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A thousand and one tumors: the promise of AI for cancer biology

Nature Methods volume 21, pages 1403–1406 (2024)Cite this article

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Breakthroughs in AI and multimodal genomics are unlocking the ability to study the tumor microenvironment. We explore promising machine learning techniques to integrate and interpret high-dimensional data, examine cellular dynamics and unravel gene regulatory mechanisms, ultimately enhancing our understanding of tumor progression and resistance.

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Fig. 1: Overview of insights into TMEs achieved with machine learning.

References

  1. Hao, Y. et al. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gayoso, A. et al. Nat. Biotechnol. 40, 163–166 (2022).

    Article  CAS  PubMed  Google Scholar 

  3. Lange, M. et al. Nat. Methods 19, 159–170 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chari, T. & Pachter, L. PLOS Comput. Biol. 19, e1011288 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nazaret, A. et al. Preprint at bioRxiv https://doi.org/10.1101/2023.11.11.566719 (2023).

  6. Lopez, R. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.1905.02269 (2019).

  7. Boyeau, P. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.10.04.510898 (2024).

  8. Rosen, Y. et al. Preprint at bioRxiv https://doi.org/10.1101/2023.11.28.568918 (2023).

  9. Welch, J. D. et al. Cell 177, 1873–1887.e17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. He, S. et al. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02173-8 (2024).

  11. Liu, Y., Jin, Y., Azizi, E. & Blumberg, A. J. BMC Bioinformatics 24, 480 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Nazaret, A., Hong, J., Azizi, E. & Blei, D. Preprint at https://doi.org/10.48550/arXiv.2311.10263 (2023).

  13. Hao, M. et al. Nat. Methods https://doi.org/10.1038/s41592-024-02305-7 (2024).

  14. Wang, Y. et al. Nat. Genet. 55, 19–25 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Maurer, K. et al. Preprint at bioRxiv https://doi.org/10.1101/2024.02.09.579677 (2024).

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Acknowledgements

We thank J. McFaline-Figueroa for helpful discussions. J.L.F. acknowledges support from the Columbia University Van C. Mow fellowship and the Avanessians doctoral fellowship. A.N. acknowledges support from the Eric & Wendy Schmidt Center Ph.D. Fellowship and the Africk Family Fund. E.A. was supported by US National Institute of Health NCI R00CA230195 and NHGRI R21HG012639, R01HG012875, National Science Foundation CBET 2144542, and grant 2022-253560 from the Chan Zuckerberg Initiative DAF.

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Author notes

  1. These authors contributed equally: Joy Linyue Fan, Achille Nazaret.

Authors and Affiliations

  1. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Joy Linyue Fan & Elham Azizi

  2. Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA

    Joy Linyue Fan, Achille Nazaret & Elham Azizi

  3. Department of Computer Science, Columbia University, New York, NY, USA

    Achille Nazaret & Elham Azizi

  4. Data Science Institute, Columbia University, New York, NY, USA

    Elham Azizi

  5. Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

    Elham Azizi

Authors
  1. Joy Linyue Fan
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  2. Achille Nazaret
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  3. Elham Azizi
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Contributions

J.L.F., A.N. and E.A. wrote the manuscript. J.L.F. designed the figure.

Corresponding author

Correspondence to Elham Azizi.

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The authors declare no competing interests.

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Fan, J.L., Nazaret, A. & Azizi, E. A thousand and one tumors: the promise of AI for cancer biology. Nat Methods 21, 1403–1406 (2024). https://doi.org/10.1038/s41592-024-02364-w

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