Oncotarget

Research Papers:

Which melanoma patient carries a BRAF-mutation? A comparison of predictive models

Thomas Eigentler _, Zeinab Assi, Jessica C. Hassel, Lucie Heinzerling, Hans Starz, Mark Berneburg, Jürgen Bauer and Claus Garbe

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Oncotarget. 2016; 7:36130-36137. https://doi.org/10.18632/oncotarget.9143

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Abstract

Thomas Eigentler1, Zeinab Assi1, Jessica C. Hassel2, Lucie Heinzerling3, Hans Starz4, Mark Berneburg5, Jürgen Bauer1, Claus Garbe1

1Department of Dermatology, Center for Dermato Oncology, University Medical Center Tübingen, Tübingen, Germany

2Department of Dermatology and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany

3Department of Dermatology, University Hospital Erlangen, Erlangen, Germany

4Department of Dermatology and Allergology, Klinikum Augsburg, Augsburg, Germany

5Department of Dermatology, University of Regensburg, Regensburg, Germany

Correspondence to:

Thomas Eigentler, email: [email protected]

Keywords: advanced melanoma, BRAF, predictive models, binary logistic regression, classification and regression trees

Received: November 27, 2015     Accepted: April 16, 2016     Published: May 02, 2016

ABSTRACT

Background: In patients with advanced melanoma the detection of BRAF mutations is considered mandatory before the initiation of an expensive treatment with BRAF/MEK inhibitors. Sometimes it is difficult to perform such an analysis if archival tumor tissue is not available and fresh tissue has to be collected.

Results: 514 of 1170 patients (44%) carried a BRAF mutation. All models revealed age and histological subtype of melanoma as the two major predictive variables. Accuracy ranged from 0.65–0.71, being best in the random forest model. Sensitivity ranged 0.76–0.84, again best in the random forest model. Specificity was low in all models ranging 0.51–0.55.

Methods: We collected the clinical data and mutational status of 1170 patients with advanced melanoma and established three different predictive models (binary logistic regression, classification and regression trees, and random forest) to forecast the BRAF status.

Conclusions: Up to date statistical models are not able to predict BRAF mutations in an acceptable accuracy. The analysis of the mutational status by sequencing or immunohistochemistry must still be considered as standard of care.


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