Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium

Abstract

Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa 5 0.76), followed by human epidermal growth factor receptor 2 (Kappa 5 0.69) and progesterone receptor (Kappa 5 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value 5 30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.Keywords: breast tumours; immunohistochemistry; tissue microarrays; digital pathology; automated scoring Received 11 March 2014; accepted 28 May 2014 † These authors jointly directed this work. Conflict of interest: The authors have declared no conflicts of interest. IntroductionBreast cancer is a biologically heterogeneous disease, which comprises multiple distinctive subtypes that are distinguishable by immunohistochemistry (IHC) [1,2] or molecular analysis such as transcriptomic profiling [3][4][5]. Clinically, IHC staining for oestrogen receptor (ER), progesterone receptor (PR) and epidermal growth factor receptor 2 (HER2) is routinely performed in most diagnostic laboratories to help select adjuvant treatment and to assess prognosis [6,7]. Research studies demonstrate that expanding this IHC panel to include markers of basal breast cancers, such as cytoke...

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

“…Given the advantages of automated versus visual scoring in terms of its potential for standardisation, reproducibility and throughput, automated methods appear to be promising alternatives to visual scoring for KI67 assessment. A potential limitation to the adoption of automated KI67 scoring in the clinical setting is that misclassification of positive nuclei as negative or malignant nuclei as benign could lead to attenuation of prognostic associations, an observation that has been reported previously for ER and PR [ 31 ] and one which we have also observed for KI67 in this analysis. This can be mitigated, however, by stringent quality control processes or by the adoption of a synergistic approach that combines the benefits of both the automated and visual scoring methods.…”

Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification

“…These limitations are balanced by several important strengths. Since automated staining of TMAs is becoming more widely used [ 17 , 33 ], we assessed automated evidence of intratumoral heterogeneity (i.e., biomarker discordance between TMA cores), and our results can therefore be used to guide manual review. Our automated image analysis methods are well-validated and produce very high agreement with manual scoring of TMAs in CBCS [ 17 ].…”

Crowdsourcing the General Public for Large Scale Molecular Pathology Studies in Cancer

“…Lymphocyte expression of ER can cause false positive results ( Pierdominici et al, 2010 ). The poor calling of cancer cells is also reported in studies with automated algorithms ( Ali et al, 2013 , Howat et al, 2015 ). This limitation is, perhaps, not surprising given that breast cancer cells are morphologically heterogeneous.…”

“…If they do identify cancer cells, the Citizen Scientist is asked to estimate: the number of cancer cells on a scale of 1 to 4 corresponding to 1 to 5, 6 to 10, 11 to 20 and more than 20 cancer cells; the proportion of nuclei staining positive on a scale of 0 to 5 corresponding to 0%, < 1%, 1 to 9%, 10 to 33%, 34 to 66% and 67 to 100%; and the intensity of staining on a scale of 0 to 3 corresponding to no staining, weak, moderate and strong staining. This scoring was designed to approximate the Allred scoring system which is commonly used in clinical practice ( Howat et al, 2015 ). The Allred system scores the proportion of nuclei staining positive on a scale of 0–5 (0%, < 1%, 1 to 9%, 10 to 33%, 34 to 66% and 67 to 100%) and the intensity of staining on a four point scale (no staining, weak, moderate and strong, 0–3).…”

“…One solution to this bottleneck is to scan the labeled sections and to use automated analysis of the digitized images of each core. Several image analysis algorithms have been shown to perform reasonably well for some IHC markers ( Giltnane and Rimm, 2004 , Bolton et al, 2010 , Ali et al, 2013 , Howat et al, 2015 ). While automated image analysis remains promising, its implementation may be complex and it has not yet replaced manual scoring in large scale molecular epidemiology studies in cancer.…”