Digital image analysis outperforms manual biomarker assessment in breast cancer

Abstract

In the spectrum of breast cancers, categorization according to the four gene expression-based subtypes 'Luminal A,' 'Luminal B,' 'HER2-enriched,' and 'Basal-like' is the method of choice for prognostic and predictive value. As gene expression assays are not yet universally available, routine immunohistochemical stains act as surrogate markers for these subtypes. Thus, congruence of surrogate markers and gene expression tests is of utmost importance. In this study, 3 cohorts of primary breast cancer specimens (total n = 436) with up to 28 years of survival data were scored for Ki67, ER, PR, and HER2 status manually and by digital image analysis (DIA). The results were then compared for sensitivity and specificity for the Luminal B subtype, concordance to PAM50 assays in subtype classification and prognostic power. The DIA system used was the Visiopharm Integrator System. DIA outperformed manual scoring in terms of sensitivity and specificity for the Luminal B subtype, widely considered the most challenging distinction in surrogate subclassification, and produced slightly better concordance and Cohen's κ agreement with PAM50 gene expression assays. Manual biomarker scores and DIA essentially matched each other for Cox regression hazard ratios for all-cause mortality. When the Nottingham combined histologic grade (Elston-Ellis) was used as a prognostic surrogate, stronger Spearman's rank-order correlations were produced by DIA. Prognostic value of Ki67 scores in terms of likelihood ratio χ 2 (LR χ 2 ) was higher for DIA that also added significantly more prognostic information to the manual scores (LR− Δχ 2 ). In conclusion, the system for DIA evaluated here was in most aspects a superior alternative to manual biomarker scoring. It also has the potential to reduce time consumption for pathologists, as many of the steps in the workflow are either automatic or feasible to manage without pathological expertise.

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers

“…FFPE archived material was sectioned in 4 um, mounted and stained according to routine protocol at the Laboratory of Clinical Pathology and Cytology at Karolinska University Hospital30.…”

Digital image analysis of Ki67 in hot spots is superior to both manual Ki67 and mitotic counts in breast cancer

“…In a previous study, we showed that applying digital image analysis (DIA) to the assessment of Ki67 and other biomarkers in breast cancer could improve this reproducibility . Furthermore, it provided better concordance with gene expression assays and histological grade, albeit without mitotic rate and survival data being available for all cohorts . Here, we used the same system to compare the scoring of Ki67 in different tumour regions with mitotic counts and the number of phosphohistone H3 (PHH3)‐positive cells in relation to patient survival.…”

Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer

“…This is consistent with reports suggesting that Ki67 is particularly challenging for DIA, with a dependency upon the specific antibody. 9,26 Robustness of Cutoff Thresholds Table 2 demonstrates that a highly significant association with survival can be obtained using DIA in almost all cases, even when using quite different methods of cutoff selection that result in very different cutoff values. For example, the Pvalues obtained using thresholds of 5 and 126.5 were almost identical when applied to ER (both o0.002), despite the fact that changing the cutoff resulted in 43 patients (16%) being assigned to different groups.…”