Full-term pregnancy at an early age confers long-term protection against breast cancer. Published data shows a specific transcriptomic profile controlling chromatin remodeling that could play a relevant role in the pregnancy-induced protection. This process of chromatin remodeling, induced by the breast differentiation caused by the first full-term pregnancy, has mainly been measured by the expression level of genes individually considered. However, genes equally expressed during the process of chromatin remodeling may behave differently in their interaction with other genes. These changes at the gene cluster level could constitute an additional dimension of chromatin remodeling and therefore of the pregnancy-induced protection. In this research, we apply Information and Graph Theories, Differential Co-expression Network Analysis, and Multiple Regression Analysis, specially designed to examine structural and informational aspects of data sets, to analyze this question. Our findings demonstrate that, independently of the changes in the gene expression at the individual level, there are significant changes in gene–gene interactions and gene cluster behaviors. These changes indicate that the parous breast, through the process of early full-term pregnancy, generates more modules in the networks, with higher density, and a genomic structure performing additional and more complex functions than those found in the nulliparous breast.

Background Strategies for breast cancer prevention in women with germline BRCA1/2 mutations are limited. We previously showed that recombinant human chorionic gonadotropin (r-hCG) induces mammary gland differentiation and inhibits mammary tumorigenesis in rats. The present study investigated hCG-induced signaling pathways in the breast of young nulliparous women carrying germline BRCA1/2 mutations. Methods We performed RNA-sequencing on breast tissues from 25 BRCA1/2 mutation carriers who received r-hCG treatment for 3 months in a phase II clinical trial, we analyzed the biological processes, reactome pathways, canonical pathways, and upstream regulators associated with genes differentially expressed after r-hCG treatment, and validated genes of interest. Results We observed that r-hCG induces remarkable transcriptomic changes in the breast of BRCA1/2 carriers, especially in genes related to cell development, cell differentiation, cell cycle, apoptosis, DNA repair, chromatin remodeling, and G protein-coupled receptor signaling. We revealed that r-hCG inhibits Wnt/β-catenin signaling, MYC, HMGA1, and HOTAIR, whereas activates TGFB/TGFBR-SMAD2/3/4, BRCA1, TP53, and upregulates BRCA1 protein. Conclusion Our data suggest that the use of r-hCG at young age may reduce the risk of breast cancer in BRCA1/2 carriers by inhibiting pathways associated with stem/progenitor cell maintenance and neoplastic transformation, whereas activating genes crucial for breast epithelial differentiation and lineage commitment, and DNA repair.

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Background The mathematical design of optimal therapies to fight cancer is an important research field in today’s Biomathematics and Biomedicine given its relevance to formulate patient-specific treatments. Until now, however, cancer optimal therapies have considered that malignancy exclusively depends on the drug concentration and the number of cancer cells, ignoring that the faster the cancer grows the worse the cancer is, and that early drug doses are more prejudicial. Here, we analyze how optimal therapies are affected when the time evolution of treated cancer is envisaged as an additional element determining malignancy, analyzing in detail the implications for imatinib-treated Chronic Myeloid Leukemia. Methods Taking as reference a mathematical model describing Chronic Myeloid Leukemia dynamics, we design an optimal therapy problem by modifying the usual malignancy objective function, unaware of any temporal dimension of cancer malignance. In particular, we introduce a time valuation factor capturing the increase of malignancy associated to the quick development of the disease and the persistent negative effects of initial drug doses. After assigning values to the parameters involved, we solve and simulate the model with and without the new time valuation factor, comparing the results for the drug doses and the evolution of the disease. Results Our computational simulations unequivocally show that the consideration of a time valuation factor capturing the higher malignancy associated with early growth of cancer and drug administration allows more efficient therapies to be designed. More specifically, when this time valuation factor is incorporated into the objective function, the optimal drug doses are lower, and do not involve medically relevant increases in the number of cancer cells or in the disease duration. Conclusions In the light of our simulations and as biomedical evidence strongly suggests, the existence of a time valuation factor affecting malignancy in treated cancer cannot be ignored when designing cancer optimal therapies. Indeed, the consideration of a time valuation factor modulating malignancy results in significant gains of efficiency in the optimal therapy with relevant implications from the biomedical perspective, specially when designing patient-specific treatments.