Improving discrimination in the grading of rat mammary tumors using two-dimensional mapping of histopathological observations

Profile of Histopathological Type and Molecular Subtypes of Mammary Cancer of DMBA-induced Rat and its Relevancy to Human Breast Cancer

BACKGROUND: Animal models with mammary cancer that closely mimic human breast cancer for treatment development purposes are still required. Induction of 7,12-dimethylbenzanthracene (DMBA) to rats shows the histopathological features and mammary cancer characterization similar to humans. Examinations of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 expressions are crucial in deciding the treatment and prognosis of breast cancer. AIM: This research aimed to view histopathology images of mammary glands and expressions of ER, PR, Ki67, and HER2 of DMBA-induced rats. METHODS: After 1-week adaptation, 11 5-weeks-old female rats were induced with 20 mg/kg body weight (BW) of DMBA 2 times a week for 5 weeks. On week 29, nodules taken from the mammary gland were examined for hematoxylin-eosin staining and immunohistochemistry with p63, ER, PR, HER2, and Ki67 antibodies. The grading score used the Nottingham Grading System and molecular classifications based on St. Gallen 2013. RESULTS: Six rats had nodules, but the histopathologic features of one nodule showed normal mammary gland without cancer. The histopathological type of mammary cancer was cribriform carcinoma, comedo carcinoma, lipid-rich carcinoma, adenocarcinoma squamous, and adenomyepithelioma. Histopathological grading showed 60% of grade 3 and 40% of grade 2. P63 expression showed 60% positive and 40% negative. The frequency of ER, PR, HER2, and Ki67 of five nodules showed positivity: 40%, 60%, 60%, and 60%, respectively. Molecular subtypes of Luminal A, B, HER2, and triple-negative were 0%, 60%, 20%, and 20%, respectively. CONCLUSION: Histopathological features and molecular subtype of mammary cancer on rats induced with 20 mg/kg BW of DMBA showed similarity to human breast cancer.

A Step Forward in Breast Cancer Research: From a Natural-Like Experimental Model to a Preliminary Photothermal Approach

Breast cancer is one of the most frequently diagnosed malignancies and common causes of cancer death in women. Recent studies suggest that environmental exposures to certain chemicals, such as 7,12-Dimethylbenzanthracene (DMBA), a chemical present in tobacco, may increase the risk of developing breast cancer later in life. The first-line treatments for breast cancer (surgery, chemotherapy or a combination of both) are generally invasive and frequently associated with severe side effects and high comorbidity. Consequently, novel approaches are strongly required to find more natural-like experimental models that better reflect the tumors' etiology, physiopathology and response to treatments, as well as to find more targeted, efficient and minimally invasive treatments. This study proposes the development and an in deep biological characterization of an experimental model using DMBA-tumor-induction in Sprague-Dawley female rats. Moreover, a photothermal therapy approach using a near-infrared laser coupled with gold nanoparticles was preliminarily assessed. The gold nanoparticles were functionalized with Epidermal Growth Factor, and their physicochemical properties and in vitro effects were characterized. DMBA proved to be a very good and selective inductor of breast cancer, with 100% incidence and inducing an average of 4.7 tumors per animal. Epigenetic analysis showed that tumors classified with worst prognosis were hypomethylated. The tumor-induced rats were then subjected to a preliminary treatment using functionalized gold nanoparticles and its activation by laser (650-900 nm). The treatment outcomes presented very promising alterations in terms of tumor histology, confirming the presence of necrosis in most of the cases. Although this study revealed encouraging results as a breast cancer therapy, it is important to define tumor eligibility and specific efficiency criteria to further assess its application in breast cancer treatment on other species.

Evaluating Skin Sensitization Via Soft and Hard Multivariate Modeling

Allergic contact dermatitis is the most frequent manifestation of immunotoxicity in humans with a prevalence rate of 15% to 20% over general population. Skin sensitization is a complex end point that was for a long time being evaluated using animal testing. Great efforts have been made to completely substitute the use of animals and replace them by integrating data from in vitro and in chemico assays with in silico calculated parameters. However, it remains undefined how to make the best use of the cumulative data in such a way that information gain is maximized and accomplished with the fewest number of tests possible. In this work, 3 skin sensitization prediction models were considered: one to discriminate sensitizers from non-sensitizers, considering a 2-level scale; one according to the GHS, considering a 3-level scale; and the other to categorize potency in a 6-level scale, according to available human data. We used a data set of known human skin allergens for which in vitro, in chemico, and in silico descriptors where available to build classifiers based on soft and hard multivariate modeling. Model building, optimization, and refinement resulted in 100% accuracy in distinguishing between sensitizers and non-sensitizers. The same model was able to perform the characterization, in 3 and 6 levels, respectively, with 98.8 and 97.5% accuracy. Combining data from in vitro and in chemico tests with in silico descriptors is relatively simple to implement and some predictors are fitting the adverse outcome pathway for skin sensitization.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Prognostic factors in MNU and DMBA-induced mammary tumors in female rats

Chemically-induced mammary tumors in rats by the carcinogens 1-methyl-1-nitrosourea- (MNU) and 7,12-dimethylbenz[a]anthracene (DMBA) are the most widely used models for studies related with human breast cancer. This study aimed to evaluate the immunoexpression of the prognostic factors estrogen receptor α (ERα), progesterone receptor (PR) and Ki-67, in MNU and DMBA-induced rat mammary tumors, in order to know the model that best suits to woman breast cancer. Twenty-eight MNU-induced and 16 DMBA-induced mammary tumors in virgin female Sprague-Dawley rats were analyzed. The expression of the prognostic markers ERα, PR and Ki-67 proliferation index (Ki-67 PI) was assessed by immunohistochemistry. Mitotic activity index (MAI) was also evaluated. More than one histological pattern was identified in each mammary tumor. Carcinomas constituted the lesions most frequently induced by both carcinogens: 33 MNU-induced carcinomas and 23 DMBA-induced carcinomas. All MNU and DMBA-induced mammary carcinomas were ER⁺/PR⁺, with a higher expression of ERα when compared with PR. Tumors' weight, the expression of ERα, PR, Ki-67 PI and MAI were higher in MNU-induced mammary carcinomas when compared with the DMBA-induced ones. Statistically significant differences between groups were observed for ERα, PR and MAI (p<0.05). The higher KI-67 PI and MAI in MNU-induced mammary carcinomas are suggestive of a higher aggressiveness of these carcinomas when compared with the DMBA-induced ones, and consequently a worse response to the therapy and a worse prognosis. In this way, the use of the rat model of MNU-induced mammary tumors is advised in experimental protocols aiming to study more aggressive mammary tumors within the group of double-positive mammary tumors (ER⁺/PR⁺).

Experimental mammary carcinogenesis - Rat models

Mammary cancer is one of the most common cancers, victimizing more than half a million of women worldwide every year. Despite all the studies in this field, the current therapeutic approaches are not effective and have several devastating effects for patients. In this way, the need to better understand the mammary cancer biopathology and find effective therapies led to the development of several rodent models over years. With this review, the authors intended to provide the readers with an overview of the rat models used to study mammary carcinogenesis, with a special emphasis on chemically-induced models.