The Evaluation of Estrogen Receptor in Primary Breast Carcinoma by Computer-Assisted Image Analysis

Transcription

1 The Evaluation of Estrogen Receptor in Primary Breast Carcinoma by Computer-Assisted Image Analysis SARA BACUS, PH.D., JULIE L. FLOWERS, B.S., MICHAEL F. PRESS, PH.D., JAMES W. BACUS, PH.D., AND KENNETH S. MCCARTY, JR., M.D., PH.D. A monoclonal antibody prepared against estrogen receptor has been shown to be specific and sensitive for the detection of estrogen receptor in human breast lesions by use of immunohistochemical methods. Two hundred selected cases of primary breast carcinoma were assayed for estrogen receptor content by biochemical and immunohistochemical procedures. Quantitative evaluation was by biochemical, immunohistochemical, and automated computer-assisted image analysis using the Cell Analysis System's CAS/100 machine (Lombard, IL), Quantitative estrogen receptor content was determined by dextran-coated charcoal analysis and sucrose density gradient analysis. Immunohistochemical evaluation incorporated both intensity and distribution of staining, yielding a subjective score, histologic score (HSCORE). An objective quantitation, also incorporating intensity and distribution of staining, was done by computer-assisted image analysis, quantitative immunocytochemical score (QIC SCORE). HSCORE analysis was done with and without methyl green counterstain with no loss of sensitivity. Comparison of QIC SCORE with the biochemical and immunohistochemical analysis of the tissues examined revealed excellent sensitivities and specificities. These data suggest that automated image analysis provides an effective qualitiative and quantitative means of evaluating estrogen receptor content in human breast cancers. (Key words: Computer-assisted image analysis; Immunohistochemistry; Estrogen receptor) Am J Clin Pathol 1988;90: IMMUNOHISTOCHEMICAL TECHNIQUES have allowed the direct visualization of specific antigens in tissue sections. The interpretation of these techniques has involved subjective, semiquantitative observations. 7,910 The development of instruments to computerize image analysis has the potential to quantitate immunohistochemically localized antigens. The availability of highly specific monoclonal antibodies to human estrogen receptor has resulted in an effective means of qualitative receptor analysis in human breast tumors. The specific aim of this project is to determine whether computer-assisted image analysis is an effective method of observing and quantitating estrogen receptor protein content in primary breast tumors. Received May 27, 1987; received revised manuscript and accepted for publication March 9, Supported by Small Business Innovation Grant No , NIH CA 38989, ROl CA 39635, and ROl CA Address reprint requests to Dr. McCarty: Box 3711, Duke University Medical Center, Durham, North Carolina Cell Analysis Systems, Lombard, and University of Chicago, Chicago, Illinois, and Duke University Medical Center, Durham, North Carolina Patient Population Materials and Methods Two hundred cases of primary breast carcinoma accessioned from from the Duke University Endocrine-Oncology Laboratory were selected for study. These cases represented primary breast cancer specimens with sufficient cancerous tissue for complete biochemical and immunocytochemical analysis. Biochemical Receptor Analysis For each case, a biochemical estrogen receptor analysis was performed at the time the tissue was first obtained by methods described previously. 1,6 These consisted of multiconcentration titration analysis (dextrancoated charcoal analysis ([DCCA]) and/or sucrose density gradient analysis (SDGA), of estrogen binding. The DCCA result was used for quantitative comparison in all cases in which sufficient tissue-extracted protein permitted both analyses, and the SDGA value was used when cytosol protein was limited. Immunohistochemical Procedures Cryostat sections of fresh-frozen tissue were fixed in 3.7% formaldehde-0.1 mol/l phosphate-buffered saline, for 10 minutes, followed by immersion in cold 100% methanol for 4 minutes and acetone for 1 minute. Monoclonal antibody H222 (ER-ICA Kit, Abbott Laboratories, Chicago, IL), prepared by Dr. Geoffrey Greene and modified by Dr. Larry Miller, against human estrogen receptor protein was used. 3 " 5,8 The peroxidase-antiperoxidase (PAP) method for immunocytochemical localization was performed as described by Sternberger.'' The blocking reagent was normal goat serum. The primary antibody (H222), which is a rat monoclonal antibody against human estrogen receptor, was used at a minimum concentration of 0.1 /tg/ml. The bridging antibody was goat antirat immu- 233

2 234 BACUS ET AL. A.J.C.P.-September Wavelength of Light (nm) 700 FIG. 1. Spectra from the cell nuclei stained with the chromagen-diaminobenzidine (X) and methyl green ( ). Both absorb similarly at approximately 650 nm and exhibit different absorption at approximately 500 nm. The DAB absorbs and methyl green transmits light. noglobulin, and the PAP complex was of rat origin. Control slides consisted of an adjacent tumor section to that stained with the primary monoclonal antibody in which the monoclonal antibody was replaced by normal rat immunoglobulin. The peroxidase localization was developed with diaminobendizidine-h The slides were rinsed in running tap water for 5 minutes, dehydrated in serial alcohols to xylene, and coverslipped with Permount without counterstaining. Evaluation of the Effect of Methyl Green Counterstain The ER-ICA-stained studies were first evaluated by use of a histologic score (HSCORE) technique (see "Quantitation of Assay Results") with no counterstain, then reevaluated and scored after methyl green counterstain with the examiner having no knowledge of the previous HSCORE. For counterstaining, the coverslips were removed by soaking the slides in toluene overnight. They were then placed in sodium acetate buffer, rehydrated through an acetone series, counterstained with methyl green, rinsed with sodium acetate buffer, dehydrated with an acetone series to xylene, and coverslipped with Permount. Computer-Assisted Image Analysis Computer-assisted image analysis was performed with the Cell Analysis System's 100 machine (CAS/100). It is configured with a standard microscope and x, y sensors to record and relocate areas on slides; and it employs high-speed digital image processing for cell and tissue measurements. The system is microcomputer based, with modular image analysis software packages for different analysis applications. For ER applications, dual-staining, two-color, "nuclear mask" imaging techniques are used. In our study, cases previously scored (HSCORE) without counterstaining were counterstained with methyl green for computer-assisted image analysis. Methyl green was selected as the counterstain because spectral studies have shown that methyl green offers the best spectral separation from diaminobenzidine, allowing the image to be digitized at two separate wavelengths, (Fig. 1). Spectral bandpass filters of 650 nm (red) and 500 nm (green) were used. The image-processing approach consists of first forming a mask of the methyl green-stained nuclei in each scene under consideration, with the red filter in the image path. This mask is stored, and an image for estrogen receptor quantitation is then acquired with the use of greenfilterwith the same image in place. The analysis is then performed by use of only those parts of the image covered by the "nuclear mask." The control slide was examined at 10X for an overall view of the tumor and counterstain intensity. An area that was representative of the tumor with no tissue folding and minimal background staining was chosen. The control antibody threshold, defined as the proportional absorbance with the red filter accounting for the methyl green counterstain and nonspecific chromagen localization, was then determined at 40X. The primary section for estrogen receptor was then evaluated. An evaluation for the acceptibility and uniformity of staining were accounted for at 10X. At 40X one cannot adequately evaluate normal and benign versus malignant components. Normal and benign components were not scored; only the malignant components of the tissue were scored. Five random fields at 40X were then evaluated for chromagen-diaminobenzidine (DAB) intensity. If the staining intensity was focally positive and negative, and heterogenous staining existed elsewhere in the section, a field representing the positive and negative site as well as the heterogenous area was obtained and averaged. In calculating the nuclear threshold for generation of the "nuclear mask" of thefield,the value taken was that in which the tumor cells were best separated from the background by viewing an image display CRT with the control slide. If an image of the tumor cells on the primary section stained with ER antibody was not complete for capturing all nuclei, the threshold was brought up or down accordingly. This was true for allfivefields. The antibody threshold was determined on the control slide by viewing the CRT and increasing or decreasing the threshold until no pixelling was present; then it was brought up one scale value to account for background staining. The primary slide was then observed and, if the histogram did not clearly separate the positive and negative cells, the first field was adjusted accord-

3 Vol. 90 No. 3 IMAGE ANALYSIS OF ER IN BREAST CARCINOMA 235 ingly. It was never adjusted more than three scale values above or below the original antibody threshold value. This was done for the first field. The five fields were averaged to give the final calculations (percentage positive and positive stain intensity) and distribution analyses. Fivefieldswere used to allow for uniformity of analyses between all cases in this study because in some examples only five fields containing tumor were available (<10%). In addition, for each case, a parameter depicting the cumulative total nuclear area analyzed was calculated. Quantitation of Assay Results Biochemical assays were summarized as femtomoles of estrogen binding per milligram of cytosol protein. The immunocytochemical localization was initially scored in a semiquantitative fashion, incorporating both the intensity and the distribution of specific staining as previously described. 7 The evaluations were recorded as percentages of positively stained target cells in each of four intensity categories, which were denoted as 0 (no staining), 1+ (weak but detectable above control), 2+ (distinct), and 3+ (intense). For each tissue, a value designated the HSCORE was derived by summing the percentages of cells staining at each intensity multiplied by the weighted intensity of staining: HSCORE = 2 Pi(i + 1) where i = 1, 2, 3 and Pi varies from 0 to 100%. An HSCORE was assigned both with and without methyl green counterstain for each specimen. Computer-assisted image analysis was completed on all of the cases, and a quantitative immunocytochemical score (QIC SCORE) was then generated by use of the following equation: QIC SCORE _ Positive percentage X positive stain intensity 10 where positive percentage is defined as percentage of nuclei with chromagen intensity above that of the control antibody threshold (see above) and positive stain intensity is defined as the median stain intensity for the pixelled nuclear image as compared with the control "nuclear mask" (see above). Positive percentage and positive stain intensity were calculated by the CAS/100 machine. Data Management and Analysis Biochemical assay values, immunocytochemical assay values, and computerized image analysis assay data were coded separately in a blinded fashion and maintained as independent files in the Time Oriented Record for Oncology (TORO) System of the Duke Cancer Center Database until completion of each phase of the study. Record identification numbers were used to match samples for the analysis. Sensitivities and specificities were done as previously described. 2 Evaluation of biochemical, HSCORE, and QIC SCORE values were done by correlation analysis. The cases were divided into two sets: a learning set (100 cases) and a test set (100 cases). The learning set was established by taking 100 cases with selected distribution to provide statistically adequate representation in each of the following four categories: negative HSCOREs, borderline HSCOREs, intermediate HSCOREs, and positive HSCOREs. The remaining 100 cases were placed in the test set. The learning set, defined as a selected complete spectrum of HSCOREs with no concern or needs regarding the biochemical values of this set, was used to establish a threshold value. Estimated correlation values (r) of QIC SCORE were made versus those of HSCORE with counterstain and without counterstain. It was intended to evaluate QIC SCORE as a means of quantitating antibody-antigen complex localization in tissues. The test set was then used to establish the sensitivity and specificity of the assays, based on the threshold value from the learning set. Correlation coefficient values were established between QIC SCORE versus Biochemical ER and HSCORE, and HSCORE versus Biochemical ER. QIC SCORE was compared with HSCORE with and without counterstain. The test set was selected to include cases with appropriate biochemical estrogen receptor content (appropriate B max and K<i) and corresponding HSCOREs. Results The use of the Cell Analysis Systems's software program for estrogen receptor quantitation permitted immunostained histologic sections to be represented as digitized images from which the optical densities of the diaminobenzidine reaction product over nuclei could be quantitated (Fig. 2). Microscopic fields from sections of breast carcinomas, immunostained either with control antibody (normal rat serum) (Fig. 1A) or with monoclonal estrogen receptor antibody (Fig. 2D), were presented as a series of computerized images demonstrating the nuclear area under consideration (Figs. 25 and E) and the amount of that area containing estrogen receptor reaction product (Figs. 2Cand F). The relative optical densities of the estrogen receptor reaction product were determined and a quantitative graphic expression of the amount of negative and positive nuclear area with the relative optical densities plotted (Figs. 2G and H).

4 236 BACUS ET AL. AJ.CP. September 1988 FIG. 2. Computerized image analysis of immunohistochemical localization of estrogen receptor in breast carcinoma. A (upper, left). \ Control section. The primary antibody (H222) is replaced with normal rat serum. Methyl green counterstain. B (center, left). The nuclear mask (yellow) of the microscopic field illustrated in A #" was established with incident light modified through a 650-nm filter (red). C (lower, left). The nuclear mask (yellow) with the amount of superimposed nuclear area covered by immunoprecipitate (brown) if present. The amount of immunoprecipitate was determined with the microscopic field of A illuminated with light modified through a 500-nmfilter(green). No immuno precipitate is identified in A or in the computerized reconstruction in C. D (upper, right). The same breast carcinoma treated with monoclonal antiestrogen receptor, H222. Methyl green counterstain. E (center, right). Nuclear mask (yellow) of the microscopic field il\v lustrated in D. This image of the nuclear area was generated with the incident light filtered through the 650-nm filter (red). F (lower, right). The nuclear area that contains immunoprecipitate (brown) is superimposed on the total nuclear area (yellow). The amount of diaminobenzidine reaction product, present in the microscopic field of E, was determined with the incident light filtered through a green filter. G (opposite page upper). Graphic histogram of the computerized analysis of the microscopic field illustrated in A. Because this was determined with the control antibody, the result establishes the threshold for immunostaining. H (opposite page lower). A distribution of the immunoprecipitate (DAB) of the positively stained nuclei (portion of the histogram in yellow). The average stain intensity and the percentage positively stained nuclei are reported. An average staining intensity, based on these plotted nuclear optical densities, was computed and the percentage of positive nuclear area was recorded. These values were combined into a summary expression, the QIC SCORE, for each case as described in "Materials and Methods." A QIC SCORE of greater than or equal to 18 was established as the threshold value for a positive assay. This involved examining the effects of variation of QIC SCORE threshold on sensitivity and specificity with the use of the previously derived value, HSCORE greater than or equal to 75 in the learning set as the criteria for positive (Fig. 3). HSCORE (sections without counterstain) versus CH222 HSCORE (sections with counterstain) showed that counterstaining did not alter the histologic score, r = 0.83 (Fig. 4). Based on the QIC SCORE threshold value from the learning set, the test set was used to establish the sensi- tivity and specificity of QIC SCORE versus CH222 HSCORE and biochemical estrogen receptor analysis. Thresholds of greater than or equal to 10 fmol/mg protein of estrogen receptor by biochemical analysis and greater than or equal to 75 for HSCORE were determined to represent the threshold values for categorization in the calculation of sensitivity and specificity. A sensitivity of 98% and specificity of 96% was observed for QIC SCORE versus CH222 HSCORE (Table 1). QIC SCORE versus biochemical estrogen receptor analysis yielded a sensitivity of 98% and a specificity of 100% (Table 2). A linear correlation was demonstrated on the quantitative comparison of QIC SCORE versus CH222 HSCORE (r = 0.79) and Log QIC SCORE versus Log biochemical estrogen receptor analysis (r = 0.86) (Figs. 5 and 6). These figures demonstrate the effectiveness of the CAS/100 system in separating the critical values near threshold.

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6 238 BACUS ET AL. AJ.C.P. September 1988 Table 1. Categorization of Cases by Semiquantitative Immunocytochemistry and Quantitative Immunocytochemistry CH222 HSCORE QIC SCORE &18 <18 75 < * Categorization of cases by semiquantitative immunocytochemistry, HSCORE and quantitative immunocytochemistry. QIC SCORE, using threshold values of greater than or equal to 75 and greater than or equal to 18, respectively. (Results of test set.) Sensitivity FIG. 3. Receiver operator curve generated by testing the effect on sensitivity versus specificity using the data from the learning set holding HSCORE threshold as greater than or equal to 75 and varying QIC score. QIC score threshold greater than or equal to 18 optimized sensitivity and specificity. The numbers along the curve indicate the number of x, y pairs at that point. Discussion Two hundred cases of primary breast carcinoma stained by immunoperoxidase techniques were analyzed for estrogen receptor content. A subjective, semiquantitative analysis by microscopic observation was completed and a histologic score (HSCORE) assigned. An objective quantitative immunocytochemical score (QIC rr o & 100- i CH222 HSCORE i 300 r 400 FIG. 4. HSCORE versus CH222 HSCORE (learning set). Sections with counterstain (CH222) showed that counterstaining did not alter the HSCORE (noncounterstained), r = SCORE) was derived by computer-assisted image analysis using Cell Analysis System's CAS/100 machine. Computer-assisted image analysis proved to be an effective method of observing and objectively quantitating estrogen receptor content in these tumors. Obtaining reproducible results requires immunohistochemical procedures to be performed in a competent way and for the computerized image analysis system to be properly maintained and calibrated so that the scale of the report histogram covers the range of staining intensities obtained in practice. Additional studies are underway to establish procedures for calibration in the stain intensity distribution for the laboratories, with possibly different staining conditions. It also necessitates that the machine operator is familiar with the pathology and cellular morphologic characteristics and components of the tissue being examined. In the case of estrogen receptor evaluation by immunoperoxidase techniques, it is important to recognize background and artifactual staining (i.e., necrotic debris, inflammatory elements, and red blood cell staining). These components should be excluded from the scoring evaluation. An experienced observer can easily make these distinctions by using the CAS/100 apparatus as configured. Counterstaining is an important issue addressed in this study. We had not previously used counterstain in case evaluations. However, for automated image analy- Table 2. Categorization of Cases by Biochemical Analysis and Quantitative Immunocytochemistry QIC SCORE oo oo Biochemical Analysis 10 < * Categorization of cases by quantitative biochemical analysis and quantitative immunocytochemistry, QIC SCORE, using threshold values of greater than or equal to 10 fmol/mg protein and greater than or equal to 18, respectively. (Results of test set.)

7 Vol. 90 No. 3 IMAGE ANALYSIS OF ER IN BREAST CARCINOMA sis the slides must be counterstained. Counterstaining did not alter the outcome of the histologic score. Computerized image analysis using the CAS/100 machine is an effective means of objectively quantitating immunocytochemistry, in this case ER binding content. It is necessary that the observer be familiar with the tissue being studied. The specificity and sensitivity are excellent as compared with previously validated biochemical methods. rr UJ e a> 3-2- e» I' o 0- ~\ ICO 400 CH222 HSCORE FIG. 5. Comparison of test set QIC SCORE versus CH222 HSCORE, r = (Test set) I 2 Log QIC SCORE FlG. 6. Comparison of test set Log QIC SCORE versus Log biochemical estrogen receptor analysis, r = (Test set). References 1. Flowers JL, Burton GS, Cox EB, et al: Use of monoclonal antiestrogen receptor antibody to evaluate estrogen receptor content in fine needle aspiration breast biopsies. Ann Surg 1986;203: Galen RS, Gambino SR: Beyond normality: The predictive value and efficiency of medical diagnosis. New York: John Wiley and Sons, 1975: Greene GL, Fitch FW, Jensen EV: Monoclonal antibodies to estrophilin: Probes for the study of estrogen receptors. Proc Natl Acad Sci USA 1980;77: Greene GL, Nolan C, Engler JP, Jensen EV: Monoclonal antibodies to human estrogen receptor. Proc Natl Acad Sci USA 1980;77: Greene GL, Jensen EV: Monoclonal antibodies as probes for estrogen receptor detection and characterization. J Steroid Biochem 1982;16: McCarty KS Jr, Barton TK, Fetter BF, Creaseman WT, McCarty KS Sr: Correlation of estrogen and progesterone receptors with histologic differentiation in endometrial adenocarcinoma. Am J Pathol 1979;96: McCarty KS Jr, Miller LS, Cox EB, Konrath J, McCarty KS Sr: Estrogen receptor analyses: Correlation of biochemical and immunohistochemical methods using monoclonal antireceptor antibodies. Arch Pathol Lab Med 1985;109: Miller LS, Tribby HE, Miles MR, Tomita JT, Nolan C: Hybridomas producing monoclonal antibodies to human estrogen receptor. Fed Proc 1982,41: Pertschuk LP, Eisenberg KB, Carter AC, Feldman JG: Immunohistologic localization of estrogen receptors in breast cancer with monoclonal antibodies: Correlation with biochemistry and clinical endocrine response. Cancer 1985;55: Press MF, Nousek-Goebl N, King WJ, Herbst AL, Greene GL: Immunohistochemical assessment of estrogen receptor distribution in the human endometrium throughout the menstrual cycle. Lab Invest 1984;51: Sternberger LA: Immunocytochemistry. New York: John Wiley and Sons, 1979: