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Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer – the INNODIAG study

Overview of attention for article published in BMC Medical Genomics, February 2015
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Title
Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer – the INNODIAG study
Published in
BMC Medical Genomics, February 2015
DOI 10.1186/s12920-015-0077-1
Pubmed ID
Authors

Tatiana Kempowsky-Hamon, Carine Valle, Magali Lacroix-Triki, Lyamine Hedjazi, Lidwine Trouilh, Sophie Lamarre, Delphine Labourdette, Laurence Roger, Loubna Mhamdi, Florence Dalenc, Thomas Filleron, Gilles Favre, Jean-Marie François, Marie-Véronique Le Lann, Véronique Anton-Leberre

Abstract

Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts. We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles. To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification. We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned. Interestingly histologic grade 2 tumors (n = 58) were split in these two molecular grade categories. We confirmed the use of fuzzy logic selection as a new tool to identify gene signatures with good reliability and increased classification power. This method based on artificial intelligence algorithms was successfully applied to breast cancers molecular grade classification allowing histologic grade 2 classification into grade 1 and grade 2 like to improve patients prognosis. It opens the way to further development for identification of new biomarker combinations in other applications such as prediction of treatment response.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Australia 1 3%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 24%
Other 5 14%
Student > Bachelor 5 14%
Student > Ph. D. Student 4 11%
Student > Master 3 8%
Other 5 14%
Unknown 6 16%
Readers by discipline Count As %
Medicine and Dentistry 7 19%
Computer Science 6 16%
Agricultural and Biological Sciences 5 14%
Biochemistry, Genetics and Molecular Biology 4 11%
Philosophy 1 3%
Other 5 14%
Unknown 9 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 April 2015.
All research outputs
#16,994,744
of 19,211,930 outputs
Outputs from BMC Medical Genomics
#849
of 1,014 outputs
Outputs of similar age
#201,663
of 244,041 outputs
Outputs of similar age from BMC Medical Genomics
#3
of 3 outputs
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So far Altmetric has tracked 1,014 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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