Evidence map›Paper›PMID 19014521›Full record

ArticleBreast cancer research : BCR2008

A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer.

Fabien Reyal, Martin H van Vliet, Nicola J Armstrong, Hugo M Horlings, Karin E de Visser, Marlen Kok, Andrew E Teschendorff, Stella Mook, Laura van 't Veer, Carlos Caldas and 3 more

Abstract readComparative Study
In one paragraph

Article in Breast cancer research : BCR, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. New tools for old drugs: Functional genetic screens to optimize current chemotherapy.Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy · 2018
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5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Fabien ReyalDepartment of Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
Martin H van Vliet
Nicola J Armstrong
Hugo M Horlings
Karin E de Visser
Marlen Kok
Andrew E Teschendorff
Stella Mook
Laura van 't Veer
Carlos Caldas
Remy J Salmon
Marc J van de Vijver
Lodewyk F A Wessels

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSeveral gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes?

methodsWe performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases.

resultsThe classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set.

conclusionsThe study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.

Indexed as

Cell ProliferationComputational BiologyGene Expression ProfilingBreast NeoplasmsDatabases, GeneticFemaleHumansImmune System PhenomenaPrognosisRNA SplicingSurvival Rate

Identifiers

PMID19014521
PMCPMC2656909

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.