0 1991 Wiley-Liss, Inc.

Cytometry 12:207-220 (1991)

Comparison of Microscopic Imaging Strategies for Evaluating Immunocytochemical (PAP) Steroid Receptor Heterogeneity' R.J. Sklarew,' S.C. Bodmer, and L.P. Pertschuk Cytokinetics & Imaging Laboratory, Department of Medicine, Research Institute, New York Medical College, Elmsford, New York 10523 (R.J.S., S.C.B.); Department of Pathology, State University of New York Health Science Center, Brooklyn, New York 11203 (L.P.P.) Received for publication J u n e 19, 1990; accepted November 6, 1990.

Receptogram analysis was compared with three other imaging strategies for immunocytochemical assay of estrogen receptors. These included nuclear-specific methods for analysis of nuclear integrated optical density (IOD) or mean optical denstiy (MOD) histograms, and field-specific methods, where the pixel optical density (POD) histogram was evaluated for the composite nuclear phase. Measurements in culture and in breast cancer cryosections were treated separately to isolate geometric considerations. In culture receptograms the modality of IOD and MOD histograms and their bivariate contour maps revealed one, two, or more subpopulations with discrete receptor content and concentration. However, when the field of nuclei was imaged as a whole, regardless of the number of subpopulations, POD histograms showed two minima, defining three intranuclear phases. This was due to mottling and variegation of intranuclear chromatin and nucleolar immuno-

staining and not to differences between nuclei. These limitations were also revealed in breast cancer sections. In POD histograms, % unstained pixels did not provide a reliable estimate of % receptor negative nuclei, as determined by their enumeration. In sections, correction of IOD for nuclear volume variability was essential to suppress artifactual peaks not representing differences in receptor content. This was achieved by multiplying nuclear IOD by the spherical nuclear radius (S) of individual slab sections. Peaks of IOD(S) then reflected receptor content on a true ratio scale. Only receptogram analysis, which incorporates these strategies, permitted objective evaluation of receptor heterogeneity at the level of tumor subpopulations. Key terms: Estrogen receptor, progesterone receptor, receptor quantification, breast cancer prognosis, imaging densitometry

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Therapy of breast cancer with anti-estrogens is predicated upon targeting of estrogen-receptor (ER) positive tumor subpopulations, which have retained a level of responsiveness to endogenous steroids. Accordingly, the assay of estrogen receptors (ER) in breast cancer has been important in identifying ER' patients who are more likely to benefit from endocrine therapy. The availability of specific monoclonal antibody has made possible immunocytochemical detection of ER in tumor nuclei using the peroxidase-antiperoxidase method (ERICA) (15,20-22). This has significant advantages over the traditional dextran-coated charcoal assay (DCC) in evaluating endocrine status. Foremost, the microscopic analysis of receptors may be restricted specifically to tumor cells, thus eliminating contamination

of the assay by benign cells, connective tissue, and inflammatory elements. Marked variability of ERICA staining intensity among tumor cells is a frequent feature in breast cancer, and is most striking when unstained and heavily stained tumor nuclei appear in the

'This work was supported by the American Cancer Society grant PDT317A (RJS),by NCI grants CA38807 (R.J.S.), CA23623 (L.P.P.), and by R.J. Sklarew Imaging Assoc., Inc., Larchmont, NY 10538. 'Address reprint requests to R.J. Sklarew, Cytokinetics & Imaging Laboratory, Dept. of Medicine, Research Institute, New York Medical College, 100 Grasslands Road, Elmsford, NY 10523.

SKLAREW ET AL.

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Table 1 Geometric Considerations in Receptor Quantification i n Whole Cell Preparations" Nuclear receDtor conient (IOD) RCP RCP

Nuclear radius (R) 10 21)

Nuclear receptor concentration (MOD) RCPilOOO RCPi8000

ing receptograms among ERICA patients which correlate with endocrine response or failure. In a retrospective study, it has demonstrated significantly higher sensitivity and specificity than DCC assay in identifying stage I11 and IV breast cancer patients who are likely to respond to endocrine therapy (34). While the importance of evaluating receptor heterogeneity has been recognized by various groups, rather diverse imaging strategies have evolved for its characterization. These have utilized either feature-specific logic, where measurements are made on individual nuclei (7,9,11,12,31), or field-specific logic, where a field of nuclei are defined by a frame, with the nuclear phase measured as a composite (1,2). Since the type of information extracted and its interpretation are dependent upon the imaging strategy and measurement set, we have programmed the various approaches for simultaneous implementation. The analyses are critically compared for whole cell preparations a s well as for cryostat sections, since these systems pose distinct geometric problems which merit separate consideration. Stereologic correction of densitometry errors in sectioned Feulgen-stained nuclei have been discussed in detail as the classic "corpuscle" problem (2,19,24,37,38). However, the impact of sectioning errors has been neither corrected nor considered in the context of steroid receptor imaging, with the exception of our studies of immunostaining in breast cancer cryosections (33,34). +

Nuclear proj area 413a(1000) 413d8000)

"The assumptions are as follows: 1) Nuclei are uniformly flattened as a prolate spheroid. The cylindrical model for approximating the nuclear geometry has a thickness (t) arbitrarily set equal to one, radius (R), and receptor content given by (RCP). 2) Nuclei are completely permeabilized so that immunocytochemical staining takes place throughout the nuclear matrix, not merely on the surface of the preparation. Under these conditions relative receptor content is directly measured by IOD. Relative receptor concentration is proportional to MOD, provided that the nuclei are uniformly flattened. Therefore, with constant receptor content, concentration is proportional to l/R3, where R is the spherical nuclear radius. However, if nuclear permeabilization is incomplete, then receptor (IOD) is inversely proportional to (t), receptor MOD is independent of (t),and the DAB reaction is confined to the exposed nuclear surface.

same microscopic field. Accordingly, the variable and unknown proportion of tumor cells, as well a s differences in ER content among co-existent tumor subpopulations, limit the significance of quantitative DCC estimates. Finally, DCC assay detects only unbound receptor, whereas the monoclonal probes bind to epitopes distal to the steroid binding site, so that both free and bound receptor are detected (17). Since visual scoring of ERICA staining is subjective, rigorous quantification by microscopic imaging has been employed to provide a sensitive and reproducible means of characterizing receptor heterogeneity (1,2,313,26,32-36). "Receptogram analysis" has been developed in our laboratory as a method for imaging and classification of immunostaining patterns (33-36). The criticality of the approach lies primarily in distinguish-

MATERIALS AND METHODS Cell Cultures Human endothelial cells were cultured and established from the umbilical veins of full-term newborns by methods previously described (14,161. They were grown on coverslips in 12 well Falcon tissue culture plates in Dulbecco's MEM media containing 20% human serum. Cultures were fixed in 3.7% buffered formalin for 10 min, washed for 5 min with PBS, overlaid with cold methanol for 5 min, and then with cold ace-

Table 2 Geometric Relationships and Nuclear Receptor Quantification i n T h i n Cryosections: Spherical Nuclear Model" Nuclear radius

10 20

Receptor content (whole nucleus)

Nuclear volume

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Nuclear mid-section projected

Nuclear mid-section receptor volume

Nuclear mid-section receptor content

Calculated receptor content

T(100) ~(400)

Tr(lOO)(l) n(400)(1)

1,000i314) 500(314)

10,000 1m o o

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1.51T 93751~

"Definitions: Receptor content of the whole nucleus (RCP) is arbitrarily assigned 1,000 IOD units. R = radius of nucleus. Section thickness (t) = 1.0 (set arbitrarily, for computational purposes. S,radius of section at distance (r)from center of nucleus with radius R; S = (R' - r2)l"$(S = R a t the mid-section). Nuclear volume = 413 nR3. Mid-section area = T R'. Mid-section volume n R2 (t). IOD of mid-section - (RCP) x 7~ R"(t) (413 mR3)-'. Concentration of mid-section = IOD of mid-section: mid-section volume. Principles: 1)Section concentration (MOD) is inversely proportional to nuclear volume and the cube of the nuclear radius. The concentration distribution is dependent upon both the amount of receptors and the nuclear volume distribution. 2) Section IOD is inversely proportional to the mid-sectional radius. Therefore the IOD distribution is volume dependent. 3) Peaks of IODiS) provide estimation of relative receptor content that is independent of the nuclear volume distribution on a true ratio scale.

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Table 3 Geometric Relationships and Nuclear Receptor Quantification in T h i n Cryosections: Elliptical Nuclear Model” AIB 1.000 1.050 1.100 1.150 1.200 1.250 1.300 1.350 1.400 1.450 1.500 1.550 1.600 1.650 1.700 1.750 1.800 1.850 1.900 1.950 2.000

A 1.000 1.033 1.066 1.098 1.129 1.160 1.191 1.221 1.251 1.281 1.310 1.339 1.368 1.396 1.424 1.452 1.480 1.507 1.534 1.561 1.587

S

B 1.000 0.984 0.969 0.954 0.941 0.928 0.916 0.905 0.894 0.884 0.874 0.864 0.855 0.846 0.838 0.830 0.822 0.815 0.807 0.800 0.794

IOD 1.000 1.016 1.032 1.048 1.063 1.077 1.091 1.105 1.119 1.132 1.145 1.157 1.170 1.182 1.193 1.205 1.216 1.228 1.239 1.249 1.260

1.000 1.008 1.016 1.024 1.031 1.038 1.045 1.051 1.058 1.064 1.070 1.076 1.081 1.087 1.092 1.098 1.103 1.108 1.113 1.118 1.122

IOD(S)

IOD(B) 1.000 0.952 0.909 0.870 0.833 0.800 0.769 0.741 0.714 0.690 0.667 0.645 0.625 0.606 0.588 0.571 0.556 0.541 0.526 0.513 0.500

1.000

1.025 1.049 1.072 1.095 1.118 1.140 1.162 1.183 1.204 1.225 1.245 1.265 1.285 1.304 1.323 1.342 1.360 1.378 1.396 1.414

“Definitions: A, major elliptic axis; B, minor elliptic axis; S, nuclear radius of spherical nuclear section; IOD, nuclear integrated optical density; IOD(S), corrected content assuming spherical nuclear model; IOD(B), corrected content for elliptic nuclei sectioned parallel to major axis plane. The values are given for nuclear mid-sections to define the potential range of error in peak assignment.

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FIG.1. Nuclear-specific receptogram analysis in primary endothelial cell cultures (umbilical vein). ER receptograms of whole cell preparations from endothelial cell cultures on days 3, 7, 10 in vitro, and PgR receptogram on day 3 in vitro.

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Fig. 2 .

RECEPTOR IMAGING STRATEGIES

tone for 1 min. They were then placed in a glucose storage solution a t -20°C prior to processing for ERICA and PgRICA.

Immunocytochemical Receptor Staining of Breast Cancer Specimens Biopsy specimens were obtained from 98 stage IIIiIV breast cancer patients prior to therapy. Four micrometer cryostat sections were prepared and stained for estrogen and progesterone receptors using the peroxidase-antiperoxidase technique and monoclonal antibodies D75Spy and KD68, respectively (20,21,34). Background staining over tumor nuclei was evaluated in controls where r a t monoclonal antibody was replaced with non-specific immune rat IgG. This served to define the sensitivity of steroid receptor detection. Imaging Instrumentation and Measurements Receptor staining in cell culture and in cryostat preparations was quantified at x 1,000 magnification using a Qantimet 720 Image Analysis System (Cambridge Instruments, Deerfield, IL) equipped with a Vidicon camera mounted on a Leitz Orthoplan microscope with HBOlOO mercury light source (E. Leitz, Inc, Rockleigh, NJ)) and automated Marzhauser stepping stage (34,35). After shading correction, the densitometer was calibrated for establishing white and black levels. Dynamic range and linearity was adjusted using 2.0 0 . D and 1.0 O.D. filters, respectively, in the incident light path (27-30). Measurements of integrated optical density (IOD), nuclear projected area (A), and perimeter (P) were made on individual nuclei a t 64 optical density thresholds over the range of 0.02 O.D. to 1.26 O.D. Nuclei were selected on the monitor screen by encircling them with the light pen of the Image Editor module. Feature boundaries were further defined a t each threshold using the autodelineation function of the 1-D

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FIG.2. Intranuclear distribution of estrogen receptors in endothelial cell cultures. Cells were selected from a video tape of the imaging sequence in a 14 days subculture. The types were distinguished on the basis of total IOD and the pattern of nucleolar immunostaining. The nucleolar cycle is characterized by the absence of nucleoli immediately after reconstituion of the mitotic nucleus. Five nucleoli, corresponding to the five pairs of nucleolar organizing chromosomes of the human genome, are formed early in G1 and show progressive receptor staining. The nucleoli undergo progressive fusion, resulting in a single large nucleolus. There is a n increase in nuclear receptor content (IOD) during this sequence. Nucleolar number is therefore a concomitant of temporal order within the cell cycle. Type 1: Finely granular and uniform nuclear staining, without nucleolar formation (nuclear IOD = 6,645; nuclear MOD = 3,829, given in arbitrary units). Type 2 Finely granular nuclear matrix with prominent staining of early multiple nucleoli (nuclear IOD = 5,973; nuclear MOD = 3,702, given in arbitrary units). Type 3: Moderately clumped nuclear matrix with intense staining of multiple nucleoli (nuclear IOD = 29,921; nuclear MOD = 6,559, given in arbitrary units). Type 4 Intense, diffuse nuclear staining, and prominent staining of a single large nucleolus (nuclear IOD = 24,440; nuclear MOD = 6,271, given in arbitrary units).

211

Autodetector module. Based upon the optical density gradient a t phase transitions, this independently sets the detection threshold on each scan line a t the midheight of the signal rise. Nuclear mean optical density (MOD) was obtained from IOD/A. The pixel optical density (POD) histogram of individual nuclei was then obtained from the change in nuclear projected area with threshold increment. This reveals the relative number of pixels detected within the incremental ranges of optical density. Nuclei were classified on the basis of chromatin and nucleolar morphology and IOD, a s follows (Fig. 2)): type 1, pale nucleus lacking a nucleolus; type 2 , pale nucleus with multiple, separate nucleoli; type 3, dark immunostained nucleus with finely granular chromatin and fused nucleoli; type 4, nucleus with densely clumped chromatin and intense immunostaining of fused nucleoli (Fig. 2). Composite POD histograms for randomly scored nuclei and for nuclei grouped according to morphologic type were obtained by summing POD histograms of individual nuclei. Such histograms are equivalent to those generated from field measurements made on groups of such nuclei; where the stained nuclear phase first defines a mask for thresholding the immunostained nuclear phase (1).A Fortran 77 program was written for sequential acquisition and processing of measurements requisite to the various imaging strategies and associated graphics.

Receptogram Representation Receptograms provide univariate log distributions of nuclear receptor content and concentration and their bivariate relationships in three-dimensional plots of scatter density, and in corresponding isometric contour plots. IOD and projected area were measured a t a n autodelineated threshold which provides delineation of immunostained nuclei and suppression of paler background cytoplasmic staining. The threshold was determined by superimposition of the detected bright-up display and the video image. In whole cell preparations, IOD corresponds directly to relative receptor content. However, MOD, which is a function of nuclear receptor concentration, is also influenced by the degree of nuclear flattening. By contrast, in thin cryosections, the number and position of nuclear IOD peaks is determined not only by nuclear receptor content but also by the nuclear volume distribution of subpopulations prior to sectioning. For a discrete subpopulation with finite receptor content and nuclear volume, there is a single IOD distribution peaking at the IOD content of the nuclear mid-section. However, for nuclear subpopulations with the same receptor content but different nuclear volumes, the position of IOD peaks is inversely proportional to the nuclear radius of the parent subpopulation, so that doubling of the radius results in halving of the IOD peak position. A correction was introduced which provides estimates of receptor content on a true ratio scale independent of nuclear volume variability. The principles were adapted from the for-

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mulation of Bins and Takens (1985) developed for stereologic correction of Feulgen IOD estimates in thin section. In cryostat sections, distributions of nuclear MOD and IOD(S) were evaluated. The radius of individual nuclear sections (S) was calculated from projected nuclear area, assuming nuclear sphericity. Peaks of IOD(S) then relate to differences in nuclear receptor content on a true ratio scale, independent of nuclear volume variability (33,341. The method is robust in identifying major peaks of receptor content for thin sections uniformly cut a t one to 4 km. The effect of varying nuclear receptor content and volume is summarized for spherical and elliptic nuclei for both whole cell preparations and infinitely thin sections to illustrate the underlying principles (Tables 13 ) .The nuclear mid-section IOD is compared, since this corresponds to the most frequent IOD class for any discrete subpopulation. For any subpopulation of discrete content and volume, the IOD(S) distribution is left skewed to zero due to random sampling of nuclear sections with progressively smaller diameters. The limits of the spherical model for peak identification are defined by the magnitude of errors obtained with increasing ellipticity (Table 3 ) . With major to minor axis ratios of < 1.30 the assignment of peak position is underestimated by no more than 14%. This is well within the range of ellipticity ascertained from the nuclear projected area to perimeter ratio of breast tumor nuclei.

Receptogram Classification Receptograms were classified according to modality of IOD and MOD peaks and bivariate contour slope, as previously described (34). Bivariate receptogram analysis of receptor content and concentration reveals the presence of either well-defined single or multiple subpopulations of receptor positive tumor cells, subpopu-

lations with marked variation in receptor concentration, and subpopulations showing various proportions of negative and positive immunostained tumor nuclei. Representative cases revealing distinct receptogram patterns were selected for illustration. These demonstrated major differences in interpretation when the sections were analyzed on the basis of either univariate nuclear IOD histograms or pixel optical density histograms.

Data Processing and Computer Graphics In receptograms, distributions of IOD vs. MOD (in cultures) or IOD(S) vs. MOD (in cryostat sections) were smoothed using a count-dependent filter (34).Prefiltering provided more precise identification of peaks than simple histogramming in that a much finer binning interval could be employed without introducing gaps in the distribution. A three cycle log scale was used to accommodate the range of values. After smoothing, log histograms were transformed so that the ordinate relates the product of frequency and abscissa value. This corrects halving of peak amplitude with doubling of abscissa position on the log scale, so that peak height reflects the population frequency of components independent of abscissa index ( 3 3 ) . In receptograms, the relationship between receptor concentration and content in individual nuclei was represented by isometric contour and three dimensional plots of bivariate scatter density (33,341. In sections, log nuclear IOD histograms were similarly smoothed and transformed for comparison of modality with IOD(S) histograms given in the receptogram. Pixel optical density histograms were plotted on a n arithmetic axis to replicate the graphics of Bacus et al. (1). RESULTS Quantification of ERICA in Whole Cell Preparations Nuclear feature-specific measurement strategies. Receptogram analysis is based upon separate measurements made on individual nuclei, a so-called feature-specific measurement strategy. In initial lag phase primary cultures (day 31, ER and PgR receptograms revealed well-defined peaks of receptor concentration and content (Fig. 1).After 7 days in vitro, the ER concentration and content distributions had broadened. By 10 days, when the cultures were nearly confluent, two major peaks of receptor concentration emerged, differing by a factor of two. This was due to bimodality of the nuclear volume distribution and to elevated ER content in the higher MOD subpopulation. In confluent 10 day subcultures, the range of nuclear ER concentration and content was broader than that shown by any of the primary cultures. This provided a n opportunity to evaluate receptor properties of individual subpopulations composing the mixed cell population. To accomplish this, a learning ;set was established to characterize cell types within different ranges of receptor IOD. Initially, a video tape of the monitor screen

213

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MOD distributions were also unimodal. The average IOD for nuclear types 1, 2, 3, and 4 was 5,765, 5,075, 33,836, and 26,689, and the average MOD was 3,752, 3,646, 7,013, and 5,931, respectively

and associated measurements was reviewed for selection and classification of prototype nuclei based upon nuclear and nucleolar morphometry and receptor staining characteristics. In man, the nucleolar cycle involves the formation of five pair of nucleoli following mitosis, each corresponding to one of the five nucleolar organizing chromosomes. The nucleoli fuse progressively during the course of G1, forming a single large nucleolus by mid S-phase. Thus, nucleolar number and morphology are useful markers for differentiating classes of cells with respect to cell cycle progression. Four discrete IOD peaks were identified, corresponding to each of the nuclear “types” defined in the learning set (Fig. 2). The ordered types represent sequential stages of the nucleolar cycle. They showed progressive nucleolar fusion and reduction in nucleolar number with increasing receptor content (IOD). The composite receptogram for the learning set showed that the overall mixed cell population was highly mosaic with respect to nuclear receptor content and concentration (Fig. 3). However, when each nuclear “type” was analyzed separately it was morphologically distinct, and uniform with respect to receptor concentration and con-

tent. This was revealed by the receptogam for each type, which showed well-defined, unimodal MOD and IOD distributions (CV -5%) and a spherical contour pattern (Fig. 4). Field-specific measurement strategies. Fieldspecific imaging approaches have been employed in a number of laboratories for defining receptor heterogeneity (1,2). This has involved evaluation of the pixel optical density (POD) distribution of the nuclear phase. When individual prototype nuclei in the learning set were imaged separately, they yielded triphasic POD histograms (Fig. 5). The optical density minima define phase transitions within the immunostained nuclear matrix. The resultant optical density ranges were designated a s phases I, 11, and 111. Phase I defined diffuse nuclear and background staining, whereas phase I1 defined finely clumped nuclear aggregates which impart a mottled staining. Phase I11 defined nucleolar staining, and was absent in the type 1nucleus, which lacked a visible nucleolus. The low level of receptor content was consistent with early G1 residence, and the earliest stage of the nucleolar cycle, immediately following nuclear reconstitution, and prior to the appearance of

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FIG.5. Pixel optical density histograms. POD histograms A-D correspond to the intranuclear receptor distribution patterns for each of the prototype nuclei shown in Figure 2. The minima in the histogram define the transition between discrete phases comprising the nuclear matrix. Histogram region 1, the palest nuclear phase, may represent estrogen receptor complex not bound to chromatin, whereas region 2, a darker phase, with clumped receptor, is thought t o represent chromatin localization Region 3 defines the concentration of receptor within the nucleolus. The occurrence of minima at precise optical density intervals confirms that the nuclei are uniformly flattened. The minima are accentuated by the threshold autodelineation logic.

FIG.6. Pixel optical density histogram of subpopulations by nuclear type. Composite POD histograms for the learning set population is shown for each of the nuclear types. The appearance of well-defined minima with the same location in each of the nuclear types demonstrates that the nuclei are uniformly flattened. The position of minima shifts to lower optical densities with greater flattening. Hence, where flattening is non-uniform, minima are not well defined in nuclear populations due to dampening and drift in the histogram. The progressive shift in receptor localization from region 1 to region 3 occurs as receptor accumulates in the nucleus. The nuclear types comprise the same populations shown in the receptograms of Figure 4.

multiple nucleoli. The optical density distribution shifted to the right when the histograms were ordered numerically by type. This shift corresponds to increasing receptor content (IOD), and reflected redistribution of receptor within the nucleus and nucleolus. Each of the subpopulations composing the various nuclear prototypes was homogeneous with respect to nuclear receptor concentration and content, as revealed by receptogram analysis (Fig. 4).However, the POD histograms compiled for populations of the respective nuclear types were broad and multimodal (Fig. 6). Operationally, the composite POD histograms are equivalent to those obtained from multi-thresholded field measurements of the overall nuclear phase, given populations of uniform nuclear type. Three intranuclear phases were present regardless of nuclear type, a s evidenced by the consistent location of distribution minima. As with the histograms for individual prototype nuclei, the population POD histograms shifted to the right in the transition from nuclear type 1 to type 4. The phase shift was smooth and continuous with increasing IOD, indicating progressive rather than abrubt morphologic differences. This was shown by plotting % nuclear area for each histogram phase a s a function of nuclear IOD in a randomly image culture population. Thus, increase in IOD corresponded to a gradual shift in the distribution of receptor staining from phases I to I1 to I11 (Fig. 7 ) . In a random population analysis, despite the admixture of nuclear types, the POD histogram for field-specific thresholding of the nuclear phase was triphasic (Fig. 81,as found in the POD histograms of individual

nuclei. Since the POD histogram defines the composite nuclear phase a s a whole, differences in intranuclear receptor localization which may distinguish co-existent subpopulations are obscured. Note that discrete unimodal peaks of concentration and content, which characterized each of the component subpopulations in receptograms, were not revealed by this approach.

Quantification of ERICA in Tissue Sections In tissue sections, differences in nuclear staining intensity (MOD) may be due to nuclear volume differences rather than to differences in receptor content. Such volume variability may be resolved by evaluating integrated optical density (IOD) and projected area (A) of nuclear cross sections. This requires high resolution feature-specific imaging of individual nuclei rather than field-specific measurements of the nuclear phase. An important aspect of receptogram analysis in tissue sections is the correction of relative receptor content for variability in nuclear volume. The analysis of receptor concentration (MOD) and content, IOD(S), of individual nuclei, and their bivariate relationship in the receptogram, permits identification of subpopulations which differ in receptor expression. Receptograms are illustrated for four breast cancer patients selected for their diverse ER staining patterns (Figs. 9-12). These cases were selected for comparative analysis using the various imaging approaches. In the receptogram of patient ER82, there were three peaks of receptor content, IOD(S), and a single mode of receptor concentration, MOD (Fig. 9). The IOD distribution also showed three peaks, indicating the presence of three

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centration. Nevertheless, the IOD distribution (Fig. 13) showed 6-8 peaks due to multiple modes of nuclear volume for each IOD(S) peak. Such "sawtooth' IOD distributions cannot be interpreted in terms of the respective contributions of nuclear volume and content variability. Clearly, multiple IOD peaks may not be indicative of differences in nuclear receptor content. The POD histogram for ER91 was left skewed with I 0 respect to that of ER82. However, the same three 20 modes and minima were found. The percentage of unstained nuclear pixels was 29%,while, in fact, the per0 centage of unlabeled nuclei was only 8%, a s shown in 0 5000 10000 15000 20000 25000 30000 35000 40000 the receptogram. Integrated Optical Density In the third example, patient ER26,the receptogram revealed three peaks of receptor content, IOD(S1, and FIG. 7. Percent nuclear area vs. nuclear IOD for intranuclear three peaks of receptor concentration, MOD (Fig. 11). phases. The plots are for 100 nuclei selected at random. Discrete The IOD distribution also showed three peaks, due to phases 1-3 are defined by optical density ranges determined by the relatively constant nuclear volumes a t each content consistent location of minima in the optical density histogram. mode (Fig. 13).However, the POD histogram, once Changes in the receptor phase distribution are continuous with inagain, showed the same distributional minima and creasing IOD. The frequency of pixels in phase 1 decline while those range found in the two previous cases (Fig. 14).Furin phase 2 and 3 increase progressively. thermore, while all the tumor nuclei in ER26 were ERf (Fig. 111,27% of the pixels in the POD histogram were below background and would be erroneously distinct volume modes, one for each ER content mode equated with a subpopulation of ER- cells. Note that (Fig. 13).This proportionality resulted in a well-de- the proportion of negative pixels in POD histograms in fined, unimodal ER concentration distribution. How- ER91 and ER26 were about the same, although ERever, field-specific thresholding of the nuclear phase tumor cells were present only in ER91. The final receptogram, patient ER29,contained a yielded a broad POD histogram (Fig. 14).The discrete minima corresponded to phase transitions within the relatively discrete peak of ER content but a highly nuclear phase. The distribution was multimodal de- skewed MOD distribution (Fig. 12).This was due to spite the uniformity of nuclear ER concentration re- marked variability of the nuclear volume distribution. The POD histogram showed the same basic features vealed by the receptogram. The receptogram of patient ER91 revealed co-exis- present in the three previous cases, even though the tent ERt and ER- subpopulations (Fig. 10).There respective tumor subpopulations differed markedly in were three positive peaks of receptor content and con- receptor distribution a t the level of individual nuclei. Phase 3

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lo Receptor MOD

Recept.01- MOD

FIG. 9. Receptogram of breast cancer cryostat sections-patient ER82. The MOD distribution relating nuclear ER concentration reveals a well-defined ER' unimodal peak. The IOD(S) distribution, relating relative nuclear ER content, shows three major overlapping peaks confined to a fourfold range. Background nuclear MOD is shown by the arrow. FIG. 10. Receptogram of breast cancer cryostat sections-patient ER91. Distribution (Mixed, ER -/ER-), Patient ER91. The MOD distribution reveals subpopulations of co-existent ER' and ER tumor cells. The ER- component composes 8%of the tumor cell population. Relative receptor concentration (MOD) in the predominant ER subpopulation is about 100-fold that of background staining (indicated by the arrow).

FIG. 11. Receptogram of breast cancer cryostat sections-patient ER26. Overlapping MOD distributions reveal three distinct ER' peaks defining a threefold concentration (MOD) range. The nuclear ER content range, IOD(S),is about tenfold. Background nuclear MOD is shown by the arrow.

FIG.12. Receptogram of breast cancer cryostat sections-patient ER29. The univariate distributions and contour map reveal a relatively well-defined peak of receptor content, IOD(S), but broad range of receptor concentration, MOD. Background nuclear MOD is shown by the arrow.

+

DISCUSSION Methods for microscopic imaging of steroid receptors in hormone-responsive cancer have been under intensive development because of the potential importance of receptor quantification in patient prognosis and treatment. The heterogeneity of monoclonal antibody staining in tumor nuclei has been regarded as a n important feature reflecting the physiologic state, functionality, and hormone responsiveness of tumor subpopulations. While the enumeration of positive and negative nuclei and characterization o f staining mosaicism may seem straightforward, receptor imaging in breast cancer has resulted in rather diverse strategies

and endpoints for clinical assay. This is, in part, a consequence of different biologic objectives as well as differences in the application of existing technology. The imaging strategies have embraced either feature-specific logic, where measurements are made on individual nuclei, or field-specific logic, where the nuclear phase is imaged as a whole for a large group of nuclei. Studies of steroid receptor imaging in breast cancer utilizing whole cell preparations are summarized in Table 4.In whole cells, a n important consideration is the degree of permeabilization of the nucleus. This determines whether the immunoperoxidase reaction takes place a t the surface or throughout the nuclear matrix. If the reaction takes place only on the surface,

RECEPTOR IMAGING STRATEGIES

1

.2

.4

1

2

4

10 20 40

.1 . 2

.4

217

1

2

4

10 20 40

IOD FLG.13. IOD distributions in breast cancer cryostat sections. The modality of the IOD distributions is similar to that of the corresponding IOD(S) distributions (Figs. 9-12) for three of the patients (ER82,

ER26, ER29). However, the IOD distribution in patient ER91 shows numerous additional peaks. The artifactual peaks are due to multiple nuclear volumes at each mode of receptor content.

dependent of flattening, and MOD decreases with cell flattening. This assumes that intranuclear receptor distribution does not limit PAP reaction efficiency. Therefore, with effective permeabilization, the estimation of relative receptor content is independent of nuclear geometry and is reliably measured by IOD. Estimation of relative receptor concentration, measured by W Patient E l 7 9 nuclear MOD, is less reliable because it requires uniI1 form cell flattening, which is difficult to verify in clinical material. Accordingly, dual evaluation of receptor concentration and content and their bivariate contour plot in receptograms of whole cells is more informative than single parameter estimation. In breast tumor as0 ' 5 1 0 1 5 80 25 0 ' 5 10 15 20 25 30 pirates and imprints studied by Charpin e t al. (9) differences in the modality of IOD and MOD receptor hisRelative Opt.ical Density tograms may be ascribed to nuclear volume variability FIG.14. Pixel optical density histogram in cryostat sections. A: In and to differences in cell flattening. Such factors also patient Er82, the composite POD histogram of stained nuclear pixels influence measurements of projected area in the biis multiphasic, with minima corresponding to the optical density of variate scatter plots of nuclear IOD and area reported intranuclear phase transitions. In contrast, in the receptogram, the MOD distribution of individual nuclei is discrete and more than ten- by Cohen et al. (10) in cell scrapings from breast tufold above background staining (Fig. 9). While the tumor population mors. is exclusively ER', the POD histogram reveals E R ~pixels within the In the present study, subpopulations with unimodal nuclear phase, with 11%of nuclear pixel area below background opdistributions of nuclear receptor concentration (MOD) tical density. Thus, in the interpretation of POD histograms, the 7c and content (IOD) were defined in a cell culture learnnegative pixels is unrelated to the % of ER- nuclei B: In patient ing set. Despite the homogeneity of each nuclear type ER91, while 8% of the nuclei are ER- (receptogram, Fig. 10) 26% of the pixels composing the nuclear phase are below background optical in receptograms, field measurements of thresholded density in the POD histogram. This is the same percentage of ER nuclear area consistently produced triphasic optical pixels as in patient ER91 with exclusively ER' nuclei. C: In patient density histograms, regardless of nuclear IOD. ReferER26, the range of nuclear MOD for this specimen is above ER background staining, and all of the nuclei are ER+. However, in the POD ence to the learning set demonstrated that minima in histogram, 27% of pixels composing the nuclear phase are below back- the pixel optical density histogram relate to phase ground optical density. D In patient ER29, the essential three phase transitions within the nuclear phase rather than to character of the POD histogram does not distinguish the receptor distinct subpopulations with different mean optical pattern from that of the previous three patient examples. densities. The heterogeneous intranuclear distribution of immunoperoxidase staining was confirmed by the triphasic histograms obtained with thresholded meathen nuclear MOD is independent of cell flattening, surements of single prototype nuclei. These findings while IOD increases with flattening (increased pro- illustrate the principle that field measurements relate jected area). If, on the other hand, the reaction takes to the properties of individual nuclei only when intraplace throughout the matrix, then nuclear IOD is in- nuclear staining is homogeneous within each subpopPatient E l 91

Patient Era2

l6

I

I1

I11

I

I1

111

SKLAREW ET AL.

218

Table 4 Summary of Imaging Strategies for Steroid Receptor Quantification in Breast Cancer

Imaging system

Receptor

Preparation

Quantimet Quantimet SAMBA SAMBA SAMBA

ER PgR ER ER ER ER

Cell cultures Cell Cultures Scrapings Cell cultures Imprints Tumor aspirates

Quantimet

ER

Sklarew 135) Franklin et al. (13) Colley et al. (12) Kommoss et al. (18) Bacus et al. (1,2)

Quantimet MicroTICAS MicroTICAS MicroTICAS CAS System

Charpin et al. (6-9)

SAMBA

Reference Whole cell preparations Sklarew et al. (4)” Sklarew 135) Cohen et al. (10) Charpin et al. (6,7) Charpin et al. (9) Cryostat sections Sklarew et al.

Prognostic endpoint

Evaluation of :staining heterogeneity

IOD, MOD

Receptogram analysis

MODXELI~ MOD“ IOD IOD

IOD vs. area scatter ELI MOD histogram IOD histogram IOD histogram

4km sections

Receptogram typing

IOD (S)distribution, MOD distribution, IOD(S) vs MOD scatter

PgR ER ER PgR ER

4pm sections Cryostat sections Cryostat sections Cyrostat sections Cryostat sections

Receptogram MOD MOD MOD QIC

(as above)

ER

5pm cryosections

IOD

(33,34,35,36)

IOD 5bm cryosections PER Charpin et al. (9) IOD ER skctioni Simonv et al. (26) - SAMBA “Present paper. bELI,estrogen receptor labeling index (the percentage of estrogen receptor positive nuclei). ‘The abcissa of the histogram is labeled staining intensity.

ulation. Consider, for example, the simple analogue of a n equal mixture of “black” (heavily stained) nuclei and “white” nuclei (unstained) versus a population with “black” and “white” checkerboard-stained nuclei. These situations would yield the same two-phase optical density histograms. One might conclude incorrectly that half of the nuclei were “white,” and therefore receptor negative in the checkerboard-stained population. Intranuclear mottling of immunostaining therefore places profound limitations on the interpretation of optical density histograms. Principles governing the use of optical density distributions were first formulated by Prewitt (23) and shown to be useful in classification of individual cells (4,23,25). Analysis of immunostaining in tissue sections poses problems distinct from those relating to whole cells, as is illustrated by studies summarized in Table 4. Charpin et al. (9) in imaging of ER-ICA breast tumor sections, has emphasized differences in the modality of the nuclear IOD histogram among patients. However, the present study has shown that IOD distributions obtained from sections may contain additional peaks due to variability of the nuclear volume distribution. These peaks may be misinterpreted as representing differences in receptor content leading to misclassification of the pattern. In sections, analysis of MOD histograms has been employed for imaging Er and PgR (12,13,18). While the MOD histogram patterns reported by these investigators correspond to the univariate classification proposed by Sklarew and Pertschuk (331, such univariate analysis is incomplete in that

MOD histogram MOD histogram MOD histogram POD histogram c/r immunostained nuclear area IOD histogram ’?c immunostained nuclear area (as above) -

multiple peaks may reflect modality of nuclear volume rather than differences in receptor content (33,341. Such effects may be detected by simultaneous evaluation of IOD(S) distributions. However, this requires high resolution feature-specific imaging of individual nuclei rather than composite imaging of low resolution fields. Receptogram analysis of tissue sections provides evaluation of nuclear receptor content and concentration. The bivariate relationship defines tumor subpopulations and contour patterns which have correlated significantly with endocrine response in DCC ‘ i ERICA ’ patients (34). Inasmuch a s accurate estimation of the fraction of ER- cells is a n important correlative of endocrine response, its evaluation from r/o positive pixels in pixel optical density histograms in the studies of Bacus et al. (1) is subject to error. The present clinical studies comparing receptograms and pixel optical density histograms show that unlabeled nuclear pixels are nearly always present in exclusively E R ’ nuclei. Thus the proportion of negative cells is grossly overestimated by the proportion of total unstained nuclear area in such field measurements. The findings also show that the modality of the optical density histogram does not reflect differences in nuclear receptor concentration among component subpopulations, but rather variegation of intranuclear receptor staining. Finally, in sections, differences in nuclear optical density which are due to nuclear volume variability are not evaluable in such histograms, so that appropriate correction is not feasible. The mean endpoints used to evaluate quantitative

RECEPTOR IMAGING STRATEGIES

receptor imaging have been selected in most instances to provide a single index for correlation with dextran coated charcoal assays (Table 4).Thus, mean IOD and MOD have been used as indices or after correction for the percentage of labeled tumor nuclei or labeled nuclear area. In our view, objective endocrine response in patients should be used as the sole criteria for defining and optimizing a n imaging discriminant. Considering the limitations and predictive value of the DCC assay, and the potential advantages of quantitative receptor immunocytochemistry, the manipulation and selection of imaging parameters to conform to DCC estimates may limit potential improvement in response prediction. The adoption of a mean index obscures the modality of IOD and MOD distributions which together define heterogeneity at the level of single tumor cells. Such heterogeneity, as expressed in the receptor staining pattern, may be a n important determinant of endocrine response. Indeed, receptogram analysis, a pattern-oriented approach developed in our laboratory, has demonstrated improved sensitivity and specificity in endocrine response prediction (34).The present comparative study confirms the criticality of this approach in assessing receptor heterogeneity within tumor subpopulations.

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