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Cancer immunotherapy

Enhanced precision in immunotherapy

Nature Cancer volume 5, pages 1136–1138 (2024)Cite this article

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Recent advancements in targeted immune checkpoint blockade (ICB) therapy have reshaped cancer treatment paradigms. However, many patients do not respond, highlighting the need for robust biomarkers. A study now introduces an approach using multi-omics data and machine learning to improve patient selection for ICB therapy, offering more effective treatment.

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Fig. 1: LORIS integrates multi-omics data and machine learning techniques to predict patient responses to ICB therapy.

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Authors and Affiliations

  1. Moores Cancer Center, University of California San Diego, La Jolla, CA, USA

    Erik N. Bergstrom & Ludmil B. Alexandrov

  2. Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA

    Erik N. Bergstrom & Ludmil B. Alexandrov

  3. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Erik N. Bergstrom & Ludmil B. Alexandrov

  4. Sanford Stem Cell Institute, University of California San Diego, La Jolla, CA, USA

    Ludmil B. Alexandrov

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  1. Erik N. Bergstrom
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  2. Ludmil B. Alexandrov
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Correspondence to Ludmil B. Alexandrov.

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Competing interests

L.B.A. is a compensated consultant and has equity interest in io9, LLC. The terms of this arrangement have been reviewed and approved by the University of California, San Diego (UCSD) in accordance with its conflict of interest policies. His spouse is an employee of Hologic, Inc. L.B.A. is a compensated member of the scientific advisory board of Inocras. E.N.B. is a compensated consultant and has equity interest in io9, LLC. E.N.B. and L.B.A. declare a US provisional patent application filed with UCSD with serial numbers 63/269,033 for artificial intelligence architecture for predicting cancer biomarkers.

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Bergstrom, E.N., Alexandrov, L.B. Enhanced precision in immunotherapy. Nat Cancer 5, 1136–1138 (2024). https://doi.org/10.1038/s43018-024-00802-4

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