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

Nature Biotechnology

Volumn 35, Issue 10, 2017, Pages 983-989

  Lapek, John D ^a   Greninger, Patricia ^a   Morris, Robert ^a   Amzallag, Arnaud ^a   Pruteanu Malinici, Iulian ^a   Benes, Cyril H ^a   Haas, Wilhelm ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELL CULTURE; DISEASES; MOLECULAR BIOLOGY; THROUGHPUT;

BREAST CANCER CELLS; CONSENSUS NETWORKS; DRUG SENSITIVITY; IDENTIFICATION OF PROTEINS; PROTEIN ASSOCIATIONS; PROTEIN COMPLEXES; PROTEIN-PROTEIN ASSOCIATIONS; PROTEOME ANALYSIS;

PROTEINS;

BIOLOGICAL MARKER; EPIDERMAL GROWTH FACTOR RECEPTOR 3; PROTEIN; PROTEIN CASC5; PROTEIN POLE; PROTEIN RPS2; PROTEIN SMARCE1; PROTEIN SUZ12; PROTEIN TPX2; SCATTER FACTOR RECEPTOR; TRANSCRIPTION FACTOR EZH2; UNCLASSIFIED DRUG;

ARTICLE; CONTROLLED STUDY; HUMAN; HUMAN CELL; INTERACTOME MAPPING BY HIGH THROUGHPUT QUANTITATIVE PROTEOME ANALYSIS; MALIGNANT NEOPLASM; MASS SPECTROMETRY; PHENOTYPE; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DEGRADATION; PROTEIN PROTEIN INTERACTION; PROTEOMICS; REGULATORY MECHANISM; SIGNAL TRANSDUCTION; BREAST TUMOR; FEMALE; HIGH THROUGHPUT SCREENING; METABOLISM; NEOPLASM; PROCEDURES; TUMOR CELL LINE;

BREAST NEOPLASMS; CELL LINE, TUMOR; FEMALE; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; NEOPLASMS; PROTEIN INTERACTION MAPS; PROTEOMICS;

EID: 85031086623     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.3955     Document Type: Article

References (50)

1. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347-355 (2016).
2. Rolland, T., et al. A proteome-scale map of the human interactome network. Cell 159, 1212-1226 (2014).
3. Huttlin, E. L., et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425-440 (2015).
4. Hein, M. Y., et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712-723 (2015).
5. McAlister, G. C., et al. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84, 7469-7478 (2012).
6. Heiser, L. M., et al. Subtype-and pathway-specific responses to anticancer compounds in breast cancer. Proc. Natl. Acad. Sci. USA 109, 2724-2729 (2012).
7. Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937-940 (2011).
8. McAlister, G. C., et al. MultiNotch MS3 enables accurate, sensitive and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150-7158 (2014).
9. Neve, R. M., et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515-527 (2006).
10. Zhang, B., et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382-387 (2014).
11. Klijn, C., et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306-312 (2015).
12. Szklarczyk, D., et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561-D568 (2011).
13. Wang, J., et al. Proteome profiling outperforms transcriptome profiling for co-expression-based gene function prediction. Mol. Cell. Proteomics http://dx. doi. org/10. 1074/mcp. M116. 060301 (2016).
14. Stefely, J. A., et al. Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling. Nat. Biotechnol. 34, 1191-1197 (2016).
15. Chick, J. M., et al. Defining the consequences of genetic variation on a proteomewide scale. Nature 534, 500-505 (2016).
16. Ruepp, A., et al. CORUM: the comprehensive resource of mammalian protein complexes-2009. Nucleic Acids Res. 38, D497-D501 (2010).
17. Heath, C. G., Viphakone, N. & Wilson, S. A. The role of TREX in gene expression and disease. Biochem. J. 473, 2911-2935 (2016).
18. Goldberg, A. L. & Dice, J. F. Intracellular protein degradation in mammalian and bacterial cells. Annu. Rev. Biochem. 43, 835-869 (1974).
19. Marcotte, R., et al. Functional genomic landscape of human breast cancer drivers, vulnerabilities and resistance. Cell 164, 293-309 (2016).
20. Berger, A. H., et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30, 214-228 (2016).
21. Lawrence, M. S., et al. Discovery and saturation analysis of cancer genes across 21 tumor types. Nature 505, 495-501 (2014).
22. Nik-Zainal, S., et al. Landscape of somatic mutations in 560 breast cancer wholegenome sequences. Nature 534, 47-54 (2016).
23. Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44-57 (2009).
24. Ioannidis, S., et al. Discovery of 5-chloro-N2-[(1S)-1-(5-fluoropyrimidin-2-yl)ethyl]-N4-(5-methyl-1H-pyrazol-3-yl)pyrimidine-2, 4-diamine (AZD1480) as a novel inhibitor of the Jak-Stat pathway. J. Med. Chem. 54, 262-276 (2011).
25. Jester, B. W., Gaj, A., Shomin, C. D., Cox, K. J. & Ghosh, I. Testing the promiscuity of commercial kinase inhibitors against the AGC kinase group using a split-luciferase screen. J. Med. Chem. 55, 1526-1537 (2012).
26. Davis, M. I., et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046-1051 (2011).
27. O'Hare, T., et al. AP24534, a pan-BCR-ABL inhibitor for chronic myeloid leukemia, potently inhibits the T315I mutant and overcomes mutation-based resistance. Cancer Cell 16, 401-412 (2009).
28. El-Mir, M. Y., et al. Dimethylbiguanide inhibits cell respiration via an indirect effect targeted on the respiratory chain complex I. J. Biol. Chem. 275, 223-228 (2000).
29. Miyadera, H., et al. Atpenins, potent and specific inhibitors of mitochondrial complex II (succinate-ubiquinone oxidoreductase). Proc. Natl. Acad. Sci. USA 100, 473-477 (2003).
30. Kagawa, Y. & Racker, E. Partial resolution of the enzymes catalyzing oxidative phosphorylation. 8. Properties of a factor conferring oligomycin sensitivity on mitochondrial adenosine triphosphatase. J. Biol. Chem. 241, 2461-2466 (1966).
31. Dephoure, N., et al. Quantitative proteomic analysis reveals posttranslational responses to aneuploidy in yeast. eLife 3, e03023 (2014).
32. Stingele, S., et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol. Syst. Biol. 8, 608 (2012).
33. Mertins, P., et al. Proteogenomics connects somatic mutations to signaling in breast cancer. Nature 534, 55-62 (2016).
34. Villén, J. & Gygi, S. P. The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry. Nat. Protoc. 3, 1630-1638 (2008).
35. Haas, W., et al. Optimization and use of peptide mass measurement accuracy in shotgun proteomics. Mol. Cell. Proteomics 5, 1326-1337 (2006).
36. Wessel, D. & Flügge, U. I. A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Anal. Biochem. 138, 141-143 (1984).
37. Edwards, A. & Haas, W. Multiplexed quantitative proteomics for high-throughput comprehensive proteome comparisons of human cell lines. Methods Mol. Biol. 1394, 1-13 (2016).
38. Thompson, A., et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895-1904 (2003).
39. Wang, Y., et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019-2026 (2011).
40. Huttlin, E. L., et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174-1189 (2010).
41. Eng, J. K., McCormack, A. L. & Yates, J. R. I. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976-989 (1994).
42. Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J. & Gygi, S. P. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J. Proteome Res. 2, 43-50 (2003).
43. Elias, J. E., Haas, W., Faherty, B. K. & Gygi, S. P. Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nat. Methods 2, 667-675 (2005).
44. Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207-214 (2007).
45. R Developmental Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria) (2011).
46. Shannon, P., et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504 (2003).
47. Marcotte, R, et al, et al. Essential gene profiles in breast, pancreatic and ovarian cancer cells. Cancer Discov. 2, 172-189 (2012).
48. Garnett, M. J., et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570-575 (2012).
49. Tong, P., et al. drexplorer: a tool to explore dose-response relationships and drug-drug interactions. Bioinformatics 31, 1692-1694 (2015).
50. Huang, D. W., et al. DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35, W169-W175 (2007).