Maternal age and risk of labor and delivery complications.

We utilized an updated nationally representative database to examine associations between maternal age and prevalence of maternal morbidity during complications of labor and delivery. We used hospital inpatient billing data from the 2009 United States Nationwide Inpatient Sample, part of the Healthcare Cost and Utilization Project. To determine whether the likelihood that maternal morbidity during complications of labor and delivery differed among age groups, separate logistic regression models were run for each complication. Age was the main independent variable of interest. In analyses that controlled for demographics and clinical confounders, we found that complications with the highest odds among women, 11-18 years of age, compared to 25-29 year old women, included preterm delivery, chorioamnionitis, endometritis, and mild preeclampsia. Pregnant women who were 15-19 years old had greater odds for severe preeclampsia, eclampsia, postpartum hemorrhage, poor fetal growth, and fetal distress. Pregnant women who were ≥35 years old had greater odds for preterm delivery, hypertension, superimposed preeclampsia, severe preeclampsia, and decreased risk for chorioamnionitis. Older women (≥40 years old) had increased odds for mild preeclampsia, fetal distress, and poor fetal growth. Our findings underscore the need for pregnant women to be aware of the risks associated with extremes of age so that they can watch for signs and symptoms of such complications.

Increased prevalence of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer.

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. Decoding the interconnections among different biological axes of plasticity is crucial to understand the molecular origins of phenotypic heterogeneity. Here, we use multi-modal transcriptomic data-bulk, single-cell, and spatial transcriptomics-from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity-two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. Mathematical modeling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and identify interventions to restrict it.

Multi-modal transcriptomic analysis unravels enrichment of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer.

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. It manifests along multiple phenotypic axes and decoding the interconnections among these different axes is crucial to understand its molecular origins and to develop novel therapeutic strategies to control it. Here, we use multi-modal transcriptomic data analysis - bulk, single-cell and spatial transcriptomics - from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity - two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. These patterns were inherent in methylation profiles, suggesting an epigenetic crosstalk between EMT and lineage plasticity in breast cancer. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes recapitulate and thus elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify possible interventions to restrict it.