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Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children’s Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/. more...

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia. more...

IMPORTANCE: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. OBJECTIVE: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. EXPOSURES: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. MAIN OUTCOMES AND MEASURES: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. RESULTS: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. CONCLUSIONS AND RELEVANCE: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB. more...

The mechanisms by which regulatory T cells (Tregs) migrate to and function within the hypoxic tumor microenvironment are unclear. Our studies indicate that specific ablation of hypoxia-inducible factor 1α (HIF-1α) in Tregs results in enhanced CD8+T cell suppression versus wild-type Tregs under hypoxia, due to increased pyruvate import into the mitochondria. Importantly, HIF-1α-deficient Tregs are minimally affected by the inhibition of lipid oxidation, a fuel that is critical for Treg metabolism in tumors. Under hypoxia, HIF-1α directs glucose away from mitochondria, leaving Tregs dependent on fatty acids for mitochondrial metabolism within the hypoxic tumor. Indeed, inhibition of lipid oxidation enhances the survival of mice with glioma. Interestingly, HIF-1α-deficient-Treg mice exhibit significantly enhanced animal survival in a murine model of glioma, due to their stymied migratory capacity, explaining their reduced abundance in tumor-bearing mice. Thus HIF-1α acts as a metabolic switch for Tregs between glycolytic-driven migration and oxidative phosphorylation-driven immunosuppression. more...

Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor and is associated with poor survival. Virotherapy is a promising candidate for the development of effective, novel treatments for GBM. Recent studies have underscored the potential of virotherapy in enhancing antitumor immunity despite the fact that its mechanisms remain largely unknown. Here, using a syngeneic GBM mouse model, we report that intratumoral virotherapy significantly modulates the tumor microenvironment. We found that intratumoral administration of an oncolytic adenovirus, AdCMVdelta24, decreased tumor-infiltrating CD4+Foxp3+regulatory T cells (Tregs) and increased IFNγ-producing CD8+T cells in treated tumors, even in late stage disease in which a highly immunosuppressive tumor microenvironment is considered to be a significant barrier to immunotherapy. Importantly, intratumoral AdCMVdelta24 treatment augmented systemically transferred tumor-antigen-specific T cell therapy. Furthermore, mechanistic studies showed (1) downregulation of Foxp3 in Tregs that were incubated with media conditioned by virus-infected tumor cells, (2) downregulation of indoleamine 2,3 dioxygenase 1 (IDO) in glioma cells upon infection by AdCMVdelta24, and (3) reprograming of Tregs from an immunosuppressive to a stimulatory state. Taken together, our findings demonstrate the potency of intratumoral oncolytic adenoviral treatment in enhancing antitumor immunity through the regulation of multiple aspects of immune suppression in the context of glioma, supporting further clinical development of oncolytic adenovirus-based immune therapies for malignant brain cancer. more...

The use of stem cells (SCs) as carriers for therapeutic agents has now progressed to early clinical trials. These clinical trials exploring SC‐mediated delivery of oncolytic adenoviruses will commence in the near future, hopefully yielding meritorious results that can provoke further scientific inquiry. Preclinical animal studies have demonstrated that SCs can be successfully loaded with conditionally‐replicative adenoviruses and delivered to the tumor, whereupon they may evoke pronounced therapeutic efficacy. In this protocol, we describe the maintenance of SCs, provide an analysis of optimal adenoviral titers for SC loading, and evaluate the optimized viral loading on SCs. © 2015 by John Wiley & Sons, Inc. more...