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Consensus genes of the literature to predict breast cancer recurrence

  • Preclinical Study
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Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Background:

Extensive efforts have been undertaken to discover genes relevant for breast cancer prognosis. Yet, in current opinion, with little overlap in findings. We aimed to reanalyze molecular prediction of breast cancer recurrence.

Methods:

From 44 published gene lists relevant for breast cancer prognosis, we extracted 374 genes, which, besides other quality criteria, are recorded at least twice. From eight published microarray datasets, a single dataset of 1,067 breast cancer patients was created, using transformation to ‘probability of expression’ scale. For recurrence analysis, the Cox proportional hazards model was applied.

Results:

The 374 genes, termed ‘374 Gene Set’, are highly enriched in cell cycle genes. The ‘374 Gene Set’ is significantly associated with breast cancer recurrence (p = 2 × 10−12, log-rank test) in the meta set of 1,067 patients, showing an estimated Hazard Ratio of recurrence for the ‘poor’ prognosis group compared to the ‘good’ prognosis group of 2.03 (95% confidence interval, 1.66–2.48). Notably, the ‘374 Gene Set’ is significantly associated with recurrence in untreated patients. In multivariate analysis, including the standard histopathological parameters, only tumor size and the ‘374 Gene Set’ remain independent predictors of recurrence. External validation further confirmed the prognostic relevance of the gene set (253 patients, p = 0.001, log-rank test).

Conclusions:

The ‘374 Gene Set’ comprises a molecular basis of metastatic breast cancer progression. Starting from this gene set it might be possible to construct a clinically relevant classifier, which then again needs to be validated.

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Acknowledgments

We thank Prof. Fritz Leisch and Prof. Fritz Pittner for constructive advice. This work was supported by FFG, project no. 809596. The sponsor had no role in any part of the study.

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Authors and Affiliations

  1. Austrian Research Centers GmbH – ARC, Molecular Diagnostics, Mendelstrasse 1, 2444, Seibersdorf, Austria

    Martin Lauss, Albert Kriegner, Klemens Vierlinger, Ilhami Visne, Ahmet Yildiz, Erkan Dilaveroglu & Christa Noehammer

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  1. Martin Lauss
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  2. Albert Kriegner
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  3. Klemens Vierlinger
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  4. Ilhami Visne
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Correspondence to Martin Lauss.

Additional information

Martin Lauss, Albert Kriegner, Klemens Vierlinger, and Christa Noehammer are named inventors on a related patent application

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ESM 1(DOC 28 kb)

Supplementary Table 1

Summary of the 44 gene lists used in this study (DOC 75 kb)

Supplementary Table 2

All 4475 gene identifiers linked to their corresponding input gene list, input identifiers and output annotation to UniGene Cluster (XLS 1098 kb)

Supplementary Table 3

Genes of the ‘374 Gene Set’ and centroids for the ‘good’ and ‘poor’ prognosis-groups (XLS 104 kb)

Supplementary Table 4

Patient data and grouping of the metaset and external validation set (XLS 362 kb)

Supplementary Figure 1

DAG diagram of the GO terms enriched in the ‘374 Gene Set’ when compared to the human genome (GIF 294 kb)

Supplementary Figure 2

KEGG pathway chart ‘cell cycle’. Genes contained in ‘374 Gene Set’ are red (GIF 87 kb)

Supplementary Figure 3

Kaplan Meier plots of recurrence-free survival (RFS) using 1067 patients that were grouped by the reduced independent gene set. Thirty-three genes remained for grouping the patients and subsequent survival analysis. Numbers at risk are given at last time of event before the fixed timepoints (0,50,100,150,200) (EMF 321 kb)

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Lauss, M., Kriegner, A., Vierlinger, K. et al. Consensus genes of the literature to predict breast cancer recurrence. Breast Cancer Res Treat 110, 235–244 (2008). https://doi.org/10.1007/s10549-007-9716-3

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