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  • 402 Implementing molecular classification for endometrial cancer through the application of betella's innovative algorithm on a retrospective series.

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03. Endometrial cancer
402 Implementing molecular classification for endometrial cancer through the application of betella's innovative algorithm on a retrospective series.
  1. Martina Arcieri1,2,
  2. Tommaso Occhiali3,
  3. Cristina Giorgiutti3,
  4. Giulia Pellecchia3,
  5. Veronica Tius3,
  6. Sara Pregnolato3,
  7. Laura Mariuzzi4,
  8. Maria Orsaria5,
  9. Carla Di Loreto4,6,
  10. Claudia Andreetta7,
  11. Francesca Titone8,
  12. Francesco Fanfani9,
  13. Alfredo Ercoli10,
  14. Lorenza Driul1,3,
  15. Giovanni Scambia9,
  16. Giuseppe Vizzielli1,3 and
  17. Stefano Restaino1
  1. 1Department of Maternal and Child Health, ’Santa Maria della Misericordia’ University Hospital, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy
  2. 2Department of Biomedical, Dental, Morphological and Functional Imaging Science, University of Messina,, Messina, Italy
  3. 3Medical Area Department (DAME), University of Udine, Udine, Italy
  4. 4Institute of Pathology, DAME, University of Udine, Udine, Italy
  5. 5Institute of Pathology, Academic Hospital, Udine, Italy
  6. 6Department of Medicine, University of Udine, Udine, Italy
  7. 7Medical Oncology Unit, Department of Oncology,, Udine, Italy
  8. 8Radiation Oncology Unit, Department of Oncology,, Udine, Italy
  9. 9Division of Gynecologic Oncology, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
  10. 10Obstetrics and Gynecology Unit, Department of Human Pathology of Adult and Childhood ``G. Baresi'', University Hospital ``G. Martino'', Messina, Italy

Abstract

Introduction/Background In 2020, the European Society of Gynecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology (ESGO/ESTRO/ESP) integrated molecular classification into the classic risk stratification based on clinicopathological features of patients with endometrial cancer (EC), thus introducing a new decision-making for clinicians in adjuvant therapy. Therefore, each surgical specimen should be tested for mutations in the tumor protein 53 (p53), in mismatch repair (MMR) proteins and Polimerase Epsilon (POLE). However, this may be too expensive and not always necessary. Betella’s algorithm (figure 1) therefore pointed to test only for mutations that would actually change the risk group and consequently the adjuvant treatment: first of all, analysis of MMR proteins; then research for p53 mutation in stages I and II of disease according to International Federation of Gynecology and Obstetrics (FIGO) 2009; finally, the sequencing of POLE when class migration occurred. Currently, available data allow class migration ’only’ in stage I-II endometrial cancers, in FIGO grade 3 and when lymphovascular spaces are involved.

Methodology The study aimed to externally validate Betella’s algorithm by retrospective analysis of EC cases treated between March 2021 and March 2023 at the Gynecology Clinic of S. Maria della Misericordia Hospital in Udine, Italy.

Results Of 102 patients underwent surgical staging, complete molecular analysis was obtained in 97%. Integrating molecular classification in the calculation of risk groups resulted in a risk group move in 11.1% of patients: 7 moved to a lower risk group due to POLE mutations, while 4 shifted to a higher risk group due to p53 mutations. By applying the Betella algorithm, we were able to spare POLE sequencing in 65 cases (65.7%) and p53 immunohistochemistry in 17 cases (17.2%) (figure 2).

Abstract 402 Figure 1
Abstract 402 Figure 1

Betella’s algorithm

Abstract 402 Figure 2
Abstract 402 Figure 2

Application of Betella’s algorithm to UDINE population

Conclusion The application of this proposed new algorithm appears safe for patients and allows rationalization of resources.

Disclosures Authors have no disclosures to declare.

https://doi.org/10.1136/ijgc-2024-ESGO.331

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