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First generation prognostic gene signatures for breast cancer predict both survival and chemotherapy sensitivity and identify overlapping patient populations

Abstract

The aims of this study were to compare the performance of six different genomic prognostic markers to predict long-term survival and chemotherapy response on the same patient cohort and assess if clinicopathological variables carry independent prognostic and predictive values. We examined seven clinical variables and six previously described prognostic signatures on 228 tumors from patients who received homogeneous preoperative chemotherapy and had long-term follow-up information for survival. We used the area under the receiver operator characteristic curve (AUC) to compare predictors and also performed univariate and multivariate analyses including the genomic and clinical variables and plotted Kaplan-Meir survival curves. All genomic prognostic markers had statistically similar AUCs and sensitivity to predict 5-year progression-free survival (PFS, sensitivities ranged from 0.591 to 0.773, and AUCs: 0.599–0.673), overall survival (OS, sensitivities: 0.590–0.769, AUCs: 0.596–0.684) and pathologic complete response (pCR, sensitivities: 0.596–0.851, AUCs: 0.614–0.805). In multivariate analysis, the genomic markers were not independent from one another; however, estrogen receptor (Odds Ratio [OR] 7.63, P < 0.001) and HER2 status (OR: 0.37, P = 0.021) showed significant independent predictive values for pCR. Nodal status remained an independent prognostic, but not predictive, variable (OR for PFS: 2.77, P = 0.021, OR for OS: 3.62, P = 0.01). There was moderate to good agreement between different prediction results in pair-wise comparisons. First-generation prognostic-gene signatures predict both chemotherapy response and long-term survival. When multiple predictors are applied to the same case discordant risk prediction frequently occurs.

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Acknowledgments

This study was supported by grants from the Breast Cancer Research Foundation (LP, WFS), National Cancer Institute (WFS), and the Faculty Department Funds (WFS).

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Correspondence to Lajos Pusztai.

Additional information

Takayuki Iwamoto and Ju-Seog Lee contributed equally to this study.

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Supplementary material 1 (XLS 24 kb)

Supplementary material 2 (XLS 33 kb)

10549_2011_1706_MOESM3_ESM.ppt

Supplementary Fig. 1. Kaplan–Meier 5-year recurrence-free survival curves by clinical T stage, nodal status and histological grade. (PPT 151 kb)

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Supplementary Fig. 2. Kaplan–Meier 5-year recurrence-free survival curves by the 70- and 76-gene prognostic signatures. (PPT 136 kb)

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Supplementary Fig. 3. Kaplan–Meier 5-year recurrence-free survival curves by the genomic version of the 21-gene recurrence score. (PPT 139 kb)

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Supplementary Fig. 4. Kaplan–Meier 5-year recurrence-free survival curves by Mitotic Kinome Score and by Ki67 expression. (PPT 137 kb)

Supplementary Fig. 5. Kaplan–Meier 5-year recurrence-free survival curves by Intrinsic Molecular Subtypes. (PPT 110 kb)

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Supplementary Fig. 6. Kaplan–Meier 5-year overall survival curves by clinical T stage, nodal status and histological grade. (PPT 153 kb)

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Supplementary Fig. 7. Kaplan–Meier 5-year overall survival curves by the 70- and 76-gene prognostic signatures. (PPT 136 kb)

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Supplementary Fig. 8. Kaplan–Meier 5-year overall survival curves by a genomic version of the 21-gene recurrence score. (PPT 136 kb)

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Supplementary Fig. 9. Kaplan–Meier 5-year overall survival curves by Mitotic Kinome Score and by Ki67 expression. (PPT 137 kb)

Supplementary Fig. 10. Kaplan–Meier 5-year overall survival curves by Intrinsic Molecular Subtypes. (PPT 110 kb)

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Iwamoto, T., Lee, JS., Bianchini, G. et al. First generation prognostic gene signatures for breast cancer predict both survival and chemotherapy sensitivity and identify overlapping patient populations. Breast Cancer Res Treat 130, 155 (2011). https://doi.org/10.1007/s10549-011-1706-9

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  • DOI: https://doi.org/10.1007/s10549-011-1706-9

Keywords

  • Genomic marker
  • Predictive marker
  • Prognostic marker
  • Gene expression profiling
  • Breast cancer