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2022-LBA-1382-ESGO Genomic signatures for the prediction of recurrence and metastasis in Endometrial Cancer
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  1. Alba Farres1,
  2. Natalia Teixeira1,
  3. Pia Espanol2,
  4. Eva Magret1,
  5. Rocio Luna1,
  6. Cristina Soler1,
  7. Pau Martin-Malpartida3,4,
  8. Maria Jesús Macías3,4,
  9. Maria Virtudes Céspedes5 and
  10. Ramon Rovira1
  1. 1Gynecology and Obstetrics; Grup d’Oncologia Ginecològica, Institut d’Investigacions Biomédiques, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
  2. 2Gynecology and Obstetrics, Hospital Universitari Son Espases, Palma de Mallorca, Spain
  3. 3Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, Barcelona, Spain
  4. 4Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
  5. 5Grup d’Oncologia Ginecològica i Peritoneal, Institut d’Investigacions Biomédiques, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain

Abstract

Introduction Lymphadenectomy is normally indicated in Endometrial Cancer (EC) staging. Only 20–25% of patients are diagnosed with nodal involvement, therefore 70% of patients will have lymphadenectomy-related risk without therapeutic benefit. Moreover, one fourth of node-negative EC recur, suggesting that some high-risk tumors are not detected using lymphadenectomy. We hypothesize that genetic/transcriptomic and clinical differences among patients might help predict tumor recurrence risk. This study aims to develop a diagnostic tool to determine the probability of relapse/metastasis using genetic indicators.

Methods Tumor samples from a cohort of patients surgically staged for EC were collected. Total RNA was obtained, then, gene and microRNA expression arrays were performed. Clinical and pathological data were extracted from patient files. Artificial intelligence using the machine learning protocol was first trained with clinical and genetic data downloaded from public databases. Re-training of the system will be performed with the newly acquired data from the current study.

Results Genetic material has been extracted from 120 of the 150 samples considered optimal. Transcriptomic profiles have been quantified and obtained from 90 of these samples. The patient samples studied were classified into low-risk (groups A and B) and high-risk tumors (remaining), and each of the groups has been subdivided according to surgical treatment (with or without lymphadenectomy). Each subgroup has been classified into reurrence (yes/no), resulting in groups of patients between A-H . With the available transcriptomic data, we can identify potential patients that although classified as low risk, would suffer recurrence. So far, these are preliminary data obtained from a pilot study. Additional samples are currently being analysed to increase the statistical value of the observations.

Conclusions With the transcriptomic data obtained to date, we can identify patients with a higher risk of recurrence despite being classified as low risk.

https://doi.org/10.1136/ijgc-2022-ESGO.1016

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