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Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities

Nature Biotechnology volume 35, pages 983–989 (2017)Cite this article

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Abstract

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein–protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein–protein associations affect the phenotype of biological systems.

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Figure 1: High-throughput multiplexed quantitative proteome mapping of 41 breast cancer cell lines and protein co-regulation analysis to identify protein–protein associations.
Figure 2: Protein–protein association network revealed by proteomics-based protein co-regulation analysis across 41 breast cancer cell lines.
Figure 3: Deviations of protein co-regulation allow the identification of cell-line-specific dysregulation of protein–protein associations, revealing cell-specific vulnerabilities.
Figure 4: Subtype-specific dysregulated functional modules reflect sensitivity of breast cancer cell lines to specific therapeutics.

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Acknowledgements

We thank S. Gygi (Harvard Medical School) for access to computational software and facilities to process the proteomics data. We are grateful to J. Boisvert for her help with culturing of the cell lines and to all members of the Haas and Benes laboratories for valuable discussions. C.H.B. is supported by grant 102696 from the Wellcome Trust. Cell lines were purchased with funds from the NIH LINCS phase 1 grant HG006097.

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Authors and Affiliations

  1. Massachusetts General Hospital Cancer Center and Department of Medicine, Harvard Medical School, Charlestown, Massachusetts, USA

    John D Lapek Jr, Patricia Greninger, Robert Morris, Arnaud Amzallag, Iulian Pruteanu-Malinici, Cyril H Benes & Wilhelm Haas

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  1. John D Lapek Jr
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  2. Patricia Greninger
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Contributions

C.H.B. and W.H. conceived and designed the study; J.D.L., C.H.B. and W.H. wrote the manuscript; J.D.L. and W.H. performed the proteomics experiments; P.G. and C.H.B. performed the drug screen and analyzed the drug screen data; and J.D.L., R.M., A.A., I.P.-M., C.H.B. and W.H. performed the analysis of the proteomics data.

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Correspondence to Cyril H Benes or Wilhelm Haas.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Spearman’s rank correlation matrix of the proteome profiles of 41 breast cancer cell lines.

Data are listed in Supplementary Tables 1 and 2.

Supplementary Figure 2 Hierarchical clustering of 36 breast cancer cell lines based on their mRNA or protein profiles.

Hierarchical clustering of 36 breast cancer cell lines based on their (a) mRNA or (b) protein profiles. Clustering was done for the 36 cell lines, which proteome profiles were mapped in this study and which mRNA profiles were also reported by Klinj et al.1 Although the dendrograms show differences in the composition of the sub-clusters, the clustering based both on mRNA and protein profiles generated similar clusters of cell lines of the basal (green), luminal (red), claudin-low (blue), and non-malignant (brown) subtypes. Unsupervised clustering was done based on the intensities of 6659 gene-products quantified in all datasets and using the JMP software (version Pro 11) applying the ward method without standardizing the data.

1. Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat Biotechnol 33, 306-312, doi:10.1038/nbt.3080 (2015).

Supplementary Figure 3 Global comparison of protein and mRNA levels for 36 breast cancer cell lines.

(a) Scatterplot of protein levels measured in two biological replicates of HCC1937 proteomes. Protein levels were calculated as the log2 ratio of the protein level determined in each duplicates over the median protein level in all proteome measurements (duplicates of 36 cell lines); log2 HCC1937 intensity/median intensity. (b) Scatterplot of protein levels measured in one biological duplicate versus mRNA level measured by RNA sequencing analysis. Both levels are given as log2 HCC1937 intensity/median intensity. (c) Spearman correlation distribution for duplicate proteomics measurements (dark blue, median Spearman’s r = 0.82) and mRNA and protein level comparisons (yellow, median ρ = 0.62).

Supplementary Figure 4 Protein co-regulation analysis identifies substantially more known protein–protein associations than mRNA co-regulation analysis across a range mRNA of precision thresholds.

The analysis was done on identical numbers of gene products monitored in 36 cell lines. The RNAseq data published by Klinj et al.1 were used for mRNA-based co-regulation analysis. High confidence known protein-protein associations were extracted from the STRING database (score ≥ 0.700)2. The co-regulation analysis is described in the Supplementary Methods Section.

1. Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat Biotechnol 33, 306-312, doi:10.1038/nbt.3080 (2015).

2. Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39, D561-568, doi:10.1093/nar/gkq973 (2011).

Supplementary Figure 5 Validating novel protein–protein associations found through protein co-regulation analysis using extended STRING database matching.

A novel association between proteins A and B is confirmed in the extended STRING database match if the association between protein B and another protein C detected in our study to also being associated with protein A is confirmed in the STRING database.

Supplementary Figure 6 Comparison of co-regulation data with AP-MS interactome maps to identify protein complexes.

Even the conservative cut-off of an FDR of 0.05 % used in our manuscript produces a set of protein-protein associations with 71 % not listed in the CORUM database, a conservatively curated database of known protein complexes. It should be noted that this compares favorably to the two prominent AP-MS studies on mapping the human interactome1,2. Only 6 % of the 23,744 interactions reported by Huttlin et al.1 are listed in the CORUM database and 7 % of the 31,944 interactions presented by Hein et al.2. We have compared our dataset with these AP-MS datasets. For this comparison we only considered associations, where both protein partners were identified in both datasets. This was done to remove experimental bias regarding the identified proteins. The number of associations from our dataset fulfilling this requirement was 8,128, out of which 678 (8 %) were also reported by Huttlin et al.. Out of the 678 associations 501 (74 %) are CORUM listed and 177 (26 %) are novel interactions. The percentage of known interactions in our sub-dataset was 35 % (2,825 associations) and 501 of those (18 %) were confirmed by Huttlin et al., while the 177 unknown interactions confirmed by Huttlin et al. constituted 3 % of the 5,303 unknown associations in our dataset. In summary, the comparison shows that associations found in both dataset are enriched for known associations but that unknown associations was also confirmed by the AP-MS interactome mapping. To put this comparison into perspective, we also compared another AP-MS mapping performed by Hein et al. with that of Huttlin et al. in the exactly same manner. 16,357 associations reported by Hein et al. fulfilled the requirement of both protein-partners being identified in interactions in both datasets. 6 % (998) of them were confirmed by Huttlin et al., of which 390 (39 %) were known and 61 % (608) were novel. Of all the known interactions in the Hein et al. sub-dataset (1,758), 22 % (390) were also found by Huttlin et al.. The overlap was 4 % (608 out of 16357) for the unknown interactions. In summary, we show that novel associations in our dataset are confirmed by AP-MS data and that our dataset compares with AP-MS data in a similar way that AP-MS datasets compare with each other.

1. Huttlin, E. L. et al. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell 162, 425-440, doi:10.1016/j.cell.2015.06.043 (2015).

2. Hein, M. Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712-723, doi:10.1016/j.cell.2015.09.053 (2015).

Supplementary Figure 7 Dysregulated protein–protein associaction networks in basal-subtype breast cancer cell lines.

Proteins with dysregulated protein-protein associations are shown in red on the entire network of all protein-protein associations defined through co-regulation analysis (Fig. 2, Supplementary Table 6). Association dysregulations were determined by bivariate outlier detection of deviations of co-regulation of any protein pair across 41 breast cancer cell lines calculating the Mahalnobis distance and using the Grubb’s outlier test (p<0.1, Supplementary Table 8).

Supplementary Figure 8 Dysregulated protein–protein associaction networks in luminal-subtype breast cancer cell lines.

Proteins with dysregulated protein-protein associations are shown in red on the entire network of all protein-protein associations defined through co-regulation analysis (Fig. 2, Supplementary Table 6). Association dysregulations were determined by bivariate outlier detection of deviations of co-regulation of any protein pair across 41 breast cancer cell lines calculating the Mahalnobis distance and using the Grubb’s outlier test (p<0.1, Supplementary Table 8).

Supplementary Figure 9 The numbers of dysregulated protein–protein associations identified in all 41 breast cancer cell lines.

Supplementary Figure 10 Gene copy number correlations correlate strongly with mRNA but not with protein profiles, explaining the difference in the derived functional gene-association networks.

(a) Protein and mRNA profile Spearman’s correlation matrices of 32 members of the 26S proteasome multi-protein complex (Supplementary Table 13). Protein co-regulation identifies 107 associations across the members and the correlation matrix reflects the proteasome substructure. Co-regulation of mRNA profiles identifies 3 high confidence interactions across the proteasome members. (b) Gene copy number variations are strongly correlated with mRNA levels of proteasome members (median ρ = 0.55, right panel) but do not correlated with protein levels (median ρ = -0.01, left panel).

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Lapek, J., Greninger, P., Morris, R. et al. Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities. Nat Biotechnol 35, 983–989 (2017). https://doi.org/10.1038/nbt.3955

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