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Background: Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new subtype of tumor, for which novel antibody-drug conjugates have shown beneficial effects. Assessment of HER2 requires several immunohistochemistry tests with an additional in situ hybridization test if a case is classified as HER2 2+. Therefore, novel cost-effective methods to speed up the HER2 assessment are highly desirable., Methods: We used a self-supervised attention-based weakly supervised method to predict HER2-low directly from 1437 histopathological images from 1351 breast cancer patients. We built six distinct models to explore the ability of classifiers to distinguish between the HER2-negative, HER2-low, and HER2-high classes in different scenarios. The attention-based model was used to comprehend the decision-making process aimed at relevant tissue regions., Results: Our results indicate that the effectiveness of classification models hinges on the consistency and dependability of assay-based tests for HER2, as the outcomes from these tests are utilized as the baseline truth for training our models. Through the use of explainable AI, we reveal histologic patterns associated with the HER2 subtypes., Conclusion: Our findings offer a demonstration of how deep learning technologies can be applied to identify HER2 subgroup statuses, potentially enriching the toolkit available for clinical decision-making in oncology., (© 2024. The Author(s).)

The search for prognostic markers in breast cancer has bumped into a typical feature of these tumors, intra and intertumoral heterogeneity. Changes in the expression profile, localization of these proteins or shedding to the surrounding stroma can be useful in the search for new markers. In this context, classification by molecular subtypes can bring perspectives for both diagnosis and screening for appropriate treatments. However, the Triple Negative (TN) subtype, which is already the one with the worst prognosis, lacks appropriate and consistent molecular markers. In this work, we analyzed 346 human breast cancer samples in tissue microarrays (TMA) from cases diagnosed with invasive breast carcinoma to assess the expression and localization pattern of Maspin and their correlation with clinical parameters. To complement our findings, we also used TCGA data to analyze the mRNA levels of these respective genes. Our data suggests that the TN subtype demonstrates a higher level of cytoplasmic Maspin compared to the other subtypes. Maspin transcript levels follow the same trend. However, TN patients with lower Maspin expression tend to have worse overall survival and free-survival metastasis rates. Finally, we used Maspin expression data to verify possible relationships with the clinicopathological information of our cohort. Our univariate analyses indicate that Maspin is related to the expression of estrogen receptor (ER) and progesterone receptor (PR). Furthermore, Maspin expression levels also showed correlation with Scarff-Bloom-Richardson (SBR) parameter, and stromal Maspin showed a relationship with lymph node involvement. Our data is not consistently robust enough to categorize Maspin as a prognostic marker. However, it does indicate a change in the expression profile within the TN subtype., (© 2024. The Author(s).)

Breast cancer is the most prevalent cancer among women, with the basal-like triple negative (TNBC) being the most agressive one, displaying the poorest prognosis within the ductal carcinoma subtype. Due to the lack of adequate molecular targets, the diagnosis and treatment of patients with the TNBC phenotype has been a great challenge. In a previous work, we identified CD90/Thy-1 as being highly expressed in the aggressive high malignancy grade Hs578T basal-like breast tumor cell line, pointing to this molecule as a promising breast tumor marker, which should be further investigated. Here, CD90 expression was analyzed in human breast cancer samples and its functional role was investigated to better assess the oncogenic nature of CD90 in mammary cells. Quantification of CD90 expression in human breast cancer samples, by tissue microarray, showed that high CD90 positivity correlates with metastasis and poor patient survival in the basal-like subtype. The functional genetic approach, by overexpression in the CD90 cDNA in a basal-like normal mammary cell line (MCF10A) and knockdown in a highly malignant cell line (Hs578T), allowed us to demonstrate that CD90 is involved with several cellular processes that lead to malignant transformation, such as: morphological change, increased cell proliferation, invasiveness, metastasis and activation of the EGFR pathway. Therefore, our results reveal that CD90 is involved with malignant transformation in breast cancer cell lines and is correlated with metastasis and poor patient survival in the basal-like subtype, being considered as a promising new breast cancer target., Competing Interests: The authors have declared that no competing interests exist.