Evaluation and comparison of different breast cancer prognosis scores based on gene expression data
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 suppor...
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | Breast Cancer Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13058-023-01612-9 |
| _version_ | 1811165640207106048 |
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| author | Avirup Chowdhury Paul D. Pharoah Oscar M. Rueda |
| author_facet | Avirup Chowdhury Paul D. Pharoah Oscar M. Rueda |
| author_sort | Avirup Chowdhury |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T15:39:56Z |
| format | Article |
| id | doaj.art-75e9314fd94f4cb1ae2212442090234b |
| institution | Directory Open Access Journal |
| issn | 1465-542X |
| language | English |
| last_indexed | 2024-04-10T15:39:56Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Breast Cancer Research |
| spelling | doaj.art-75e9314fd94f4cb1ae2212442090234b2023-02-12T12:26:27ZengBMCBreast Cancer Research1465-542X2023-02-012511810.1186/s13058-023-01612-9Evaluation and comparison of different breast cancer prognosis scores based on gene expression dataAvirup Chowdhury0Paul D. Pharoah1Oscar M. Rueda2Centre for Cancer Genetic Epidemiology, University of CambridgeCentre for Cancer Genetic Epidemiology, University of CambridgeMRC Biostatistics Unit, University of CambridgeAbstract 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.https://doi.org/10.1186/s13058-023-01612-9PREDICTBreast cancerPrognosisGenomic scoreChemotherapyCalibration |
| spellingShingle | Avirup Chowdhury Paul D. Pharoah Oscar M. Rueda Evaluation and comparison of different breast cancer prognosis scores based on gene expression data Breast Cancer Research PREDICT Breast cancer Prognosis Genomic score Chemotherapy Calibration |
| title | Evaluation and comparison of different breast cancer prognosis scores based on gene expression data |
| title_full | Evaluation and comparison of different breast cancer prognosis scores based on gene expression data |
| title_fullStr | Evaluation and comparison of different breast cancer prognosis scores based on gene expression data |
| title_full_unstemmed | Evaluation and comparison of different breast cancer prognosis scores based on gene expression data |
| title_short | Evaluation and comparison of different breast cancer prognosis scores based on gene expression data |
| title_sort | evaluation and comparison of different breast cancer prognosis scores based on gene expression data |
| topic | PREDICT Breast cancer Prognosis Genomic score Chemotherapy Calibration |
| url | https://doi.org/10.1186/s13058-023-01612-9 |
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