Evaluation and comparison of different breast cancer prognosis scores based on gene expression data.
Breast Cancer Res
; 25(1): 17, 2023 02 08.
Article
en En
| MEDLINE
| ID: mdl-36755280
ABSTRACT
BACKGROUND:
Breast cancer is one of the three most common cancers worldwide and is the most common malignancy in women. Treatment approaches for breast cancer are diverse and varied. Clinicians must balance risks and benefits when deciding treatments, and models have been developed to support this decision-making. Genomic risk scores (GRSs) may offer greater clinical value than standard clinicopathological models, but there is limited evidence as to whether these models perform better than the current clinical standard of care.METHODS:
PREDICT and GRSs were adapted using data from the original papers. Univariable Cox proportional hazards models were produced with breast cancer-specific survival (BCSS) as the outcome. Independent predictors of BCSS were used to build multivariable models with PREDICT. Signatures which provided independent prognostic information in multivariable models were incorporated into the PREDICT algorithm and assessed for calibration, discrimination and reclassification.RESULTS:
EndoPredict, MammaPrint and Prosigna demonstrated prognostic power independent of PREDICT in multivariable models for ER-positive patients; no score predicted BCSS in ER-negative patients. Incorporating these models into PREDICT had only a modest impact upon calibration (with absolute improvements of 0.2-0.8%), discrimination (with no statistically significant c-index improvements) and reclassification (with 4-10% of patients being reclassified).CONCLUSION:
Addition of GRSs to PREDICT had limited impact on model fit or treatment received. This analysis does not support widespread adoption of current GRSs based on our implementations of commercial products.Palabras clave
Texto completo:
1
Colección:
01-internacional
Asunto principal:
Neoplasias de la Mama
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Female
/
Humans
Idioma:
En
Revista:
Breast Cancer Res
Asunto de la revista:
NEOPLASIAS
Año:
2023
Tipo del documento:
Article
País de afiliación:
Reino Unido