Molecular characterization of mixed-histology endometrial carcinoma provides prognostic and therapeutic value over morphologic findings

We performed molecular analysis of a single-institution cohort of clinically diagnosed mixed-histology endometrial carcinoma (MEC). A gynecologic pathologist confirmed that 72 cases met diagnostic criteria for MEC based on WHO 2020 guidelines, and these were molecularly classified using both a DNA-based and histologic approach. Tumors were classified as: POLE-mutated (13.9%), microsatellite instability (MSI)-high/mismatch repair deficient (MMRd) (26.4%), TP53/p53 abnormal (p53abnl) (48.6%), no specific molecular profile (NSMP) (11.1%). Recurrence risk significantly differed based upon molecular class, but not histology. 44% of MEC cases had a HER2 IHC score of 2-3+, and this was not limited to p53abnl tumors. Transcriptional analysis demonstrated 93 differentially expressed genes between p53abnl and NSMP tumors, including many associated with the innate immune response and DNA damage repair. While p53abnl and NSMP tumors have similarly poor outcomes, transcriptome analysis revealed biologic differences that could impact targeted therapeutics in this high-risk group.

Authors

Institutions

Publication Details

Journal
npj Precision Oncology
Published
2025-02-08
DOI
https://doi.org/10.1038/s41698-025-00803-1
Citations
8
Primary Topic
Endometrial and Cervical Cancer Treatments
Type
article
Field-Weighted Citation Impact
16.46

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Molecular characterization of mixed-histology endometrial carcinoma provides prognostic and therapeutic value over morphologic findings

Alexis Chassen, Ashley Patton, David E. Cohn, Ashwini Kumar Esnakula et al.
8 citations
npj Precision Oncology
Endometrial and Cervical Cancer Treatments
16.46
article

Molecular characterization of mixed-histology endometrial carcinoma provides prognostic and therapeutic value over morphologic findings

Alexis Chassen, Ashley Patton, David E. Cohn, Ashwini Kumar Esnakula, Paulina Haight, Casey M. Cosgrove, Courtney Riedinger, Jessica L. Gillespie, Adrian A. Suarez
article en
8 citations

Abstract

We performed molecular analysis of a single-institution cohort of clinically diagnosed mixed-histology endometrial carcinoma (MEC). A gynecologic pathologist confirmed that 72 cases met diagnostic criteria for MEC based on WHO 2020 guidelines, and these were molecularly classified using both a DNA-based and histologic approach. Tumors were classified as: POLE-mutated (13.9%), microsatellite instability (MSI)-high/mismatch repair deficient (MMRd) (26.4%), TP53/p53 abnormal (p53abnl) (48.6%), no specific molecular profile (NSMP) (11.1%). Recurrence risk significantly differed based upon molecular class, but not histology. 44% of MEC cases had a HER2 IHC score of 2-3+, and this was not limited to p53abnl tumors. Transcriptional analysis demonstrated 93 differentially expressed genes between p53abnl and NSMP tumors, including many associated with the innate immune response and DNA damage repair. While p53abnl and NSMP tumors have similarly poor outcomes, transcriptome analysis revealed biologic differences that could impact targeted therapeutics in this high-risk group.

npj Precision OncologyVol. 9(1)
The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (US), The Ohio State University Wexner Medical Center (US)
Ohio State University, National Institutes of Health
Openalex Percentile: Top 1%
Endometrial and Cervical Cancer Treatments
16.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.