Abstract
Background/Aim: Automated measurement of immunostained samples can enable more convenient and objective prediction of treatment outcome from radiotherapy. We aimed to validate the performance of the QuPath image analysis software in immune cell markers detection by comparing QuPath cell counting results with those of physician manual cell counting. Patients and Methods: CD8- and FoxP3-stained cervical, CD8-stained oropharyngeal, and Ku70-stained prostate cancer tumor sections were analyzed in 104 cervical, 92 oropharyngeal, and 58 prostate cancer patients undergoing radiotherapy at our Institution. Results: QuPath and manual counts were highly correlated. When divided into two groups using ROC curves, the agreement between QuPath and manual counts was 89.4% for CD8 and 88.5% for FoxP3 in cervical cancer, 87.0% for CD8 in oropharyngeal cancer and 80.7% for Ku70 in prostate cancer. In cervical cancer, the high CD8 group based on QuPath counts had a better prognosis and the low CD8 group had a significantly worse prognosis [p=0.0003; 5-year overall survival (OS), 65.9% vs. 34.7%]. QuPath counts were more predictive than manual counts. Similar results were observed for FoxP3 in cervical cancer (p=0.002; 5-year OS, 62.1% vs. 33.6%) and CD8 in oropharyngeal cancer (p=0.013; 5-year OS, 80.2% vs. 47.2%). In prostate cancer, high Ku70 group had worse and low group significantly better outcome [p=0.007; 10-year progression-free survival (PFS), 56.0% vs. 93.8%]. Conclusion: QuPath showed a strong correlation with manual counting, confirming its utility and accuracy and potential applicability in clinical practice.
Immunohistochemistry (IHC) is widely used to measure the expression of various proteins (biomarkers) in tissue samples for diagnostic and prognostic purposes. In the field of radiotherapy, the development of biomarker-based treatment response prediction has also been investigated. In our previous studies, radiotherapy response prediction was investigated by analyzing the expression of proteins involved in DNA damage repair, such as Ku70, and those involved in tumor immunity, such as CD8 and FoxP3 (1-5). In addition, characteristics of the tumor immune microenvironment may be relevant to treatment efficacy. Since radiotherapy induces immunogenic cell death, leading to release of neoantigens and ultimately to activation of T cell-mediated immunity, this concept can be applied to predict the efficacy of chemoradiotherapy.
Historically, pathologists have often evaluated IHC-stained tissue samples and manually assessed biomarker expression and staining patterns. Although many different methods for assessing the immune cell composition of tumors have been described in the literature, there are no standardized scoring methods, and visual inspection is inherently semi-quantitative and subjective. In addition, the process is labor-intensive and time-consuming, which may limit the scope of studies (6). To overcome these obstacles, there is a need for standardized and preferably automated IHC measurements. QuPath is an open source bio-image analysis software with the ability to evaluate biomarkers in digitized histopathology sections (7). QuPath is fully designed to handle slide images and provides the ability to determine the presence of biomarkers and evaluate their distribution throughout the tumor tissue. Recent advances in digital image analysis (DIA) may overcome some of these shortcomings.
In this paper, we aimed to validate the performance of QuPath in detecting positive cells and positive regions for the immune cell markers CD8 and FoxP3 and the DNA double-strand break repair-related protein Ku70 by comparing the results of cell counting by QuPath with visual assessment by a physician. We evaluated the utility of QuPath by examining tissue from different types of cancer: cervical cancer, oropharyngeal cancer, and prostate cancer.
Patients and Methods
Patients. This retrospective study was approved by the ethics committee of our institution (No. 312-168). For cervical cancer, 104 patients with cervical cancer who received chemoradiotherapy or radiotherapy at Sapporo Medical University School of Medicine between 2010 and 2017 were analyzed. For oropharyngeal cancer, 92 patients who received radiotherapy or chemoradiotherapy or bio-radiotherapy at our Institution between May 2005 and February 2016 were analyzed. For prostate cancer, 58 patients with localized prostate cancer who received intensity-modulated radiation therapy (IMRT) at our institution between August 2007 and October 2010 were analyzed. The treatment modalities for cervical and oropharyngeal cancer and prostate cancer have previously been described in detail (1-4).
Immunohistochemical staining. Immunohistochemical staining was performed as described in previous studies (1-4). Briefly, formalin-fixed, paraffin-embedded specimens obtained from pretreatment biopsy specimens for cervical cancer, endoscopic biopsy specimens for oropharyngeal cancer, and needle biopsy specimens for prostate cancer were cut into 3-μm-thick slices and mounted on glass slides. Primary antibodies were used against the following antigens: CD8 (dilution 1:100, clone C8/144B; Dako, Glostrup, Denmark), FoxP3 [dilution 1:100, clone 236A/E7 (ab20034); Abcam plc., Cambridge, UK], Ku70 monoclonal antibody (MC- 351, clone N3H10, Kamiya Biochemical Company, Tukwila, WA, USA) (8). For CD8 and FoxP3, regions containing tumor and adjacent stroma were selected, observed at high magnification (400′), and the number of expressed cells counted. Similarly, for Ku70, regions of tumor cells were selected and the number of positive cells was counted.
QuPath scoring. All slide images were analyzed using the QuPath v0.4.3 biological image analysis software to detect tumors and stroma in representative fields of view. Tumor cells and immune cells (both positive and negative staining) within each region were automatically counted using CD8-stained and FoxP3-stained cervical cancer tumor sections, CD8-stained oropharyngeal cancer tumor sections, and Ku70-stained prostate cancer tumor sections (Figure 1). For each sample, a 0.2 mm2 region of interest (ROI) was scored using both QuPath counts and manual counts. Qupath counts and manual counts counted the same fields of view. Both positively and negatively stained immune cells were quantified and prognostic analysis was performed using positive cell counts for CD8, FoxP3, and Ku70. All data were extracted from QuPath and further calculated in Microsoft Excel.
Representative images of immunohistochemically stained biopsy specimens with low (top) and high (bottom) scores. Overlay of positive/negative cells in original (left) and Qupath analysis images (right) for CD8 cervical cancer (A), FoxP3 cervical cancer (B), CD8 oropharyngeal cancer (C), and Ku70 prostate cancer (D), respectively. In Qupath analysis images, immune cells stained positively (red) and immune cells stained negatively (blue).
Statistical analysis. Overall survival (OS) and progression-free survival (PFS) curves were constructed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between groups. Receiver operating characteristic (ROC) curves were constructed for all markers based on staining levels and corresponding prognoses. Cutoff values for each marker were determined from the Youden index of the ROC curves and were used to differentiate between positive and negative for grouping purposes. All statistical analyses were performed using EZR software version 1.76 (9).
Results
Comparison of QuPath and manual counts. To confirm the accuracy of QuPath, a comparison was made between QuPath counts and manual counts. Comparison of QuPath and manual counts for CD8 and FOX-P3 staining in cervical cancer tumor sections showed a highly significant correlation between the two methods, as shown in Figure 2, which correlates QuPath and manual counts. For CD8, Qupath counts were higher than manual counts in most cases, and there were 4 cases with high manual counts and low Qupath counts, but the difference in counts was less than 5% of the total counts. Similarly, for FoxP3, the Qupath count was higher than the manual count in most cases, and there were 10 cases with higher manual counts and lower Qupath counts. Of these, 2 cases had a difference in counts of 5% or more of the total counts. The average measurement time was 45 seconds per case for the Qupath counts and 4 minutes per case for the manual counts (paired t-test; p<0.001). Positive cell counts for both CD8 and FoxP3 tended to be underestimated by manual counts.
Correlation between QuPath and manual counts of CD8 and FoxP3 protein staining in cervical cancer tumors. Comparison of QuPath and manual counts of CD8 and FoxP3 staining showed a highly significant correlation between the two methods.
ROC curves were generated for all markers based on the prognostic value for overall survival corresponding to each level of staining and to the QuPath and manual counts, and the differences in the area under the curve were tested. For CD8 staining in cervical cancer, the area under curve (AUC) for QuPath counts was 0.713 and for manual counts was 0.649, a significant difference between the two groups (p=0.010; Figure 3A and 3B). For FoxP3 staining in cervical cancer, the AUC for QuPath count was 0.618 and for manual count was 0.621, with no significant difference between the two groups (p=0.895; Figure 3C and 3D).
ROC curves for all markers based on staining levels for overall survival and prognosis corresponding to Qupath counts and manual counts, respectively, for CD8 and FoxP3 staining in cervical cancer tumor sections. (A) Qupath count for CD8 cervical cancer, (B) manual count for CD8 cervical cancer, (C) Qupath count for FoxP3 cervical cancer, manual count for FoxP3 cervical cancer. ROC: Receiver operating characteristic.
Patients were divided into two groups according to cutoff values based on ROC curves. Table I shows the number of high and low groups in the QuPath and manual counts, respectively. In the case of CD8 for cervical cancer, the results of QuPath count and manual count agreed in 93 cases and disagreed in 11 cases, with an agreement rate of 89.4% (A). In the case of Fox-P3 for cervical cancer, the results of the QuPath count and the manual count agreed in 92 cases and disagreed in 12 cases, for an agreement rate of 88.5% (B). Similarly, in the case of CD8 for oropharyngeal cancer, the results of the QuPath count and the manual count agreed in 80 cases and disagreed in 12 cases, for an agreement rate of 87.0% (C), and in the case of Ku70 for prostate cancer, the results of the QuPath count and the manual count agreed in 46 cases and disagreed in 11 cases, for an agreement rate of 80.7%. The agreement rate was 80.7% (D).
Comparison between QuPath and manual counts of the number of high and low CD8, FoxP3 and Ku70 expression groups in each cancer type.
Overall survival and PSA recurrence-free survival by protein expression. In cervical cancer, when specimens were evaluated for CD8 positivity based on QuPath counts, the high group had a better prognosis and the low group had a significantly worse prognosis (p=0.0003; 5-year OS, 65.9% vs. 34.7%; Figure 4A, left). When specimens were evaluated for CD8 positivity based on manual counts, the results were similar to the QuPath counts (p=0.014; 5-year OS, 60.9% vs. 40.7%; Figure 4A, right).
Overall survival and PSA recurrence-free survival by expression of different proteins (left: QuPath count, right: manual count). (A) CD8-positive vs. CD8-negative in cervical cancer. (B) FoxP3 positive vs. FoxP3 negative in cervical cancer. (C) CD8 positive vs. CD8 negative in oropharyngeal cancer. (D) Ku70 positive vs. Ku70 negative in prostate cancer. In cervical and oropharyngeal cancer, the high CD8 or FoxP3 group had a better prognosis, while the low CD8 or FoxP3 group had a significantly worse prognosis. In prostate cancer, the low Ku70 group had a better prognosis, while the high Ku70 group had a significantly worse prognosis. PSA: Prostate specific antigen.
Next, when specimens were evaluated for FoxP3 positivity, both QuPath and manual counts showed that the high group had a better prognosis and the low group had a worse prognosis (p=0.002; 5-year OS, 62.1% vs. 33.6%; Figure 4B, left; p=0.0001; 5-year OS, 60.4% vs. 32.1%; Figure 4B, right). Next, when specimens were evaluated by CD8 positivity based on QuPath counts in oropharyngeal cancer, the high group had a better survival, while the low group had a significantly worse survival (p=0.013; 5-year OS, 80.2% vs. 47.2%; Figure 4C, left). When specimens were evaluated by CD8 positivity based on manual counts, the results were comparable (p=0.031; 5-year OS, 77.1% vs. 47.1%; Figure 4C, right).
When specimens were scored for Ku70 positivity based on QuPath counts in prostate cancer, the high group had a worse outcome and the low group had a significantly better outcome (p=0.007; 10-year PFS, 56.0% vs. 93.8%; Figure 4D left). Evaluation of specimens for Ku70 positivity based on manual counts showed similar results (p=0.035; 10-year PFS, 61.9% vs. 91.7%; Figure 4D, right).
Discussion
Radiation therapy has been known to be effective for some individuals while ineffective for others, and in recent years. Key to radiation therapy effectiveness is the cell’s ability to repair DNA double-strand breaks (10) and activate tumor immunity (11). It is, therefore, possible that the expression of proteins involved in these processes could be used as biomarkers to individualize radiotherapy. However, there are issues to be addressed in order to disseminate the expression of specific proteins in cancer tissues as biomarkers in actual clinical practice, such as quantitative, objective, and labor-saving.
The aim of the study was to evaluate the reliability of quantification of immunohistochemically stained immune cells using QuPath, an open-source software platform for digital pathology and image analysis. This study demonstrated that QuPath’s positive cell detection feature facilitated the identification of CD8 and FoxP3 in previously annotated tumor tissues. Herein, we evaluated the computerized scoring with QuPath regarding its ability to identify and count positively stained immune cells, and compared it to manual cell counting to validate the usefulness and accuracy of QuPath on CD8 and FoxP3 markers. There was a strong correlation between QuPath and manual counting for all parameters evaluated, confirming the usefulness and accuracy of QuPath. On the other hand, manual counting underestimated the number of cells to be counted compared to QuPath counting.
In our previous study, we reported that CD8 and FoxP3 expression were significant prognostic factors in cervical cancer patients undergoing radical radiotherapy (1, 2). Similarly, we reported that the OS of patients with oropharyngeal cancer with high CD8-positive cell rate was superior to that of patients with low CD8-positive cell rate (3). In the present study, when specimens were evaluated for CD8 and FoxP3 positivity based on QuPath counts, the prognosis for both groups was better in the high group and significantly worse in the low group (Figure 4). Similar results were obtained for tissue samples from different cancer types, suggesting the usefulness of QuPath. Our previous study also reported that the expression of proteins involved in DNA double-strand break repair, such as Ku70, in prostate cancer biopsy specimens is a significant prognostic factor for recurrence (4, 5). In this study, we demonstrated that the expression of proteins involved in DNA double-strand break repair can be analyzed in the same manner using QuPath.
Nakane et al. measured a simplified immunoscore in colorectal cancer by assessing CD3/CD45RO in addition to the CD8/FoxP3 markers we used in our study and showed that per-tumor assessment of CD3/CD8 and FoxP3 is useful for prognostic prediction (12). The additional workload of such immunostaining may increase the burden of clinical implementation, but automated assessment may reduce this burden while promoting more accurate prediction of treatment response.
The time required to evaluate tumor sections with QuPath depends on the sample size and manual annotation sessions; when counting with QuPath, even whole tumor sections could be analyzed in less than 1 minute per slide. In contrast, manual counting within a tumor section as small as 100 mm2 would require at least 4 minutes per slide. Thus, QuPath dramatically reduces the time required to assess immune infiltration of an entire tumor section, making quantification of all slides more realistic. This reduces the selection bias that can result from limited counting of preselected tumor areas and can affect the interpretation of results.
This study demonstrates that digital, cell-based analysis of IHC staining in biopsy tissue is feasible. In the past, studies have focused primarily on tissue microarray (TMA) material and specific regions of interest (13-16), but clinical practice requires robust methods for processing whole slide tissue biopsies. Biopsy tissue contains many artifacts and non-neoplastic tissue, and also deals with variability in tissue area size and IHC lots. Methods that can accurately assess the positivity of biopsy material can increase the size of the study and also overcome the common problem of interobserver variability in assessing the positivity of IHC staining (17, 18).
Evaluation of biopsy specimens sometimes involves specimens with necrotic tissue, making it difficult to automate ROI selection. In the present study, ROI definition was performed by measuring manually defined areas, which is a limitation of the study. Bogajewska-Rylko et al. reported CK5/6, CK20, p63, Melan A, SOX-10 for assessment of necrotic tissue within melanoma and adenocarcinoma and squamous cell carcinoma tumors, CKAE/AE3 and CK7, and reported that immunostaining with antibodies such as CKAE/AE3 and CK7 could be used for assessment with relatively high specificity and sensitivity (19).
A limitation of this study is that it examined carcinomas with different treatment modalities. In this study, when specimens were evaluated for CD8 positivity based on QuPath counts, prognosis was better in the high-expression group and significantly worse in the low-expression group for both cervical and oropharyngeal cancer. On the other hand, treatment differences by cancer would affect the prognosis analysis, but since the purpose of the present study was to examine the usefulness of QuPath counts for manual counting using data from previous studies, we did not examine treatment differences by cancer.
A further limitation of the study was the use of relative positive cutoffs. If this method is to be applied to other data and IHC markers, close collaboration with an experienced pathologist is recommended. However, because absolute intensity thresholds are used, they are susceptible to DAB intensity heterogeneity, artifacts, noise, and batch-to-batch variation. Therefore, color normalization of DAB staining prior to analysis is ideal to overcome these issues (20). A further step to make this study more accurate would be to incorporate an object classifier that can separate tumor cells from stromal tissue, immune cells, and artifacts. Implementation of such a method would save annotation time and improve tissue throughput time.
Conclusion
This study demonstrated that the QuPath image analysis software has a positive cell detection capability and facilitated the identification of CD8, FoxP3, and Ku70 protein markers in previously annotated tumor tissues. A very strong correlation between QuPath and manual counting was observed, thus confirming the utility and accuracy of the QuPath software and re-enforcing its applicability in clinical practice.
Acknowledgements
This study was supported by JSPS KAKENHI Grants [grant number 22K07671].
Footnotes
Authors’ Contributions
TH and MS participated in the data collection and interpretation, performed the analysis and participated in the drafting and final revising of the manuscript. TT, YF, and TG participated in data collection. SM participated and helped the analysis. MK, RO, JK, AO, RK, AA, and YT participated in the patient care, data collection and interpretation. YH and TT participated importantly in the conception of the study and helped to draft the manuscript. KS participated importantly in the conception and design and helped to draft the manuscript. All Authors read and approved the final manuscript.
Conflicts of Interest
The Authors declare that they have no conflicts of interest.
- Received January 12, 2024.
- Revision received February 15, 2024.
- Accepted February 21, 2024.
- Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
