Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Amyloid-like p53 as prognostic biomarker in serous ovarian cancer—a study of the OVCAD consortium

Oncogene volume 42, pages 2473–2484 (2023)Cite this article

Subjects

Abstract

TP53 is the most commonly mutated gene in cancer and has been shown to form amyloid-like aggregates, similar to key proteins in neurodegenerative diseases. Nonetheless, the clinical implications of p53 aggregation remain unclear. Here, we investigated the presence and clinical relevance of p53 aggregates in serous ovarian cancer (OC). Using the p53-Seprion-ELISA, p53 aggregates were detected in 46 out of 81 patients, with a detection rate of 84.3% in patients with missense mutations. High p53 aggregation was associated with prolonged progression-free survival. We found associations of overall survival with p53 aggregates, but they did not reach statistical significance. Interestingly, p53 aggregation was significantly associated with elevated levels of p53 autoantibodies and increased apoptosis, suggesting that high levels of p53 aggregates may trigger an immune response and/or exert a cytotoxic effect. To conclude, for the first time, we demonstrated that p53 aggregates are an independent prognostic marker in serous OC. P53-targeted therapies based on the amount of these aggregates may improve the patient’s prognosis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The p53-Seprion-ELISA was used to quantify p53 aggregates in 81 fresh-frozen serous OC tissues.
Fig. 2: Verification of the p53-Seprion-ELISA results.
Fig. 3: Analysis of p53 aggregation propensity of specific mutations.
Fig. 4: Kaplan–Meier curves of the OC patients stratified by the amount of p53 aggregates (negative, moderate, and high).
Fig. 5: Association of p53 aggregates with p53 autoantibodies, cleaved caspase-3, and cleaved PARP.

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Surveillance Epidemiology, and End Results (SEER) Program. Cancer Stat Facts: Ovarian Cancer. 2022. https://seer.cancer.gov/statfacts/html/ovary.html.

  2. Vousden KH, Lane DP. p53 in health and disease. Nat Rev Mol Cell Biol. 2007;8:275–83.

    Article  CAS  PubMed  Google Scholar 

  3. Knowles TPJ, Vendruscolo M, Dobson CM. The amyloid state and its association with protein misfolding diseases. Nat Rev Mol Cell Biol. 2014;15:384–96.

    Article  CAS  PubMed  Google Scholar 

  4. Chiti F, Dobson CM. Protein misfolding, functional amyloid, and human disease. Annu Rev Biochem. 2006;75:333–66.

    Article  CAS  PubMed  Google Scholar 

  5. Xu J, Reumers J, Couceiro JR, De Smet F, Gallardo R, Rudyak S, et al. Gain of function of mutant p53 by coaggregation with multiple tumor suppressors. Nat Chem Biol. 2011;7:285–95.

    Article  CAS  PubMed  Google Scholar 

  6. Ghosh S, Salot S, Sengupta S, Navalkar A, Ghosh D, Jacob R, et al. p53 amyloid formation leading to its loss of function: implications in cancer pathogenesis. Cell Death Differ. 2017;24:1784–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. De Smet F, Saiz Rubio M, Hompes D, Naus E, De Baets G, Langenberg T, et al. Nuclear inclusion bodies of mutant and wild-type p53 in cancer: a hallmark of p53 inactivation and proteostasis remodelling by p53 aggregation. J Pathol. 2017;242:24–38.

    Article  PubMed  Google Scholar 

  8. Levy CB, Stumbo AC, Ano Bom AP, Portari EA, Cordeiro Y, Silva JL, et al. Co-localization of mutant p53 and amyloid-like protein aggregates in breast tumors. Int J Biochem Cell Biol. 2011;43:60–4.

    Article  CAS  PubMed  Google Scholar 

  9. Soragni A, Janzen DM, Johnson LM, Lindgren AG, Thai-Quynh Nguyen A, Tiourin E, et al. A designed inhibitor of p53 aggregation rescues p53 tumor suppression in ovarian carcinomas. Cancer Cell. 2016;29:90–103.

    Article  CAS  PubMed  Google Scholar 

  10. Yang-Hartwich Y, Soteras MG, Lin ZP, Holmberg J, Sumi N, Craveiro V, et al. p53 protein aggregation promotes platinum resistance in ovarian cancer. Oncogene. 2015;34:3605–16.

    Article  CAS  PubMed  Google Scholar 

  11. Yang-Hartwich Y, Bingham J, Garofalo F, Alvero AB, Mor G. Detection of p53 protein aggregation in cancer cell lines and tumor samples. Methods Mol Biol. 2015;1219:75–86.

    Article  CAS  PubMed  Google Scholar 

  12. Iwahashi N, Ikezaki M, Komohara Y, Fujiwara Y, Noguchi T, Nishioka K, et al. Cytoplasmic p53 aggregates accumulated in p53-mutated cancer correlate with poor prognosis. PNAS Nexus. 2022;1:pgac128.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Neal A, Lai T, Singh T, Rahseparian N, Grogan T, Elashoff D, et al. Combining ReACp53 with carboplatin to target high-grade serous ovarian cancers. Cancers. 2021;13:5908.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Heinzl N, Koziel K, Maritschnegg E, Berger A, Pechriggl E, Fiegl H, et al. A comparison of four technologies for detecting p53 aggregates in ovarian cancer. Front Oncol. 2022;12:976725.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Maritschnegg E, Heinzl N, Wilson S, Deycmar S, Niebuhr M, Klameth L, et al. Polymer-ligand-based ELISA for robust, high-throughput, quantitative detection of p53 aggregates. Anal Chem. 2018;90:13273–9.

    Article  CAS  PubMed  Google Scholar 

  16. Powell B, Soong R, Iacopetta B, Seshadri R, Smith DR. Prognostic significance of mutations to different structural and functional regions of the p53 gene in breast cancer. Clin Cancer Res. 2000;6:443–51.

    CAS  PubMed  Google Scholar 

  17. Samowitz WS, Curtin K, Ma KN, Edwards S, Schaffer D, Leppert MF, et al. Prognostic significance of p53 mutations in colon cancer at the population level. Int J Cancer. 2002;99:597–602.

    Article  CAS  PubMed  Google Scholar 

  18. Chen S, Wu JL, Liang Y, Tang YG, Song HX, Wu LL, et al. Arsenic trioxide rescues structural p53 mutations through a cryptic allosteric site. Cancer Cell. 2021;39:225–39.e8.

    Article  PubMed  Google Scholar 

  19. Eisenhauer EA, Gore M, Neijt JP. Ovarian cancer: should we be managing patients with good and bad prognostic factors in the same manner?. Ann Oncol. 1999;10:9–15.

    Article  PubMed  Google Scholar 

  20. Ano Bom AP, Rangel LP, Costa DC, de Oliveira GA, Sanches D, Braga CA, et al. Mutant p53 aggregates into prion-like amyloid oligomers and fibrils: implications for cancer. J Biol Chem. 2012;287:28152–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Costa DC, de Oliveira GA, Cino EA, Soares IN, Rangel LP, Silva JL. Aggregation and prion-like properties of misfolded tumor suppressors: is cancer a prion disease? Cold Spring Harb Perspect Biol. 2016;8:a023614.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Silva JL, De Moura Gallo CV, Costa DC, Rangel LP. Prion-like aggregation of mutant p53 in cancer. Trends Biochem Sci. 2014;39:260–7.

    Article  CAS  PubMed  Google Scholar 

  23. Silva JL, Rangel LP, Costa DC, Cordeiro Y, De Moura Gallo CV. Expanding the prion concept to cancer biology: dominant-negative effect of aggregates of mutant p53 tumour suppressor. Biosci Rep. 2013;33:e00054.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lewkowicz E, Jayaraman S, Gursky O. Protein amyloid cofactors: charged side-chain arrays meet their match? Trends Biochem Sci. 2021;46:626–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wiech M, Olszewski MB, Tracz-Gaszewska Z, Wawrzynow B, Zylicz M, Zylicz A. Molecular mechanism of mutant p53 stabilization: the role of HSP70 and MDM2. PLoS ONE. 2012;7:e51426.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kirilyuk A, Shimoji M, Catania J, Sahu G, Pattabiraman N, Giordano A, et al. An intrinsically disordered region of the acetyltransferase p300 with similarity to prion-like domains plays a role in aggregation. PLoS ONE. 2012;7:e48243.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Nieva J, Song BD, Rogel JK, Kujawara D, Altobel L 3rd, Izharrudin A, et al. Cholesterol secosterol aldehydes induce amyloidogenesis and dysfunction of wild-type tumor protein p53. Chem Biol. 2011;18:920–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kovachev PS, Banerjee D, Rangel LP, Eriksson J, Pedrote MM, Martins-Dinis MMDC, et al. Distinct modulatory role of RNA in the aggregation of the tumor suppressor protein p53 core domain. J Biol Chem. 2017;292:9345–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Soussi T. p53 antibodies in the sera of patients with various types of cancer: a review. Cancer Res. 2000;60:1777–88.

    CAS  PubMed  Google Scholar 

  30. Lasagna-Reeves CA, Clos AL, Castillo-Carranza D, Sengupta U, Guerrero-Muñoz M, Kelly B, et al. Dual role of p53 amyloid formation in cancer; loss of function and gain of toxicity. Biochem Biophys Res Commun. 2013;430:963–8.

    Article  CAS  PubMed  Google Scholar 

  31. Zhang Y, Xu L, Chang Y, Li Y, Butler W, Jin E, et al. Therapeutic potential of ReACp53 targeting mutant p53 protein in CRPC. Prostate Cancer Prostatic Dis. 2020;23:160–71.

    Article  CAS  PubMed  Google Scholar 

  32. Ferraz da Costa DC, Campos NPC, Santos RA, Guedes-da-Silva FH, Martins-Dinis MMDC, Zanphorlin L, et al. Resveratrol prevents p53 aggregation in vitro and in breast cancer cells. Oncotarget. 2018;9:29112–22.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rangel LP, Ferretti GDS, Costa CL, Andrade S, Carvalho RS, Costa DCF, et al. p53 reactivation with induction of massive apoptosis-1 (PRIMA-1) inhibits amyloid aggregation of mutant p53 in cancer cells. J Biol Chem. 2019;294:3670–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Palanikumar L, Karpauskaite L, Al-Sayegh M, Chehade I, Alam M, Hassan S, et al. Protein mimetic amyloid inhibitor potently abrogates cancer-associated mutant p53 aggregation and restores tumor suppressor function. Nat Commun. 2021;12:3962.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chen Z, Chen J, Keshamouni VG, Kanapathipillai M. Polyarginine and its analogues inhibit p53 mutant aggregation and cancer cell proliferation in vitro. Biochem Biophys Res Commun. 2017;489:130–4.

    Article  CAS  PubMed  Google Scholar 

  36. Chen Z, Kanapathipillai M. Inhibition of p53 mutant peptide aggregation in vitro by cationic osmolyte acetylcholine chloride. Protein Pept Lett. 2017;24:353–7.

    Article  PubMed  Google Scholar 

  37. Gallardo R, Ramakers M, De Smet F, Claes F, Khodaparast L, Khodaparast L, et al. De novo design of a biologically active amyloid. Science. 2016;354:aah4949.

    Article  PubMed  Google Scholar 

  38. Agupitan AD, Neeson P, Williams S, Howitt J, Haupt S, Haupt Y. P53: a guardian of immunity becomes its saboteur through mutation. Int J Mol Sci. 2020;21:3452.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hsiue EH-C, Wright KM, Douglass J, Hwang MS, Mog BJ, Pearlman AH, et al. Targeting a neoantigen derived from a common TP53 mutation. Science. 2021;371:eabc8697.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chekerov R, Braicu I, Castillo-Tong DC, Richter R, Cadron I, Mahner S, et al. Outcome and clinical management of 275 patients with advanced ovarian cancer International Federation of Obstetrics and Gynecology II to IV inside the European Ovarian Cancer Translational Research Consortium—OVCAD. Int J Gynecol Cancer. 2013;23:268–75.

    Article  PubMed  Google Scholar 

  41. Hofstetter G, Berger A, Schuster E, Wolf A, Hager G, Vergote I, et al. 133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br J Cancer. 2011;105:1593–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yang W-L, Gentry-Maharaj A, Simmons A, Ryan A, Fourkala EO, Lu Z, et al. Elevation of TP53 autoantibody before CA125 in preclinical invasive epithelial ovarian cancer. Clin Cancer Res. 2017;23:5912–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gleiss A, Zeillinger R, Braicu EI, Trillsch F, Vergote I, Schemper M. Statistical controversies in clinical research: the importance of importance. Ann Oncol. 2016;27:1185–9.

    Article  CAS  PubMed  Google Scholar 

  44. Markuch R. Adjusted survival curve estimation using covariates. J Chronic Dis. 1982;35:437–43.

    Article  Google Scholar 

  45. Uno H, Cai TX, Pencina MJ, D'Agostino RB, Wei LJ. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011;30:1105–17.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Schemper M. Predictive accuracy and explained variation. Stat Med. 2003;22:2299–308.

    Article  PubMed  Google Scholar 

  47. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81:515–26.

    Article  Google Scholar 

  48. FIRTH D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27–38.

    Article  Google Scholar 

  49. Heinze G, Schemper M. A solution to the problem of monotone likelihood in Cox regression. Biometrics. 2001;57:114–9.

    Article  CAS  PubMed  Google Scholar 

  50. Davis A, Tinker AV, Friedlander M. “Platinum resistant” ovarian cancer: what is it, who to treat and how to measure benefit? Gynecol Oncol. 2014;133:624–31.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Mina Fogel (Kaplan Medical Center, Rehovot, Israel), Silvia Darb-Esfahani, and Carsten Denkert (Department of Pathology, Charité University Medicine, Campus Charité, Berlin, Germany; CD current address: Institute of Pathology, University of Marburg, Marburg, Germany) for preparing tissue microarrays as well as Reinhard Horvat (Department of Pathology, Medical University of Vienna, Vienna, Austria) for the evaluation of the tumor cell percentage and p53 protein expression.

Funding

This study received support from Microsens Biotechnologies (London, UK) in the form of an in-kind contribution of Seprion-coated microplates and capture buffer. This work was supported by the Fellinger Krebsforschungsverein (project: “The role of p53 prions in ovarian cancer and possible implications” to EM); and the Sixth Framework Programme (FP6) Project of the European Union called “Ovarian Cancer: Diagnosis of a silent killer–OVCAD” (grant number LSHC-CT-2005-018698). The autoantibody assays were supported by the National Cancer Institute (NCI) Early Detection Research Network (grant number 5 U01 CA200462-02 to RCB Jr); MD Anderson Ovarian SPOREs (grant numbers P50 CA83639, P50CA217685 to RCB Jr); National Cancer Institute, Department of Health and Human Services; the Cancer Prevention Research Institute of Texas (grant number RP160145 to RCB Jr); and generous donations from the Ann and Henry Zarrow Foundation, the Mossy Foundation, the Roberson Endowment, Stuart and Gaye Lynn Zarrow, Barry Elson, Arthur, and Sandra Williams. The Switch laboratory was supported by the Flanders Institute for Biotechnology (VIB, grant number C0401); KU Leuven (grant number C24E/19/070); The Research Foundation—Flanders (FWO, grant number IWT-SBO 140037); and the Stichting Tegen Kanker (grant number FAF-F/2018/1174). Funders had no role in the study design, data collection, data analyses, interpretation, or writing of the report and the decision to submit the paper for publication.

Author information

Authors and Affiliations

  1. Department of Obstetrics and Gynaecology, Molecular Oncology Group, Comprehensive Cancer Center-Gynaecologic Cancer Unit, Medical University of Vienna, Vienna, Austria

    Nicole Heinzl, Elisabeth Maritschnegg, Eva Obermayr, Eva Schuster, Barbara Holzer & Robert Zeillinger

  2. Switch Laboratory, VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium

    Elisabeth Maritschnegg, Frederic Rousseau & Joost Schymkowitz

  3. Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, Box 802, 3000, Leuven, Belgium

    Elisabeth Maritschnegg, Frederic Rousseau & Joost Schymkowitz

  4. Department of Gynaecology and Obstetrics, Innsbruck Medical University, Innsbruck, Austria

    Katarzyna Koziel & Nicole Concin

  5. Section for Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria

    Christine Schilhart-Wallisch & Georg Heinze

  6. Department of Experimental Therapeutics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

    Wei-Lei Yang & Robert C. Bast

  7. Department of Gynaecology, European Competence Center for Ovarian Cancer, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Virchow Klinikum, Berlin, Germany

    Jalid Sehouli & Elena I. Braicu

  8. Department of Obstetrics and Gynecology, Stanford University, Stanford, CA, USA

    Elena I. Braicu

  9. Division of Gynaecologic Oncology, University Hospital Leuven, Leuven, Belgium

    Ignace Vergote & Toon Van Gorp

  10. Leuven Cancer Institute, KU Leuven, Leuven, Belgium

    Ignace Vergote & Toon Van Gorp

  11. Department of Gynaecology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Sven Mahner

  12. Department of Obstetrics and Gynaecology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany

    Sven Mahner

  13. Department of Obstetrics and Gynaecology, Division of General Gynaecology and Gynaecologic Oncology, Comprehensive Cancer Center-Gynaecologic Cancer Unit, Medical University of Vienna, Vienna, Austria

    Valentina Paspalj & Christoph Grimm

Authors
  1. Nicole Heinzl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elisabeth Maritschnegg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katarzyna Koziel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christine Schilhart-Wallisch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Georg Heinze
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wei-Lei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Robert C. Bast
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jalid Sehouli
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Elena I. Braicu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ignace Vergote
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Toon Van Gorp
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sven Mahner
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Valentina Paspalj
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Christoph Grimm
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Eva Obermayr
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Eva Schuster
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Barbara Holzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Frederic Rousseau
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Joost Schymkowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Nicole Concin
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Robert Zeillinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

NH: conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing—original draft; EM: conceptualization, funding acquisition, investigation, writing—review and editing; KK, RCB, EO, FR, and JS: writing—review and editing; CSW and GH: formal analysis, writing—review and editing; WLY, ES, and BH: investigation; JS, EIB, IV, TVG, SM, VP, CG, and NC: resources, writing—review and editing; RZ: conceptualization, funding acquisition, project administration, supervision, writing—review and editing. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Robert Zeillinger.

Ethics declarations

Competing interests

All authors have read the journal’s authorship agreement and policy on disclosure of potential conflicts of interest and report the following conflict of interests: NH: Employment: OncoLab Diagnostics GmbH; Patents, Royalties, Other Intellectual Property: European Patent Application No. 21197391.2, titled “METHOD OF DETERMINING TUMOR-ASSOCIATED PROTEIN AGGREGATES”; Travel, Accommodations, Expenses: OncoLab Diagnostics GmbH; JS: Consulting for: Eisai, F. Hoffmann-La Roche Ltd, Genmab, GSK, Karyopharm, MSD, Novocure, Novartis, AstraZeneca, Clovis; Grant/Corporate Sponsored Research: Amgen, F. Hoffmann-La Roche; Accommodations, travel expenses: MSD, Tesaro, AstraZeneca, GSK F. Hoffmann-La Roche; EIB: Advisory Board, Steering Committees: AstraZeneca, Clovis, GSK-Tesaro, MSD, EISAI, Roche; Travel, Accommodations, Expenses: AstraZeneca, Clovis, Roche, Tesaro; Research Funding: AstraZeneca, Bayer, GSK, MSD-Merck, Roche Diagnostics; IV: Consulting for: Agenus, Aksebio, AstraZeneca, Bristol Myers Squibb (2021), Deciphera Pharmaceuticals, Eisai, Elevar Therapeutics, F. Hoffmann-La Roche Ltd, Genmab, GSK, Immunogen Inc., Jazzpharma, Karyopharm, Mersana, MSD, Novocure, Novartis, Oncoinvent AS, Seagen, Sotio a.s., Verastem Oncology, Zentalis; Contracted Research (via KU Leuven): Oncoinvent AS (2019-20); Grant/Corporate Sponsored Research: Amgen, F. Hoffmann-La Roche; Accommodations, travel expenses: Amgen, MSD, Tesaro, AstraZeneca, Karyopharm, F. Hoffmann-La Roche; TVG: Consulting for: AstraZeneca, Eisai Europe, OncXerna Therapeutics, MSD/Merck, GSK; Grant/Corporate Sponsored Research: Amgen, Roche; Accommodations, travel expenses: Immunogen, MSD, AstraZeneca, PharmaMar; SM: Research support, advisory board, honoraria and travel expenses: AbbVie, AstraZeneca, Clovis, Eisai, GlaxoSmithKline, Medac, MSD, Novartis, Olympus, PharmaMar, Pfizer, Roche, Sensor Kinesis, Teva, Tesaro; CG: Consulting for: AstraZeneca, Celgene, MSD, PharmaMar, Roche, GSK, Vifor Pharma, Clovis; Grant/Corporate Sponsored Research: Meda Pharma, Roche Diagnostics, AstraZeneca; Accommodations, travel expenses: AstraZeneca, PharmaMar, Roche, GSK; NC: Consulting/Advisory: Seagen, Akesobio, Ensai, GSK, AstraZeneca, Mersana, Seattle Genetics, eTheRNA immunotherapies NV; Travel Expenses: Roche, Genmab, Amgen; Educational fees: MSD, Medscape Oncology, TouchIME; RZ: Employment: OncoLab Diagnostics GmbH; Leadership: OncoLab Diagnostics GmbH; Stock and Other Ownership Interests: OncoLab Diagnostics GmbH; Patents, Royalties, Other Intellectual Property: European Patent Application No. 21197391.2, titled “METHOD OF DETERMINING TUMOR-ASSOCIATED PROTEIN AGGREGATES”; Patents at OncoLab Diagnostics GmbH. The other authors have no additional financial interests.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heinzl, N., Maritschnegg, E., Koziel, K. et al. Amyloid-like p53 as prognostic biomarker in serous ovarian cancer—a study of the OVCAD consortium. Oncogene 42, 2473–2484 (2023). https://doi.org/10.1038/s41388-023-02758-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41388-023-02758-8

Search

Advanced search

Quick links