Research Papers:
Gene expression information improves reliability of receptor status in breast cancer patients
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Abstract
Michael Kenn1, Karin Schlangen1, Dan Cacsire CastilloTong2, 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, A1090 Vienna, Austria
2Translational Gynecology Group, Department of Obstetrics and Gynecology, Comprehensive Cancer Center, Medical University of Vienna, A1090 Vienna, Austria
3Department of General Gynecology and Gynecologic Oncology, Medical University of Vienna, A1090 Vienna, Austria
Correspondence to:
Wolfgang Schreiner, email: wolfgang.schreiner@meduniwien.ac.at
Keywords: gene expression, breast cancer, receptor status, data science, mathematical oncology
Received: April 12, 2017 Accepted: July 06, 2017 Published: August 24, 2017
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 missclassified. 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 lookupplot’, 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.
INTRODUCTION
The selection of an optimum breast cancer therapy has to include the expression of estrogen receptors (ER), progesterone (PGR) and human epidermal growth factor 2 (HER2) receptor proteins in an individual patient. The reliable assessment of these 3 receptor status is hence mandatory for optimum individualized therapy.
Although immunohistochemistry (IHC) is considered the gold standard for status determination, doubts have been raised [1–3] by reported differences between readers repeatedly examining the very same data. While almost complete concordance was found for ER^{}status, up to 20% of patients assigned ER^{+} status may be erroneously classified, according to reports in the literature [4, 5]. Of note, we also found a considerable number of ER^{} that might be misclassified.
First, correct assignment of ERstatus is most important for the individualized choice of treatment [6]. As gene expression measurements can be achieved by different techniques, we investigate if the assessment of receptor status could be improved by additionally exploiting gene expression data.
Second, gene expression signatures for the prediction of individual therapeutic outcome have been established [7–11] and biomarker discovery methods developed [12–14], as recently reviewed [4, 15]. Each of these signaturealgorithms includes receptor status as decisive variables upon which calculated prognosis crucially depends. Correct prognostic algorithms can thus be developed only on the basis of reliable receptor status [16].
Hence, 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 [17–20]. In the present work we restrict ourselves to a single measurement platform for gene expression (Affymetrix U133A+2.0), in order to avoid interplatform batch effects.
The present work deals with three receptors, ER, PGR and HER2.
RESULTS
For the present work we used and curated the datasets listed in Table 1.
Table 1: Expression datasets used
GEO Accession number  Citation  Number of samples  

N_{sample}  ER  ER+  PGR  PGR+  HER2  HER2+  
GSE5460  [36]  29  11  18  0  0  21  8 
GSE11001  [37]  30  12  18  16  14  23  7 
GSE12777  [38]  51  0  0  0  0  34  17 
GSE16179  [39]  18  0  0  0  0  0  18 
GSE16391  [40]  55  0  55  0  0  42  3 
GSE16446  [41]  120  0  0  0  0  90  28 
GSE18728  [42]  61  29  32  36  25  44  17 
GSE18864  [43]  84  53  31  53  31  64  18 
GSE19615  [44]  115  45  70  51  64  79  36 
GSE20685  [45]  327  0  0  0  0  0  0 
GSE20711  [46]  88  45  42  0  0  62  26 
GSE23177  [47]  116  0  116  0  0  116  0 
GSE26639  [48]  226  88  138  128  95  145  81 
GSE27120  [49]  28  4  24  12  16  26  2 
GSE29431  [50]  54  0  0  0  0  12  41 
GSE31448  [51]  353  162  188  154  167  234  31 
GSE32646  [52]  115  44  71  70  45  0  0 
GSE42568  [53]  104  34  67  0  0  0  0 
GSE43365  [54]  111  18  93  34  77  96  13 
GSE48390  [55]  81  28  53  0  0  47  34 
GSE50948  [56]  156  104  52  121  35  42  114 
GSE58812  [57]  107  107  0  107  0  107  0 
GSE61304  58  25  28  23  22  0  0  
GSE71258  [60]  128  48  51  61  38  77  22 
GSE76124  198  198  0  198  0  198  0  
GSE76274  67  18  49  25  42  28  22  
Total 
 2880  1073  1196  1089  671  1587  538 
GSEnumbers refer to GEO [25, 26], using Affymetrix platform U133A+2.0. Number of samples are given for each study: in total (N_{sample}) and also separately for receptor positive and receptor negative patients according to IHC measurements.
Receptor status obtained from gene expression by conventional methods
Given the IHCestimate of a receptorstatus, e.g. $E{R}_{\text{IHC}}^{}$ or $E{R}_{\text{IHC}}^{+}$, two distributions of expression values (${x}_{i}$, i = 1,…, N_{sample}, number of patients/samples) of the corresponding gene (e.g. ESR1. see Table 2) were estimated, see Figure 1. These plots show results for the estrogen receptor and also illustrate the computational procedure, see section ‘Methods and models for expression of receptor genes’.
Table 2: Probe sets for receptor genes ER, PGR and HER2 for the Affymetrix platform U133A+2.0
Receptor  ER  PGR  HER2 

Probeset ID  205225_at  208305_at  216836_s_at 
HUGO Nomenclature  ESR1  PGR  ERBB2 
ENTREZID  2099  5241  2064 
Figure 1: Distribution of gene expression for estrogenreceptor (ESR1): Data, models and thresholds for Affymetrix platform U133A+2.0. Xaxis: gene expression. Upper panel: Boxplots for expression values of receptor gene ESR1 (Affymetrixprobe set 205225_at), grouped according to IHCestimate ($\text{ER+}\triangleq \text{\hspace{0.17em}}{\text{ER}}_{\text{IHC}}^{+}$, $\text{ER}\triangleq \text{\hspace{0.17em}}{\text{ER}}_{\text{IHC}}^{}$). Lower panel: Yaxis: probability density. Normalized histograms for expression values (blue: ${\text{ER}}_{\text{IHC}}^{}$, yellow: ${\text{ER}}_{\text{IHC}}^{+}$). Estimated probability density functions (PDFs) modelled as sums of two normal distributions. PDF obtained ‘classically’ from means and standard deviations: green curves. Dashed lines: individual distributions for ${\text{ER}}_{\text{IHC}}^{+}$ and ${\text{ER}}_{\text{IHC}}^{}$. Solid: superposition. For methods used see legend. Green dashed curves represent portions for ${\text{ER}}_{\text{IHC}}^{+}$ and ${\text{ER}}_{\text{IHC}}^{}$  patients (integrals < 1), adding up to the solid green curve (integral = 1). Cutpoints (vertical lines) resulting from different computational approaches (see legend and text), discriminating receptor negative (left) and receptor positive (right) patients. Note that cutpoints for MaxLike and ExMax coincide (only ExMax is shown in the figure).
Obtaining receptor status ROC curves
As a prerequisite, we assumed IHCestimates (e.g.: $E{R}_{\text{IHC}}^{+}$, $E{R}_{\text{IHC}}^{}$) to be ‘true’ and computed a ROCcurve (blue curve in Figure 2) by classifying a patient as receptor positive/negative if the expression value, ${x}_{i}$, of gene ESR1 was above/below a running threshold, $\widehat{x}$. The area under the curve resulted as AUC = 0.94.
Figure 2: ROCcurve for estrogenreceptor gene (ESR1). ROCcurve from IHCestimates (e.g.: $E{R}_{\text{IHC}}^{+}$, $E{R}_{\text{IHC}}^{}$) and RMAnormalized expression values of gene ESR1 (Affymetrixprobe set 205225_at) yields AUC = 0.94 for U133A+2.0. Several concepts have been applied (see legend) to obtain thresholds for discrimination between ER^{+} and ER^{}. Sensitivity and specificity of each threshold can be read off the diagram axes.
Cutpoints resulting from models of receptor gene expression
In order to evaluate gene expression we assumed expression values, ${x}_{i}$, to be approximately normally distributed ($N({\mu}^{+},{\sigma}^{+})$ for $E{R}_{\text{IHC}}^{+}$ and $N({\mu}^{},{\sigma}^{})$ for $E{R}_{\text{IHC}}^{}$), see Figure 1, lower panel. Adding both normal distributions with proper weights (${\lambda}^{+}+{\lambda}^{}=1$) yielded a bimodal distribution, see the stacked bars in Figure 1. Likewise, we obtained bimodal distributions for progesterone (gene PGR) and human epidermal growth factor receptor 2 (HER2, gene ERBB2), see Figure 3.
Figure 3: ROCcurves and probability density (distribution) of PGR and HER2receptor genes’ expression (PGR and ERBB2, respectively): Data, models and thresholds. Left panels: PGR. Right panels: HER2. Upper panels: ROCcurves constructed from PGR and HER2values from IHC and RMAnormalized expression values of genes PGR and ERBB2 (Affymetrixprobe sets 208305_at and 216836_s_at, respectively). The areas under the curves (AUC) are 0.90 (for PGR) and 0.91 (for HER2). To obtain thresholds for discrimination (between PGR^{+} vs. PGR^{} and HER2^{+} vs. HER2^{}), the same concepts have been applied as described for the estrogen receptor, see legend and text for details. Mid panels: Box plots for gene expression after RMAnormalization for PGR and HER2, with boxes classified according to IHC (+/). For numbers of samples see Table 1. Lower panels: Normalized histograms of expression values (blue bars). xaxis: gene expression after RMAnormalization over all samples. Yaxis: estimated PDF. PDFs obtained ‘classically’ from means and standard deviations: green curves, see the dashed lines for individual distributions of IHC^{+} and IHC^{}, respectively. PDF obtained via maximum likelihood: red curve.
To obtain thresholds discriminating receptor positive versus negative we evaluated 5 different methods: MaxLike, ParEst, LogReg, Youden and ExMax, for maths see the ‘Methods and models for expression of receptor genes’ in ‘Material and methods’. We decided to finally adopt method ExMax, see section ‘Why to adopt ExMax’ in ‘Material and methods’, and obtained the cutpoints given in Table 3.
Table 3: Distribution parameters and cutpoints for receptors ER, PGR and HER2
Receptor  Method  CP  μ^{−}  μ^{+}  σ^{−}  σ^{+}  λ^{−}  λ^{+}  π^{+} 

ER  MaxLike  9.453  6.515  11.624  1.415  0.947  0.483  0.517  n/a 
 ParEst  9.099  6.835  11.300  1.870  1.580  0.483  0.517  n/a 
 LogReg  9.177  n/a  n/a  n/a  n/a  n/a  n/a  n/a 
 Youden  8.832  n/a  n/a  n/a  n/a  n/a  n/a  n/a 
 ExMax  9.363  6.557  11.472  1.534  1.013  n/a  n/a  0.519 
PGR  MaxLike  4.046  3.587  5.560  0.197  1.483  0.542  0.458  n/a 
 ParEst  4.399  3.647  5.336  0.409  1.555  0.542  0.458  n/a 
 LogReg  4.145  n/a  n/a  n/a  n/a  n/a  n/a  n/a 
 Youden  3.929  n/a  n/a  n/a  n/a  n/a  n/a  n/a 
 ExMax  4.164  3.619  5.722  0.241  1.486  n/a  n/a  0.454 
HER2  MaxLike  11.680  9.481  12.982  0.870  0.585  0.754  0.246  n/a 
 ParEst  11.053  9.396  11.912  0.867  1.484  0.754  0.246  n/a 
 LogReg  11.101  n/a  n/a  n/a  n/a  n/a  n/a  n/a 
 Youden  10.633  n/a  n/a  n/a  n/a  n/a  n/a  n/a 
 ExMax  12.304  9.492  13.209  1.251  0.538  n/a  n/a  0.124 
Cutpoints (CP) as obtained by 5 different methods (MaxLike, ParEst, LogReg, Youden and ExMax), cf. the colored dots in Figure 2 and Figure 3.
Similar results were obtained for PGR and HER2, see the bottom rows in Figure 3. Computationally, we proceeded along the very same lines as for ER. Note that the majority of patients was HER2. As a consequence, thresholds shifted towards larger expression values (as compared to ER) and specificity of discrimination resulted fairly large.
Although HER2+ has much lower prevalence then HER2, the ExMaxestimate performs outstandingly well, as can be seen from Figure 3.
Agreement between receptor status obtained by IHC versus gene expression cutpoints
Using the cutpoints (CP) shown in Table 3, we obtained receptor status from gene expression as compared to IHCestimates, see Table 4.
Table 4: Agreement (contingency tables) between receptor status from IHC versus gene expression
 estrogen ER  progesterone PGR  HER2  

GE–  GE+  GE–  GE+  GE–  GE+  
IHC−  943  130  892  197  1573  14 
IHC+  112  1084  184  487  305  233 
log(DOR) ± 95%  4.25±0.27  2.48±0.23  4.45±0.55  
Cohen’s Kappa  0.79  0.54  0.52  
Kruscal’s Gamma  0.97  0.85  0.98  
Rate of discordant samples  11%  22%  15%  
Rate of conclusive samples  89%  78%  85% 
Cutpoints for gene expression were those derived by the ExMaxalgorithm, see Table 3. Four measures of agreement are given: log(DOR) (natural logarithm of the Diagnostic Odds Ratio, DOR), Cohen’s Kappa (interrater agreement), Kruscal’s Gamma (strength of association) and the rate of discordant samples (conjugate to the rate of conclusive samples).
Association between IHC and gene expression was quantified by Kruscal’s gamma, Cohen’s Kappa (cf. Table 4) and also by the diagnostic odds ratio, DOR, and its 95% confidence intervals [21].
Disagreement between IHC and GE, as quantified by the rate of discordant samples, is highly problematic in the clinical setting, and a clinical decision should not be made on the basis of IHC data only.
PGR showed less agreement than ER due to its lower expression values, entailing a broad distribution of PGR expression values for ${\text{PGR}}_{\text{IHC}}^{+}$. Regarding HER2, the precision of determination suffered from an imbalance in the data: Much more patients are HER2 negative than positive. Note that missing IHCestimates, as considered in the next chapter, were not included here.
In order to improve this situation we enhanced the decision process by furnishing GE with a responsibility function and a critical domain.
Improved decision quality due to responsibility function and critical domain
Instead of using a single cutpoint for each receptor we introduced a ‘critical domain’ $\left[{X}_{\text{lower}},\text{\hspace{0.17em}}{X}_{\text{upper}}\right]$ for a probability level $\alpha =\text{0}\text{.05}$, see Figure 4 and, for mathematical details, the chapter ‘Material and methods’. For expression values $x<{X}_{\text{lower}}$ we decided for receptor negative, for $x>{X}_{upper}$ receptor positive: In both cases we classified the expression value as ‘informative’. As opposed, expression values within the critical domain ${X}_{\text{lower}}\le x\le {X}_{\text{upper}}$ were classified as ‘undecidable’, and we refrained from a decision. Critical domains thus constructed are given in Table 5 and their practical application is described in section, Expression lookup plot‘ in ‘Material and methods’.
Figure 4: Responsibility functions ${r}^{}$ for receptors ER, PGR and HER2. Critical domains, where no decision is possible, are indicated as colored stripes. For PGR, ${f}^{+}$is extremely wide (very large ${\sigma}^{+}$) and causes the unexpected shape of ${r}^{}$. Borders of critical domains result from the intersections of the respective ${r}^{}$ with the 5% and 95% limits (dotted lines).
Table 5: Critical domains of gene expression for receptor status determination
Receptor  Probeset  X_{lower}  X_{upper} 

ER  205225_at  8.517  10.354 
PGR  208305_at  3.664  4.407 
HER2  216836_s_at  11.783  13.070 
The critical domain for expression values x is given by ${X}_{\text{lower}}\le x\le {X}_{\text{upper}}$, based on the ExMaxalgorithm and probe sets listed in Table 2. The section ‘Expression lookup plot’ in ‘Material and methods’ describes how to actually apply the criterion ${X}_{\text{lower}}\le x\le {X}_{\text{upper}}$
The information conveyed by gene expression was thus refined, yielding the results for GE 0, shown in Table 6 and the yellow subbars in Figure 5.
Figure 5: Number of concordant and discordant samples. Left panel: Bars according to IHC receptor estimate. Height of bar represents number of samples with gene expression concordant (green), in critical domain (yellow) and discordant (red). Note that samples without IHC estimate are not included. Right panel: Bars according to GE receptor estimate. Height of bar represents number of samples with IHC concordant (green), missing (yellow) and discordant (red).
Table 6: Number of samples for which IHC and gene expression yield receptor positive/unknown/negative
 gene expression  

ER  PGR  HER2  
GE –  GE 0  GE +  GE –  GE 0  GE +  GE –  GE 0  GE +  
IHC –  887  100  86  590  357  142  1560  20  7 
IHC 0  273  81  257  365  282  473  593  88  74 
IHC +  70  157  969  75  143  453  271  119  148 
Critical GE 
 338 

 782 

 227 

Rate of discordant samples 
 5% 

 8% 

 10% 

Rate of conclusive samples 
 92% 

 79% 

 87% 

IHC 0: no IHC measurement available.
GE 0: expression value within the critical domain, and no decision can be made. Boundaries of critical domains were computed by the ExMaxmethod.
Bold print: discordant samples.
Note that results shown in this table are obtained step by step via the methods explained in ‘Material and Methods’.
A critical domain regarding GE indicates whether or not to trust in GE expression alone. This is especially useful in cases of lacking IHC estimates. For ER in particular, 81 cases revealed as untrustworthy, since missing IHC was accompanied by GE within the critical domain.
Introducing a critical domain is in particular useful in the case of PGR, since GE estimates of PGR offer only limited power of discrimination (leaving as many as 782 samples in column GE 0). But also for ER, 338 samples were revealed untrustworthy (column GE 0).
HER2 estimates revealed a special problem: Many ${\text{HER2}}_{\text{IHC}}^{+}$ samples had low (i.e. contradicting) GE, suggesting they might be false positives from IHC measurement, see the red part of the rightmost bar in left panel of Figure 5.
Therapy allocation check and consequences
In order to scrutinize if the additional information conveyed by GE might change therapeutic decisions based on IHC only, we compared four groups:
1. $\left(E{R}_{\text{IHC}}^{+}\wedge E{R}_{GE}^{+,0}\right)\vee \left(PG{R}_{\text{IHC}}^{+}\wedge PG{R}_{GE}^{+,0}\right)$ (see Figure 6, blue curves, left column, 329 Pat.): Receptor positive patients according to IHC, had received hormone treatment. Since GE confirmed IHC, the choice of systemic therapy deems correct in these cases.
Figure 6: Correct and possibly missled assignment of systemic therapy influences survival time free of symptoms. Kaplan Meier estimate of overall survival and 95% confidence intervals. Blue curves: patients with IHC status in accordance with gene expression (correctly assigned therapy). Violet: patients with IHC status contradicted by GE and possibly having received suboptimal therapy.
2. $\left(E{R}_{\text{IHC}}^{}\wedge PG{R}_{\text{IHC}}^{}\right)\wedge \left(E{R}_{GE}^{0,}\wedge PG{R}_{GE}^{0,}\right)$ (see Figure 6, blue curves, right column, 137 Pat.): Receptor negative patients according to IHC, had not received hormone treatment, accordingly. Since GE confirmed IHC, the choice of systemic therapy deems correct in these cases.
3. $\left(E{R}_{\text{IHC}}^{+}\vee PG{R}_{\text{IHC}}^{+}\right)\wedge \left(E{R}_{GE}^{}\wedge PG{R}_{GE}^{}\right)$ (see Figure 6, violet, upper left, lower right, 18 Pat.): Patients deemed receptor positive according to IHC, had received hormone treatment, accordingly. Since GE contradicted IHC, the choice of systemic therapy might have been incorrect in two possible ways: Those patients who had received neoadjuvant hormone therapy (in addition to adjuvant chemotherapy) would not have benefitted from it (negligible harm). Those patients receiving only hormone therapy without chemotherapy would have been given an ineffective therapy while at the same time being deprived of the adequate, livesaving therapy.
4. $\left(E{R}_{\text{IHC}}^{}\wedge PG{R}_{\text{IHC}}^{}\right)\wedge \left(E{R}_{GE}^{+}\vee PG{R}_{GE}^{+}\right)$ (see Figure 6, violet, upper right, lower left, 9 pat.): Patients deemed receptor negative according to IHC, not having received hormone treatment, accordingly. Since GE contradicted IHC, the choice of systemic therapy might have been suboptimal in these cases.
For each group, we calculated the Kaplan Meier product limit estimator for time free from relapse (see Figure 6) and obtained 95% confidence limits via the Greenwood formula [22].
In group 1 (blue, left column), a patient’s positive hormonereceptor status according to IHC was not contradicted by GE. Accordingly, these patients are likely to have been allocated the ‘correct’ i.e. antihormone treatment and have actually benefitted therefrom.
Patients in group 2 (blue, right column) had been assessed hormone receptor negative via IHC, and this was confirmed by GE: They are likely to have received optimum systemic therapy.
In contrast, patients in group 3 (violet, upper left and lower right) had been assessed hormone receptor positive via IHC, however, this was contradicted by GE: they might have received a hormone therapy but may not have benefitted from it (since they are likely to lack receptors, according to GE). It might even be the case that adjuvant chemotherapy was spared although being mandatory, relying on just hormone therapy (which did not work in these patients).
Patients in group 4 (violet, upper right and lower left) had been assessed hormone receptor negative via IHC, however, this was contradicted by GE: They might have been deprived of a hormone therapy they could have benefitted from.
The above grouping is weakly formulated by intention, in the sense that IHC estimates (used as basis for therapeutic decision) were questioned if and only if GE actually contradicted, not if GE was just within its critical domain ([GE+, GE0] for receptor positive, [GE, GE0] for receptor negative). Tightening the definition of errorprone groups (not tolerating ‘0’ anymore), might even aggravate the contrasts, concomitantly reducing the number of patients in each group, however. We decided to stick to the broader (and weaker) definition so as not to withhold possibly decisive warnings to patients with borderline receptor status.
DISCUSSION
Achievements due to responsibility function and critical domain
For the majority of patients (89%), the IHCassessment of estrogen receptors (ER_{IHC}) and gene expression (ER_{GE}, gene ESR1) data yielded compatible results and we may consider such an estimate true. This percentage was similar to the portion of IHCestimates in the literature suspected to be true [23]. In this work we paved the way to consistently deal with missing as well as contradicting estimates as follows, see Table 6.
In cases of missing IHCestimates one cannot simply trust GE. Instead, in order to strengthen decision performance, we rather have to introduce a critical region for GE. It effectively flags those cases where GE alone deems untrustworthy – 81, 282 and 88 for ER, PGR and HER2, respectively – and thereby provides a ‘quality filter’.
Available IHC estimates which are contradicted by GE when based on simple cutpoints (crisp decision), represent a large number of discordant cases:
(a) For ER, about (11% of patients in our cohort, ER_{IHC} and ER_{GE} disagreed, with about equal numbers (130/112) being found in each of the two possible modes of disagreement see Table 4.
(b) For PGR, discordant cases were similarly balanced (197/184), the rate of discordant samples (22%) being twice that of ER, however.
(c) For HER2 finally, discordant cases were severely imbalanced (14/305), with a rate of 15% of discordant samples, cf. Table 4 and Table 6.
We have to consider, however, that samples with formerly ‘contradicting’ GE (according to a crisp decision based on a simple cutpoint for GE) may switch to noncontradicting, i.e. ‘compliant’, if GE lied just within (and not beyond the limits of) its critical domain.
Thus, considering a critical domain for GE, the rate of discordant samples decreased while the rate of conclusive samples increased, as can be seen by comparing Table 4 and Table 6.
Results for patients on top of those for statistics
If the goal were just to build a cohort of patients for developing a geneexpression signature statistically, one could, of course, retreat to the save side and consider only cases in agreement. However, even this might bias the results and call for an improvement of receptor estimate quality.
However, if the goal are decisions upon therapeutic interventions, these have to be made for all patients and in utmost quality  even in cases of disagreement between IHC and gene expression. We therefore tried to reduce the risk of wrong decisions by improving the cutpointmethod by the additional concept of a ‘critical domain’.
Improving receptor security for a newly diagnosed patient
For a newly diagnosed patient, we proposed a procedure to improve receptor security, see section ‘Expression lookup plot’ in ‘Material and methods’. The ‘critical domain’ denotes nondecidable cases with responsibilities between 0.05 and 0.95.
Note that such a direct procedure is only possible due to the nature of RMAnormalization of expression data, see section ‘Receptor status obtained from gene expression’ in ‘Results’.
This straight forward procedure for the newly diagnosed patient is easily applicable (and renders any renormalization on the fly, etc., unnecessary).
Note however, that the ‘expression lookupplot’ may critically depend on expression chip processing, despite RMAnormalization. We hence recommend building the ‘expression lookupplot’ on the basis of gene expression measurements performed in the very same environment as data from newly diagnosed patients are analysed.
As a consequence and achievement, treatment quality of newly diagnosed patients will benefit, thus conveying a significant step towards precision medicine.
Balancing the risks of erroneous receptor status
We have shown that additional information from GEdata including a critical domain may improve receptor security in a considerable portion of cases, see Table 6.
a) ${\text{ER}}_{\text{IHC}}^{}$
Out of 1073 patients classified as ${\text{ER}}_{\text{IHC}}^{}$ only 92% were not contradicted by gene expression. Without our proposed reassessment, the remaining 8% may receive chemotherapy although hormone therapy might suffice. Thus, applying our proposed method, unnecessarily severe sideeffects could possibly be saved in approximately 8% of that patient cohort.
b) ${\text{ER}}_{\text{IHC}}^{+}$
Out of 1196 patients classified as ${\text{ER}}_{\text{IHC}}^{+}$ only 94% were not contradicted by gene expression. Without our proposed reassessment, the remaining 6% may erroneously receive noneffective endocrine therapy while chemotherapy is withheld, which may be lethal. Thus, identifying these patients, may save lives in approximately 6% of that patient cohort.
c) ${\text{PGR}}_{\text{IHC}}^{}$
Out of 1089 patients classified ${\text{PGR}}_{\text{IHC}}^{}$ only 87% were not contradicted by gene expression. Without our proposed reassessment, the remaining 13% would possibly receive undertreatment (if also ${\text{ER}}_{\text{IHC}}^{}$), since endocrine therapy would be falsely withheld from this population.
d) ${\text{PGR}}_{\text{IHC}}^{+}$
Out of 671 patients classified ${\text{PGR}}_{\text{IHC}}^{+}$ only 89% were not contradicted by gene expression. Without our proposed reassessment, the remaining 11% would be falsely misclassified into endocrine sensitive and would thus be exposed to the side effects of endocrine therapy without a therapeutic benefit.
e) ${\text{HER2}}_{\text{IHC}}^{}$
Out of 1587 patients classified ${\text{HER2}}_{\text{IHC}}^{}$, 99.6% were not contradicted by gene expression. Without our proposed reassessment, the remaining 0.4% would be exposed to Herceptinassociated side effects without deriving a benefit from this expensive and sideeffectrelated therapy.
f) ${\text{HER2}}_{\text{IHC}}^{+}$
Out of 538 patients classified ${\text{HER2}}_{\text{IHC}}^{+}$ only 50% were not contradicted by gene expression. Without our proposed reassessment, the remaining 50% would not be able to receive HER2targeted treatment such as Herceptin, and would be undertreated.
Balancing the consequences of above listed decisions in therapy allocation we adhere to the general consensus that withholding endocrine therapy when it should be delivered is more harmful than applying endocrine therapy when not needed. Hence,
• the therapy allocation error in c) is more serious than it is in d).
• the therapy allocation error in f) is more serious than it is in e).
• the most serious allocation error is listed in b), concerning patients falsely diagnosed as receptor positive.
Characteristics of selected procedures
This section critically enumerates assumptions underlying the results presented here.
Dependence of procedures on IHC  measurements
Expectation maximization (ExMax) does not draw on IHCmeasurements at all, i.e. all 5 parameters of the two normal distributions (${\pi}^{+},{\mu}^{+},{\sigma}^{+},{\mu}^{},{\sigma}^{}$)^{1} are estimated simultaneously from expression data, see Table 3. On the contrary, for methods Youden, ParEst, LogReg and MaxLike, ${\lambda}^{+}$ has to be computed from IHCvalues in advance (i.e. these methods depend on IHCmeasurements).
Table 7: Distribution parameters of gene expression and their 95% confidenceintervals
Receptor  μ−  μ+  σ−  σ+ 

ER  6.557 ± 0.047  11.472 ± 0.031  1.534 ± 0.043  1.013 ± 0.027 
PGR  3.619 ± 0.007  5.722 ± 0.050  0.241 ± 0.006  1.486 ± 0.031 
HER2  9.492 ± 0.047  13.209 ± 0.008  1.251 ± 0.036  0.538 ± 0.008 
Parameters have been obtained by the ExMaxmethod applied to 2880 samples. 95% Confidence intervals have been obtained via expected Fisher information (see ‘Material and methods’, section ‘Obtaining confidence intervals for expression distribution parameters’) and verified by bootstrapping (see chapter 8 in [35]).
^{1}${\lambda}^{+}$ and ${\lambda}^{}$ are normalized to unity, hence only ${\lambda}^{+}$ is free to be adjusted.
Dichotomous IHCestimates
IHC assays initially produce raw values which are normally dichotomized into $E{R}_{\text{IHC}}^{+}$ and $E{R}_{\text{IHC}}^{}$ according to some thresholds. Using more than one threshold yields results such as ‘low’, ‘moderate’, ‘high’, etc.
A straight forward improvement of the current methodology could draw on raw values of IHCassays and file them into the analysis as real values similar to gene expression values. Such an approach could even outperform the one presented here and may be the scope of future investigations.
A drawback of this proposal is, however, that in most cases we only have dichotomized IHCestimates at hand. No large datasets are available on the net to pursue the issue if gradual IHC estimates could improve precision.
Goals achieved and clinical impact
Receptor status estimated via IHC is a key ingredient of precision medicine for breast cancer patients. However, significant doubts have been raised regarding reliability, when comparing disparate receptor estimates of different readers evaluating the same data [5].
On the other hand, receptor estimates from gene expression, which are usually not used in clinical settings, have been given even more credit than IHC estimates [24].
Taking GE in account in addition to IHC clearly boosts credibility of concordant cases but, at first glance, reduced the number of decidable cases, since contradicting cases also emerged. However, in a second step, concordance could be reimproved by considering a critical domain of GE, thereby again reducing the number of contradictory cases.
In this work we scrutinized possible mathematical ways to enrich receptor information from IHC by gene expression data and thus put decisions on therapies on more solid grounds. To these ends we developed a procedure to incorporate both, IHC estimates and gene expression data.
This work used Affymetrix data together with clinical parameters downloaded from GEO in search for an improved receptor status determination. Translated into clinical reality, the expression status of those few genes involved will most probably be determined via RTqPCR. Gene expression for single, newly diagnosed patients can be made interpretable and meaningful by our proposed ‘expressionlookuptable’ and lends itself as a valuable tool for improved receptor status assessment.
MATERIALS AND METHODS
Data for gene expression and receptor status
Scanning the gene expression omnibus, GEO [25–27], for studies on breast cancer, using Affymetrix U133A+2.0 arrays and also containing receptor data resulted in 28 studies, two of which are redundant. The 26 nonredundant studies comprise a total number of N_{sample }= 2880 individual breast cancer samples, after excluding samples completely lacking clinical information, control samples as well as duplicate samples.
Affymetrixprobe sets for receptors are given in Table 2.
Normalization
RMAnormalization was performed on all 54675 probe sets on the Affymetrix U133A+2.0 expression chip using the rmafunction from the Bioconductor’s affy package [28–30].
For the analyses we restricted ourselves to the subset of the 22215 probe sets U133A+2.0 has in common with U133A, in order to be downwards compatible.
Methods and models for expression of receptor genes
To exploit information from the expression of receptor genes we considered five methods: MaxLike, ParEst, LogReg, Youden and ExMax.
Method 1: Youden point
The conventional, most straight forward approach is to compute the Youden point [31] along the ROCcurve by maximizing
$$J=sensitivity+specificity1$$(1)
see the label ‘Youden’ in Figure 1, Figure 2 and Figure 3, lower panels. The expanded formula reads:
$$J=\frac{\text{true}\text{\hspace{0.17em}}\text{positives}}{\text{true}\text{\hspace{0.17em}}\text{positives+false}\text{\hspace{0.17em}}\text{positives}}+\frac{\text{true}\text{\hspace{0.17em}}\text{negatives}}{\text{true}\text{\hspace{0.17em}}\text{negatives+false}\text{\hspace{0.17em}}\text{positives}}$$(2)
Method 2: logistic regression (LogReg)
Another simple approach is logistic regression [32] of receptor status versus expression values, ${x}_{i}$. The cutpoint is set at equal probabilities (0.5, 0.5) for receptor positive and negative, respectively, see the label ‘LogReg’ in Figure 1, Figure 2 and Figure 3, lower panels. Parameters of distributions ($N({\mu}^{+},{\sigma}^{+})$ and $N({\mu}^{},{\sigma}^{})$) are not considered.
Method 3: parameter estimation from bimodal distribution (ParEst)
Normal distributions are evaluated classically by computing mean and standard deviation $\left({\mu}^{+},{\sigma}^{+}\right)$ from ${N}^{+}$ receptor positive patients and $\left({\mu}^{},{\sigma}^{}\right)$ from ${N}^{}$ receptor negative patients, see the dashed green curves in Figure 1, Figure 2 and Figure 3. Weights (${\lambda}^{+}={N}^{+}/({N}^{+}+{N}^{})$, ${\lambda}^{}={N}^{}/({N}^{+}+{N}^{})$) are preset from relative abundances of IHCmeasurements (not fitted). This yields the overall probabilitydensity functions (PDF, solid green line).
$$f\left(x\right)={\lambda}^{+}{f}^{+}\left({\mu}^{+},{\sigma}^{+}x\right)+{\lambda}^{}{f}^{}\left({\mu}^{},{\sigma}^{}x\right)$$(3)
The threshold is set at equal Bayesian probabilities $\mathrm{Pr}\left(+x\right)=\mathrm{Pr}\left(x\right)=0.5$ [33], where the Bayesian probability is given by:
$$\mathrm{Pr}\left(Ax\right)=\frac{{f}^{\left(A\right)}\left(x\right)\cdot {\lambda}^{\left(A\right)}}{{\displaystyle \sum _{A}{f}^{\left(A\right)}\left(x\right)\cdot {\lambda}^{\left(A\right)}}}$$(4)
Here ${f}^{\left(A\right)}$ represents the probability density for receptorstatus $A\in \left\{,+\right\}$ and x is the measured expression value of the respective gene.
Equating $\mathrm{Pr}\left(+x\right)=\mathrm{Pr}\left(x\right)$ yields the following solution for the cutpoint x:
$$\begin{array}{cc}a=\frac{1}{{\left({\sigma}^{}\right)}^{2}}\frac{1}{{\left({\sigma}^{+}\right)}^{2}}& b=2\left(\frac{{\mu}^{}}{{\left({\sigma}^{}\right)}^{2}}\frac{{\mu}^{+}}{{\left({\sigma}^{+}\right)}^{2}}\right)\\ c={\left(\frac{{\mu}^{}}{{\sigma}^{}}\right)}^{2}\frac{{\left({\mu}^{+}\right)}^{2}}{{\left({\sigma}^{+}\right)}^{2}}+\mathrm{log}\left(\frac{{\sigma}^{}}{{\sigma}^{+}}\frac{{\lambda}^{+}}{{\lambda}^{}}\right)& x=\frac{b+\sqrt{{b}^{2}4ac}}{2a}\end{array}$$(5)
Method 4: maximum likelihood estimator of bimodal distribution (MaxLike)
Weights of both normal distributions are preset (not fitted), according to relative abundance ${N}^{+}/{N}^{}$, as in ‘ParEst’. Distribution parameters $\left({\mu}^{+},{\sigma}^{+}\right)$ and $\left({\mu}^{},{\sigma}^{}\right)$ are then computed as the maximum likelihood estimator [34] via a 4 parameter fit (gradient descent method), directly from the bimodal geneexpression histogram. This fit has to act upon a very flat target function, and therefore we implemented the Newtongradient algorithm to draw on analytical derivatives.
$$L\left({\mu}^{+},{\sigma}^{+},{\mu}^{},{\sigma}^{}\overrightarrow{x}\right)={\displaystyle \prod _{j=1}^{{N}_{\text{sample}}}\left({\lambda}^{+}{f}^{+}\left({\mu}^{+},{\sigma}^{+}{x}_{j}\right)+{\lambda}^{}{f}^{}\left({\mu}^{},{\sigma}^{}{x}_{j}\right)\right)}\to \mathrm{max}$$(6)
$$\mathrm{Pr}\left(Ax\right)=\frac{{f}^{\left(A\right)}\left(x\right)\cdot {\lambda}^{\left(A\right)}}{{\displaystyle \sum _{A}{f}^{\left(A\right)}\left(x\right)\cdot {\lambda}^{\left(A\right)}}}$$(7)
Computing the cutpoint is achieved again via eq. (4) and eq. (5).
Method 5: expectation maximization (ExMax)
The ExMaxalgorithm is a general and very potent mathematical concept, see p. 275 in [35], which can fruitfully be broken down and applied to receptor estimation, as follows.
ExMax not only estimates the parameters of both normal distributions, $\left({\mu}^{+},{\sigma}^{+}\right)$ and $\left({\mu}^{},{\sigma}^{}\right)$, but also their relative weight as an additional parameter, ${\pi}^{+}$, yielding $\left({\pi}^{}=1{\pi}^{+}\right)$ due to normalization. Hence, ExMax is able to work without any IHCestimates.
In short the process is as follows: We start from an initial guess for parameters of the two normal distributions ($\left({\widehat{\mu}}_{1},{\widehat{\sigma}}_{1}\right)$, $\left({\widehat{\mu}}_{2},{\widehat{\sigma}}_{2}\right)$ and ${\widehat{\pi}}^{+}={N}^{+}/({N}^{+}+{N}^{})$. It is only this initial guess – not the data used later for the actual estimation – which may be obtained from IHCdata.
Instead of maximizing the likelihood straight away (as in method 4), it is expanded by additional ${N}_{\text{sample}}$ latent parameters
$${\widehat{\gamma}}_{i},\text{\hspace{0.17em}}i=\mathrm{1....}{N}_{\text{sample}}$$(8)
${\widehat{\gamma}}_{i}$ represents a guess (probability) for sample i to be receptorpositive.
a) These guesses are computed (via a special formula) from the guesses for the ‘real’ parameters of the two normal distributions $\left({\widehat{\mu}}_{1},{\widehat{\sigma}}_{1}\right)$, $\left({\widehat{\mu}}_{2},{\widehat{\sigma}}_{2}\right)$ and ${\widehat{\pi}}^{+}={N}^{+}/({N}^{+}+{N}^{})$.
b) From this expanded maximum likelihood function, new estimates for the ‘real’ parameters are obtained via maximization.
Steps a) and b) are repeated in a loop until convergence. Probability density functions based on these fitted parameters are shown as blue lines in Figure 1, Figure 2 and Figure 3.
Cutpoints are obtained via eq. (4) and eq. (5), but λ’s are replaced by calculated π’s.
Note that IHCresults need to be known in advance and individually for each patient to apply methods Youden, LogReg and ParEst (i.e. supervised methods). As opposed, MaxLike draws on the relative abundances ${\lambda}^{+}={N}^{+}/({N}^{+}+{N}^{})$ only (semisupervised method). ExMax does not need IHC data at all (unsupervised method), and hence lends itself for analyzing cohorts totally lacking IHC information.
Why to adopt ExMax
Screening the five methods resulted in the following valuation:
• LogReg draws on IHC estimates which may partly be wrong, possibly blurring the decision.
• Youden yields only a cutpoint without a confidence interval.
• MaxLike and ExMax yield almost identical results. However, according to our tests, MaxLike seems to perform well only if the abundances of false positive and false negative estimates are about equal, which is incidentally the case in our data. As opposed, ExMax works unaffected by relative abundances. Hence, we preferred ExMax over MaxLike.
• ParEst yields larger confidence intervals than ExMax, see section ‘Obtaining confidence intervals for expression distribution parameters’.
• LogReg and Youden do not draw on normal distributions. Finally, each method yields a specific cutpoint for classifying the expression value, ${x}_{i}$, see the marks on the ROCcurve (Figure 2), corresponding to the vertical lines in Figure 1 and Figure 3, lower panels.
All proposed methods were found to yield compatible cutpoints, see Table 3, and ExMax was finally selected. It yields a PDF (${f}^{}$) for receptor negative and a PDF (${f}^{+}$) for receptor positive.
Obtaining confidence intervals for expression distribution parameters
For the methods ParEst, MaxLike and ExMax, outlined to model receptor gene expression, the accuracy of parameters can be estimated by several methods, such as bootstrap, LeaveOneOut (LOO), crossvalidation (CV, via training and validation set) or else by Fisher’s information criterion, FIC, [35]. We will stick to FIC, and it works as follows:
We start from the likelihood function, plug in all parameters (means and standard deviations) as a ‘vector of parameters’, $\overrightarrow{\theta}$, and get:
$$L\left({\mu}^{+},{\sigma}^{+},{\mu}^{},{\sigma}^{}\overrightarrow{x}\right)=L\left(\overrightarrow{\theta}\overrightarrow{x}\right)$$(9)
The socalled Fisher Information matrix is obtained as second partial derivative:
$$I(\overrightarrow{\theta})={\displaystyle \sum _{i=1}^{{N}_{\text{sample}}}\frac{{\partial}^{2}\mathrm{log}L\left(\overrightarrow{\theta}{\overrightarrow{x}}_{i}\right)}{\partial \overrightarrow{\theta}\text{\hspace{0.05em}}\partial {\overrightarrow{\theta}}^{T}}}$$(10)
Next, we would like to evaluate the expectation value of $I(\overrightarrow{\theta})$ by integrating over all measured values:
$$i(\overrightarrow{\theta})={\displaystyle \int I\left(\overrightarrow{\theta}{\overrightarrow{x}}_{i}\right)}\cdot f\left(\overrightarrow{\theta}{\overrightarrow{x}}_{i}\right)d\overrightarrow{x}$$(11)
For a bimodal normal distribution this is not feasible. As an approximation, we instead evaluate $I(\overrightarrow{\theta})$ for the maximum likelihood estimate of the parameters, $\widehat{\overrightarrow{\theta}}$, as arguments: $I\left(\widehat{\overrightarrow{\theta}}\overrightarrow{x}\right)$. It can be shown mathematically [35] that this converges towards the expectation value.
From statistics one knows that the parameter estimates are normally distributed
$$\widehat{\overrightarrow{\theta}}\approx N\left(\widehat{\overrightarrow{\theta}},\text{\hspace{0.17em}}\text{\hspace{0.17em}}i{(\widehat{\overrightarrow{\theta}})}^{1}\right)$$(12)
We take the main diagonal of this matrix and therefrom construct 95% confidence limits through multiplication by 1.96
$${\widehat{\overrightarrow{\theta}}}_{j}\pm 1.96\cdot \sqrt{{\left(i{(\widehat{\overrightarrow{\theta}})}^{1}\right)}_{jj}}$$(13)
For results see Tˆable 7.ˆ
Responsibility functions define lower and upper bounds for gene expression
Each of the four estimation methods (LogReg, ParEst, MaxLike, ExMax) lends itself to construct a ‘responsibility function’, ${r}^{}(x)$, for the respective sample being receptornegative (likewise, ${r}^{+}(x)$ can be constructed for a sample being receptor positive). Each method yields a separate responsibility function, see Figure 7.
Figure 7: Responsibility functions for estrogen receptornegative. Each of the four computational concepts (see legend) yields a separate responsibility function, ${r}^{}$, showing the probability that the sample from a given patient is estrogen receptornegative, based on the gene expression value. A responsibility = 0.5 determines the cutpoint in terms of gene expression. Note that the methods MaxLike and ExMax yield coinciding curves (blue and red, appearing as violet). It becomes quite obvious: the steeper the responsibility function declines, the more accurate the limits $\left[{X}_{\text{lower}},\text{\hspace{0.17em}}{X}_{\text{upper}}\right]$ can be determined. In any case ${r}^{{}_{}}(x)$ is monotonously declining from 1 to 0.
For the methods ParEst, MaxLike and in particular for ExMax, which we finally adopted, responsibility functions are constructed as follows:
Given the two PDFs ${f}^{\text{\hspace{0.05em}}}$ and ${f}^{\text{\hspace{0.05em}}+}$ (see Figure 1 and Figure 3), two ‘responsibility functions’ are constructed
$${r}^{}=\frac{{f}^{}}{{f}^{}+{f}^{+}}$$ and ${r}^{+}=\frac{{f}^{+}}{{f}^{}+{f}^{+}}$(14)
and may be directly expressed in terms of the distribution parameters:
$$\begin{array}{c}{r}^{}(x)=\frac{\left(1{\pi}^{+}\right)\cdot f\left(x{\mu}^{},{\sigma}^{}\right)}{\left(1{\pi}^{+}\right)\cdot f\left(x{\mu}^{},{\sigma}^{}\right)+{\pi}^{+}\cdot f\left(x{\mu}^{+},{\sigma}^{+}\right)}\\ {r}^{+}(x)=\frac{{\pi}^{+}\cdot f\left(x{\mu}_{+},{\sigma}_{+}\right)}{\left(1{\pi}^{+}\right)\cdot f\left(x{\mu}^{},{\sigma}^{}\right)+{\pi}^{+}\cdot f\left(x{\mu}^{+},{\sigma}^{+}\right)}\end{array}$$(15)
The Bayesian concept enters via interpreting ${\pi}^{+}$ as an a priori probability of positive (and $1{\pi}^{+}$ of negative) receptor status for a given patient. In other words, responsibility functions indicate if the positive or the negative mode (${r}^{+}\left(x\right)$ or ${r}^{}\left(x\right)$) is better suited to explain a measured expression value, x. Boundaries of the critical region $\left[{X}_{\text{lower}},\text{\hspace{0.17em}}{X}_{\text{upper}}\right]$ are obtained by putting
$${r}^{}\left({X}_{\text{lower}}\right)={r}^{+}\left({X}_{\text{upper}}\right)=1\alpha $$(16)
The ‘critical domain’ denotes nondecidable cases with responsibilities between 0.05 and 0.95.
Expression lookup plot
As a prearrangement take a gene expression database of a similar patient cohort and some expression platform, e.g. like the one we have used in this work, as a basis. Perform RMAnormalization as described and generate an ‘expression lookupplot’ similar to Figure 8. It serves as a reference in the following procedure for the newly diagnosed patient:
Figure 8: Expression lookupplot: expression versus rank for the estrogen receptor. Probes (N_{probes =} 22.215) are ranked according to expression on the xaxis. Arrows indicate the proposed procedure for the newly diagnosed patient: The rank of a receptor gene is embedded within the ranks of all other genes for the very same sample, projected against the curve and the normalized expression value read off the yaxis. This yields a processed value ready for decision making. Values are given for Affymetrix U133A+2.0. Projections for three different ranks are shown: 18.515 → 8.05 (red), 4.319 → 4.75 (blue), 21.917 → 11.19 (green).
a) Take the raw expression values of all probe sets, order them by size and obtain ranks 1 – 22.215 (This number depends on the platform used, here a subset of Affymetrix U133A+2.0 as mentioned in section ‘Normalization’).
b) Locate the rank r of the receptor probe set (e.g. ESR1) among the others e.g. rank 18.515 (within that sample), see Figure 8.
c) Within the ‘expression lookupplot’ locate the rank of the receptor gene on the rankaxis (xaxis), project it onto the curve and read off the value from the expression axis (yaxis). In math terms, one computes the x = r / N_{probes}  quantile.
d) This value, x, comes already in correct dimensions and may directly be compared with the limits of the ‘critical domain’ $\left[{X}_{\text{lower}},\text{\hspace{0.17em}}{X}_{\text{upper}}\right]$.
Thus the expression lookup plot yields information directly relevant for improving decision quality based on receptor status.
Abbreviations
IHC: Immunohistochemistry; GE: Gene Expression; ER: Estrogen Receptors; PGR: Progesterone; HER2: Human epidermal growth factor 2; U133A+2.0: Affymetrix Human Genome U133A 2.0 Gene Expression Array; U133A: Affymetrix Human Genome U133A Gene Expression Array; GEO: Gene Expression Omnibus; ESR1: HUGO gene name for ER; RMA normalization: Robust Multiarray Average normalization; ROC curve: ReceiverOperatingCharacteristic curve; CP: CutPoint; PDFs: Probability Density Functions; DOR: Diagnostic Odds Ratio; ER_{IHC}: Estrogen Receptor, assessed by IHC; ER_{GE}: Estrogen Receptor, assessed by Gene Expression; ExMax: Expectation Maximization; LogReg: Logistic Regression; ParEst: Parameter Estimation of bimodal distribution; MaxLike: Maximum Likelihood estimator of bimodal distribution; RTqPCR: RealTime quantitative Polymerase Chain Reaction; LOO: LeaveOneOut; CV: CrossValidation; AUC: Area Under the Curve HUGO: HUman Genome Organisation; ERBB2: HUGO gene name for HER2.
Author contributions
MK designed the statistical concept, developed the software and performed the calculations.
KS performed data search, retrieval, download and sample evaluations.
DCCT posed the biomedical question and contributed the discussion on IHC measurement quality.
CFS and HK posed the clinical question and formulated the sections on clinical consequences, risk balancing and medical relevance.
MC performed data manipulation and software programming.
WS organized the study and wrote the manuscript.
ACKNOWLEDGMENTS
The software for the analysis is available on request from the authors.
CONFLICTS OF INTEREST
The authors declare that there are no conflicts of interest regarding the publication of this paper.
FUNDING
No financial support was used.
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