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Learning Cancer Drug Sensitivities in Large-Scale Screens from Multi-omics Data with Local Low-Rank Structure

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12313))

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Abstract

The molecular characterization of tumor samples by multiple omics data sets of different types or modalities (e.g. gene expression, mutation, CpG methylation) has become an invaluable source of information for assessing the expected performance of individual drugs and their combinations. Merging relevant information from the omics data modalities provides the statistical basis for determining suitable therapies for specific cancer patients. Different data modalities may each have their own specific structures that need to be taken into account during inference. In this paper, we assume that each omics data modality has a low-rank structure with only a few relevant features that affect the prediction and we propose to use a composite local nuclear norm penalization for learning drug sensitivity. Numerical results show that the composite low-rank structure can improve the prediction performance compared to using a global low-rank approach or elastic net regression.

The first two authors contributed equally. L.R. is supported by The Norwegian Research Council 237718 through the Big Insight Center for research-driven innovation. The research of T.T.M. and J.C. are supported by the European Research Council (SCARABEE, no. 742158).

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Notes

  1. 1.

    https://cran.r-project.org/package=rrpack.

  2. 2.

    https://cran.r-project.org/package=glmnet.

  3. 3.

    ftp://ftp.sanger.ac.uk/pub4/cancerrxgene/releases/release-5.0/.

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Authors and Affiliations

  1. Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway

    The Tien Mai, Leiv Rønneberg, Zhi Zhao, Manuela Zucknick & Jukka Corander

  2. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

    Jukka Corander

Authors
  1. The Tien Mai
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  2. Leiv Rønneberg
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  3. Zhi Zhao
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  4. Manuela Zucknick
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  5. Jukka Corander
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Corresponding authors

Correspondence to The Tien Mai or Leiv Rønneberg .

Editor information

Editors and Affiliations

  1. University of Bergamo, Bergamo, Italy

    Paolo Cazzaniga

  2. University of Milano-Bicocca, Milan, Italy

    Daniela Besozzi

  3. National Research Council, Segrate, Italy

    Ivan Merelli

  4. Università degli Studi di Trieste, Trieste, Italy

    Luca Manzoni

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Mai, T.T., Rønneberg, L., Zhao, Z., Zucknick, M., Corander, J. (2020). Learning Cancer Drug Sensitivities in Large-Scale Screens from Multi-omics Data with Local Low-Rank Structure. In: Cazzaniga, P., Besozzi, D., Merelli, I., Manzoni, L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science(), vol 12313. Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-63061-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63060-7

  • Online ISBN: 978-3-030-63061-4

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