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      Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome

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          Abstract

          Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures—unrelated to cancer—of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R 2 = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.

          Author Summary

          Proving that research findings from in vitro or animal models are relevant to human diseases is a major bottleneck in medical science. Hundreds of researchers have suggested the human relevance of oncogenic mechanisms from the statistical association between gene expression markers of these mechanisms and disease outcome. Such evidence has become easier to obtain recently with the advent of microarray screens and of large public-domain genome-wide expression datasets with patient follow-up. We demonstrated that in breast cancer any set of 100 genes or more selected at random has a 90% chance to be significantly associated with outcome. Thus, investigators are bound to find an association however whimsical their marker is. For example, we could establish outcome associations for a signature of postprandial laughter and a signature of social defeat in mice. Association was not stronger than expected at random for 28 (60%) of 47 published breast cancer signatures. The odds of association are 5–17% with random single gene markers—a finding relevant to older breast cancer studies. We explained these results by showing that much of the breast cancer transcriptome is correlated with proliferation, which integrates most prognostic information in this disease.

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          Most cited references40

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          R: A language and environment for statistical computing

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            Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up.

            Morphological assessment of the degree of differentiation has been shown in numerous studies to provide useful prognostic information in breast cancer, but until recently histological grading has not been accepted as a routine procedure, mainly because of perceived problems with reproducibility and consistency. In the Nottingham/Tenovus Primary Breast Cancer Study the most commonly used method, described by Bloom & Richardson, has been modified in order to make the criteria more objective. The revised technique involves semiquantitative evaluation of three morphological features--the percentage of tubule formation, the degree of nuclear pleomorphism and an accurate mitotic count using a defined field area. A numerical scoring system is used and the overall grade is derived from a summation of individual scores for the three variables: three grades of differentiation are used. Since 1973, over 2200 patients with primary operable breast cancer have been entered into a study of multiple prognostic factors. Histological grade, assessed in 1831 patients, shows a very strong correlation with prognosis; patients with grade I tumours have a significantly better survival than those with grade II and III tumours (P less than 0.0001). These results demonstrate that this method for histological grading provides important prognostic information and, if the grading protocol is followed consistently, reproducible results can be obtained. Histological grade forms part of the multifactorial Nottingham prognostic index, together with tumour size and lymph node stage, which is used to stratify individual patients for appropriate therapy.
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              PCNA, the maestro of the replication fork.

              Inheritance requires genome duplication, reproduction of chromatin and its epigenetic information, mechanisms to ensure genome integrity, and faithful transmission of the information to progeny. Proliferating cell nuclear antigen (PCNA)-a cofactor of DNA polymerases that encircles DNA-orchestrates several of these functions by recruiting crucial players to the replication fork. Remarkably, many factors that are involved in replication-linked processes interact with a particular face of PCNA and through the same interaction domain, indicating that these interactions do not occur simultaneously during replication. Switching of PCNA partners may be triggered by affinity-driven competition, phosphorylation, proteolysis, and modification of PCNA by ubiquitin and SUMO.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                October 2011
                October 2011
                20 October 2011
                : 7
                : 10
                : e1002240
                Affiliations
                [1 ]IRIDIA-CoDE, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
                [2 ]IRIBHM, Université Libre de Bruxelles (U.L.B.), Campus Erasme, Brussels, Belgium
                [3 ]WELBIO, Université Libre de Bruxelles (U.L.B.), Campus Erasme, Brussels, Belgium
                Jefferson Medical College/Thomas Jefferson University, United States of America
                Author notes

                Conceived and designed the experiments: VD. Performed the experiments: DV VD. Analyzed the data: DV VD. Wrote the paper: DV JED VD.

                Article
                PCOMPBIOL-D-11-00571
                10.1371/journal.pcbi.1002240
                3197658
                22028643
                3f4629d3-f0a6-4644-897d-30392549246b
                Venet et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 27 April 2011
                : 7 September 2011
                Page count
                Pages: 8
                Categories
                Research Article
                Biology
                Genomics
                Systems Biology
                Medicine
                Oncology

                Quantitative & Systems biology
                Quantitative & Systems biology

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