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An efficient and effective method to identify significantly perturbed subnetworks in cancer

Nature Computational Science volume 1, pages 79–88 (2021)Cite this article

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Abstract

The identification of key functional biological networks from high-dimensional genomics data is pivotal for cancer research. Here, we introduce FDRnet, a method for the detection of molecular subnetworks in cancer, which addresses several challenges in pathway analysis. FDRnet detects key subnetworks by solving a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint, and minimizing a conductance score to find dense subgraphs around seed genes. A large-scale benchmark study was performed on both simulation and cancer genomics data. FDRnet outperformed other methods in the ability to detect functionally homogeneous subnetworks in a scale-free biological network, to control FDRs of the genes in detected subnetworks, to improve computational efficiency and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases.

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Fig. 1: Overview of the proposed method.
Fig. 2: Comparison of six methods in terms of their abilities to detect target genes and modular structures and to control FDRs of identified subnetworks using simulation data.
Fig. 3: Detecting significantly mutated subnetworks in breast cancer using The Cancer Genome Atlas copy number and somatic mutation data.
Fig. 4: Detecting pathways differentially expressed between germinal center B-cell like (GCB) and activated B-cell like (ABC) diffuse large B-cell lymphoma using gene expression data.
Fig. 5: Running time of six methods applied to simulation, breast cancer and lymphoma data.

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Data availability

The breast cancer somatic mutation and copy number data (dbGaP study accession no. phs000178) were downloaded from the TCGA Firehose website (https://gdac.broadinstitute.org). The iRefIndex9.0 PPI network, the BioGRID v3.5.187 PPI network and the ReactomeFI v2019 PPI network were downloaded from http://compbio-research.cs.brown.edu/pancancer/hotnet2/, https://thebiogrid.org and https://reactome.org, respectively, without any restriction. For the lymphoma study, the gene expression data and the interactome data (HPRD PPI network) were obtained from the BioNet package (https://www.bioconductor.org/packages/release/bioc/html/BioNet.html) without any restriction. Source data are provided with this paper.

Code availability

The software and user manual are available at https://github.com/yangle293/FDRnet (https://doi.org/10.5281/zenodo.4121885; ref. 61) and www.acsu.buffalo.edu/~yijunsun/lab/FDRnet.html.

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Acknowledgements

This work is supported in part by NIH R01AI125982 (Y.S.), NIH R01DE024523195 (Y.S.) and NIH R01CA241123 (S.G.).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The State University of New York at Buffalo, Buffalo, NY, USA

    Le Yang, Runpu Chen & Yijun Sun

  2. Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA

    Steve Goodison

  3. Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY, USA

    Yijun Sun

  4. Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY, USA

    Yijun Sun

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Contributions

L.Y., S.G. and Y.S. designed the study. L.Y., R.C. and Y.S. performed the data analysis. S.G. performed the biological discussions. L.Y., S.G. and Y.S. wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Steve Goodison or Yijun Sun.

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Yang, L., Chen, R., Goodison, S. et al. An efficient and effective method to identify significantly perturbed subnetworks in cancer. Nat Comput Sci 1, 79–88 (2021). https://doi.org/10.1038/s43588-020-00009-4

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