Bioinformatic analysis of computational identified differentially expressed genes in tumor stoma of pregnancy‑associated breast cancer

Exposure to brominated flame retardants during pregnancy and lactation increases the prevalence of breast lesions and cancer-associated pathways in sprague-dawley rats

The mammary gland undergoes significant changes during pregnancy, lactation, and involution, making it highly susceptible to endocrine-disrupting chemicals such as brominated flame retardants (BFRs). Despite being restricted in many countries, some BFRs persist in the environment and accumulate in human tissues, including the mammary gland and human milk. This study investigates the effects of BFRs exposure during pregnancy and lactation on mammary gland development and breast cancer risk in a rat model. Dams were exposed to a mixture of polybrominated diphenyl ethers (PBDEs) and hexabromocyclododecane (HBCDD), formulated based on relative congener levels found in house dust. Post-weaning, dams were treated with 7,12-dimethylbenz[a]anthracene (DMBA) to initiate tumor formation. The results revealed that both low and high doses of BFRs induced lesions in mammary epithelium, with an increase in total lesion number in low dose. Molecular analysis revealed disruptions in the Wnt/β-catenin signaling pathway, leading to an increase in oncogene expression, including c-Myc and c-Jun. RNA sequencing also indicated dysregulation in calcium signaling and glucose metabolism pathways. Our findings suggest that BFR exposure during the critical window of mammary gland involution compromises the cancer-protective effects of pregnancy and lactation. These effects are particularly significant at low exposure levels, demonstrating a non-monotonic dose-response. The study underscores the potential long-term health risks associated with environmental BFR exposure and highlights the need for further research on its implications on the risks of developing breast cancer later in life.

Characterization of the immune environment in pregnancy-associated breast cancer

Pregnancy-associated breast cancer (PrBC) is a rare and clinically challenging condition. Specific immune mechanisms and pathways are involved in maternal-fetal tolerance and tumor-host immunoediting. The comprehension of the molecular processes underpinning this immune synergy in PrBC is needed to improve patients' clinical management. Only a few studies focused on the immune biology of PrBC and attempted to identify bona fide biomarkers. Therefore, clinically actionable information remains extremely puzzling for these patients. In this review article, we discuss the current knowledge on the immune environment of PrBC, in comparison with pregnancy-unrelated breast cancer and in the context of maternal immune changes during pregnancy. A particular emphasis is given to the actual role of potential immune-related biomarkers for PrBC clinical management.

Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review

Background

The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.

Objective

The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.

Methods

The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.

Results

The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.

Conclusions

In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.

Clinical outcomes and prognostic biomarkers among pregnant, post-partum and nulliparous women with breast cancer: a prospective cohort study

PURPOSE

To compare clinical-pathologic characteristics and outcomes of pregnancy-associated, post-partum (PP) and nulliparous (NP) breast cancer (BC) patients and explore mediators of the poor prognosis associated with post-partum BC.

METHODS

A prospective database of 233 women ≤ 40 years of age diagnosed with BC between February 2008 and January 2015 was analysed. Clinical-pathologic characteristics and outcomes among pregnant, PP and NP patients were compared using chi-square or Kruskal-Wallis tests. The Kaplan-Meier method was used to estimate disease-free survival (DFS), distant DFS and overall survival (OS). Survival curves were compared using the log-rank test. Univariable Cox proportional hazards regression models were used to evaluate factors that were potentially prognostic for the clinical outcomes of interest; a multivariable Cox model was constructed using a forward stepwise selection process. Androgen receptor (AR), GATA3, PDL1 status and the presence/absence of tumour-infiltrating lymphocytes (TILs) were assessed when possible. Pre-treatment neutrophil and lymphocyte counts were abstracted retrospectively. Statistical significance was defined as a p value ≤ 0.05.

RESULTS

Women ≤ 2 years PP had a numerically higher incidence of lymph node-positive and high-grade disease and were significantly more likely to have estrogen receptor-negative BC compared to NP controls. With a median follow-up of 7.2 years, increasingly poor outcomes were observed among NP (longest OS), > 2 years PP, ≤ 2 years PP and pregnant (shortest OS) patients, but these differences were not statistically significant. The ≤ 2 years PP group had significantly lower AR expression, a strong trend toward higher PDL1 expression and a higher expression of stromal TILs compared to NP women.

CONCLUSIONS

PPBC patients had numerically lower DFS and OS compared to NP controls. Higher PDL1 and stromal TILs in PPBC suggest that adjuvant immunotherapy may be effective in the post-partum BC subgroup.

The Genomic Profile of Pregnancy-Associated Breast Cancer: A Systematic Review

Breast cancer is the most common malignancy diagnosed during pregnancy. Strong data on the genomic profile of pregnancy-associated breast cancer are lacking. This systematic review aims to integrate and analyze all existing data from the literature regarding the genomic background and the gene mutational patterns of pregnancy-associated breast cancer. Using various genomic analysis methods, multiple differentially expressed genes and numerous non-silent mutations have been detected. More particularly, our review demonstrates the aberrant expression of several oncogenes (e.g., MYC, SRC, FOS), tumor suppressor genes (e.g., TP53, PTEN, CAV1), apoptosis regulators (e.g., PDCD4, BCL2, BIRC5), transcription regulators (e.g., JUN, KLF1, SP110), genes involved in DNA repair mechanisms (e.g., Sig20, BRCA1, BRCA2, FEN1), in cell proliferation (e.g., AURKA, MKI67), in the immune response (e.g., PD1, PDL1), and in other significant biological processes (e.g., protein modification, internal cell motility). Further research on the genomic profile of pregnancy-associated breast cancer is urgently required in order to identify potential biomarkers facilitating early-stage diagnosis and individualized therapy.