Single‐cell multiplex immunocytochemistry in cell block preparations of metastatic breast cancer confirms sensitivity of GATA‐binding protein 3 over gross cystic disease fluid protein 15 and mammaglobin
Metastatic breast cancers are frequently encountered in routine cytology practice. , GATA‐binding protein 3 (GATA3), gross cystic disease fluid protein 15 (GCDFP15), and mammaglobin (MMG) are markers that indicate primary breast cancers, and are often used in clinical diagnosis. , Cytology specimens, compared to core and excisional biopsies, are relatively smaller in volume, and thus lower in tumor cellularity. An efficient immunocytochemistry (ICC) panel would not only save on cost but allow for accurate diagnoses in low‐cellularity specimens. With multiplex ICC (mICC), more than one marker can be analyzed on a single section, and (co)expression patterns can be observed at a single‐cell level. This study aimed to validate mICC in cell block preparations in comparison to traditional ICC results in cell blocks prepared from aspirates of metastatic breast cancers. Additionally, single‐cell expression pattern analysis was performed to identify the single markers and combinations of markers that are the most sensitive in different subtypes of breast cancer.   Case collection Aspiration cytology specimens with a diagnosis of metastatic carcinoma from patients with a hospital diagnosis code of breast cancer from 2017 to 2022 were retrieved. Cytology slides were reviewed for cellularity, confirmation of diagnosis, and GATA3, GCDFP15, and MMG staining (the proportion of tumor cells that were positive). Patient case notes and previous pathology reports were reviewed for histological diagnosis and surrogate molecular subtype marker status (estrogen receptor [ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]). Formalin‐fixed paraffin‐embedded cell blocks for the cases were retrieved and sectioned for mICC. mICC and image acquisition GATA3, GCDFP15, and MMG antibodies were paired with OptiView 3,3′‐diaminobenzidine (DAB) and Ventana DISCOVERY Purple and Blue, respectively, which are chromogens that are optically distinct by digital analysis and visual inspection. The selection of nuclear (GATA3) and cytoplasmic (GCDFP15 and MMG) antibodies was also intentional to minimize overlap by staining different cellular compartments (Figure ). Slides were cyclically and serially stained and analyzed by bright‐field imaging with the Mantra 2 system (Akoya Biosciences), a quantitative pathology workstation with digital microscopy and image analysis capabilities. Regions of interest were selected during imaging at 400× magnification with output images at a resolution of 0.25 µm/pixel. Brightness was adjusted to maximize contrast between the nuclei and the background and to minimize noise. Color deconvolution Images were exported and analyzed by the inForm Tissue Finder (Akoya Biosciences), the software included for image analysis in the Mantra 2 system. Single‐color reference slides of each stain were created for purple, teal (DISCOVERY Blue), Mayer hematoxylin, and DAB. The reference slides were imaged by Mantra 2 under the same brightness settings as the sample slides. The stain profiles were extracted by reading the reflected wavelengths and intensity, and the recorded data were compiled into a curve representing the color. Cell detection, phenotyping, and annotation An inbuilt algorithm was used to perform nuclear segmentation by locating nuclear boundaries on the basis of hematoxylin and DAB stains and edge detection of membranes on the basis of teal and purple stains. Data were fed into the inbuilt phenotyping algorithm in the inForm Tissue Finder, which resulted in annotations on three layers corresponding to each antibody (i.e., three layers of binary classification for each cell). A phenotype map was generated, and the centroids of the cell objects were marked and color coded with a unique color identifying one out of eight phenotypes (including a complete negative). Phenotype maps of separate stains were also exported and stacked on each other in sequence at 50% opacity, and color values of the background white pixels were changed to transparent pixels to facilitate overlaying. Resultant color values were measured and crosschecked with stain expression. Deconvoluted hematoxylin channel images were exported for review and annotation (selection) of tumor cells. Five exported images of high‐power fields containing on average over 100 tumor cells were generated for each cell block preparation. Tumor cells were selected on the basis of cytomorphology. Overlapping detections and detections not attributable to tumor cells were purposefully omitted (Figure ). Statistical analysis Assessment of staining was performed by two pathologists, and annotations were performed by one pathologist and reexamined by another pathologist. Discrepancies were resolved by reviewing the slides or images together until a consensus was reached. Statistical analysis was performed with SPSS (version 23.0; IBM). The Pearson χ 2 test was used for comparing the immunocytochemical markers at different expression cutoffs as categorical variables. The kappa statistic was used for measuring concordance in expression of the immunocytochemical markers. A p value of <.05 was considered significant. A flowchart of the study design is illustrated in Figure . The study was approved by the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee, and was granted exemption from requiring written informed consent (2022.061).   Aspiration cytology specimens with a diagnosis of metastatic carcinoma from patients with a hospital diagnosis code of breast cancer from 2017 to 2022 were retrieved. Cytology slides were reviewed for cellularity, confirmation of diagnosis, and GATA3, GCDFP15, and MMG staining (the proportion of tumor cells that were positive). Patient case notes and previous pathology reports were reviewed for histological diagnosis and surrogate molecular subtype marker status (estrogen receptor [ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]). Formalin‐fixed paraffin‐embedded cell blocks for the cases were retrieved and sectioned for mICC.   GATA3, GCDFP15, and MMG antibodies were paired with OptiView 3,3′‐diaminobenzidine (DAB) and Ventana DISCOVERY Purple and Blue, respectively, which are chromogens that are optically distinct by digital analysis and visual inspection. The selection of nuclear (GATA3) and cytoplasmic (GCDFP15 and MMG) antibodies was also intentional to minimize overlap by staining different cellular compartments (Figure ). Slides were cyclically and serially stained and analyzed by bright‐field imaging with the Mantra 2 system (Akoya Biosciences), a quantitative pathology workstation with digital microscopy and image analysis capabilities. Regions of interest were selected during imaging at 400× magnification with output images at a resolution of 0.25 µm/pixel. Brightness was adjusted to maximize contrast between the nuclei and the background and to minimize noise.   Images were exported and analyzed by the inForm Tissue Finder (Akoya Biosciences), the software included for image analysis in the Mantra 2 system. Single‐color reference slides of each stain were created for purple, teal (DISCOVERY Blue), Mayer hematoxylin, and DAB. The reference slides were imaged by Mantra 2 under the same brightness settings as the sample slides. The stain profiles were extracted by reading the reflected wavelengths and intensity, and the recorded data were compiled into a curve representing the color.   An inbuilt algorithm was used to perform nuclear segmentation by locating nuclear boundaries on the basis of hematoxylin and DAB stains and edge detection of membranes on the basis of teal and purple stains. Data were fed into the inbuilt phenotyping algorithm in the inForm Tissue Finder, which resulted in annotations on three layers corresponding to each antibody (i.e., three layers of binary classification for each cell). A phenotype map was generated, and the centroids of the cell objects were marked and color coded with a unique color identifying one out of eight phenotypes (including a complete negative). Phenotype maps of separate stains were also exported and stacked on each other in sequence at 50% opacity, and color values of the background white pixels were changed to transparent pixels to facilitate overlaying. Resultant color values were measured and crosschecked with stain expression. Deconvoluted hematoxylin channel images were exported for review and annotation (selection) of tumor cells. Five exported images of high‐power fields containing on average over 100 tumor cells were generated for each cell block preparation. Tumor cells were selected on the basis of cytomorphology. Overlapping detections and detections not attributable to tumor cells were purposefully omitted (Figure ).   Assessment of staining was performed by two pathologists, and annotations were performed by one pathologist and reexamined by another pathologist. Discrepancies were resolved by reviewing the slides or images together until a consensus was reached. Statistical analysis was performed with SPSS (version 23.0; IBM). The Pearson χ 2 test was used for comparing the immunocytochemical markers at different expression cutoffs as categorical variables. The kappa statistic was used for measuring concordance in expression of the immunocytochemical markers. A p value of <.05 was considered significant. A flowchart of the study design is illustrated in Figure . The study was approved by the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee, and was granted exemption from requiring written informed consent (2022.061).   A total of 50 aspirates of metastatic carcinomas of the breast to lymph node ( n = 41), chest wall ( n = 3), lung ( n = 3), and thyroid ( n = 1) were retrieved. In 14 cases, mICC was not performed because of low cellularity on further sectioning and/or unsatisfactory tissue quality (crushing artifacts, poor preservation, and suboptimal immunoreactivity to controls). The majority of cases were invasive breast carcinomas of no special type (46 of 50; 92%) and hormone receptor positive and HER2 negative (30 of 50; 60%). The most common site of aspiration was lymph node (41 of 50; 82%), followed by lung (3 of 50; 6%) and soft tissue (3 of 50; 6%) (Table ). The expression rates of GATA3, GCDFP15, and MMG in traditional ICC and mICC were similar for cases in the cohort. At a 1% cutoff, the highest rate of expression was seen for GATA3 (>80%), whereas GCDFP15 and MMG showed similar expression rates of 40%–50% (Table ). In a review of cases by surrogate molecular subtype, MMG expression was the highest for ER+/PR+/HER2+ metastatic breast cancers, and GATA3 expression was the highest for all remaining subtypes (ER+/PR+/HER2−, ER−/PR−/HER2+, and ER−/PR−/HER2−) (Table ). GATA3, GCDFP15, and MMG expression in traditional ICC and mICC demonstrated good concordance. The lowest concordance was observed for GATA3 at 1% expression as cutoff, with a kappa of 0.365. Concordance of GATA3 at 5% expression cutoff and GCDFP15 and MMG at all cutoffs was at a kappa of >0.5 ( p = .002–<.001) (Table ). The correlations between GATA3, GCDFP15, and MMG expression were explored by traditional ICC and mICC. By traditional ICC, only GATA3 and GCDFP15 showed significant (positive) correlation when adopting a 1% expression cutoff ( p = .022). mICC revealed different results, with significant correlation seen only between GATA3 and MMG at a 5% cutoff for positivity ( p = .016) (Table ). A significant proportion of tumor cells (38.0%–48.2%) were negative to all three markers despite being overall positive to at least one marker. Single marker–positive tumor cells outnumbered double marker– and triple marker–positive tumor cells in all subgroups. When breast marker expression was examined on individual tumor cells, cases considered overall GCDFP15 or MMG positive, the proportion of tumor cells showing expression of the corresponding marker was lower than that of GATA3 (GCDFP15 positive: 34.2% [GATA3] vs. 2.9% [GCDFP15]; MMG positive: 25.6% [GATA3] vs. 9.1% [MMG]). On the contrary, in cases considered overall positive to GATA3, the percentage of tumor cells expressing any GCDFP15 and/or MMG while being negative to GATA3 was lower than 5% (MMG positive, 4.7%; GCDFP15 positive, 1.4%; MMG and GCDFP15 positive, 0.3%). For tumor cells expressing two markers, combinations including GATA3 (GATA3/MMG positive and GATA3/GCDFP15 positive) were more frequent (2.5%–19.4%) than MMG/GCDFP15 positive (>0.5%) tumor cells (Table ). Limited to single marker–positive tumor cells, GATA3 positivity led all molecular subtypes except for ER+/PR+/HER2+, of which single‐MMG positivity was the most common immunophenotype. Interestingly, two molecular subtypes showed distinct distributions for double marker– and triple marker–positive tumor cells. GATA3/GCDFP15 double positivity was not seen for ER+/PR+/HER2+ cases, and the proportion of tumor cells with triple positivity was 0.1%. In triple‐negative cases, despite a high percentage of GATA3 only–positive tumor cells (17.9%), GATA3/MMG–coexpressing tumor cells, including triple marker–positive cells, were entirely absent (0%) (Table ; Figure ).   Carcinomas of the breast display a high propensity for metastasis and are commonly encountered in aspiration cytology. , The histomorphology and cytomorphology of breast carcinomas are heterogeneous, and breast carcinomas cannot be reliably distinguished from metastatic carcinomas of other primary organ cancers. In most cases, ICC is necessary for confirmation of primary breast cancer for metastatic carcinomas. , , , GATA3, GCDFP15, and MMG , , are markers frequently used in clinical diagnostic settings for such purposes. Single‐marker , , and multimarker studies , , on GATA3, GCDFP15, and MMG are available in the literature detailing their respective expression percentage (i.e., sensitivity), and GATA3 has been described as the most sensitive among these three markers. With the advent of multiplex staining techniques, a single cell can be stained with multiple markers or by digital reconstruction, such as the subtractive coordinate immunoreactivity pattern approach by characterizing cells of multiple phenotypic populations , or virtual mICC. Bright‐field multiplexes use translucent chromogens to allow the visualization of coexpressing phenotypes. In addition, by selecting a panel of markers that are expressed in different cellular compartments, the nucleus for GATA3 and the cytoplasm for GCDFP15 and MMG, the expression pattern of multiple markers in a single cell can be accurately assessed. Traditional ICC is scored by a semiquantitative overall assessment. Multimarker comparisons performed with traditional ICC on a single specimen would include tumor cells of different immunophenotypes, which would compound the sampling error introduced by sequential sections. Correlations identified by mICC indicate coexpression on a single‐cell level, and subsequent statistical analysis is based on single cells as the smallest unit rather than gross scoring of a section. The results generated are more precise and reproducible than those of traditional ICC. As compared to immunofluorescence‐based multiplex techniques, bright‐field mICC was adopted for the current study, and cell detection results were reviewed and revised by two pathologists. The results generated are more comparable to ICC performed in diagnostic practices. High concordance was observed between traditional ICC and mICC for cell block preparation in the current cohort. The difference in positive rates at cutoffs of 1%, 5%, and 50% rarely exceeded 5%, and kappa values were largely over 0.5 ( p < .05). Additionally, in line with the literature, GATA3, GCDFP15, and MMG showed preferential positivity in ER+ cases, , , and GATA3 showed superior sensitivity to the other markers in both traditional ICC and mICC. , , In order to identify complementary markers, correlation analysis and crosstabulation between different markers were performed. A high correlation would indicate similar expression patterns and limited value in applying both markers together. GATA3 and GCDFP15 showed significant correlation with traditional ICC (at a 1% cutoff), whereas GATA3 and MMG showed greater correlation with mICC (at a 50% cutoff). However, further examination revealed that there were no GATA3−/GCDFP15+ or GATA3−/MMG+ cases, and all discordances were from false‐negative GCDFP15 and MMG results. These findings demonstrate that an overall assessment of the entire slide or hotspots lacks sensitivity. Breast cancers display high intratumoral heterogeneity, often with multiple subclones present. Positivity in a small population of tumor cells would be interpreted as an overall positive result. Such results from summated semiquantitative assessment would not be applicable to low‐cellularity or small‐volume specimens, and it is in these specimens that consuming tissue for overlapping markers should be avoided. By examining marker expression with mICC, complementary and mutually exclusive markers can be accurately identified at a single‐cell level. For single marker–positive tumor cells, GATA3 positivity was the most common, except for MMG in ER+/PR+/HER2+ breast cancers. When limited to only two markers (i.e., single marker positive and double marker positive), the combination of GATA3 and MMG included the greatest number of tumor cells for luminal breast cancers (ER+/PR+/HER2+, 60.6%; ER+/PR+/HER2+, 31.4%), whereas HER2‐overexpressed breast cancers (27.4%) and triple‐negative breast cancers (26.4%) favored the combination of GATA3 plus GCDFP15 (Table ). The combination of MMG together with GCDFP15 was lowest in all breast cancer subtypes (3.3%–15.7%). The proportions of double marker–positive cells are similar, with GATA3/GCDFP15–coexpressing cells being the dominant double marker–positive population in luminal breast cancers (ER+/PR+/HER2+, 93.0%; ER+/PR+/HER2+, 98.6%), and GATA3/MMG–coexpressing cells being the most populated for HER2‐overexpressed breast cancers (8.14%) and triple‐negative breast cancers (91.2%) (Table ). These findings confirm that GATA3 is the best performing single marker. For hormone receptor‐positive breast cancers, the addition of MMG increases sensitivity over GCDFP15, and vice versa for hormone receptor‐negative breast cancers. Triple marker–positive cells were rare and composed of not more than 2% of tumor cells on average in a specimen, which indicates that tumor cells rarely express all three markers, and the so‐called coexpression with traditional ICC is likely attributable to the presence of multiple tumor subclones that are independently positive to different markers. This is also consistent with the proportion of single marker–positive tumor cells outnumbering double marker– and triple marker–positive tumor cells. With the low number of triple marker–positive tumor cells, it is advisable to prioritize the more sensitive GATA3 and to add another marker, preferably according to hormone receptor expression. The detection rate of a randomly selected marker would be significantly lower. In conclusion, the current study provides evidence that mICC is robust and demonstrates good concordance with traditional single‐marker ICC. mICC further refines marker assessment in small specimens, which allows coexpression patterns to be examined at a single‐cell level, which is more accurate for cytology and small specimens. Among GATA3, GCDFP15, and MMG, GATA3 displayed the highest sensitivity as a single marker. Along with GATA3, the addition of MMG for hormone receptor‐positive breast cancers and GCDFP15 for hormone receptor‐negative breast cancers further increased sensitivity. Single marker–positive tumor cells outnumbered double marker–positive tumor cells. Triple marker–positive tumor cells were exceedingly rare. Such findings suggest that the previously observed coexpression of markers is more likely attributable to different expression patterns of tumor subclones due to intratumoral heterogeneity. As such, particularly in limited specimens, careful selection of markers is advised.   Joshua J. X. Li : Conceptualization, data curation, investigation, methodology, visualization, and writing–original draft. Hiu Yu Cheng : Investigation, methodology, visualization, and writing–original draft. Conrad H. C. Lee : Data curation, investigation, methodology, and writing–original draft. Joanna K. M. Ng : Investigation and validation. Julia Y. Tsang : Data curation, formal analysis, resources, and visualization. Gary M. Tse : Conceptualization, methodology, supervision, validation, and writing–review and editing.   The authors declare no conflicts of interest.   