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The epichaperome is an integrated chaperome network that facilitates tumour survival

Abstract

Transient, multi-protein complexes are important facilitators of cellular functions. This includes the chaperome, an abundant protein family comprising chaperones, co-chaperones, adaptors, and folding enzymes—dynamic complexes of which regulate cellular homeostasis together with the protein degradation machinery1,2,3,4,5,6. Numerous studies have addressed the role of chaperome members in isolation, yet little is known about their relationships regarding how they interact and function together in malignancy7,8,9,10,11,12,13,14,15,16,17. As function is probably highly dependent on endogenous conditions found in native tumours, chaperomes have resisted investigation, mainly due to the limitations of methods needed to disrupt or engineer the cellular environment to facilitate analysis. Such limitations have led to a bottleneck in our understanding of chaperome-related disease biology and in the development of chaperome-targeted cancer treatment. Here we examined the chaperome complexes in a large set of tumour specimens. The methods used maintained the endogenous native state of tumours and we exploited this to investigate the molecular characteristics and composition of the chaperome in cancer, the molecular factors that drive chaperome networks to crosstalk in tumours, the distinguishing factors of the chaperome in tumours sensitive to pharmacologic inhibition, and the characteristics of tumours that may benefit from chaperome therapy. We find that under conditions of stress, such as malignant transformation fuelled by MYC, the chaperome becomes biochemically ‘rewired’ to form a network of stable, survival-facilitating, high-molecular-weight complexes. The chaperones heat shock protein 90 (HSP90) and heat shock cognate protein 70 (HSC70) are nucleating sites for these physically and functionally integrated complexes. The results indicate that these tightly integrated chaperome units, here termed the epichaperome, can function as a network to enhance cellular survival, irrespective of tissue of origin or genetic background. The epichaperome, present in over half of all cancers tested, has implications for diagnostics and also provides potential vulnerability as a target for drug intervention.

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Figure 1: A subset of cancer cells are enriched in stable multimeric chaperome complexes.
Figure 2: The epichaperome facilitates cancer cell survival.
Figure 3: MYC is a driver of chaperome rewiring into the epichaperome.
Figure 4: More than half of all tumours tested express the epichaperome.

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Data deposits

Primary data, such as raw mass spectrometry files, Mascot generic format files and proteomics data files created by Scaffold have been deposited in the MassIVE database (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp); MassIVE accession ID: MSV000079877.

References

  1. Brehme, M. et al. A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Reports 9, 1135–1150 (2014)

    Article  CAS  Google Scholar 

  2. Finka, A. & Goloubinoff, P. Proteomic data from human cell cultures refine mechanisms of chaperone-mediated protein homeostasis. Cell Stress Chaperones 18, 591–605 (2013)

    Article  CAS  Google Scholar 

  3. Taipale, M. et al. A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways. Cell 158, 434–448 (2014)

    Article  CAS  Google Scholar 

  4. Echtenkamp, F. J. & Freeman, B. C. Expanding the cellular molecular chaperone network through the ubiquitous cochaperones. Biochim. Biophys. Acta 1823, 668–673 (2012)

    Article  CAS  Google Scholar 

  5. Echeverría, P. C., Bernthaler, A., Dupuis, P., Mayer, B. & Picard, D. An interaction network predicted from public data as a discovery tool: application to the Hsp90 molecular chaperone machine. PLoS One 6, e26044 (2011)

    Article  ADS  Google Scholar 

  6. Balchin, D., Hayer-Hartl, M. & Hartl, F. U. In vivo aspects of protein folding and quality control. Science 353, aac4354 (2016)

    Article  Google Scholar 

  7. Moulick, K. et al. Affinity-based proteomics reveal cancer-specific networks coordinated by Hsp90. Nature Chem. Biol. 7, 818–826 (2011)

    CAS  Google Scholar 

  8. Woodford, M. R. et al. Impact of posttranslational modifications on the anticancer activity of Hsp90 inhibitors. Adv. Cancer Res. 129, 31–50 (2016)

    Article  CAS  Google Scholar 

  9. Kamal, A. et al. A high-affinity conformation of Hsp90 confers tumour selectivity on Hsp90 inhibitors. Nature 425, 407–410 (2003)

    Article  CAS  ADS  Google Scholar 

  10. Alarcon, S. V. et al. Tumor-intrinsic and tumor-extrinsic factors impacting Hsp90-targeted therapy. Curr. Mol. Med. 12, 1125–1141 (2012)

    Article  CAS  Google Scholar 

  11. Mollapour, M. & Neckers, L. Post-translational modifications of Hsp90 and their contributions to chaperone regulation. Biochim. Biophys. Acta 1823, 648–655 (2012)

    Article  CAS  Google Scholar 

  12. Assimon, V. A., Southworth, D. R. & Gestwicki, J. E. Specific binding of tetratricopeptide repeat proteins to heat shock protein 70 (Hsp70) and heat shock protein 90 (Hsp90) is regulated by affinity and phosphorylation. Biochemistry 54, 7120–7131 (2015)

    Article  CAS  Google Scholar 

  13. Zuehlke, A. & Johnson, J. L. Hsp90 and co-chaperones twist the functions of diverse client proteins. Biopolymers 93, 211–217 (2010)

    Article  CAS  Google Scholar 

  14. Shrestha, L. & Young, J. C. Function and chemotypes of human Hsp70 chaperones. Curr. Top. Med. Chem. 16, 2812–2828 (2016)

    Article  CAS  Google Scholar 

  15. Smith, J. R. et al. Restricting direct interaction of CDC37 with HSP90 does not compromise chaperoning of client proteins. Oncogene 34, 15–26 (2015)

    Article  CAS  Google Scholar 

  16. Radons, J. The human HSP70 family of chaperones: where do we stand? Cell Stress Chaperones 21, 379–404 (2016)

    Article  CAS  Google Scholar 

  17. Patel, P. D. et al. Paralog-selective Hsp90 inhibitors define tumor-specific regulation of HER2. Nature Chem. Biol. 9, 677–684 (2013)

    CAS  Google Scholar 

  18. Nayar, U. et al. Targeting the Hsp90-associated viral oncoproteome in gammaherpesvirus-associated malignancies. Blood 122, 2837–2847 (2013)

    Article  CAS  Google Scholar 

  19. Shrestha, L., Patel, H. J. & Chiosis, G. Chemical tools to investigate mechanisms associated with HSP90 and HSP70 in disease. Cell Chemical Biology 23, 158–172 (2016)

    Article  CAS  Google Scholar 

  20. Taldone, T., Ochiana, S. O., Patel, P. D. & Chiosis, G. Selective targeting of the stress chaperome as a therapeutic strategy. Trends Pharmacol. Sci. 35, 592–603 (2014)

    Article  CAS  Google Scholar 

  21. Taldone, T. et al. Design, synthesis, and evaluation of small molecule Hsp90 probes. Bioorg. Med. Chem. 19, 2603–2614 (2011)

    Article  CAS  Google Scholar 

  22. Rodina, A. et al. Affinity purification probes of potential use to investigate the endogenous Hsp70 interactome in cancer. ACS Chem. Biol. 9, 1698–1705 (2014)

    Article  CAS  Google Scholar 

  23. Taldone, T. et al. Synthesis of purine-scaffold fluorescent probes for heat shock protein 90 with use in flow cytometry and fluorescence microscopy. Bioorg. Med. Chem. Lett. 21, 5347–5352 (2011)

    Article  CAS  Google Scholar 

  24. Chiosis, G. et al. Uses of labeled hsp90 inhibitors. US patent 20140242602 A1 (2014)

  25. Taldone, T. et al. Radiosynthesis of the iodine-124 labeled Hsp90 inhibitor PU-H71. J. Labelled Comp. Radiopharm. 59, 129–132 (2016)

    Article  CAS  Google Scholar 

  26. Rodina, A. et al. Identification of an allosteric pocket on human Hsp70 reveals a mode of inhibition of this therapeutically important protein. Chem. Biol. 20, 1469–1480 (2013)

    Article  CAS  MathSciNet  Google Scholar 

  27. Taldone, T. et al. Heat shock protein 70 inhibitors. 2. 2,5′-thiodipyrimidines, 5-(phenylthio)pyrimidines, 2-(pyridin-3-ylthio)pyrimidines, and 3-(phenylthio)pyridines as reversible binders to an allosteric site on heat shock protein 70. J. Med. Chem. 57, 1208–1224 (2014)

    Article  CAS  Google Scholar 

  28. Kang, Y. et al. Heat shock protein 70 inhibitors. 1. 2,5′-thiodipyrimidine and 5-(phenylthio)pyrimidine acrylamides as irreversible binders to an allosteric site on heat shock protein 70. J. Med. Chem. 57, 1188–1207 (2014)

    Article  CAS  Google Scholar 

  29. Miyajima, N. et al. The HSP90 inhibitor ganetespib synergizes with the MET kinase inhibitor crizotinib in both crizotinib-sensitive and -resistant MET-driven tumor models. Cancer Res . 73, 7022–7033 (2013)

    Article  CAS  Google Scholar 

  30. Whitesell, L., Mimnaugh, E. G., De Costa, B., Myers, C. E. & Neckers, L. M. Inhibition of heat shock protein HSP90-pp60v-src heteroprotein complex formation by benzoquinone ansamycins: essential role for stress proteins in oncogenic transformation. Proc. Natl Acad. Sci. USA 91, 8324–8328 (1994)

    Article  CAS  ADS  Google Scholar 

  31. Corben, A. D. et al. Ex vivo treatment response of primary tumors and/or associated metastases for preclinical and clinical development of therapeutics. J. Vis. Exp. 92, e52157 (2014)

    Google Scholar 

  32. Hassane, D. C. et al. Chemical genomic screening reveals synergism between parthenolide and inhibitors of the PI-3 kinase and mTOR pathways. Blood 116, 5983–5990 (2010)

    Article  CAS  Google Scholar 

  33. Miller, J. D. et al. Human iPSC-based modeling of late-onset disease via progerin-induced aging. Cell Stem Cell 13, 691–705 (2013)

    Article  CAS  Google Scholar 

  34. Moulick, K. et al. Synthesis of a red-shifted fluorescence polarization probe for Hsp90. Bioorg. Med. Chem. Lett. 16, 4515–4518 (2006)

    Article  CAS  Google Scholar 

  35. Fan, A. C. et al. Nanofluidic proteomic assay for serial analysis of oncoprotein activation in clinical specimens. Nat. Med. 15, 566–571 (2009)

    Article  CAS  Google Scholar 

  36. Lin, C. H., Jackson, A. L., Guo, J., Linsley, P. S. & Eisenman, R. N. Myc-regulated microRNAs attenuate embryonic stem cell differentiation. EMBO J . 28, 3157–3170 (2009)

    Article  CAS  Google Scholar 

  37. Rodina, A. et al. Selective compounds define Hsp90 as a major inhibitor of apoptosis in small-cell lung cancer. Nat. Chem. Biol. 3, 498–507 (2007)

    Article  CAS  Google Scholar 

  38. Zong, H. et al. A hyperactive signalosome in acute myeloid leukemia drives addiction to a tumor-specific Hsp90 species. Cell Reports 13, 2159–2173 (2015)

    Article  CAS  Google Scholar 

  39. Caldas-Lopes, E. et al. Hsp90 inhibitor PU-H71, a multimodal inhibitor of malignancy, induces complete responses in triple-negative breast cancer models. Proc. Natl Acad. Sci. USA 106, 8368–8373 (2009)

    Article  CAS  ADS  Google Scholar 

  40. Taldone, T., Zatorska, D., Kang, Y. & Chiosis, G. A facile and efficient synthesis of d6-labeled PU-H71, a purine-scaffold Hsp90 inhibitor. J. Labelled Comp. Radiopharm. 53, 47–49 (2010)

    CAS  Google Scholar 

  41. Kessner, D., Chambers, M., Burke, R., Agus, D. & Mallick, P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008)

    Article  CAS  Google Scholar 

  42. Skarra, D. V. et al. Label-free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5. Proteomics 11, 1508–1516 (2011)

    Article  CAS  Google Scholar 

  43. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2013)

  44. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol . 5, R80 (2004)

    Article  Google Scholar 

  45. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res . 43, e47 (2015)

    Article  Google Scholar 

  46. Sarkar, D. Lattice: Multivariate Data Visualization with R. (Springer, 2008)

  47. Scales, M., Jäger, R., Migliorini, G., Houlston, R. S. & Henrion, M. Y. visPIG—a web tool for producing multi-region, multi-track, multi-scale plots of genetic data. PLoS One 9, e107497 (2014)

    Article  ADS  Google Scholar 

  48. 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)

    Article  CAS  Google Scholar 

  49. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res . 13, 2498–2504 (2003)

    Article  CAS  Google Scholar 

  50. Licata, L. et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res . 40, D857–D861 (2012)

    Article  CAS  Google Scholar 

  51. Brown, K. R. & Jurisica, I. Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biol . 8, R95 (2007)

    Article  Google Scholar 

  52. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015)

  53. Brown, K. R. & Jurisica, I. Online predicted human interaction database. Bioinformatics 21, 2076–2082 (2005)

    Article  CAS  Google Scholar 

  54. Chandriani, S. et al. A core MYC gene expression signature is prominent in basal-like breast cancer but only partially overlaps the core serum response. PLoS One 4, e6693 (2009)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We thank D. Gewirth, A. Younes, J. Young and E. Devaney for reading the manuscript. This work was supported by the NIH (R01 CA172546, R01 CA155226, P01 CA186866, P30 CA08748, P50 CA86438, DP2 OD007399), the Jane H. Gordon Breast Cancer Research Fund, the Breast Cancer Research Fund, Susan G. Komen for the Cure, W. H. Goodwin, A. Goodwin and the Commonwealth Foundation for Cancer Research, and the Experimental Therapeutics Center of MSKCC, the Hirshberg Foundation for Pancreatic Cancer, the Irma T. Hirschl Foundation and the Rubenstein Center for Pancreatic Research. A.R. was supported by the National Center for Advancing Translational Sciences of the NIH under award number UL1TR000457.

Author information

Authors and Affiliations

Authors

Contributions

A.R., T.W., P.Y., E.D.G., M.P.S.D., M.R., J.F.G., M.A., N.P., A.C., H.Z., J.K. III, B.B., C.P., C.X., R.T., H.J.P., P.P., M.F.F., F.S., E.C.-L., A.B., S.K., C.Y., O.L. and H.E.-B. performed experiments. S.M., T.T., C.H., N.P., L.C., A.M., E.C., L.S., J.S.L. and G.J.R. provided reagents. N.P., A.R., E.D.G., T.W., P.Y., P.Z., B.B., R.L., A.M., S.M.L., J.S.L., M.P.S.D., R.I.P., F.C., J.F.G., H.E-B., G.C. and M.L.G. participated in the design and analysis of various experiments, S.P., R.I.P. and T.W. performed statistical analyses and G.C. and M.L.G. wrote the paper. All authors read the manuscript and/or discussed the experiments.

Corresponding authors

Correspondence to Monica L. Guzman or Gabriela Chiosis.

Ethics declarations

Competing interests

Memorial Sloan Kettering Cancer Centre holds the intellectual rights to PU-H71, YK-derivatives and uses of labelled HSP90 inhibitors. Samus Therapeutics, of which G.C. has partial ownership, has licensed PU-H71 and PU-PET.

Additional information

Reviewer Information Nature thanks J. W. Kelly and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Summary of the experimental design and findings.

a, Schematic of the experimental approach to address four key questions concerning the chaperome in cancer: (1) what are the molecular characteristics and composition of the chaperome in cancer; (2) what molecular factors drive chaperome networks to crosstalk in tumours; (3) what distinguishes the chaperomes of tumours that are sensitive to pharmacologic inhibition from those that are not; and (4) what are the characteristics of tumours that may benefit from chaperome therapy? To retain the endogenous proteome/chaperome make-up and function, we applied a variety of chemical biology tools and biochemical methods that retain native protein conformations and complexes. This approach minimally interferes with the system it interrogates, thus providing answers closer to the reality of disease. When applicable, data were validated by alternative and complementary methods, as indicated. This approach led to the discovery of a novel mechanism of tumour regulation. Specifically, we have identified and characterized the epichaperome, a modified chaperome network. Our data demonstrate that heterogeneous and stable, multimeric chaperome complexes nucleating on HSP90 and HSP70, and incorporating co-chaperones, isomerases, scaffolding proteins, and transport proteins, bring about the effective physical and functional integration of the chaperome machinery into the epichaperome. Chaperome rewiring into the epichaperome is fuelled by powerful transcription activators such as MYC. Only under conditions in which the chaperome becomes tightly integrated both functionally and physically to form the epichaperome are tumours addicted to individual chaperome members. The epichaperome is the survival mechanism for type 1 tumours; when the epichaperome is dismantled by ablation of a component chaperome, the chaperome network collapses leading to cell death. In contrast, in type 2 tumours in which the integration of the chaperome is only partial and most chaperome members function as insular communities, depletion of chaperome members only ‘locally’ compromises the chaperome, maintaining overall cellular survival. b, c, Therapeutic and diagnostic implications of the findings. We propose the epichaperome as a biomarker to stratify patients for chaperome therapy, such as HSP90 inhibitors. This work also provides several ways to measure the epichaperome in clinic, that is, a non-invasive imaging assay (PU-PET) for solid tumours, a flow cytometry assay based on PU-FITC for liquid tumours and a native protein separation and analysis for minute biopsy specimens (isoelectric focusing; NanoPro technique) (b). We also propose that HSP90 is a cancer target when integrated into the epichaperome. Thus, HSP90 inhibitors that are specific for HSP90 when part of the epichaperome would be preferred for clinical use. Non-discriminate pharmacological agents that target chaperome members regardless of whether they are in the epichaperome or are part of dynamic complexes, such as in normal cells, could lead to toxicities and a low therapeutic index. For example, GI and ocular toxicities have been associated with some HSP90 inhibitors and not others due to chronic HSP90 inhibition in these normal tissues.

Extended Data Figure 2 Biochemical profile of chaperome members in cancer cell lines and primary specimens.

a, The biochemical profile of HSP90 in cell lines was analysed by native capillary isoelectric focusing. The ‘heat map’ representation shows snapshots of HSP90 complexes as detected under different antibody blotting exposure times. See also Fig. 1a. b, The biochemical profile of HSP90 in samples denatured with urea. RT, room temperature; ON, overnight. Data were repeated independently twice with representative data shown. ce, The biochemical profile of indicated chaperome members in cell lines (c, n = 4) and primary specimens (breast cancer, n = 5 (d), and acute myeloid leukaemia (AML), n = 2 (e)) was analysed by native capillary isoelectric focusing (IEF), native-PAGE and SDS–PAGE. The schematic for the isolation and separation of AML blasts for biochemical analyses is shown in e, top. TNBC, triple negative breast cancer; HER2+, HER2-overexpressing breast cancer. For uncropped gel data, see Supplementary Fig. 1.

Extended Data Figure 3 PU-H71 and its labelled versions are reliable tools to perturb, identify and measure the expression of the high-molecular-weight, multimeric HSP90 complexes in tumours.

a, Correlative analysis between binding of a fluorescently (FITC) labelled PU-H71 (PU-FITC) to the panel of cancer cells shown in Fig. 1a (n = 6) and their apoptotic sensitivity to HSP90 inhibition (Pearson’s r, two-tailed). Each data point represents a cell line. b, MDA-MB-468 (type 1) and ASPC1 (type 2) contain similar levels of HSP90 but only MDA-MB-468 expresses the high-molecular-weight chaperome species (see also Fig. 1a). HSP90α and HSP90β levels were quantified by fluorescence microscopy (n = 50; mean ± s.d.; unpaired t-test, NS). Scale bar, 10 μm. c, Association and dissociation of PU-FITC (a FITC labelled PU-H71) from HSP90 was probed in cell homogenates by fluorescence polarization. Average from technical duplicates is shown on the graph. The experiment was carried out twice with similar results. d, Association and dissociation of PU-H71-bait (a solid-support immobilised PU-H71) from HSP90 was probed in cell homogenates by chemical precipitation followed by analyses of HSP90 in the supernatant and of HSP90 isolated on the solid support. A solid-support containing immobilized PU-H71 and an HSP90-inert molecule (control bait) were incubated with cell homogenates for 2 h. The bait-captured cargo was isolated and analysed by western blot (bait). The HSP90 species in the supernatant were separated and analysed as indicated. For isoelectric focusing, both the gel (for experimental duplicates) and the heat map representations of different exposure times are shown for each experimental condition. HMEC cells are shown for reference. Data were repeated independently twice with representative data shown. e, f, Association and dissociation of PU-H71 from type 1 and 2 tumours, measured in cells. Binding of PU-FITC to live cells was analysed by flow cytometry and fluorescence microscopy, as indicated. PU-FITC (1 μM for flow and 5 μM for microscopy) was added to cells and incubated for 4 h before fluorescence signal detection. To show specificity of binding, the signal was competed off in a dose-dependent manner with unlabelled PU-H71. Control FITC, a triethylene glycol labelled FITC. e, Mean from two technical replicates; f, mean ± s.d., n = 50 individual cells, unpaired t-test, ****P < 0.0001. The fluorescence intensity of PU-FITC staining was quantified by ImageJ. Scale bar, 10 μm. g, Association and dissociation of PU-H71 from type 1 and 2 tumours, measured in vivo. Biodistribution of 124I-PU-H71 (a 124I radiolabelled version of PU-H71) was monitored live in tumour-bearing mice. Each mouse bears one xenografted MDA-MB-468 and one ASPC1 tumour, of similar volume, as indicated. Following intraveneous (iv) injection of a tracer amount of the 124I-PU-H71 agent, mice were monitored by micro-positron emission tomography (PET) imaging. Representative images taken at the indicated times post-injection are shown. Note that immediately after injection (1 h timepoint image), the agent is widely and uniformly distributed throughout the body and in each tumour. The off rate from type 1 tumours is slower compared to type 2 and non-transformed tissues (that is, distinct koff from type 1 tumours versus type 2 tumours). The image is representative of five individual mice. In an independent experiment, radioactivity was measured in a gamma-counter upon mouse euthanasia and data were graphed to monitor the time-dependent distribution of PU-H71. Graph; radioactivity, measured as %IDg; injected dose per gram, versus time upon euthanasia (mean ± s.d., n = 8, ASPC1; n = 34, MDA-MB-468, pooled experiments of mice bearing individual tumours). Means were compared by unpaired t-tests between MDA-MB-468 and ASPC1 at each time point (NS, not significant; ***P < 0.001; ****P < 0.0001). h, Same as in g for a therapeutic dose of injected PU-H71, as indicated. Levels of intact PU-H71 in the indicated tumours, tissues and plasma were determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) in mice (n = 5) euthanized at the indicated times post-PU-H71 injection. Graph; mean ± s.d., unpaired t-tests between MDA-MB-468 and ASPC1 (NS, not significant; ***P < 0.001). i, Dose- and time-dependent binding of PU-H71 and H9010 (an anti-HSP90 antibody) to HSP90 species expressed in type 1 and type 2 tumour cells. C, control beads containing an HSP90-inert molecule. PU, 10 μl PU-H71 wet beads; 2×PU, 20 μl PU-H71 wet beads; H9010; 2 μl antibody immobilized on agarose beads. Because the IgG interferes with the HSP90 signal (see the high molecular smear in the native gels), native lysates were used for a control (input). Graph shows quantification of time-dependent changes in HSP90 species. j, Representative fluorescence microscopy images of live cells stained with PU-FITC (top) as compared to antibodies specific for HSP90 (bottom). rbtIgG, rabbit IgG control, msIgG, mouse IgG control. Scale bar, 10 μm. Micrograph is representative of four captured images. For uncropped gel data, see Supplementary Fig. 1.

Source data

Extended Data Figure 4 Binding affinity of PU-H71 for cellular HSP90 is independent of the expression of HSP90 and other chaperome members, and is not affected by intracellular ATP concentration variations.

a, Correlative analysis for PU-H71-sensitive HSP90 species abundance, as measured by PU-FITC capture, and cell viability upon a 48 h treatment with PU-H71 (1 μM), as measured by annexin V staining (Pearson’s r, two-tailed). Each data point represents a cell line (n = 17); data points are the mean from two biological replicates ran in duplicate or triplicate. To account for intercellular background signal variability, HL60 cells were spiked in and used as internal control for each cell line; thus binding is presented as a ratio of the signal obtained in the analysed cell over that in HL60 cells. y axis, log values of the binding ratios. b, Cell lines analysed in a were lysed and total levels of the indicated chaperome members were determined by western blot. β-actin; protein loading control. c, A correlative analysis was performed between total chaperome levels, as obtained in b, and cell viability values, as determined in a; no significant and/or robust relationship was observed (Pearson’s r, two-tailed). d, e, In a panel of type 1, type 2 and non-transformed cells (n = 9), binding to PU-H71 and cell killing by PU-H71 (d) was compared to intracellular ATP levels (e). d, Correlation, Pearson’s r, two-tailed; e, mean ± s.d., each symbol represents an experimental replicate (MDA-MB-468, n = 23; OCI-LY1, n = 15; BCP-1, n = 8; HCC-1806, n = 16; ASPC1, n = 15; MDA-MB-415, n = 8; MCF7, n = 8; HMEC, n = 7; MRC5, n = 16). f, Schematic showing the experimental design for the isolation and analysis of primary AML samples. g, AML samples were stained with PU-FITC, and blasts (malignant) and lymphocytes (normal) were separated and analysed by flow cytometry. The signal in blasts over lymphocytes (used as internal standard) was graphed to classify clones as type 1 (>2.1 PU-FITC binding ratio of signal in blast versus lymphocytes) and type 2 (<2.1 PU-FITC binding ratio) (mean ± s.d., n = 9, unpaired t-test, **P < 0.01). h, Total HSP90 levels were measured by staining with an HSP90 antibody after cell fixation and permeabilization (mean ± s.d., n = 9, unpaired t-test, NS, not significant). i, Viability of blasts following exposure to PU-H71 (1 μM) for 48 h was measured by annexin V/7AAD staining (mean ± s.d., n = 9, unpaired t-test, ***P < 0.001). j, PU-FITC staining of live and fixed/permeabilized unfractionated AMLs was visualized by fluorescence microscopy. Scale bar, 100 μm. Micrograph is representative of two captured images. The biochemical profile of AML no. 1 and AML no. 2 is presented in Extended Data Fig. 2e. For uncropped gel data, see Supplementary Fig. 1.

Source data

Extended Data Figure 5 Chaperome networks in type 1, type 2 and non-transformed cells.

a, b, Heat maps illustrating all chaperome members (a) and the interactome of HSP90 (b) isolated by the HSP90 bait and identified upon mass spectrometry and bioinformatics analyses enriched (P < 0.1) in type 1 tumours over type 2 tumours and non-transformed cells. Protein sorting was based on hierarchical clustering. Last lane of the heat map in a shows the enrichment of these proteins on the HSP70 bait. c, Network illustrating the connectivity of proteins isolated by the HSP90 bait and identified upon mass spectrometry and bioinformatics analyses; chaperome members and proteins with scaffolding, adaptor, protein interface modulator roles and significantly enriched (P < 0.1) in type 1 tumours over type 2 tumours and non-transformed cells are shown. The thickness of the edges (connection lines) represents the robustness of the functional interaction. The colour of nodes represents protein abundance. For comparison, type 2 and non-transformed cells are also show. Core interactions are shown in Fig. 1f. d, e, The cargo or interacting proteins of HSP90 (d) and HSP70 (e) isolated by the PU-H71 and YK-chemical baits from the indicated cell homogenates. Protein levels in individual cell homogenates (input) were analysed by IEF and SDS–PAGE, as indicated. Proteins precipitated on the chemical bait were analysed by SDS–PAGE. Protein levels from each experimental condition were quantified and graphed (bottom). Data were repeated independently twice with representative data shown. fi, Changes in multimeric chaperone complexes in cells challenged with multiple siRNAs against HSP90α or HSP90β (f), HSP90α/β, AHA1, HOP or HSP110 (g) and in cell homogenates challenged with antibodies specific for the indicated HSP70 paralogues and for HOP (h, i), as indicated. Levels of proteins in the homogenate were probed by SDS–PAGE or native-PAGE, as indicated. All data were repeated independently twice with representative data shown. For uncropped gels, see Supplementary Fig. 1.

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Extended Data Figure 6 HSP90 is functional and susceptible to exogenous inhibitors in type 2 as well as in type 1 cells, but only inhibition of HSP90 in type 1 cells is toxic to the cell.

a, The response of type 1 and 2 tumours classified by PU-PET avidity, to PU-H71 treatment, is shown. Patients were treated as part of the NCT01393509 clinical study. Each picture is a scan of data taken of an individual patient. PU-PET images were taken at 24 h after 124I-PU-H71 tracer administration. Scale bars (bottom of panel); PET window display intensity scales for FDG and PU-PET fusion PET-CT images. Numbers in the scale bars indicate upper and lower SUV thresholds that define pixel intensity on PET images. b, Changes in HSP90 machinery function upon pharmacologic inhibition (PU-H71, 1 μM for 24 h). Inhibition of PI3K/AKT activity was monitored; see p-S6K surrogate for AKT activity in cell lines and p-AKT in primary specimens (below). Data in cell lines were repeated independently twice with representative data shown. For HSP90α/β knockdown data, see Extended Data Fig. 5g. cf, Treatment schematic and representative examples of primary breast cancer specimens (n = 2) treated ex vivo with PU-H71. c, Workflow for the analysis of the primary specimens. d, Molecular signature of tumour and adjacent normal tissue of the surgical specimen as analysed by native, nanofluidic proteomic assay (NanoPro; native IEF), for HSP90 and HSP70 (gel representation), and AKT (chromatogram representation). e, Molecular response of tumour sections treated for 24 h ex vivo with PU-H71 (1 μM). AKT (an HSP90 client) activity was probed with the indicated antibody. BCL2 was chosen as a loading standard; this protein is insensitive to HSP90 inhibition in the analysed primary breast specimens (native IEF, chromatogram representation). f, Apoptotic response of the indicated tumour specimens to ex vivo treatment with PU-H71 or vehicle. Apoptosis and necrosis of the tumour cells (as percentage) is assessed by reviewing all the haematoxylin and eosin (H&E) slides of the case (controls and treated ones) in toto, blindly, allowing for better estimation of the overall treatment effect to the tumour. Image representative of the entire specimen section. gj, Response profile of a panel of pancreatic cancer cells to HSP90 inhibition. g, Changes in cell viability following HSP90 pharmacologic inhibition by three chemically distinct agents, as indicated. Mean from two to three technical replicates is shown. Subclassification of the analysed cell lines by PU-FITC binding is shown on the left. h, The effect of PU-H71 on cell growth was measured with an assay that analyses intracellular ATP levels. Cells were treated for 72 h with PU-H71 and the half maximal inhibitory growth concentration (IC50) was determined. Mean ± s.d.; n = 6. i, Representative examples of type 1 and type 2 cells treated for 24 h with the indicated concentrations of PU-H71. Inhibition of HSP90 is demonstrated by a decrease in HSP90 client function (p-S6K and p-ERK) and by HSP70 induction, and evidenced in both type 1 and 2 tumour cells. Induction of apoptosis, as demonstrated by the appearance of cleaved PARP (cPARP), is however, specific to type 1 tumour cells. β-actin, protein loading control. The HSP90 biochemical signature of the select cells is shown on the right. The blue arrows indicate the close relationship between the growth inhibitory IC50 values and HSP90 function inhibition, suggesting that HSP90 inactivation is sufficient to inhibit growth (that is, have a static effect) in both type 1 and 2 tumours. In contrast, substantial induction of apoptosis is specific to type 1 tumours. Thus, HSP90 is functional in type 2 and is engaged by the HSP90 inhibitors—the resistance phenotype of type 2 tumours cannot be explained by an inability of the HSP90 inhibitor to engage HSP90. Data are representative of two independent experiments. For uncropped gel data, see Supplementary Fig. 1.

Extended Data Figure 7 The epichaperome facilitates cancer cell survival.

The expression of the epichaperome was altered by epichaperome components knockdown (a) or by titrating into cells siRNAs that targeted both the HSP90α and the HSP90β paralogues (b–g) to test whether the epichaperome facilitates survival in type 1 tumours. a, Epichaperome levels were altered by AHA1 siRNA knockdown or a control (scramble, Scr) siRNA (left panel) and cell viability, as measured by PARP cleavage, was determined in cells treated for 24 h with increasing concentrations of PU-H71 (0, 0.5, 1 and 2 μM) (right panel). See also Extended Data Fig. 5g for biochemical signature of cells after AHA1 knockdown. Data are representative of two independent experiments. be, Total protein (b, e), mRNA (c) and multimeric species of indicated chaperome members (d) levels were monitored in MDA-MB-468 type 1 cells in which several concentrations of siRNAs against HSP90α/β (n = 14) were titrated in. 1 and 8 are control scramble; 2, 3, 4, 9, 10, 11 are 0.915, 1.83, 3.66, 0.366, 1.83 and 14.64 nM of siRNA no. 1, respectively; and 5, 6, 7, 12, 13, 14 are 0.366, 0.915, 3.66, 0.0229, 0.0915 and 0.366nM of siRNA no. 2, respectively. Cell viability in each condition was monitored by LDH release. Values for each experimental condition (as percentage control scramble) were quantified and are noted under the native gels in a. HMW, high molecular weight. For gels, experiments were repeated independently twice with representative data shown. For graphs, mean ± s.d., n = 6. f, Changes in chaperome members (mRNA) were monitored following siRNA knockdown of the indicated individual chaperome members in MDA-MB-468 and ASPC1 cells. Error bars show mean ± s.d., n = 6. g, Same as for bd in ASPC1, type 2 cells (1 through 4 siRNA concentrations, as in d. Error bars represent mean ± s.d., n = 6, unpaired t-test, NS, not significant). For uncropped gel data, see Supplementary Fig. 1.

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Extended Data Figure 8 MYC is a driver of chaperome rewiring into the epichaperome.

a, The top eight transcriptional regulators ranked by significant overlap between dataset genes and known targets regulated by the indicated transcription regulators in type 1 tumours. b, Heat map illustrating the enrichment in type 1 tumours of MYC target genes, MYC transcriptional signature and of proteins that regulate MYC function and/or expression.

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Extended Data Figure 9 Chaperome rewiring into the epichaperome is fuelled by powerful transcription activators such as MYC.

a–e, Establishment and characterization of HSP90-inhibitor-resistant cells. a, Schematic detailing the establishment and separation of clones cross-resistant to PU-H71, PU-DZ13 and 17-DMAG. These compounds are chemically distinct HSP90 inhibitors. b, RNA-seq and western blot analyses of clone no. 22 indicate that cellular resistance to HSP90 inhibitors is associated with MYC downregulation. RPKM, reads per kilobase of exon per million mapped reads. Western blot data are representative of two independent experiments. c, HSP90 remains functional in the HSP90-inhibitor resistant clones, as well as in cancer cells in which MYC expression is reduced by shRNA knockdown. Cells were treated for 24 h with the indicated concentrations of PU-H71, and HSP90 client protein function (p-AKT and p-S6K levels) was analysed by western blot. For gels, experiments were repeated independently twice with a representative gel shown. d, PU-FITC binding to the indicated resistant clones (n = 10) presented as relative, mean fluorescence intensity values, was measured by flow cytometry. The parental OCI-LY1 (type 1 tumour cell) is shown for comparison. Inset shows total HSP90 levels measured by western blot in the indicated clones (n = 8). IB; anti-HSP90 (F8) sc-13119. e, Binding of a fluorescently labelled geldanamycin derivative (GM-cy3B) to the indicated cell homogenates was measured by fluorescence polarization. Graph shows mean from three technical replicates. f, Experimental design for the creation and characterization of MYC-expressing ASPC1 cells. g, Levels of MYC and HSP90 were analysed by western blot in the indicated infection conditions (day 4 post-lentiviral infection). Data were repeated independently twice with a representative blot shown. h, Transcriptional activity of MYC in infected cells was measured using the TransAM c-Myc Transcription Factor ELISA. Mean from three technical replicates is shown. i, Flow cytometry confirmed the expression of MYC in infected ASPC1 cells. mCerulean and MYC were co-expressed with a 2A peptide linker, which was self-cleaved after protein translation. Data were repeated independently twice with representative data shown. jl, Viability of ASPC1 cells infected with either empty vector or MYC was assessed using an assay that quantifies annexin V/7AAD-stained cells (j), ATP levels (k), LDH release (l) following treatment with PU-H71, as indicated. k, Mean of four technical replicates. l, Error bars show mean ± s.d., n = 6, unpaired t-test, ****P < 0.0001. mp, HSP90 oncogenic kinase clients do not require the epichaperome for cell transforming activity. m, PU-FITC binding to the indicated live cells presented as a ratio (fluorescent signal of measured cells over signal in HL60 cells; HL60, internal standard). np, Changes in multimeric chaperome complexes (n, o) and total protein (p) in the indicated conditions. All data were repeated independently twice with representative data shown. OCI-LY1 (type 1 cells) and OCI-LY1 rewired to type 2 following MYC loss are presented for direct comparison purposes. For uncropped gels, see Supplementary Fig. 1.

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Extended Data Figure 10 The apoptotic response profile of a panel of cancer cells following HSP90 inhibition is independent of levels of chaperome members, HSP90 client proteins and anti-apoptotic molecules, tissue of origin or causal genetic mutations.

a, Total levels of the indicated chaperome members, HSP90 client proteins and anti-apoptotic molecules were analysed by western blot in a panel of pancreatic cancer cells (n = 12). GAPDH and β-actin, protein loading controls. Protein levels were quantified and graphed against the viability of these cells upon HSP90 inhibition. A correlative analysis was performed (Pearson’s r, two-tailed). Each data point represents a cell line. PU-FITC binding is shown for comparison. b, Correlative analysis of epichaperome abundance, as measured by PU-FITC staining, and cell viability upon a 48 h treatment with PU-H71 (1 μM), as measured by annexin V staining (Pearson’s r, two-tailed). Each data point represents a cell line (n = 95); data are the mean from two orthree biological replicates. Cells representing pancreatic, gastric, lung, and breast cancers, along with lymphomas and leukaemias were chosen for analysis. c, Same as above for the treatment of cancer cells (n = 12) with three chemically distinct HSP90 inhibitors. d, Same as b, but for each cell line, known genetic lesions were added. No specific genetic alteration could be found distinguishing the two tumour types; whereas p53-, Ras-, Myc-, HER-, PI3K/AKT-, and JAK- cell cycle-related defects were found in tumours that were sensitive to PU-H71, they were also evident in PU-H71 resistant cells. We found genetic defects in major chaperome members to be rare, with BCP-1 cells only carrying an HSP90AA1 missense mutation (P596S). No mutations in HSP90AB1, HSPH1, HSPA8, STIP1 and AHSA1 were reported in this large panel of cell lines. These were obtained from the cBioPortal for Cancer Genomics website (http://www.cbioportal.org/).

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Supplementary information

Supplementary Information

This file contains a Supplementary Discussion, Supplementary References, Supplementary Figure 1, gel source data. (PDF 10891 kb)

Supplementary Table 1

Sheet A contains a table corresponding to Figure 1f, it contains core chaperome networks in type 1, type 2 and nontransformed cells. Sheet B contains a table corresponding to Extended Data Figure 5a, showing comprehensive chaperome networks in type 1, type 2 and non-transformed cells. Sheet C contains a table corresponding to Extended Data Figure 5b and Extended Data Figure 8b, it shows the HSP90 interactome isolated by the HSP90 bait in type 1, type 2 and non-transformed cells. Sheet D contains a table corresponding to Extended Data Figure 8a, showing upstream transcriptional regulators that explain the protein signature of type1 tumours. Sheet E contains metastasis-related proteins characteristic of type 1 tumours. Sheets F-Q contain peptide matches and scoring information that demonstrate the proteomics data processing. (XLSX 17764 kb)

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Rodina, A., Wang, T., Yan, P. et al. The epichaperome is an integrated chaperome network that facilitates tumour survival. Nature 538, 397–401 (2016). https://doi.org/10.1038/nature19807

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