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A novel algorithm to implement the molecular classification according to the new ESGO/ESTRO/ESP 2020 guidelines for endometrial cancer
  1. Ilaria Betella1,
  2. Caterina Fumagalli2,3,
  3. Paola Rafaniello Raviele4,
  4. Gabriella Schivardi1,5,
  5. Luigi Antonio De Vitis1,
  6. Maria Teresa Achilarre1,
  7. Alessia Aloisi1,
  8. Annalisa Garbi1,
  9. Matteo Maruccio1,
  10. Vanna Zanagnolo1,
  11. Giovanni Aletti1,6,
  12. Elena Guerini-Rocco4,6,
  13. Andrea Mariani5,
  14. Angelo Maggioni1,
  15. Massimo Barberis4,
  16. Nicoletta Colombo1,7 and
  17. Francesco Multinu1
  1. 1Department of Gynecology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
  2. 2Clinical Unit of Oncogenomics, European Institute of Oncology (IEO), IRCCS, Milan, Italy
  3. 3Department of Diagnostic Services, Division of Pathology, Azienda Socio Sanitaria Territoriale della Valle Olona, Gallarate, Italy
  4. 4Department of Pathology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
  5. 5Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, New York, USA
  6. 6Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
  7. 7Faculty of Medicine and Surgery, Universita degli Studi di Milano-Bicocca, Milan, Italy
  1. Correspondence to Dr Ilaria Betella, Department of Gynecology, European Institute of Oncology (IEO), IRCCS, Milan, Lombardy, Italy; ilaria.betella{at}ieo.it

Abstract

Objective To compare the risk class attribution with molecular classification unknown to those with molecular classification known, according to the European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology (ESGO/ESTRO/ESP) 2020 guidelines on endometrial cancer, with a focus on risk group migration. Additionally, to evaluate the capability of a novel molecular analysis algorithm to reduce the number of required tests.

Methods We conducted a retrospective study including all consecutive patients with endometrial cancer undergoing surgery and comprehensive molecular analyses between April 2019 and December 2021. Molecular analyses including immunohistochemistry for p53 and mismatch repair (MMR) proteins, and DNA sequencing for POLE exonuclease domain were performed to classify tumors as POLE-mutated (POLE), MMR-deficient (MMR-d), p53 abnormal (p53abn), or non-specific molecular profile (NSMP). The two risk classifications of the ESGO/ESTRO/ESP 2020 guidelines were compared to estimate the proportion of patients in which the molecular analysis was able to change the risk class attribution. We developed a novel algorithm where the molecular analyses are reserved only for patients in whom incorporation of the molecular classification could change the risk class attribution.

Results A total of 278 patients were included. Molecular analyses were successful for all cases, identifying the four subgroups: 27 (9.7%) POLE, 77 (27.7%) MMR-d, 49 (17.6%) p53abn, and 125 (45.0%) NSMP. Comparison of risk class attribution between the two classification systems demonstrated discordance in the risk class assignment in 19 (6.8%, 95% CI 4.2% to 10.5%) cases. The application of our novel algorithm would have led to a reduction in the number of POLE sequencing tests by 67% (95% CI 61% to 73%) and a decrease of p53 immunohistochemistry by 27% (95% CI 22% to 33%), as compared with the application of molecular classification to all patients.

Conclusion Molecular categorization of endometrial cancer allows the reallocation of a considerable proportion of patients in a different risk class. Furthermore, the application of our algorithm enables a reduction in the number of required tests without affecting the risk classification.

  • Endometrial Neoplasms
  • Pathology

Data availability statement

Data are available upon reasonable request.

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Data availability statement

Data are available upon reasonable request.

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Footnotes

  • NC and FM are joint senior authors.

  • Twitter @IBetella, @LuigiDEvitis, @Fmultinu

  • Contributors IB - Project development, data collection, data analysis, manuscript writing, guarantor. CF - Project development, data collection, data analysis. PRR - Project development, data collection, data analysis. GS - Project development, data collection, data analysis, manuscript writing. LADV - Data collection, data analysis, manuscript writing. MTA - Project development, data collection. AA - Project development, data collection. AG - Project development, data collection. MM - Project development, data collection. VZ - Project development, data collection, data analysis. GA - Project development, data collection, data analysis. EG-R - Data analysis. AMar - Project development, data analysis. AMag - Project development, data collection. MB - Project development, data analysis. NC - Project development, data collection, data analysis, manuscript writing. FM - Project development, data collection, data analysis, manuscript writing, guarantor.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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