Abstract 6416: Early stage endometrioid endometrial cancer recurrence risk stratification using a machine learning RNA-seq gene expression signature
Authors: Rafi Pelossof, Talal Ahmed, Seung Won Hyun, Chithra Sangali, Ben Terdich, Calvin Chao, Michael Toboni, Casey Cosgrove, Elizabeth Kidd, Brook Howitt, Timothy Taxter
Journal: Cancer Research (2024)

Abstract Endometrioid endometrial cancer (EEC) adjuvant therapy decisions rely on risk stratification using histology, grade, stage, and lymphovascular space invasion (LVSI). Recently, molecular classification systems originating from TCGA, evaluated in GOG-210 and PORTEC-3 defined four prognostic subtypes based on POLE, MSI-H/MMR-D, and p53 alterations. Although valuable, this molecular approach still has significant limitations such as applicability to the majority of EEC patients categorized as no-specific molecular profile (NSMP) and the potential need to resolve pathogenic and prognostic heterogeneity within MMR-D, and TP53 subtypes. These unmet needs were key motivators for Tempus to develop a molecular classifier predicting distant recurrence risk in early-stage EEC with a focus on high intermediate-risk (HIR) patients. The RNA-seq-based gene expression profiler (GEP) was trained using a machine-learning pipeline with TCGA data which resulted in a 24 gene signature that classifies EEC patients as molecular risk (MR) high or low (MR-high, MR-low). The GEP MR test was then evaluated on a de-identified cohort of EEC patients (N=1037) from Tempus to test associations with known pathologic or molecular prognostic features. The GEP-MR risk predictor showed significant enrichment of MR high-risk in G3 versus G1/2 histology (p-value &amp;lt; 5e-8). A high correlation was found between the MR score and copy number alteration score (t-test p-value &amp;lt; 1e-5). Next, a clinical evaluation was performed in an early-stage EEC case-control cohort of patients with documented recurrence or no recurrence event at four years (N=109), from Stanford. In the entire cohort, the MR-high group had a significantly higher rate of distant recurrence in comparison to the MR-low group (HR=4.8, N=109). Next, we performed a subgroup analysis in the clinically important HIR patients. In this subgroup, the MR-high group had a significantly higher rate of distant recurrence in comparison to the MR-low group (HR=8.0, N=56). Lastly, given the significance of genomic biomarkers in the evolution of EEC FIGO staging, we stratified outcomes by the established TCGA subtypes as a reference standard and performed a subgroup analysis in patients classified as having no specific molecular profile (NSMP). Among patients who were NSMP, the MR-high group showed a significantly higher rate of distant recurrence in comparison to the MR-low group (HR=7.92, N=67). These evaluation studies demonstrate the performance of the GEP MR test in early-stage EEC distant recurrence risk stratification, specifically HIR patients, necessitating further studies to validate the test for clinical use in informing adjuvant clinical management. Citation Format: Rafi Pelossof, Talal Ahmed, Seung Won Hyun, Chithra Sangali, Ben Terdich, Calvin Chao, Michael Toboni, Casey Cosgrove, Elizabeth Kidd, Brook Howitt, Timothy Taxter. Early stage endometrioid endometrial cancer recurrence risk stratification using a machine learning RNA-seq gene expression signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6416.