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01.12.2017 | Research article | Ausgabe 1/2017 Open Access

Breast Cancer Research 1/2017

Detecting gene signature activation in breast cancer in an absolute, single-patient manner

Breast Cancer Research > Ausgabe 1/2017
E. R. Paquet, R. Lesurf, A. Tofigh, V. Dumeaux, M. T. Hallett
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s13058-017-0824-7) contains supplementary material, which is available to authorized users.
This article has been updated to correct an error in the abbreviations.
An erratum to this article is available at http://​dx.​doi.​org/​10.​1186/​s13058-017-0842-5.



The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation).


Using a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes.


We described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR) <5%). Interestingly, 74.5% (1291/1733) of the models are able to distinguish patients with luminal A cancer from those with luminal B cancer (Fisher’s exact test FDR <5%).


AIPS represents the first tool that would allow an individual breast cancer patient to obtain a thorough knowledge of the molecular processes active in their tumor from only one individual gene expression (N-of-1) profile.
Additional file 1: Supplemental methods. (PDF 589 kb)
Additional file 2: Figure S1. Instability of current pathway activation tools in function of grade and Her2. Figure S2. The ROIq method is able to identify samples with either low or high activation. Figure S3. The ROIq method is able to identify samples with either low or high activation. Figure S4. Comparing pathway scores from Gatza et al. to AIPS assignments. Figure S5. Comparing genes selected in AIPS models with genes in the original gene signature. (PDF 782 kb)
Additional file 3: Table S1. Information on the 1733 selected AIPS models. (XLS 3145 kb)
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