Skip to main content
SpringerLink
Log in

It is time to implement molecular classification in endometrial cancer

  • Review
  • Published:
Archives of Gynecology and Obstetrics Aims and scope Submit manuscript

Abstract

A huge effort has been done in redefining endometrial cancer (EC) risk classes in the last decade. However, known prognostic factors (FIGO staging and grading, biomolecular classification and ESMO-ESGO-ESTRO risk classes stratification) are not able to predict outcomes and especially recurrences. Biomolecular classification has helped in re-classifying patients for a more appropriate adjuvant treatment and clinical studies suggest that currently used molecular classification improves the risk assessment of women with EC, however, it does not clearly explain differences in recurrence profiles. Furthermore, a lack of evidence appears in EC guidelines. Here, we summarize the main concepts why molecular classification is not enough in the management of endometrial cancer, by highlighting some promising innovative examples in scientific literature studies with a clinical potential significant impact.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

N.A.

References

  1. Fremond S, Koelzer VH, Horeweg N, Bosse T (2022) The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning. Front Oncol. https://doi.org/10.3389/fonc.2022.928977

    Article  PubMed  PubMed Central  Google Scholar 

  2. Njoku K, Barr CE, Crosbie EJ (2022) Current and emerging prognostic biomarkers in endometrial cancer. Front Oncol 12:1682

    Article  Google Scholar 

  3. Colombo, N. et al (2013) Endometrial cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 24 Suppl 6

  4. Creutzberg CL et al (2000) Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: Multicentre randomised trial. Lancet 355:1404–1411

    Article  CAS  PubMed  Google Scholar 

  5. Keys HM et al (2004) A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a gynecologic oncology group study. Gynecol Oncol 92:744–751

    Article  PubMed  Google Scholar 

  6. P, B. et al (2009) Adjuvant external beam radiotherapy in the treatment of endometrial cancer (MRC ASTEC and NCIC CTG EN.5 randomised trials): pooled trial results, systematic review, and meta-analysis. Lancet 373:137–146

    Article  Google Scholar 

  7. Kong A, Johnson N, Kitchener HC, Lawrie TA (2012) Adjuvant radiotherapy for stage I endometrial cancer: an updated Cochrane systematic review and meta-analysis. J Natl Cancer Inst 104:1625–1634

    Article  PubMed  Google Scholar 

  8. Levine DA (2013) Integrated genomic characterization of endometrial carcinoma. Nature 497:67–73

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  9. Travaglino A et al (2020) TCGA molecular subgroups in endometrial undifferentiated/dedifferentiated carcinoma. Pathology & Oncology Research 26:1411–1416

    Article  CAS  Google Scholar 

  10. Talhouk A et al (2015) A clinically applicable molecular-based classification for endometrial cancers. Br J Cancer 113:299–310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Talhouk A et al (2017) Confirmation of ProMisE: a simple, genomics-based clinical classifier for endometrial cancer. Cancer 123:802–813

    Article  CAS  PubMed  Google Scholar 

  12. Concin N et al (2021) ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Virchows Arch 478:153–190

    Article  PubMed  ADS  Google Scholar 

  13. Leon-Castillo A et al (2020) Molecular classification of the PORTEC-3 trial for high-risk endometrial cancer: impact on prognosis and benefit from adjuvant therapy. J Clin Oncol 38:3388–3397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. León-Castillo A et al (2020) Clinicopathological and molecular characterisation of ‘multiple-classifier’ endometrial carcinomas. J Pathol 250:312–322

    Article  PubMed  PubMed Central  Google Scholar 

  15. Giustozzi A et al (2021) Refining adjuvant therapy for endometrial cancer: new standards and perspectives. Biology (Basel) 10:845

    CAS  PubMed  Google Scholar 

  16. Vizza E et al (2020) Pattern of recurrence in patients with endometrial cancer: a retrospective study. Eur J Surg Oncol 46:1697–1702

    Article  CAS  PubMed  Google Scholar 

  17. Vermij L, Smit V, Nout R, Bosse T (2020) Incorporation of molecular characteristics into endometrial cancer management. Histopathology 76(1):52–63. https://doi.org/10.1111/his.14015.PMID:31846532;PMCID:PMC6972558

    Article  PubMed  Google Scholar 

  18. Arciuolo D, Travaglino A, Raffone A, Raimondo D, Santoro A, Russo D, Varricchio S, Casadio P, Inzani F, Seracchioli R, Mollo A, Mascolo M, Zannoni GF (2022) TCGA molecular prognostic groups of endometrial carcinoma: current knowledge and future perspectives. Int J Mol Sci 23(19):11684. https://doi.org/10.3390/ijms231911684.PMID:36232987;PMCID:PMC9569906

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kasius JC, Trozzi R, Pijnenborg J, Baert T, Laenen A, Van Rompuy AS, Zapardiel I, Vizzielli G, Knez J, Fanfani F, Amant F (2023) Improving Endometrial cancer assessment by combining the new techniqUe of GENomic profiling with surgical Extra uterIne disEase assessment (EUGENIE). Int J Gynecol Cancer. https://doi.org/10.1136/ijgc-2023-004289. (Epub ahead of print. PMID: 36977506)

    Article  PubMed  Google Scholar 

  20. Stelloo E et al (2016) Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer-combined analysis of the PORTEC cohorts. Clin Cancer Res 22:4215–4224

    Article  CAS  PubMed  Google Scholar 

  21. Stelloo E et al (2015) Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a TransPORTEC initiative. Mod Pathol 28:836–844

    Article  CAS  PubMed  Google Scholar 

  22. Nout RA et al (2010) Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial. Lancet 375:816–823

    Article  CAS  PubMed  Google Scholar 

  23. Auguste A et al (2018) Refinement of high-risk endometrial cancer classification using DNA damage response biomarkers: a transPORTEC initiative. Mod Pathol 31(12 31):1851–1861

    Article  CAS  PubMed  Google Scholar 

  24. Miller RE et al (2020) ESMO recommendations on predictive biomarker testing for homologous recombination deficiency and PARP inhibitor benefit in ovarian cancer. Ann Oncol 31:1606–1622

    Article  CAS  PubMed  Google Scholar 

  25. Konstantinopoulos PA, Lheureux S, Moore KN (2020) PARP Inhibitors for ovarian cancer: current indications, future combinations, and novel assets in development to target DNA damage repair. Am Soc Clin Oncol Educ Book 40:1–16

    PubMed  Google Scholar 

  26. van den Heerik ASVM et al (2020) PORTEC-4a: International randomized trial of molecular profile-based adjuvant treatment for women with high-intermediate risk endometrial cancer. Int J Gynecol Cancer 30:2002–2007

    Article  PubMed  PubMed Central  Google Scholar 

  27. Romani C et al (2022) L1CAM expression as a predictor of platinum response in high-risk endometrial carcinoma. Int J Cancer 151:637

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. He X et al (2020) Deregulation of cell adhesion molecules is associated with progression and poor outcomes in endometrial cancer: analysis of the cancer genome atlas data. Oncol Lett 19:1906–1914

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Ledinek Ž, Sobočan M, Knez J (2022) The role of CTNNB1 in endometrial cancer. Dis Mark. https://doi.org/10.1155/2022/1442441

    Article  Google Scholar 

  30. Dong D et al (2021) POLE and mismatch repair status, checkpoint proteins and tumor-infiltrating lymphocytes in combination, and tumor differentiation: identify endometrial cancers for immunotherapy. Front Oncol. https://doi.org/10.3389/fonc.2021.640018

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gargiulo P et al (2016) Tumor genotype and immune microenvironment in POLE-ultramutated and MSI-hypermutated endometrial cancers: new candidates for checkpoint blockade immunotherapy? Cancer Treat Rev 48:61–68

    Article  CAS  PubMed  Google Scholar 

  32. Yu Z et al (2022) Single-cell sequencing reveals the heterogeneity and intratumoral crosstalk in human endometrial cancer. Cell Prolif. https://doi.org/10.1111/cpr.13249

    Article  PubMed  PubMed Central  Google Scholar 

  33. Herrero C et al (2019) Extracellular vesicles-based biomarkers represent a promising liquid biopsy in endometrial cancer. Cancers (Basel). https://doi.org/10.3390/cancers11122000

    Article  PubMed  Google Scholar 

  34. Hao X et al (2017) DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci U S A 114:7414–7419

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  35. Ying J, Xu T, Wang Q, Ye J, Lyu J (2018) Exploration of DNA methylation markers for diagnosis and prognosis of patients with endometrial cancer. Epigenetics 13:490–504

    Article  PubMed  PubMed Central  Google Scholar 

  36. Farkas SA, Sorbe BG, Nilsson TK (2017) Epigenetic changes as prognostic predictors in endometrial carcinomas. Epigenetics 12:19

    Article  PubMed  Google Scholar 

  37. Huo X et al (2019) (2019) Identification of prognosis markers for endometrial cancer by integrated analysis of DNA methylation and RNA-Seq data. Sci Rep 9(1 9):1–10

    Google Scholar 

  38. Wang Y, Liu D, Jin X, Song H, Lou G (2019) Genome-wide characterization of aberrant DNA methylation patterns and the potential clinical implications in patients with endometrial cancer. Pathol Res Pract 215:137–143

    Article  CAS  PubMed  Google Scholar 

  39. Zeng Z et al (2020) A 14-Methylation-driven differentially expressed RNA as a signature for overall survival prediction in patients with uterine corpus endometrial carcinoma. DNA Cell Biol 39:975–991

    Article  CAS  PubMed  Google Scholar 

  40. Shen PC et al (2022) Developing a novel DNA methylation risk score for survival and identification of prognostic gene mutations in endometrial cancer: a study based on TCGA data. Jpn J Clin Oncol 52:992–1000

    PubMed  Google Scholar 

  41. Dou Y et al (2020) Proteogenomic characterization of endometrial carcinoma. Cell 180:729-748.e26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Si W, Shen J, Zheng H, Fan W (2019) The role and mechanisms of action of microRNAs in cancer drug resistance. Clin Epigenetics 11(1):1–24

    Article  Google Scholar 

  43. Cui M et al (2019) Circulating MicroRNAs in cancer: potential and challenge. Front Genet. https://doi.org/10.3389/fgene.2019.00626

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lehmann TP et al (2020) In Vitro and in silico analysis of miR-125a with rs12976445 polymorphism in breast cancer patients. App Sci 10:7275

    Article  CAS  Google Scholar 

  45. Garzon R, Calin GA, Croce CM (2008). MicroRNAs in Cancer. https://doi.org/10.1146/annurev.med.59.053006.104707

    Article  Google Scholar 

  46. Wang Z, Jensen MA, Zenklusen JC (2016) A practical guide to the cancer genome atlas (TCGA). Methods Mol Biol 1418:111–141

    Article  PubMed  Google Scholar 

  47. Denschlag D, Ulrich U, Emons G (2010) The diagnosis and treatment of endometrial cancer: progress and controversies. Dtsch Arztebl Int 108:571–577

    PubMed  Google Scholar 

  48. Ni L et al (2022) Construction of a miRNA-based nomogram model to predict the prognosis of endometrial cancer. J Pers Med. https://doi.org/10.3390/jpm12071154

    Article  PubMed  PubMed Central  Google Scholar 

  49. Yi R et al (2012) Multi-omic profiling of multi-biosamples reveals the role of amino acid and nucleotide metabolism in endometrial cancer. Front Oncol. https://doi.org/10.3389/fonc.2022.861142

    Article  Google Scholar 

  50. Lianidou E, Pantel K (2019) Liquid biopsies. Genes Chromosom Cancer 58:219–232

    Article  CAS  PubMed  Google Scholar 

  51. Clemmens H, Lambert DW (2018) Extracellular vesicles: translational challenges and opportunities. Biochem Soc Trans 46:1073–1082

    Article  CAS  PubMed  Google Scholar 

  52. Steinbichler TB, Dudás J, Riechelmann H, Skvortsova II (2017) The role of exosomes in cancer metastasis. Semin Cancer Biol 44:170–181

    Article  CAS  PubMed  Google Scholar 

  53. Peinado H et al (2012) Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med 18:883

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Muinelo-Romay L, Casas-Arozamena C, Abal M (2018) Liquid biopsy in endometrial cancer: new opportunities for personalized oncology. Int J Mol Sci. https://doi.org/10.3390/ijms19082311

    Article  PubMed  PubMed Central  Google Scholar 

  55. Mariscal J et al (2019) Proteomic characterization of epithelial-like extracellular vesicles in advanced endometrial cancer. J Proteome Res 18:1043–1053

    Article  CAS  PubMed  Google Scholar 

  56. Mathai RA et al (2019) Potential utility of liquid biopsy as a diagnostic and prognostic tool for the assessment of solid tumors: implications in the precision oncology. J Clin Med. https://doi.org/10.3390/jcm8030373

    Article  PubMed  PubMed Central  Google Scholar 

  57. Vizza E et al (2018) Serum DNA integrity index as a potential molecular biomarker in endometrial cancer. J Exp Clin Cancer Res. https://doi.org/10.1186/s13046-018-0688-4

    Article  PubMed  PubMed Central  Google Scholar 

  58. Cicchillitti L et al (2017) Circulating cell-free DNA content as blood based biomarker in endometrial cancer. Oncotarget 8:115230–115243

    Article  PubMed  PubMed Central  Google Scholar 

  59. Dobrzycka B et al (2010) Circulating free DNA, p53 antibody and mutations of KRAS gene in endometrial cancer. Int J Cancer 127:612–621

    Article  CAS  PubMed  Google Scholar 

  60. Moss EL et al (2020) Utility of circulating tumor DNA for detection and monitoring of endometrial cancer recurrence and progression. Cancers (Basel) 12:1–13

    Article  Google Scholar 

  61. Pereira E et al (2015) Personalized circulating tumor DNA biomarkers dynamically predict treatment response and survival in gynecologic cancers. PLoS One. https://doi.org/10.1371/journal.pone.0145754

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bolivar AM et al (2019) Targeted next-generation sequencing of endometrial cancer and matched circulating tumor DNA: identification of plasma-based, tumor-associated mutations in early stage patients. Mod Pathol 32:405–414

    Article  CAS  PubMed  Google Scholar 

  63. Lemech CR et al (2016) Enumeration and molecular characterisation of circulating tumour cells in endometrial cancer. Oncology 91:48–54

    Article  CAS  PubMed  Google Scholar 

  64. Bogani G et al (2015) Detection of circulating tumor cells in high-risk endometrial cancer. Anticancer Res 35:683–687

    PubMed  Google Scholar 

  65. Hoivik EA et al (2021) A radiogenomics application for prognostic profiling of endometrial cancer. Commun Biol. https://doi.org/10.1038/nrclinonc.2017.141

    Article  PubMed  PubMed Central  Google Scholar 

  66. Lambin P et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. https://doi.org/10.1038/nrclinonc.2017.141

    Article  PubMed  Google Scholar 

  67. Yan BC et al (2021) Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study. Eur Radiol 31:411–422

    Article  CAS  PubMed  Google Scholar 

  68. Veeraraghavan H, et al. (2020) Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers. Scientific Reports, 10:1 10, 1–10

  69. Fasmer KE et al (2021) Whole-volume tumor MRI radiomics for prognostic modeling in endometrial cancer. J Magn Reson Imagin 53:928–937

    Article  ADS  Google Scholar 

  70. Xu X et al (2019) Multiplanar MRI-based predictive model for preoperative assessment of lymph node metastasis in endometrial cancer. Front Oncol. https://doi.org/10.3389/fonc.2019.01007

    Article  PubMed  PubMed Central  Google Scholar 

  71. Chen J et al (2021) MRI-based radiomic model for preoperative risk stratification in stage I endometrial cancer. J Cancer 12:726

    Article  PubMed  PubMed Central  Google Scholar 

  72. de Boer SM et al (2017) Clinical consequences of upfront pathology review in the randomised PORTEC-3 trial for high-risk endometrial cancer. Ann Oncol 29:424–430

    Article  PubMed Central  Google Scholar 

  73. Tanos P, Dimitriou S, Gullo G, Tanos V (2022) Biomolecular and genetic prognostic factors that can facilitate fertility-sparing treatment (FST) decision making in early stage endometrial cancer (ES-EC): a systematic review. Int J Mol Sci. https://doi.org/10.3390/ijms23052653

    Article  PubMed  PubMed Central  Google Scholar 

  74. Jamieson A, Bosse T, McAlpine JN (2021) The emerging role of molecular pathology in directing the systemic treatment of endometrial cancer. Ther Adv Med Oncol. https://doi.org/10.1177/17588359211035959

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Prof. Maureen Rinaldi for her careful help in the revision of this manuscript in English language.

Funding

The authors declare that no funds, grants, or other support were received to support the work.

Author information

Authors and Affiliations

  1. Gynecologic Oncology Unit, Department of Experimental Clinical Oncology, IRCCS “Regina Elena” National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy

    Valentina Bruno, Benito Chiofalo, Emanuela Mancini & Enrico Vizza

  2. Department of Maternal and Child Health and Urological Sciences, University of Rome “Sapienza”, Policlinico “Umberto I”, Rome, Italy

    Alessandra Logoteta

  3. Biostatistics, Bioinformatics and Clinical Trial Center, IRCCS—Regina Elena National Cancer Institute, Rome, Italy

    Martina Betti

  4. Department of Anesthesiology, IRCCS—Regina Elena National Cancer Institute, Rome, Italy

    Luana Fabrizi

  5. Department of Surgical Sciences, Catholic University Our Lady of Good Counsel, Tirane, Albania

    Emilio Piccione

Authors
  1. Valentina Bruno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessandra Logoteta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benito Chiofalo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emanuela Mancini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martina Betti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luana Fabrizi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Emilio Piccione
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Enrico Vizza
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, literature revision and writing—original draft preparation: VB and AL. Data analysis, writing—review and editing: BC and EM. Critically revision: MB, LF, EP and EV.

Corresponding author

Correspondence to Benito Chiofalo.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

Note required.

Consent to participate

Not required.

Consent to publish

Not required.

Additional information

Publisher's Note

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

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

Bruno, V., Logoteta, A., Chiofalo, B. et al. It is time to implement molecular classification in endometrial cancer. Arch Gynecol Obstet 309, 745–753 (2024). https://doi.org/10.1007/s00404-023-07128-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00404-023-07128-z

Keywords

Search

Navigation