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Clin Cancer Res. 2015 Sep 15;21(18):4194-200. doi: 10.1158/1078-0432.CCR-14-2910. Epub 2015 Apr 15.

Gene Expression Profiling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival.

Author information

1
Aix Marseille Univ, CRO2, INSERM U911, Marseille, France. APHM, Timone Hospital, Department of Medicine, Division of Adult Oncology, Marseille, France. sebastien.salas@ap-hm.fr.
2
Department of Pathology, INSERM U916, Bergonie Institute, Bordeaux, France.
3
Department of Pathology, Gustave Roussy Institute, Villejuif, France.
4
Department of Pathology, Leon Berard Center, Lyon, France.
5
University Institute of Pathology, Lausanne, Switzerland.
6
Department of Pathology, Institut Curie, Paris, France.
7
Department of Pathology, AlexisVautrin Center, Nancy, France.
8
Department of Pathology, Saint-Louis Hospital, Paris, France.
9
Department of Oncology and Hematology, University Hospital, Strasbourg, France.
10
Department of Surgery, Gustave Roussy Institute, Villejuif, France.
11
Department of Medicine, Leon Berard Center, Lyon, France.
12
Department of Medicine, Gustave Roussy Institute, Villejuif, France.
13
Department of Pathology, INSERM U916, Bergonie Institute, Bordeaux, France. Victor Ségalen University Bordeaux, Bordeaux, France.
14
Department of Pathology, INSERM U916, Bergonie Institute, Bordeaux, France. Translational Research, Bergonie Institute, Bordeaux, France.

Abstract

PURPOSE:

Because desmoid tumors exhibit an unpredictable clinical course, translational research is crucial to identify the predictive factors of progression in addition to the clinical parameters. The main issue is to detect patients who are at a higher risk of progression. The aim of this work was to identify molecular markers that can predict progression-free survival (PFS).

EXPERIMENTAL DESIGN:

Gene-expression screening was conducted on 115 available independent untreated primary desmoid tumors using cDNA microarray. We established a prognostic gene-expression signature composed of 36 genes. To test robustness, we randomly generated 1,000 36-gene signatures and compared their outcome association to our define 36-genes molecular signature and we calculated positive predictive value (PPV) and negative predictive value (NPV).

RESULTS:

Multivariate analysis showed that our molecular signature had a significant impact on PFS while no clinical factor had any prognostic value. Among the 1,000 random signatures generated, 56.7% were significant and none was more significant than our 36-gene molecular signature. PPV and NPV were high (75.58% and 81.82%, respectively). Finally, the top two genes downregulated in no-recurrence were FECH and STOML2 and the top gene upregulated in no-recurrence was TRIP6.

CONCLUSIONS:

By analyzing expression profiles, we have identified a gene-expression signature that is able to predict PFS. This tool may be useful for prospective clinical studies.

PMID:
25878329
DOI:
10.1158/1078-0432.CCR-14-2910
[Indexed for MEDLINE]
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