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Translational research biomarkers
1057 Development and validation of a machine-learning-derived RNAseq prognostic signature in endometrial cancer
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  1. G Beinse1,2,
  2. MA Le Frere Belda3,
  3. J Pierre-Alexandre4,5,
  4. N Bekmezian3,5,
  5. M Koual6,
  6. S Garinet7,
  7. K Leroy5,7,
  8. N Delanoy8,
  9. H Blons2,7,
  10. C Gervais5,8,
  11. C Durdux5,9,
  12. C Chapron5,10,
  13. F Goldwasser1,5,
  14. B Terris4,5,
  15. C Badoual3,5,
  16. P Laurent-Puig2,7,
  17. V Taly2,
  18. B Borghese2,10,
  19. AS Bats2,6 and
  20. J Alexandre1,2
  1. 1Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of medical oncology, Hopital Cochin, PARIS, France
  2. 2Centre de Recherche des Cordeliers, « Equipe labélisée Ligue Contre le Cancer », Sorbonne Université, Université de Paris, INSERM, PARIS, France
  3. 3Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of pathology, Hopital Européen Georges Pompidou, Paris, France
  4. 4Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of pathology, Hopital Cochin, Université de Paris, PARIS, France
  5. 5Université de Paris, Paris, France
  6. 6Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of gynecological surgery, Hopital Européen Georges Pompidou, Paris, France
  7. 7Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of Biology, Hopital Européen Georges Pompidou, Paris, France
  8. 8Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of medical oncology, Hopital Européen Georges Pompidou, Paris, France
  9. 9Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of radiotherapy, Hopital Européen Georges Pompidou, Paris, France
  10. 10Institut du Cancer Paris CARPEM, AP-HP, APHP.Centre, Department of gynecological surgery, Hopital Cochin, Paris, France

Abstract

Introduction/Background*Because of inter-tumor heterogeneity of endometrial carcinoma (EC), prognostication remains challenging. We aimed to develop a RNAseq signature to stratify EC patient prognosis beyond molecular subtyping.

Methodology A prognostic signature was identified using a LASSO-penalized Cox regression model on TCGA (N=543 patients). A polyA-RNAseq-based method was developed for validation of the signature in a cohort of stage I-IV EC patients treated in two Paris Hospitals between 2010 and 2017. Model performances were evaluated using time-dependent ROC curves (prediction of disease-specific-survival (DSS)). The additional value of the RNAseq signature was evaluated using uni/multivariable Cox models (hazard ratio (HR) with [95% confidence interval]) and Kaplan-Meier analysis.

Result(s)*Among 209 patients included in the validation cohort (median follow-up 55 months IQR [41-69]), 61 (30%), 10 (5%), 52 (25%), and 82 (40%), had mismatch repair-deficient, POLE-mutated, TP53-mutated tumors, and tumors with no specific molecular profile, respectively. The 38-genes signature accurately predicted DSS (AUC=80%). Using a composite classifier accounting for the RNAseq signature and the TP53-mutated group, three groups were identified: good prognosis tumors based on RNAseq signature and without TP53 mutation, characterized by excellent outcome (N=103 patients, 5-years DSS rates of 99%) (reference), poor prognosis tumors based on RNAseq signature and without TP53 mutation (N=49 patients, 5-years DSS rates of 81%; HR: 5.86 [1.16; 29.7]), and TP53-mutated tumors whatever the RNAseq signature (N=52 patients, 5-years DSS rates of 52%; HR: 11.14 [2.40; 51.7]) (HR adjusted on FIGO stage). In 81 (38%) patients with adverse features (2020 ESGO/ESTRO/ESP guidelines: non-endometrioid histology or stage III-IVA or TP53-mutated tumors), TP53-mutated molecular group was not significantly associated with poor prognosis (p=0.18). A The composite classifier identified three classes within this subgroup: RNAseq-good prognosis (N=24), non-TP53/RNAseq-poor prognosis (N=16), and TP53-mutated tumors (N=41), with 5-years DSS rates of 100%, 59%, and 71%, respectively (p=0.015). Transcriptome analyses suggested the underlying involvement of immune deprivation and wound healing processes in tumors with poor prognosis.

Conclusion*We demonstrate that RNAseq characterization can refine prognostication in EC beyond molecular subgroups and main prognostic features, and warrants validation for potential RNAseq-based adjuvant therapeutic strategies in EC.

https://doi.org/10.1136/ijgc-2021-ESGO.602

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