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Endometrial and Cervical Cancer Treatments / Ferroptosis and Cancer Prognosis · Journal article
npj Precision Oncology · September 5, 2026
Encouraging direction, but not yet definitive.
This retrospective proteogenomic study demonstrates that quantitative proteomic profiling identifies tumor programs independently associated with progression and endometrial cancer-specific death in high-risk disease, beyond conventional clinical and genomic classifiers. A derived proteomic risk score successfully stratified progression risk across training, testing, and independent validation cohorts. While the study shows promise in improving prognostic stratification, prospective validation and evaluation of clinical utility are required before implementation.
Retrospective cohort study with integrated proteogenomic analysis and independent validation. Patients with stage I-III high-risk endometrial carcinoma treated with curative intent with long-term follow-up; archival FFPE primary tumor tissue.. Intervention: Quantitative proteomic profiling and molecular subtyping. Compared with: TCGA molecular subtype classification and clinical-genomic risk modeling. n = 274. Not stated.
Among non-POLE tumors, TCGA molecular subtype and TP53 mutation status did not significantly stratify progression or EC-specific mortality Proteomic profiling identified reproducible tumor programs independently associated with lethal outcomes after adjustment for clinicopathologic and molecular subtypes A proteomic risk score stratified progression risk across training, testing, and independent cohorts (73 independent HR-EC patients)
Among non-POLE tumors, TCGA molecular subtype and TP53 mutation status did not significantly stratify progression or EC-specific mortality
If prospectively validated, proteomic risk stratification could improve prognostic discrimination in high-risk endometrial cancer beyond existing clinical-genomic models and potentially guide treatment intensification or surveillance strategies. Current data support translational investigation but are insufficient for clinical decision-making without prospective validation.
A well-designed retrospective proteogenomic study with independent validation cohort showing that proteomic profiling improves risk stratification beyond clinical-genomic classifiers in high-risk endometrial cancer, but limited by single-center design, archival tissue, and lack of prospective validation or intervention outcome data.
As stated by the source record.
Quoted from the source exactly as published.
If prospectively validated, proteomic risk stratification could improve prognostic discrimination in high-risk endometrial cancer beyond existing clinical-genomic models and potentially guide treatment intensification or surveillance strategies. Current data support translational investigation but are insufficient for clinical decision-making without prospective validation.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Patients with high-risk endometrial carcinoma (HR-EC) experience substantial mortality despite multimodality therapy, molecular classification and contemporary care. We determined whether proteomic tumor programs capture lethal risk beyond established clinical and genomic classifiers and may inform translational investigations. We performed integrated clinical, genomic, and quantitative proteomic profiling of archival FFPE primary tumors from 274 patients with stage I-III HR-EC treated with curative intent and long-term follow-up. Molecular subtyping, clinical-genomic risk modeling, and proteomic analyses evaluated relationships with endometrial cancer (EC)-specific death and progression. A proteomic risk score for progression was developed and validated in 73 independent HR-EC patients. Among patients with non- POLE tumors, TCGA molecular subtype and TP53 mutation status did not significantly stratify progression or EC-specific mortality. Clinical-genomic classification and regression tree analysis identified distinct risk groups with divergent outcomes. Quantitative proteomic profiling identified reproducible tumor programs independently associated with lethal outcomes after adjustment for clinicopathologic and molecular subtypes. Semi-supervised protein clusters classified high vs. low risk of progression in either serous or grade 3 endometrioid carcinoma. A proteomic risk score stratified progression risk across training, testing, and independent cohorts and recapitulated adverse proteomic biology. Proteomic integration improved prognostic risk stratification and warrants further validation and translational investigation in HR-EC.
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