Research Papers:

Gene expression information improves reliability of receptor status in breast cancer patients

Michael Kenn, Karin Schlangen, Dan Cacsire Castillo-Tong, Christian F. Singer, Michael Cibena, Heinz Koelbl and Wolfgang Schreiner _

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Oncotarget. 2017; 8:77341-77359. https://doi.org/10.18632/oncotarget.20474

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Michael Kenn1, Karin Schlangen1, Dan Cacsire Castillo-Tong2, Christian F. Singer2, Michael Cibena1, Heinz Koelbl3 and Wolfgang Schreiner1

1Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, A-1090 Vienna, Austria

2Translational Gynecology Group, Department of Obstetrics and Gynecology, Comprehensive Cancer Center, Medical University of Vienna, A-1090 Vienna, Austria

3Department of General Gynecology and Gynecologic Oncology, Medical University of Vienna, A-1090 Vienna, Austria

Correspondence to:

Wolfgang Schreiner, email: [email protected]

Keywords: gene expression, breast cancer, receptor status, data science, mathematical oncology

Received: April 12, 2017     Accepted: July 06, 2017     Published: August 24, 2017


Immunohistochemical (IHC) determination of receptor status in breast cancer patients is frequently inaccurate. Since it directs the choice of systemic therapy, it is essential to increase its reliability.

We increase the validity of IHC receptor expression by additionally considering gene expression (GE) measurements. Crisp therapeutic decisions are based on IHC estimates, even if they are borderline reliable. We further improve decision quality by a responsibility function, defining a critical domain for gene expression. Refined normalization is devised to file any newly diagnosed patient into existing data bases. Our approach renders receptor estimates more reliable by identifying patients with questionable receptor status. The approach is also more efficient since the rate of conclusive samples is increased. We have curated and evaluated gene expression data, together with clinical information, from 2880 breast cancer patients. Combining IHC with gene expression information yields a method more reliable and also more efficient as compared to common practice up to now.

Several types of possibly suboptimal treatment allocations, based on IHC receptor status alone, are enumerated. A ‘therapy allocation check’ identifies patients possibly miss-classified. Estrogen: false negative 8%, false positive 6%. Progesterone: false negative 14%, false positive 11%. HER2: false negative 2%, false positive 50%. Possible implications are discussed.

We propose an ‘expression look-up-plot’, allowing for a significant potential to improve the quality of precision medicine.

Methods are developed and exemplified here for breast cancer patients, but they may readily be transferred to diagnostic data relevant for therapeutic decisions in other fields of oncology.

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