Abstract
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.
Highlights
The selection of an optimum breast cancer therapy has to include the expression of estrogen receptors (ER), progesterone (PGR) and human epidermal growth factor2 (HER2) receptor proteins in an individual patient
As gene expression measurements can be achieved by different techniques, we investigate if the assessment of receptor status could be improved by exploiting gene expression data
To obtain thresholds discriminating receptor positive versus negative we evaluated 5 different methods: maximum likelihood estimator of bimodal distribution (MaxLike), parameter estimation from bimodal distribution (ParEst), logistic regression (LogReg), Youden and expectation maximization (ExMax), for maths see the ‘Methods and models for expression of receptor genes’ in ‘Material and methods’
Summary
The selection of an optimum breast cancer therapy has to include the expression of estrogen receptors (ER), progesterone (PGR) and human epidermal growth factor. Gene expression signatures for the prediction of individual therapeutic outcome have been established [7,8,9,10,11] and biomarker discovery methods developed [12,13,14], as recently reviewed [4, 15] Each of these signature-algorithms includes receptor status as decisive variables upon which calculated prognosis crucially depends. Correct prognostic algorithms can be developed only on the basis of reliable receptor status [16] It is of paramount value for both, patient treatment and research, to increase the reliability or even impute receptor status by the use of additional information, e.g., gene expression measurements [17,18,19,20]. HER2-, the ExMax-estimate performs outstandingly well, as can be seen from Figure 3
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