Assessment of proliferative activity in breast cancer
Approaches, significance and challenges
Cell proliferation is a hallmark of cancer associated with aggressiveness and response to chemotherapy. In breast cancer (BC), assessment of proliferation constitutes the main component of grading and help in management. Herein, we address the most common approaches for BC proliferation assessment,
their limitations, and evolving applications to overcome these challenges.
Cell proliferation in breast cancer
Uncontrolled cell proliferation is a hallmark of cancer initiation and progression, and predicts the response to cytotoxic chemotherapy. Tumour proliferation is a complex process that is tightly controlled by several factors including growth factors, hormones, genetics, epigenetics and the tumour microenvironment. The ultimate effect of these factors is cell division through the several phases of cell cycle (Fig. 1). Understanding the underlying mechanisms that control proliferation could help in identifying therapeutic targets to suppress tumour growth. The proliferative activity of a tumour is typically determined by measuring the percentage of tumour cells in the different stages of the cell cycle, including the interphase and mitotic phases.
Tumour proliferative activity in breast cancer (BC) has important implications not only for diagnostic but also for prognostic and therapeutic purposes [1]. The Nottingham grading system (NGS) for BC relies on assessment of three components, which are (i) mitotic scores, which reflects the BC proliferative activity; (ii) degree of tubule formation; and (iii) pleomorphism [2]. NGS is a key component of various prognostic indices which are used for risk stratification of BC patients, such as Nottingham prognostic index, Nottingham Px [3], and recently has been included in the tumour, node, metastasis (TNM) prognostic staging [4]. Ki67, which is an immunohistochemical (IHC) marker associated with cell proliferation has attracted a lot of attention as a prognostic marker in BC. Moreover, proliferation-related genes make up the main component of the prognostic multigene signatures.
Methods of proliferation assessment in BC
Methods of proliferation assessment in BC include estimation of the percentage of cells in the cell cycle using molecular assays, assessment of the dividing cells in the mitotic phase using either morphology (mitotic figure counting), or molecular assays such as IHC with mitotic specific markers (e.g. PHH3). Although complex multigene assays can be used to assess set(s) of proliferation-associated genes, perhaps the simplest, easiest and most practical method for proliferation assessment in BC is counting mitosis in hematoxylin and eosin (H&E)-stained tissue sections. The following sections will briefly highlight the various proliferation assessment methods in BC with emphasis on the limitations and challenges of each method. The potential advantages of digital pathology (DP) and artificial intelligence (AI) to improve proliferation assessment in BC will be also discussed.
Mitotic count
Counting mitotic figures in H&E sections remains the most standardized method for assessment of proliferation in BC that forms the main component of NGS. According to guideline recommen-dations, the counting of mitoses should be carried out within the hotspots, defined as areas of the highest number of mitoses within the tumour, which are usually at the invasive tumour edge [5]. BC is a heterogeneous disease and a substantial variation in mitotic rates is present in different areas of the same tumour; however, it is the highest proliferative components which are the most likely to be associated with aggressive tumour behaviour. Therefore, the hotspot approach is recommended over the average mitotic scores.
For practical reasons and to standardize mitotic assessment in BC, counting using microscopes should be performed in 10 high-power fields (HPFs) with adjustment of the field-of-view diameter to produce a mitotic count per specific area size. Counting should exclude non-invasive tumour areas, such as in situ carcinoma, necrosis, fibrosis, or inflammatory cells. A guidance table with the various mitotic score cut-off values dependent on the microscope field diameter is provided by the World Health Organization’s classification of BC. Detailed histological features of mitotic figures including the atypical forms (Fig. 2), which are considered as part of the mitotic scores are published [6].
Although widely used, counting mitoses using visual assessment has limitations with low concordance among observers (Kappa, 0.3–0.5) [7]. Given the presence of intra-tumoral heterogeneity, the choice of hotspots can be a major cause of such low reproducibility. Other factors that may contribute to low concordance are tissue fixation, staining quality, mitosis mimickers and microscope field diameter.
Although counting all mitosis forms (regardless of whether typical or atypical) is used to provide the overall mitosis score for BC grading, assessment of atypical forms may provide added value. In a recent study, we found that the atypical-to-typical mitoses ratio has prognostic value independent of the overall mitotic count in BC and could predict the response to chemotherapy in triple-negative BC [8].
Other techniques used to assess proliferation in BC
Various techniques other than mitotic count are used to estimate proliferative activity in BC. Bromodeoxyuridine and tritiated thy-midine [9] was used in earlier studies; however, this technique is impractical as it involves invasive procedures where patients need to be injected intravenously and fresh tissue should be examined. Flow cytometry was used to measure the percentage of cells in S phase of the replicative cycle, but it seems to be impractical [10]. Evaluation of the proliferative activity using IHC represents an easy, and reliable method [11]. Many biomarkers including, Ki67, DNA topoisomerase 2-alpha (TOP2A), phospho-histone H3 (PHH3), proliferating cell nuclear antigen (PCNA), and minichromosome maintenance (MCM) proteins are expressed in the different phases of the cell cycle and can be used to assess proliferation in BC (Figs 2 & 3); however, Ki67 is the most widely used protein [12,13]. Ki67 localizes in the nucleus and attaches to the perichromosomal layer of dividing cells which can be visualized using IHC (Fig. 4).
Ki67 is used to categorize luminal A from luminal B tumours, which show a higher proliferation index. Several methods for Ki67 assessment in BC were addressed in the literature, including (i) estimation of the average percentage of positive tumour cells in the whole section, (ii) the proportion of positive tumour cells within the hotspots only, or (iii) to count 1000 invasive tumour cells within hotspots and calculate the percentage of positive nuclei among them. Although the latter method is tedious and time-consuming, it is a widely accepted method among pathologists, and it showed the best association with tumour behaviour and outcome.
There are still several challenges in Ki67 assessment which result in limited clinical applicability. These include pre-analytic factors, such as tissue fixation, antibody used and staining platform, or analytic-related causes such as scoring method and identification of the hotspots (intra-tumoral heterogeneity of Ki67 is not uncommon). Post-analytic factors in terms of data handing and cut-off values to categorize high versus low proliferative groups are also considered. Ki67 expression follows a continuous distribution for prognosis where every 10% increase in the index is associated with about 20% increase in mortality risk [14]. The international Ki67 Working Group published recommendations to reduce the inter-laboratory variability to increase Ki67 applicability in routine practice. These include:
1. use of the mouse anti-human Ki67 monoclonal antibody MIB1 is warranted;
2. core and whole tissue sections are suitable for assessment;
3. proper specimen fixation is needed;
4. prolonged exposure of cut sections to air should be avoided;
5. heat-induced retrieval is recommended;
6. proper training for scoring should be provided;
7. at least three HPFs (×40 objective) should be selected to represent the spectrum of staining among the whole section;
8. the cells at the invasive edge of the tumour should be scored as it represents the biologically active part;
9. scoring should involve the counting of at least 500 (or more preferable 1000) invasive tumour cells;
10. Ki67 score/index should be expressed as the percentage of positively stained cells among the total number of invasive cells scored; and
11. cut-offs for prognosis, and monitoring should be applied if the results from local practice have been validated [15].
Should we assess mitosis or the overall proliferative pool in BC?
The previous sections highlighted the value of both mitosis and Ki67 as indicators of the tumour proliferation rate. This would raise the question if they are alternatives or complementary to each other? Although both can act as surrogate markers for proliferation in BC, we believe that they complementary. In a recent study, we have quantified the proportion of BC cells in the cell cycle and in mitosis. We observed that the mean proportion of BC cells in mitosis was only 5%. A high proportion of cells entering mitosis was significantly associated with poor prognostic parameters.
Although mitotic counting is easy and more defined as the cut-off for NGS mitotic scores are standardized and universally accepted, Ki67 performs very well and better than mitotic count in poorly fixed tumours and can highlight BC with very low and very high proliferative activity better than the three-tier mitotic score. Ki67 can also provide additional prognostic information in intermediate (grade 2) BC cases. However, the use of Ki67 in BC still has some limitations, such as the lack of agreement of a cut-off value, is time- and cost-consuming compared with mitotic counts, and there is variation between staining platforms.
Other proliferation and genetic profiles
A number of multi-parameter genomic tools such as Oncotype DX® (Genomic Health), MammaPrint® (Agendia), Prediction Analysis of Microarray 50 (PAM50; Prosigna) and EndoPredict® (EP) are useful for prognostication for BC patients [16]. It is noted that, proliferation markers are an integral component among most of these assays. For example, the Ki67 gene (MKI67) is one of the more heavily weighted of the 16 genes in the Oncotype DX assay which provides a measure of recurrence risk for women with hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative disease. However, these assays are expensive, have high levels of discordant results and can be replaced in many situations by the cheap and readily available morphological features and IHC-based markers [17].
Assessment of BC proliferative activity in the era of DP and AI
DP is a relatively new diagnostic technique, that entails converting diagnostic glass slides into whole-slide images (WSIs). Mitosis scoring was a perfect target for computer-assisted detection algorithms as it is very time-consuming and has low concordance levels. The quality of the hardware (the scanners and related infrastructure) and viewing software used can significantly and synergistically affect the computer-assisted mitosis detection [6].
The ability to identify mitotic figures and to differentiate them from their mimickers is different between using conventional light microscope and digitalized images. The former relies on the recognition of some fine details that can be lost in the latter. In BC grading studies using WSIs, the inter-observer and intra-observer agreement on mitotic scores were higher on glass slides. It is recognized that loss of fine-tuning capability in WSI and lack of refined, objective criteria for mitosis identification can impede detection of true mitotic figures on WSI. We have published a recent study to refine the criteria of mitosis detection in WSIs. We found that 58% of mitoses had an absence of hair-like projections in WSI, whereas 89% retained their ragged nuclear border, which distinguished them from mimickers including apoptotic cells, lymphocytes and dark elongated hyperchromatic structures [18]. As a result, all morphological features of mitosis should be considered in WSI to enable recognition and differentiation from their mimickers.
Another challenge to note is that counting mitoses on WSIs was more time-consuming than counting mitoses using conventional microscopes. This extra time is usually spent on annotating the area for counting, marking each mitotic figure, and counting the total number of annotations. The size of the area to count mitotic figures as well as the area for counting (hotspots) that should be chosen on WSI are not defined. Recently, we have defined selection criteria for hotspot identification and assessed the reproducibility, representativeness, time, and association with BC outcome [19]. We concluded that visual mitosis scoring on WSI can be performed reliably by adjusting the number of monitor screens to achieve 3mm2 area for mitosis counting. We believe this research, in addition to similar studies, are essential as preliminary evidence-based findings before training AI based algorithms for mitosis detection and counting, which started to show promising results.
Similar applications can be provided to score Ki67 in BC. The Working Group developed a standardized web-based calibration tool (www.gpec.ubc.ca/calibrator) that uses a formal counting methodology in order to harmonize scoring, which showed a significant trend of improvement in reproducibility [20].
Summary
To summarize, this review provides a brief highlight into the importance of proliferation assessment in BC with emphasis on the methods of evaluation, challenges and potential future approaches that could improve proliferation assessment in BC.
The authors
Michael S. Toss1,2 PhD, Ayat Lashen1, Asmaa Ibrahim1 and Emad Rakha*1 MD, PhD
1 Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK,
2 Department of Histopathology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
* Corresponding author E-mail: emad.rakha@nottingham.ac.uk, emad.rakha@nuh.nhs.uk
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