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Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

The standardized preanalytical code (SPREC) aggregates warm ischemia (WIT), cold ischemia (CIT), and fixation times (FIT) in a precise format. Despite its growing importance underpinned by the European in vitro diagnostics regulation or broad preanalytical programs by the National Institutes of Health, little is known about its empirical occurrence in biobanked surgical specimen. In several steps, the Tissue Bank Bern achieved a fully informative SPREC code with insights from 10,555 CIT, 4,740 WIT, and 3,121 FIT values. During process optimization according to LEAN six sigma principles, we identified a dual role of the SPREC code as a sample characteristic and a traceable process parameter. With this preanalytical study, we outlined real‐life data in a variety of organs with specific differences in WIT, CIT, and FIT values. Furthermore, our FIT data indicate the potential to adapt the SPREC fixation toward concrete paraffin‐embedding time points and to extend its categories beyond 72 h due to weekend delays. Additionally, we identified dependencies of preanalytical variables from workload, daytime, and clinics that were actionable with LEAN process management. Thus, streamlined biobanking workflows during the day were significantly resilient to workload peaks, diminishing the turnaround times of native tissue processing (i.e. CIT) from 74.6 to 46.1 min under heavily stressed conditions. In conclusion, there are surgery‐specific preanalytics that are surgico‐pathologically limited even under process optimization, which might affect biomarker transfer from one entity to another. Beyond sample characteristics, SPREC coding is highly beneficial for tissue banks and Institutes of Pathology to track WIT, CIT, and FIT for process optimization and monitoring measurements. [ABSTRACT FROM AUTHOR]

Aim: Tumour budding ('attacker') and CD8+ T cells ('defender') are recognised as important parameters for risk stratification in colon cancers and, combined, may have an even stronger clinical impact. Here, we determine the value of tumour budding and CD8+ in rectal cancer patients treated with/without neoadjuvant therapy. Methods and results: Using digital scans of all tumour slides/case, we analysed CD8+ T cell counts in two patient cohorts: 45 neoadjuvantly treated and 47 primarily surgically treated (totalling n = 543 slides) after double‐staining of the surgical resection specimen for pan‐cytokeratin and CD8+. Tumour buds in hot‐spots were manually counted (area = 0.785 mm2) and CD8+ T cell counts were analysed separately both in tumour budding hot‐spots and the densest CD8+ regions throughout the tumour. In neoadjuvantly treated patients, only tumour budding and not CD8+ T cells was associated with tumour features, including more advanced ypT (P = 0.0062), venous invasion (P = 0.002), lymphatic invasion (P = 0.0003) and perineural invasion (P = 0.0017), as well as higher American Joint Committee on Cancer (AJCC) tumour regression score (P = 0.0035), indicating less tumour response. Overall survival was also worse in patients with high‐grade budding in univariate analysis only. In contrast, all three variables, namely tumour budding (P = 0.0347), CD8+ T cells in budding hot‐spots (P = 0.0382) and CD8+ T cells in the densest areas (P = 0.0117) were also associated with worse (budding) and better (CD8) survival time in the multivariate setting. Conclusion: In rectal cancer, tumour budding has clinical relevance in both primarily surgically treated patients and in those with neoadjuvantly treated patients, where it characterises highly aggressive residual disease. CD8+ T cell counts appear not to have prognostic relevance in the neoadjuvant context. [ABSTRACT FROM AUTHOR]

Tumour budding in colorectal cancer, defined as single tumour cells or small clusters containing four or fewer tumour cells, is a robust and independent biomarker of aggressive tumour biology. On the basis of published data in the literature, the evidence is certainly in favour of reporting tumour budding in routine practice. One important aspect of implementing tumour budding has been to establish a standardised and evidence‐based scoring method, as was recommended by the International Tumour Budding Consensus Conference (ITBCC) in 2016. Further developments have aimed at establishing methods for automated tumour budding assessment. A digital approach to scoring tumour buds has great potential to assist in performing an objective budding count but, like the manual consensus method, must be validated and standardised. The aim of the present review is to present general considerations behind the ITBCC scoring method, and a broad overview of the current situation and challenges regarding automated tumour budding detection methods. [ABSTRACT FROM AUTHOR]