High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium

TMA-Mate: An open-source modular toolkit for constructing tissue microarrays of arbitrary layouts

Biomedical research and quality control procedures often demand a variety of microscopic analysis of numerous formalin-fixed and paraffin-embedded (FFPE) tissue samples from different individuals of both healthy and diseased regions of interest. Depending on the number of samples to be analyzed, conventional processing of each FFPE block separately can be laborious or impracticable. This effort can be drastically reduced by using tissue microarrays (TMAs). TMAs have a wide range of applications and can be considered as a high-throughput method to process up to hundreds of miniaturized tissue samples simultaneously on a single microscopy slide, in order to reduce labor, costs and sample consumption, and to increase results comparability. Several commercial and self-made solutions to fabricate TMAs with varying degrees of automation are available. However, these solutions may not be suitable for every situation, either due to high costs, high complexity, lack of precision or lack of flexibility, especially when diagnostically oriented pathology institutes or laboratories with constrained resources are considered. This article introduces the TMA-Mate, an open-source 3D printable modular toolkit for constructing high-density TMAs of arbitrary layouts, providing an affordable, lightweight, and accessible procedure to implement TMAs into existing histology processing pipelines. Step-by-step demonstrations for replicating the hardware and constructing TMAs are included.

The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature

Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1′s expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient’ s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO—IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters.

Nonredundant Upregulation of CD112R (PVRIG) and PD-1 on Cytotoxic T Lymphocytes Located in T Cell Nests of Colorectal Cancer

Focal T lymphocyte aggregates commonly occur in colorectal cancer; however, their biological significance is unknown. To study focal aggregates of T lymphocytes, a deep learning-based framework for automated identification of T cell accumulations (T cell nests) was developed using CD8, PD-1, CD112R, and Ki67 multiplex fluorescence immunohistochemistry. To evaluate the clinical significance of these parameters, a cohort of 523 colorectal cancers with clinical follow-up data was analyzed. Spatial analysis of locally enriched CD8⁺ T cell density and cell-to-cell contacts identified T cell nests in the tumor microenvironment of colorectal cancer. CD112R and PD-1 expressions on CD8⁺ T cells located in T cell nests were found to be elevated compared with those on CD8⁺ T cells in all other tumor compartments (P < .001 each). Although the highest mean CD112R expression on CD8⁺ T cells was observed at the invasive margin, the PD-1 expression on CD8⁺ T cells was elevated in the center of the tumor (P < .001 each). Across all tissue compartments, proliferating CD8⁺ T cells showed higher relative CD112R and PD-1 expressions than those shown by non-proliferating CD8⁺ T cells (P < .001 each). Integration of all available spatial and immune checkpoint expression parameters revealed a superior predictive performance for overall survival (area under the curve, 0.65; 95% CI, 0.60-0.70) compared with the commonly used CD8⁺ tumor-infiltrating lymphocyte density (area under the curve, 0.57; 95% CI, 0.53-0.61; P < .001). Cytotoxic T cells with elevated CD112R and PD-1 expression levels are orchestrated in T cell nests of colorectal cancer and predict favorable patient outcomes, and the spatial nonredundancy underlies fundamental differences between both inhibitory immune checkpoints that provide a rationale for dual anti-CD112R/PD-1 immune checkpoint therapy.

Reliability of a computational platform as a surrogate for manually interpreted immunohistochemical markers in breast tumor tissue microarrays

BACKGROUND

Pathologist and computational assessments have been used to evaluate immunohistochemistry (IHC) in epidemiologic studies. We compared Definiens Tissue Studio® to pathologist scores for 17 markers measured in breast tumor tissue microarrays (TMAs) [AR, CD20, CD4, CD8, CD163, EPRS, ER, FASN, H3K27, IGF1R, IR, Ki67, phospho-mTOR, PR, PTEN, RXR, and VDR].

METHODS

5 914 Nurses' Health Study participants, diagnosed 1976-2006 (NHS) and 1989-2006 (NHS-II), were included. IHC was conducted by the Dana-Farber/Harvard Cancer Center Specialized Histopathology Laboratory. The percent of cells staining positive was assessed by breast pathologists. Definiens output was used to calculate a weighted average of percent of cells staining positive across TMA cores for each marker. Correlations between pathologist and computational scores were evaluated with Spearman correlation coefficients. Receiver-operator characteristic curves were constructed, using pathologist scores as comparison.

RESULTS

Spearman correlations between pathologist and Definiens assessments ranged from weak (RXR, rho=-0.05; CD163, rho = 0.10) to strong (Ki67, rho = 0.79; pmTOR, rho = 0.77). The area under the curve was >0.70 for all markers except RXR.

CONCLUSION

Our data indicate that computational assessments exhibit variable correlations with interpretations made by an expert pathologist, depending on the marker evaluated. This study provides evidence supporting the use of computational platforms for IHC evaluation in large-scale epidemiologic studies, with the caveat that pilot studies are necessary to investigate agreement with expert assessments. In sum, computational platforms may provide greater efficiency and facilitate high-throughput epidemiologic analyses.

Interobserver concordance in visual assessment of Ki67 immunohistochemistry in surgical excision specimens from patients with lymph node-negative breast cancer

PURPOSE

This study aimed to determine the interobserver concordance of two methods for proliferation assessment in breast cancer using Ki67 immunohistochemistry.

METHODS

Ki67 was independently assessed in randomly selected tumour samples from patients with lymph node-negative breast cancer using two different methods: either cell counting or visual estimation of hot spot areas. For hot spot cell counting, positive and negative cell numbers were recorded for total cell counts of 300-500, 500-800 and 800-1000 cells. Visual estimation involved allocation of a score from 1 to 5 using a visual scale to estimate percentage positivity. Interobserver agreement for hot spot counting was calculated using a two-way fixed effects intraclass correlation model, and by using Cohen's kappa measure for visual assessment. Prognostic concordance between the two methods was also calculated using Cohen's kappa.

RESULTS

Samples from 96 patients were included in this analysis. Interobserver agreement for hot spot cell counting was excellent (> 0.75) across all three cell count ranges, with correlation coefficients of 0.88 (95% CI 0.84-0.92), 0.87 (95% CI 0.82-0.91) and 0.89 (95% CI 0.85-0.92), respectively. Interobserver agreement with visual estimation was greatest for hot spots compared with areas of intermediate or low proliferation, with kappa scores of 0.49, 0.42 and 0.40, respectively. Both assessment methods demonstrated excellent prognostic agreement.

CONCLUSIONS

Interobserver and prognostic concordance in Ki67 immunohistochemistry assessments was high using either hot spot cell counting or visual estimation, further supporting the utility and reproducibility of these cost-efficient methods to assess proliferation.

Cutoff of 25% for Ki67 expression is a good classification tool for prognosis in colorectal cancer in the AJCC‑8 stratification

Ki‑67 expression has been widely used in clinical practice as an index to evaluate the proliferative activity of tumor cells. The cutoff for Ki67 expression in order to increase the prognostic value of Ki67 expression in colorectal cancer varies. The present study assessed the relationship between the 25% cutoff for Ki67 expression and prognosis in colorectal cancer in the AJCC‑8 (American Joint Committee on Cancer 8 edition) stratification. The current trial included 1,090 colorectal cancer patients enrolled from 2006 to 2012 at Huzhou Central Hospital. Ki67 expression was classified according to 25% intervals, dividing the patients into four groups. Measurement data were analyzed by ANOVA, and count data by Crosstabs. Bivariate correlation analysis was performed to assess clinicopathological indicators based on Ki67 expression. Disease‑free survival (DFS) and overall survival (OS) based on Ki67 levels were analyzed by the Kaplan‑Meier method. A total of 1,090 patients of the 2,080 enrolled CRC cases were evaluated (52.4%). Invasive depth, tumor differentiation, tumor size, AJCC‑8, positive number of lymph nodes and chemotherapy status showed significant differences in the various Ki67 expression groups (all P<0.05), with significant correlations (Spearman rho: 0.170, 0.456, 0.22, 0.195, 0.514 and ‑0.201, respectively, all P<0.001). DFS and OS for the different Ki67 level groups based on AJCC‑8 stratification were analyzed, and no significance was found in stage IV (P=0.334). DFS and OS survival rates were assessed at different Ki67 expression levels, and no significant differences were found (all P>0.05). Cox regression analysis showed that invasive depth, lymph node metastasis, tumor differentiation, AJCC‑8 and Ki67 were independent factors affecting colorectal cancer (P=0.030, all others P<0.001). In conclusion, a cutoff of 25% for Ki67 expression is a good classification tool. High Ki67 has a close association with poor prognosis in colorectal cancer and independently predicts prognosis in the AJCC‑8 stratification.

Combined quantitative measures of ER, PR, HER2, and KI67 provide more prognostic information than categorical combinations in luminal breast cancer

Although most women with luminal breast cancer do well on endocrine therapy alone, some will develop fatal recurrence thereby necessitating the need to prospectively determine those for whom additional cytotoxic therapy will be beneficial. Categorical combinations of immunohistochemical measures of ER, PR, HER2, and KI67 are traditionally used to classify patients into luminal A-like and B-like subtypes for chemotherapeutic reasons, but this may lead to the loss of prognostically relevant information. Here, we compared the prognostic value of quantitative measures of these markers, combined in the IHC4-score, to categorical combinations in subtypes. Using image analysis-based scores for all four markers, we computed the IHC4-score for 2498 patients with luminal breast cancer from two European study populations. We defined subtypes (A-like (ER + and PR + : and HER2- and low KI67) and B-like (ER + and/or PR + : and HER2 + or high KI67)) by combining binary categories of these markers. Hazard ratios and 95% confidence intervals for associations with 10-year breast cancer-specific survival were estimated in Cox proportional-hazard models. We accounted for clinical prognostic factors, including grade, tumor size, lymph-nodal involvement, and age, by using the PREDICT-score. Overall, Subtypes [hazard ratio (95% confidence interval) B-like vs. A-like = 1.64 (1.25-2.14); P-value < 0.001] and IHC4-score [hazard ratio (95% confidence interval)/1 standard deviation = 1.32 (1.20-1.44); P-value < 0.001] were prognostic in univariable models. However, IHC4-score [hazard ratio (95% confidence interval)/1 standard deviation = 1.24 (1.11-1.37); P-value < 0.001; likelihood ratio chi-square (LRχ2) = 12.5] provided more prognostic information than Subtype [hazard ratio (95% confidence interval) B-like vs. A-like = 1.38 (1.02-1.88); P-value = 0.04; LRχ2 = 4.3] in multivariable models. Further, higher values of the IHC4-score were associated with worse prognosis, regardless of subtype (P-heterogeneity = 0.97). These findings enhance the value of the IHC4-score as an adjunct to clinical prognostication tools for aiding chemotherapy decision-making in luminal breast cancer patients, irrespective of subtype.

Comparison of protein expression between formalin-fixed core-cut biopsies and surgical excision specimens using a novel multiplex approach

PURPOSE

We evaluated whether multiplex protein quantification using antibody bar-coding with photocleavable oligonucleotides (NanoString) can be applied to evaluate protein expression in breast cancer FFPE specimens. We also assessed whether diagnostic core-cuts fixed immediately at time of procedures and surgical excision sections from routinely fixed breast cancers are affected by the same fixation related differences noted using immunohistochemistry (IHC).

METHODS

The expression of 26 proteins was analysed using NanoString technology in 16 pairs of FFPE breast cancer core-cuts and surgical excisions. The measurements yielded were compared with those by IHC on Ki67, PgR and HER2 biomarkers and pAKT and pERK1/2 phosphorylated proteins.

RESULTS

When considered irrespective of sample type, expression measured by the two methods was strongly correlated for all markers (p < 0.001; ρ = 0.69-0.88). When core-cuts and excisions were evaluated separately, the correlations between NanoString and IHC were weaker but significant except for pAKT in excisions. Surgical excisions showed lower levels of 8/12 phosphoproteins and higher levels of 4/13 non-phosphorylated proteins in comparison to core-cuts (p < 0.01). Reduced p4EBP1, pAMPKa, pRPS6 and pRAF1 immunogenicity in excisions was correlated with tumour size and mastectomy specimens showed lower p4EBP1 and pRPS6 expression than lumpectomy (p < 0.05).

CONCLUSIONS

Our study supports the validity of the new multiplex approach to protein analysis but indicates that, as with IHC, caution is necessary for the analysis in excisions particularly of phosphoproteins. The specimen type, tumour size and surgery type may lead to biases in the quantitative analysis of many proteins of biologic and clinical interest in excision specimens.

Practical approaches to automated digital image analysis of Ki-67 labeling index in 997 breast carcinomas and causes of discordance with visual assessment

The Ki-67 labeling index (LI) is an important prognostic factor in breast carcinoma. The Ki-67 LI is traditionally calculated via unaided microscopic estimation; however, inter-observer and intra-observer variability and low reproducibility are problems with this visual assessment (VA) method. For more accurate assessment and better reproducibility with Ki-67 LI, digital image analysis was introduced recently. We used both VA and automated digital image analysis (ADIA) (Ventana Virtuoso image management software) to estimate Ki-67 LI for 997 cases of breast carcinoma, and compared VA and ADIA results. VA and ADIA were highly correlated (intraclass correlation coefficient 0.982, and Spearman’s correlation coefficient 0.966, p<0.05). We retrospectively analyzed cases with a greater than 5% difference between VA and ADIA results. The cause of these differences was: (1) tumor heterogeneity (98 cases, 56.0%), (2) VA interpretation error (32 cases, 18.3%), (3) misidentification of tumor cells (26 cases, 14.9%), (4) poor immunostaining or slide quality (16 cases, 9.1%), and (5) Estimation of non-tumor cells (3 cases, 1.7%). There were more discrepancies between VA and ADIA results in the group with a VA value of 10–20% compared to groups with <10% and ≥20%. Although ADIA is more accurate than VA, there are some limitations. Therefore, ADIA findings require confirmation by a pathologist.

Etiology of hormone receptor positive breast cancer differs by levels of histologic grade and proliferation

Limited epidemiological evidence suggests that the etiology of hormone receptor positive (HR+) breast cancer may differ by levels of histologic grade and proliferation. We pooled risk factor and pathology data on 5,905 HR+ breast cancer cases and 26,281 controls from 11 epidemiological studies. Proliferation was determined by centralized automated measures of KI67 in tissue microarrays. Odds ratios (OR), 95% confidence intervals (CI) and p-values for case-case and case-control comparisons for risk factors in relation to levels of grade and quartiles (Q1-Q4) of KI67 were estimated using polytomous logistic regression models. Case-case comparisons showed associations between nulliparity and high KI67 [OR (95% CI) for Q4 vs. Q1 = 1.54 (1.22, 1.95)]; obesity and high grade [grade 3 vs. 1 = 1.68 (1.31, 2.16)] and current use of combined hormone therapy (HT) and low grade [grade 3 vs. 1 = 0.27 (0.16, 0.44)] tumors. In case-control comparisons, nulliparity was associated with elevated risk of tumors with high but not low levels of proliferation [1.43 (1.14, 1.81) for KI67 Q4 vs. 0.83 (0.60, 1.14) for KI67 Q1]; obesity among women ≥50 years with high but not low grade tumors [1.55 (1.17, 2.06) for grade 3 vs. 0.88 (0.66, 1.16) for grade 1] and HT with low but not high grade tumors [3.07 (2.22, 4.23) for grade 1 vs. 0.85 (0.55, 1.30) for grade 3]. Menarcheal age and family history were similarly associated with HR+ tumors of different grade or KI67 levels. These findings provide insights into the etiologic heterogeneity of HR+ tumors.

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer

Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.

An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer

Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring. In this article, we suggest an advanced deep learning model for computerized recognition of candidate hotspots and subsequent proliferation rate scoring by quantifying Ki-67 appearance in breast cancer immunohistochemical images. Unlike existing Ki-67 scoring techniques, our methodology uses Gamma mixture model (GMM) with Expectation-Maximization for seed point detection and patch selection and deep learning, comprises with decision layer, for hotspots detection and proliferation scoring. Experimental results provide 93% precision, 0.88% recall and 0.91% F-score value. The model performance has also been compared with the pathologists’ manual annotations and recently published articles. In future, the proposed deep learning framework will be highly reliable and beneficial to the junior and senior pathologists for fast and efficient Ki-67 scoring.

Comparison of immunohistochemistry with PCR for assessment of ER, PR, and Ki-67 and prediction of pathological complete response in breast cancer

Background

Proliferation may predict response to neoadjuvant therapy of breast cancer and is commonly assessed by manual scoring of slides stained by immunohistochemistry (IHC) for Ki-67 similar to ER and PgR. This method carries significant intra- and inter-observer variability. Automatic scoring of Ki-67 with digital image analysis (qIHC) or assessment of MKI67 gene expression with RT-qPCR may improve diagnostic accuracy.

Methods

Ki-67 IHC visual assessment was compared to the IHC nuclear tool (AperioTM) on core biopsies from a randomized neoadjuvant clinical trial. Expression of ESR1, PGR and MKI67 by RT-qPCR was performed on RNA extracted from the same formalin-fixed paraffin-embedded tissue. Concordance between the three methods (vIHC, qIHC and RT-qPCR) was assessed for all 3 markers. The potential of Ki-67 IHC and RT-qPCR to predict pathological complete response (pCR) was evaluated using ROC analysis and non-parametric Mann-Whitney Test.

Results

Correlation between methods (qIHC versus RT-qPCR) was high for ER and PgR (spearman´s r = 0.82, p < 0.0001 and r = 0.86, p < 0.0001, respectively) resulting in high levels of concordance using predefined cut-offs. When comparing qIHC of ER and PgR with RT-qPCR of ESR1 and PGR the overall agreement was 96.6 and 91.4%, respectively, while overall agreement of visual IHC with RT-qPCR was slightly lower for ER/ESR1 and PR/PGR (91.2 and 92.9%, respectively). In contrast, only a moderate correlation was observed between qIHC and RT-qPCR continuous data for Ki-67/MKI67 (Spearman’s r = 0.50, p = 0.0001). Up to now no predictive cut-off for Ki-67 assessment by IHC has been established to predict response to neoadjuvant chemotherapy. Setting the desired sensitivity at 100%, specificity for the prediction of pCR (ypT0ypN0) was significantly higher for mRNA than for protein (68.9% vs. 22.2%). Moreover, the proliferation levels in patients achieving a pCR versus not differed significantly using MKI67 RNA expression (Mann-Whitney p = 0.002), but not with qIHC of Ki-67 (Mann-Whitney p = 0.097) or vIHC of Ki-67 (p = 0.131).

Conclusion

Digital image analysis can successfully be implemented for assessing ER, PR and Ki-67. IHC for ER and PR reveals high concordance with RT-qPCR. However, RT-qPCR displays a broader dynamic range and higher sensitivity than IHC. Moreover, correlation between Ki-67 qIHC and RT-qPCR is only moderate and RT-qPCR with MammaTyper® outperforms qIHC in predicting pCR. Both methods yield improvements to error-prone manual scoring of Ki-67. However, RT-qPCR was significantly more specific.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3111-1) contains supplementary material, which is available to authorized users.

Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups

BACKGROUND

The value of KI67 in breast cancer prognostication has been questioned due to concerns on the analytical validity of visual KI67 assessment and methodological limitations of published studies. Here, we investigate the prognostic value of automated KI67 scoring in a large, multicentre study, and compare this with pathologists' visual scores available in a subset of patients.

METHODS

We utilised 143 tissue microarrays containing 15,313 tumour tissue cores from 8088 breast cancer patients in 10 collaborating studies. A total of 1401 deaths occurred during a median follow-up of 7.5 years. Centralised KI67 assessment was performed using an automated scoring protocol. The relationship of KI67 levels with 10-year breast cancer specific survival (BCSS) was investigated using Kaplan-Meier survival curves and Cox proportional hazard regression models adjusted for known prognostic factors.

RESULTS

Patients in the highest quartile of KI67 (>12 % positive KI67 cells) had a worse 10-year BCSS than patients in the lower three quartiles. This association was statistically significant for ER-positive patients (hazard ratio (HR) (95 % CI) at baseline = 1.96 (1.31-2.93); P = 0.001) but not for ER-negative patients (1.23 (0.86-1.77); P = 0.248) (P-heterogeneity = 0.064). In spite of differences in characteristics of the study populations, the estimates of HR were consistent across all studies (P-heterogeneity = 0.941 for ER-positive and P-heterogeneity = 0.866 for ER-negative). Among ER-positive cancers, KI67 was associated with worse prognosis in both node-negative (2.47 (1.16-5.27)) and node-positive (1.74 (1.05-2.86)) tumours (P-heterogeneity = 0.671). Further classification according to ER, PR and HER2 showed statistically significant associations with prognosis among hormone receptor-positive patients regardless of HER2 status (P-heterogeneity = 0.270) and among triple-negative patients (1.70 (1.02-2.84)). Model fit parameters were similar for visual and automated measures of KI67 in a subset of 2440 patients with information from both sources.

CONCLUSIONS

Findings from this large-scale multicentre analysis with centrally generated automated KI67 scores show strong evidence in support of a prognostic value for automated KI67 scoring in breast cancer. Given the advantages of automated scoring in terms of its potential for standardisation, reproducibility and throughput, automated methods appear to be promising alternatives to visual scoring for KI67 assessment.

Impact of intratumoural heterogeneity on the assessment of Ki67 expression in breast cancer

In breast cancer (BC), the prognostic value of Ki67 expression is well-documented. Intratumoural heterogeneity (ITH) of Ki67 expression is amongst the several technical issues behind the lag of its inclusion into BC prognostic work-up. The immunohistochemical (IHC) expression of anti-Ki67 antibody (MIB1 clone) was assessed in four full-face (FF) sections from different primary tumour blocks and their matched axillary nodal (LN) metastases in a series of 55 BC. Assessment was made using the highest expression hot spots (HS), lowest expression (LS), and overall/average expression scores (AS) in each section. Heterogeneity score (Hes), co-efficient of variation, and correlation co-efficient were used to assess the levels of Ki67 ITH. Ki67 HS, LS, and AS scores were highly variable within the same section and between different sections of the primary tumour, with maximal variation observed in the LS (P < 0.001). The least variability between the different slides was observed with HS scoring. Although the associations between Ki67 and clinicopathological and molecular variables were similar when using HS or AS, the best correlation between AS and HS was observed in tumours with high Ki67 expression only. Ki67 expression in LN deposits was less heterogeneous than in the primary tumours and was perfectly correlated with the HS Ki67 expression in the primary tumour sections (r = 0.98, P < 0.001). In conclusion, assessment of Ki67 expression using HS scoring method on a full-face BC tissue section can represent the primary tumour growth fraction that is likely to metastasise. The association between Ki67 expression pattern in the LN metastasis and the HS in the primary tumour may reflect the temporal heterogeneity through clonal expansion.