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Comprehensive characterization of protein–protein interactions perturbed by disease mutations

Nature Genetics volume 53, pages 342–353 (2021)Cite this article

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Abstract

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein–protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

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Fig. 1: Proof of concept of protein–protein interaction–perturbing alleles in human diseases.
Fig. 2: Network perturbation by missense somatic mutations in human cancers.
Fig. 3: Landscape of protein–protein interaction–perturbing mutations across 33 cancer types.
Fig. 4: Pharmacogenomics landscape of protein–protein interaction–perturbing alleles.
Fig. 5: Protein–protein interaction–perturbing alleles in histone H4 complex.
Fig. 6: Experimental investigation of alleles with perturbed physical protein–protein interactions.
Fig. 7: Mutants of RXRA and ALOX5 promote cancer cell growth.

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Data availability

All mapping interface mutations, network-predicted oncoPPIs across pan-cancer and 33 individual cancer types, the human protein–protein interactome and predicted drug responses and patient survival analysis are freely available at the websites https://mutanome.lerner.ccf.org/ and https://github.com/ChengF-Lab/oncoPPIs. Publicly available databases used in the present study include the RCSB protein data bank (https://www.rcsb.org), Interactome3D (v.2017.12, https://interactome3d.irbbarcelona.org), Interactome INSIDER (v.2018.3, http://interactomeinsider.yulab.org), GeneCards (http://www.genecards.org/), NCBI (https://www.ncbi.nlm.nih.gov), TCGA GDC Data Portal (https://portal.gdc.cancer.gov), HGMD (http://www.hgmd.cf.ac.uk/ac/index.php), 1000 Genomes (phase 3, 2,504 individuals, https://www.internationalgenome.org), ExAC database (v.0.3.1, 60,706 individuals, https://gnomad.broadinstitute.org) and GDSC (http://www.cancerrxgene.org/).

Code availability

All codes written for and used in this study are available from https://github.com/ChengF-Lab/oncoPPIs.

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Acknowledgements

We thank S. Tribuna for expert technical assistance. A portion of this work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract no. DE-AC52-07NA27344 (release no. LLNL-JRNL-797982). This work was supported by National Institutes of Health (NIH) grant nos. K99 HL138272, R00 HL138272, 3R01AG066707-01S1 and R01AG066707 to F.C. This work was also supported in part by NIH grant nos. U01 HG007690, P50 GM107618, U54 HL119145, R01 HL155107 and R01 HL155096 to J.L., as well as by AHA grant nos. D700382 and CV-19 to J.L. F.C.L. was supported by AHA CRADA no. TC02274.0. C.E. is the Sondra J. and Stephen R. Hardis Endowed Chair in Cancer Genomic Medicine at the Cleveland Clinic, and an ACS Clinical Research Professor. M.V. and D.E.H. were supported by NIH grant nos. P50 HG004233 and U41 HG001715 from NHGRI. This work has been also supported in part by the VeloSano Pilot Program (Cleveland Clinic Taussig Cancer Institute) to F.C.

Author information

Author notes

  1. These authors contributed equally: Feixiong Cheng, Junfei Zhao, Yang Wang, Weiqiang Lu.

Authors and Affiliations

  1. Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Feixiong Cheng, Yadi Zhou, William R. Martin, Yuan Hou, Jessica A. Castrillon, Jiansong Fang & Charis Eng

  2. Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Feixiong Cheng, Justin D. Lathia & Charis Eng

  3. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Feixiong Cheng, Justin D. Lathia, Ruth A. Keri & Charis Eng

  4. Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY, USA

    Junfei Zhao & Raul Rabadan

  5. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Junfei Zhao & Raul Rabadan

  6. Center for Cancer Systems Biology (CCSB), Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Yang Wang, Tong Hao, Hong Yue, Jing Ma, David E. Hill & Marc Vidal

  7. Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Yang Wang, Tong Hao, Hong Yue, David E. Hill & Marc Vidal

  8. Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China

    Weiqiang Lu

  9. Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China

    Zehui Liu & Jin Huang

  10. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Ruisheng Wang & Joseph Loscalzo

  11. Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China

    Jing Ma

  12. Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China

    Jiansong Fang

  13. Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Ruth A. Keri

  14. Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, CA, USA

    Felice C. Lightstone

  15. Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Elliott Marshall Antman

  16. Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Charis Eng

  17. Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA

    Charis Eng

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  1. Feixiong Cheng
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Contributions

J.L. and F.C. conceived the study. F.C., J.Z., Y.W., W.L. and Z.L. performed experiments and data analysis. Y.Z., W.R.M., H.Y., J.H., J.M., R.W., T.H., D.E.H., J.A.C., J.F., Y.H., J.D.L., R.A.K., F.C.L., E.M.A., R.R., C.E. and M.V. interpreted the data analysis. F.C., J.Z., Y.W., W.L. and J.L. drafted the manuscript and critically revised the manuscript. All authors critically revised and gave final approval of the manuscript.

Corresponding author

Correspondence to Joseph Loscalzo.

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Competing interests

J.L. is the scientific cofounder of Scipher Medicine, Inc., a startup company that uses network medicine to identify biomarkers for disease and specific pathway targets for drug development. M.V. is a shareholder and scientific advisor of seqWell, Inc. and founder and scientific advisor of Gloucester Marine Genomics Institute, Inc. The other authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Leng Han, Ulrich Stelzl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The 13 selected pan-cancer oncoPPIs with crystal structure-based PPI interface mutations.

The images were prepared by PyMOL (https://pymol.org/2/) using the Protein Data Bank (PDB) IDs (highlighted in figures) downloaded from PDB database (https://www.rcsb.org). Structural views of all oncoPPIs in pan-cancer and individual cancer types/subtypes are freely available: https://mutanome.lerner.ccf.org/.

Extended Data Fig. 2 Survival analyses of p53-SRSF1 PPI perturbing-mutations and p53 mutations alone.

Three exemplary cancer types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and colon adenocarcinoma (COAD), are illustrated. Survival analyses of p53-SRSF1 PPI perturbing-mutations across other cancer types/subtypes are provided in Supplementary Fig. 14. The p-value (P) was computed by log-rank test. All oncoPPI-predicted survival analyses for 33 cancer types/subtypes are freely available at the following website: https://mutanome.lerner.ccf.org/.

Supplementary information

Supplementary Information

Supplementary Figs. 1–22 and Table 1

Reporting Summary

Supplementary Data 1

Lists of oncoPPIs in pan-cancer and individual cancer analyses

Supplementary Data 2

Landscape of pharmacogenomics predicted by PPI-perturbing mutations in cancer cell lines

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Cheng, F., Zhao, J., Wang, Y. et al. Comprehensive characterization of protein–protein interactions perturbed by disease mutations. Nat Genet 53, 342–353 (2021). https://doi.org/10.1038/s41588-020-00774-y

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