A B S T R A C TBreast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time-and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

The anti-tumor immune response was recently reported to play a critical role in the chemotherapeutic sensitivity of breast cancer. Therefore, we investigated the correlation between CD8+ and FOXP3+ tumor-infiltrating lymphocytes and the pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), in conjunction with neoangiogenesis, basal and proliferation markers. CD8+ and FOXP3+ lymphocytes were assessed in biopsy specimens by double-staining immunohistochemistry, in combination with immunostaining of vasohibin-1, CD31, EGFR, CK5/6, and Ki-67. Earlier age, pre-menopausal status, smaller tumor size, and high Ki-67 were significantly associated with pCR, as in high CD8+, high CD8+/FOXP3+ ratio, and low vasohibin-1 positive ratio. Multivariate analysis did reveal that a high CD8+/FOXP3+ ratio was a strong predictor of pCR with an odds ratio of 5.32 (P = 0.005). High Ki-67 was also significantly associated with pCR (P = 0.002). TNBCs with a high CD8+/FOXP3+ ratio and high Ki-67 had the highest pCR rate (70%) following NAC. However, the pCR rate of the patients with low CD8+/FOXP3+ ratio and low Ki-67 was only 5%. The pCR rates of a high CD8+/FOXP3+ ratio and low Ki-67 patients and those with a low CD8+/FOXP3+ ratio and high Ki-67 were 24 and 21%, respectively. TNBCs with a high CD8+/FOXP3+ ratio were more sensitive to anthracycline and taxane-based chemotherapeutic regimens, and the CD8+/FOXP3+ ratio in conjunction with Ki-67 could predict pCR following NAC in TNBC. This predictor may represent a new surrogate for testing the efficacy of investigational agents in the neoadjuvant setting.

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Aromatase inhibitors (AIs) are considered the gold standard of endocrine therapy for oestrogen receptor-positive postmenopausal breast cancer patients. AI treatment was reported to result in marked alterations of genetic profiles in cancer tissues but its detailed molecular mechanisms have not been elucidated. Therefore, we profiled miRNA expression before and after treatment with letrozole in MCF-7 co-cultured with primary breast cancer stromal cells. Letrozole significantly altered the expression profiles of cancer miRNAs in vitro. Among the elevated miRNAs following letrozole treatment, computational analysis identified let-7f, a tumour-suppressor miRNA which targeted the aromatase gene (CYP19A1) expression. Quantitative real-time PCR assay using MCF-7 and SK-BR-3 cells as well as clinical specimens of a neoadjuvant study demonstrated a significant inverse correlation between aromatase mRNA and let-7f expression. In addition, high let-7f expression was significantly correlated with low aromatase protein levels evaluated by both immunohistochemistry and the western blotting method in breast cancer cases. Results of 3'UTR luciferase assay also demonstrated the actual let-7f binding sites in CYP19A1, indicating that let-7f directly targets the aromatase gene. Subsequent WST-8 and migration assays performed in let-7f-transfected MCF-7 and SK-BR-3 cells revealed a significant decrement of their proliferation and migration. These findings all demonstrated that let-7f, a tumour suppressor miRNA in breast cancer, directly targeted the aromatase gene and was restored by AI treatment. Therefore, AIs may exert tumour-suppressing effects upon breast cancer cells by suppressing aromatase gene expression via restoration of let-7f.