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Predicting high-risk endometrioid carcinomas using proteins.

Di Du¹, Wencai Ma¹, Melinda S Yates²

¹Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

|May 8, 2018

Summary

This summary is machine-generated.

A new biomarker model, Protein Scoring of Endometrioid Endometrial Cancer Staging (PSES), accurately predicts surgical stage in endometrioid endometrial cancer (EEC). This tool aids in identifying high-risk tumors and understanding tumor biology.

Keywords:

RPPA biomarker endometrioid carcinoma protein stage

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Area of Science:

Oncology
Biomarker Discovery
Molecular Diagnostics

Background:

Endometrioid endometrial cancer (EEC) lethality is linked to advanced disease stages.
Accurate prediction of surgical stage at diagnosis is crucial for effective treatment planning.

Purpose of the Study:

To develop and validate a novel biomarker model for predicting the surgical stage of EEC at clinical diagnosis.
To identify proteins and pathways associated with EEC staging.

Main Methods:

Developed a Protein Scoring of EEC Staging (PSES) scheme using reverse-phase protein array data from 210 EEC cases (TCGA).
Validated the PSES model in an independent cohort of 184 EEC cases (MDACC) using ROC analysis.
Assessed the association of PSES with patient outcomes via Kaplan-Meier survival analysis and identified pathways using Ingenuity pathway analysis.

Main Results:

PSES significantly correlated with surgical stage in both TCGA (P < 0.0001) and validation (P = 0.0003) cohorts.
Elevated PSES was observed in advanced-stage tumors, even within lower grade classifications (Grade 1-2).
Positive PSES scores were associated with significantly shorter progression-free survival in both cohorts.

Conclusions:

The PSES model shows potential for clinically predicting high-risk EEC tumors.
PSES offers novel insights into the underlying tumor biology of EEC.