Lymph node metastases and recurrence in pT1 colorectal cancer: Prediction with the International Budding Consortium Score-A retrospective, multi-centric study

BACKGROUND

The International Collaboration on Cancer Reporting proposes histological tumour type, lymphovascular invasion, tumour grade, perineural invasion, extent, and dimensions of invasion as risk factors for lymph node metastases and tumour progression in completely endoscopically resected pT1 colorectal cancer (CRC).

OBJECTIVE

The aim of the study was to propose a predictive and reliable score to optimise the clinical management of endoscopically resected pT1 CRC patients.

METHODS

This multi-centric, retrospective International Budding Consortium (IBC) study included an international pT1 CRC cohort of 565 patients. All cases were reviewed by eight expert gastrointestinal pathologists. All risk factors were reported according to international guidelines. Tumour budding and immune response (CD8+ T-cells) were assessed with automated models using artificial intelligence. We used the information on risk factors and least absolute shrinkage and selection operator logistic regression to develop a prediction model and generate a score to predict the occurrence of lymph node metastasis or cancer recurrence.

RESULTS

The IBC prediction score included the following parameters: lymphovascular invasion, tumour buds, infiltration depth and tumour grade. The score has an acceptable discrimination power (area under the curve of 0.68 [95% confidence intervals (CI) 0.61-0.75]; 0.64 [95% CI 0.57-0.71] after internal validation). At a cut-off of 6.8 points to discriminate high-and low-risk patients, the score had a sensitivity and specificity of 0.9 [95% CI 0.8-0.95] and 0.26 [95% 0.22, 0.3], respectively.

CONCLUSION

The IBC score is based on well-established risk factors and is a promising tool with clinical utility to support the management of pT1 CRC patients.

Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials.

ARID3B expression in primary breast cancers and breast cancer-derived cell lines

BACKGROUND

ARID3B (AT-rich interaction domain 3) is a member of the family of ARID proteins, which constitutes evolutionarily conserved transcription factors implicated in normal development, differentiation, cell cycle regulation and chromatin remodeling. In addition, ARID3B has been linked to cellular immortalization, epithelial-mesenchymal transition (EMT) and tumorigenesis. Given the emerging role of ARID3B in tumor development, we examined its expression in primary patient-derived breast cancer samples and breast cancer-derived cell lines.

METHODS

Immunohistochemistry (IHC) was used to detect ARID3B expression in 63 formalin-fixed paraffin-embedded (FFPE) invasive breast cancer samples. In addition, a panel of 6 (estrogen receptor-positive and -negative, ERBB2-positive and -negative) breast cancer-derived cell lines and immortalized non-tumorigenic epithelial breast cells were used for ARID3B expression analysis using RT-PCR. Specific primers and Western blotting were used to detect ARID3B isoforms.

RESULTS

Using IHC, nuclear, cytoplasmic and low levels of membranous ARID3B staining were detected in all 63 primary invasive breast tumors. Nuclear ARID3B staining positively correlated with estrogen receptor (ER) status and negatively correlated with tumor grade, mitotic index and ERBB2 status of the patients. Increased nuclear expression of ARID3B was confirmed in breast cancer-derived cell lines expressing ERα. In addition, two out of three ERBB2-positive breast cancer cell lines were found to lack full length ARID3B. Three ARID3B isoforms were found to be present in normal breast epithelial cells as well as in breast cancer cells.

CONCLUSION

We report a positive correlation between ER positivity and nuclear ARID3B expression in primary breast cancers, along with a negative correlation with the ERBB2 status. Very similar correlations were noted in breast cancer-derived cell lines. Since in the recent past ARID3B expression has increasingly been related to cancer-associated proteins and microRNAs, knowledge on ARID3B expression and function may be instrumental for gaining further insight into potentially important cancer-related networks.