt and unwavering support in setting up the lab space. M.P. was supported by grants to Y.G. including the Career Award at the Scientific Interface from BWF (1020614.01) and start-up funds from Northwestern University, and KVPY Fellowship (Department of Science and Technology, Government of India). E.H. acknowledges support from the Career Award at the Scientific Interface from BWF (1020614.01) and Northwestern University’s Biomedical Engineering Department for the BME Summer Undergraduate Research Grant Award (SURA). E.H. thanks E. Hojel and M. Hojel for the support and encouragement to pursue her passions. M.K.J. acknowledges support from Ramanujan Fellowship awarded by the Science and Engineering Research Board, Department of Science and Technology, Government of India (SB/S2/RJN-049/2018), and from the InfoSys Foundation, Bangalore. Y.G. acknowledges support from the Career Award at the Scientific Interface from BWF (1020614.01), start-up funds from Northwestern University, and a grant (10063150.01) from Research Catalyst Program from the McCormick School of Engineering at Northwestern University.

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Authors and Affiliations

  1. Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Maalavika Pillai, Emilia Hojel & Yogesh Goyal

  2. Center for Synthetic Biology, Northwestern University, Chicago, IL, USA

    Maalavika Pillai, Emilia Hojel & Yogesh Goyal

  3. Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Maalavika Pillai & Yogesh Goyal

  4. Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India

    Maalavika Pillai & Mohit Kumar Jolly

  5. Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA

    Emilia Hojel & Yogesh Goyal

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  1. Maalavika Pillai
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Contributions

M.K.J. and Y.G. conceived the project, wrote the first draft of the manuscript and contributed to figure design. M.P. revised the initial draft, contributed to figure design, and prepared the boxes and the table. E.H. contributed to the figure, box and table design and literature survey. All authors contributed to revisions.

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Correspondence to Mohit Kumar Jolly or Yogesh Goyal.

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Competing interests

Y.G. received consultancy fee from the Schmidt Science Fellows and the Rhodes Trust. The other authors declare no competing interests.

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Nature Computational Science thanks Gábor Balázsi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Ananya Rastogi and Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Pillai, M., Hojel, E., Jolly, M.K. et al. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. Nat Comput Sci 3, 301–313 (2023). https://doi.org/10.1038/s43588-023-00427-0

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