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Prenatal exposure to toxic metals is associated with altered placental function and adverse infant and child health outcomes. Adverse outcomes include those that are observed at the time of birth, such as low birthweight, as well as those that arise later in life, such as neurological impairment. It is often the case that these adverse outcomes show sex-specific responses in relation to toxicant exposures. While the precise molecular mechanisms linking in utero toxic metal exposures with later-in-life health are unknown, placental inflammation is posited to play a critical role. Here, we sought to understand whether in utero metal exposure is associated with alterations in the expression of the placental proteome by identifying metal associated proteins (MAPs). Within the Extremely Low Gestational Age Newborns (ELGAN) cohort (n = 230), placental and umbilical cord tissue samples were collected at birth. Arsenic (As), cadmium (Cd), lead (Pb), selenium (Se), and manganese (Mn) concentrations were measured in umbilical cord tissue samples via ICP-MS/MS. Protein expression was examined in placental samples using an LC-MS/MS-based, global, untargeted proteomics analysis measuring more than 3400 proteins. MAPs were then evaluated for associations with pregnancy and neonatal outcomes, including placental weight and gestational age. We hypothesized that metal levels would be positively associated with the altered expression of inflammation/immune-associated pathways and that sex-specific patterns of metal-associated placental protein expression would be observed. Sex-specific analyses identified 89 unique MAPs expressed in female placentas and 41 unique MAPs expressed in male placentas. Notably, many of the female-associated MAPs are known to be involved in immune-related processes, while the male-associated MAPs are associated with intracellular transport and cell localization. Further, several MAPs were significantly associated with gestational age in males and females and placental weight in males. These data highlight the linkage between prenatal metal exposure and an altered placental proteome, with implications for altering the trajectory of fetal development. [ABSTRACT FROM AUTHOR]

Children born preterm are at heightened risk of neurodevelopmental impairments, including Autism Spectrum Disorder (ASD). The placenta is a key regulator of neurodevelopmental processes, though the precise underlying molecular mechanisms remain unclear. Here, we employed a multi‐omic approach to identify placental transcriptomic and epigenetic modifications related to ASD diagnosis at age 10, among children born preterm. Working with the extremely low gestational age (ELGAN) cohort, we hypothesized that a pro‐inflammatory placental environment would be predictive of ASD diagnosis at age 10. Placental messenger RNA (mRNA) expression, CpG methylation, and microRNA (miRNA) expression were compared among 368 ELGANs (28 children diagnosed with ASD and 340 children without ASD). A total of 111 genes displayed expression levels in the placenta that were associated with ASD. Within these ASD‐associated genes is an ASD regulatory complex comprising key genes that predicted ASD case status. Genes with expression that predicted ASD case status included Ewing Sarcoma Breakpoint Region 1 (EWSR1) (OR: 6.57 (95% CI: 2.34, 23.58)) and Bromodomain Adjacent To Zinc Finger Domain 2A (BAZ2A) (OR: 0.12 (95% CI: 0.03, 0.35)). Moreover, of the 111 ASD‐associated genes, nine (8.1%) displayed associations with CpG methylation levels, while 14 (12.6%) displayed associations with miRNA expression levels. Among these, LRR Binding FLII Interacting Protein 1 (LRRFIP1) was identified as being under the control of both CpG methylation and miRNAs, displaying an OR of 0.42 (95% CI: 0.17, 0.95). This gene, as well as others identified as having functional epimutations, plays a critical role in immune system regulation and inflammatory response. In summary, a multi‐omic approach was used to identify functional epimutations in the placenta that are associated with the development of ASD in children born preterm, highlighting future avenues for intervention. Lay Summary: Children who are born preterm have an increased risk for neurodevelopmental impairments, including autism spectrum disorder (ASD). The placenta is known to play a pivotal role in child development, and is posited to regulate neurodevelopment as well. Our research reveals a significant number of placental genes expressed in relation to ASD, and that many of these genes are under the control of epigenetic processes. This research is important for understanding how the placenta may contribute to the development of ASD later in life in children born preterm. [ABSTRACT FROM AUTHOR]

Social determinants of health (SDoH) are defined as the conditions in which people are born, grow, live, work, and age. The distribution of these conditions is influenced by underlying structural factors and may be linked to adverse pregnancy outcomes through epigenetic modifications of gestational tissues. A promising modification is epigenetic gestational age (eGA), which captures 'biological' age at birth. Measuring eGA in placenta, an organ critical for foetal development, may provide information about how SDoH 'get under the skin' during pregnancy to influence birth outcomes and ethnic/racial disparities. We examined relationships of placental eGA with sociodemographic factors, smoking, and two key clinical outcomes: Apgar scores and NICU length of stay. Using the Robust Placental Clock, we estimated eGA for placental samples from the Extremely Low Gestational Age Newborns cohort (N = 408). Regression modelling revealed smoking during pregnancy was associated with placental eGA acceleration (i.e., eGA higher than chronologic gestational age). This association differed by maternal race: among infants born to mothers racialized as Black, we observed greater eGA acceleration (+0.89 week, 95% CI: 0.38, 1.40) as compared to those racialized as white (+0.27 week, 95% CI: -0.06, 0.59). Placental eGA acceleration was also correlated with shorter NICU lengths of stay, but only among infants born to mothers racialized as Black (-0.08 d/week-eGA, 95% CI: -0.12, -0.05). Together, these observed associations suggest that interpretations of epigenetic gestational aging may be tissuespecific. [ABSTRACT FROM AUTHOR]

Cerebral white matter is the most common anatomic location of neonatal brain injury in preterm newborns. Factors that predispose preterm newborns to white matter damage are understudied. In relation to studies of the placenta-brain-axis, dysregulated placental gene expression may play a role in preterm brain damage given its implication in programming early life origins of disease, including neurological disorders. There is a critical need to investigate the relationships between the placental transcriptome and white matter damage in the neonate. In a cohort of extremely low gestational age newborns (ELGANs), we aimed to investigate the relationship between the placental transcriptome and white matter damage as assessed by neonatal cranial ultrasound studies (echolucency and/or ventriculomegaly). We hypothesized that genes involved in inflammatory processes would be more highly expressed in placentas of ELGANs who developed ultrasound-defined indicators of white matter damage. Relative to either form of white matter damage, 659 placental genes displayed altered transcriptional profiles. Of these white matter damage-associated genes, largely distinct patterns of gene expression were observed in the study (n = 415/659 genes). Specifically, 381 genes were unique to echolucency and 34 genes were unique to ventriculomegaly. Pathways involved in hormone disruption and metabolism were identified among the unique echolucency or ventriculomegaly genes. Interestingly, a common set of 244 genes or 37% of all genes was similarly dysregulated in the placenta relative to both echolucency and ventriculomegaly. For this common set of white matter damage-related genes, pathways involved in inflammation, immune response and apoptosis, were enriched. Among the white matter damage-associated genes are genes known to be involved in Autism Spectrum Disorder (ASD) and endocrine system disorders. These data highlight differential mRNA expression patterning in the placenta and provide insight into potential etiologic factors that may predispose preterm newborns to white matter damage. Future studies will build upon this work to include functional measures of neurodevelopment as well as measures of brain volume later in life. [ABSTRACT FROM AUTHOR]

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction. Author summary: In the burgeoning field of personalized medicine, genetic variation is recognized as a major contributor to patients' differential responses to drugs. Lymphoblastoid cell lines (LCLs) are a consistent and convenient representation of cells used for in vitro research. Human genome sequencing with LCLs can identify new genes that influence individuals' drug responses, including the dose-response relationship, which describes the relationship between physiological response and the amount of exposure to a substance. In this work, we conduct high-throughput screening and genome-wide association mapping using 680 LCLs from the 1000 Genomes Project to identify new genes that influence individual response to 44 widely used anticancer drugs. We found the NQO1 gene to be associated with the dose-response of several drugs, namely arsenic trioxide, erlotinib, trametinib, and the paclitaxel + epirubicin combination, and performed follow-up analyses to better understand its functional role in drug response. Our results indicate NQO1 expression is correlated with increased drug resistance and provide some evidence that SNP rs1800566 influences drug response by altering protein activity for these four treatments. With further research, NQO1 has potential use as a therapeutic target, for example, suppressing NQO1 expression to increase sensitivity to particular drugs. [ABSTRACT FROM AUTHOR]

[Figure: see text]. [ABSTRACT FROM AUTHOR]

Lymphoblastoid cell lines (LCLs) are a highly successful model for evaluating the genetic etiology of cancer drug response, but applications using this model have typically focused on single drugs. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. In the present work, we demonstrate that synergy occurs and can be quantified in LCLs across a range of clinically important drug combinations. Lymphoblastoid cell lines have been commonly employed in association mapping in cancer pharmacogenomics, but it is so far untested as to whether synergistic effects have a genetic etiology. Here we use cell lines from extended pedigrees to demonstrate that there is a substantial heritable component to synergistic drug response. Additionally, we perform linkage mapping in these pedigrees to identify putative regions linked to this important phenotype. This demonstration supports the premise of expanding the use of the LCL model to perform association mapping for combination therapies. [ABSTRACT FROM AUTHOR]

The idea of synergistic interactions between drugs and chemicals has been an important issue in the biomedical world for over a century. As complex diseases, especially cancer, are being treated with various drug cocktails, understanding the interactions among these drugs is increasingly vital to ensuring successful treatment regimens. However, the idea of synergy is not limited to only the biomedical realm and these ideas have developed across many different disciplines, as well. In this review, we first discuss the various terminology surrounding the idea of synergy, providing a comprehensive list of terms defined across numerous disciplines. We then review the most commonmethodology for detection and quantification of synergy, including the two most prominent reference models for describing additive interactions: Loewe Additivity and Bliss Independence. We also discuss advantages and limitations to each method, with a focus on the Chou-Talalay Combination Index method. Finally, we describe how methods development and terminology have developed among disciplines outside of biomedicine and pharmacology, to synthesize the literature for readers. [ABSTRACT FROM AUTHOR]

Objective: To examine the association between neonatal cranial ultrasound (CUS) abnormalities among infants born extremely preterm and neurodevelopmental outcomes at 10 years of age.Study Design: In a multicenter birth cohort of infants born at <28 weeks of gestation, 889 of 1198 survivors were evaluated for neurologic, cognitive, and behavioral outcomes at 10 years of age. Sonographic markers of white matter damage (WMD) included echolucencies in the brain parenchyma and moderate to severe ventricular enlargement. Neonatal CUS findings were classified as intraventricular hemorrhage (IVH) without WMD, IVH with WMD, WMD without IVH, and neither IVH nor WMD.Results: WMD without IVH was associated with an increased risk of cognitive impairment (OR 3.5, 95% CI 1.7, 7.4), cerebral palsy (OR 14.3, 95% CI 6.5, 31.5), and epilepsy (OR 6.9; 95% CI 2.9, 16.8). Similar associations were found for WMD accompanied by IVH. Isolated IVH was not significantly associated these outcomes.Conclusions: Among children born extremely preterm, CUS abnormalities, particularly those indicative of WMD, are predictive of neurodevelopmental impairments at 10 years of age. The strongest associations were found with cerebral palsy. [ABSTRACT FROM AUTHOR]

Prenatal exposure to organophosphorus pesticides (OPPs) has been associated with neurodevelopmental deficits in children, however evidence linking OPPs with specific cognitive mechanisms, such as executive function (EF), is limited. This study aims to evaluate the association between prenatal exposure to OPPs with multiple measures of EF in preschool-aged children, while considering the role of variant alleles in OPP metabolism genes. We included 262 children with preschool attention-deficit/hyperactivity disorder (ADHD), and 78 typically developing children, from the Preschool ADHD substudy of the Norwegian, Mother, Father, and Child Cohort Study. Participants who gave birth between 2004 and 2008 were invited to participate in an on-site clinical assessment when the child was approximately 3.5 years; measurements of EF included parent and teacher rating on Behavior Rating Inventory of Executive Function-Preschool (BRIEF-P), and three performance-based assessments. We measured OPP metabolites in maternal urines collected at ∼17 weeks' gestation to calculate total dimethyl- (ΣDMP) and diethyl phosphate (ΣDEP) metabolite concentrations. We estimated multivariable adjusted β′s and 95% confidence intervals (CIs) corresponding to a change in z-score per unit increase in log-ΣDMP/DEP. We further characterized gene-OPP interactions for maternal variants in PON1 (Q192R, M55L) , CYP1A2 (1548T > C) , CYP1A1 (IntG > A) and CYP2A6 (-47A > C). Prenatal OPP metabolite concentrations were associated with worse parent and teacher ratings of emotional control, inhibition, and working memory. A one log-∑DMP increase was associated with poorer teacher ratings of EF on the BRIEF-P (e.g. emotional control domain: β = 0.55, 95% CI: 0.35, 0.74), when weighted to account for sampling procedures. We found less consistent associations with performance-based EF assessments. We found some evidence of modification for PON1 Q192R and CYP2A6 -47A > C. Association with other variants were inconsistent. Biomarkers of prenatal OPP exposure were associated with more adverse teacher and parent ratings of EF in preschool-aged children. • We examined prenatal organophosphate pesticide exposure and child executive functions. • Higher prenatal OPP exposure was associated with worse teacher and parent rated EF. • Associations of OPP with performance assessments of EF were less consistent. • There was some evidence for modification by Q192R in PON1 and -47A > C in CYP2A6. [ABSTRACT FROM AUTHOR]

Toxicology research has rapidly evolved, leveraging increasingly advanced technologies in high-throughput approaches to yield important information on toxicological mechanisms and health outcomes. Data produced through toxicology studies are consequently becoming larger, often producing high-dimensional data. These types of data hold promise for imparting new knowledge, yet inherently have complexities causing them to be a rate-limiting element for researchers, particularly those that are housed in "wet lab" settings (i.e., researchers that use liquids to analyze various chemicals and biomarkers as opposed to more computationally focused, "dry lab" researchers). These types of challenges represent topics of ongoing conversation amongst our team and researchers in the field. The aim of this perspective is to i) summarize hurdles in analyzing high-dimensional data in toxicology that require improved training and translation for wet lab researchers, ii) highlight example methods that have aided in translating data analysis techniques to wet lab researchers; and iii) describe challenges that remain to be effectively addressed, to date, in toxicology research. Specific aspects include methodologies that could be introduced to wet lab researchers, including data pre-processing, machine learning, and data reduction. Current challenges discussed include model interpretability, study biases, and data analysis training. Example efforts implemented to translate these data analysis techniques are also mentioned, including online data analysis resources and hands-on workshops. Questions are also posed to continue conversation in the toxicology community. Contents of this perspective represent timely issues broadly occurring in the fields of bioinformatics and toxicology that require ongoing dialogue between wet and dry lab researchers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Payton, Roell, Rebuli, Valdar, Jaspers and Rager.)

Children born preterm are at heightened risk of neurodevelopmental impairments, including Autism Spectrum Disorder (ASD). The placenta is a key regulator of neurodevelopmental processes, though the precise underlying molecular mechanisms remain unclear. Here, we employed a multi-omic approach to identify placental transcriptomic and epigenetic modifications related to ASD diagnosis at age 10, among children born preterm. Working with the extremely low gestational age (ELGAN) cohort, we hypothesized that a pro-inflammatory placental environment would be predictive of ASD diagnosis at age 10. Placental messenger RNA (mRNA) expression, CpG methylation, and microRNA (miRNA) expression were compared among 368 ELGANs (28 children diagnosed with ASD and 340 children without ASD). A total of 111 genes displayed expression levels in the placenta that were associated with ASD. Within these ASD-associated genes is an ASD regulatory complex comprising key genes that predicted ASD case status. Genes with expression that predicted ASD case status included Ewing Sarcoma Breakpoint Region 1 (EWSR1) (OR: 6.57 (95% CI: 2.34, 23.58)) and Bromodomain Adjacent To Zinc Finger Domain 2A (BAZ2A) (OR: 0.12 (95% CI: 0.03, 0.35)). Moreover, of the 111 ASD-associated genes, nine (8.1%) displayed associations with CpG methylation levels, while 14 (12.6%) displayed associations with miRNA expression levels. Among these, LRR Binding FLII Interacting Protein 1 (LRRFIP1) was identified as being under the control of both CpG methylation and miRNAs, displaying an OR of 0.42 (95% CI: 0.17, 0.95). This gene, as well as others identified as having functional epimutations, plays a critical role in immune system regulation and inflammatory response. In summary, a multi-omic approach was used to identify functional epimutations in the placenta that are associated with the development of ASD in children born preterm, highlighting future avenues for intervention., (© 2023 International Society for Autism Research and Wiley Periodicals LLC.)

Background: The increased risk of developing attention-deficit hyperactivity disorder (ADHD) in extremely preterm infants is well-documented. Better understanding of perinatal risk factors, particularly those that are modifiable, can inform prevention efforts., Methods: We examined data from the Extremely Low Gestational Age Newborns (ELGAN) Study. Participants were screened for ADHD at age 10 with the Child Symptom Inventory-4 ( N = 734) and assessed at age 15 with a structured diagnostic interview (MINI-KID) to evaluate for the diagnosis of ADHD ( N = 575). We studied associations of pre-pregnancy maternal body mass index (BMI), pregestational and/or gestational diabetes, maternal smoking during pregnancy (MSDP), and hypertensive disorders of pregnancy (HDP) with 10-year and 15-year ADHD outcomes. Relative risks were calculated using Poisson regression models with robust error variance, adjusted for maternal age, maternal educational status, use of food stamps, public insurance status, marital status at birth, and family history of ADHD. We defined ADHD as a positive screen on the CSI-4 at age 10 and/or meeting DSM-5 criteria at age 15 on the MINI-KID. We evaluated the robustness of the associations to broadening or restricting the definition of ADHD. We limited the analysis to individuals with IQ ≥ 70 to decrease confounding by cognitive functioning. We evaluated interactions between maternal BMI and diabetes status. We assessed for mediation of risk increase by alterations in inflammatory or neurotrophic protein levels in the first week of life., Results: Elevated maternal BMI and maternal diabetes were each associated with a 55-65% increase in risk of ADHD, with evidence of both additive and multiplicative interactions between the two exposures. MSDP and HDP were not associated with the risk of ADHD outcomes. There was some evidence for association of ADHD outcomes with high levels of inflammatory proteins or moderate levels of neurotrophic proteins, but there was no evidence that these mediated the risk associated with maternal BMI or diabetes., Conclusion: Contrary to previous population-based studies, MSDP and HDP did not predict ADHD outcomes in this extremely preterm cohort, but elevated maternal pre-pregnancy BMI, maternal diabetes, and perinatal inflammatory markers were associated with increased risk of ADHD at age 10 and/or 15, with positive interaction between pre-pregnancy BMI and maternal diabetes., Competing Interests: Author JF has received grant or research support from Healix and Syneos Health, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cochran, Jensen, Frazier, Jalnapurkar, Kim, Roell, Joseph, Hooper, Santos, Kuban, Fry and O’Shea.)

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to "TAME" data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor ST declared a past collaboration with the author JER., (Copyright © 2022 Roell, Koval, Boyles, Patlewicz, Ring, Rider, Ward-Caviness, Reif, Jaspers, Fry and Rager.)

Background: Prenatal exposures to metallic and metalloid trace elements have been linked to altered immune function in animal studies, but few epidemiologic studies have investigated immunological effects in humans. We evaluated the risk of bacterial sepsis (an extreme immune response to bacterial infection) in relation to prenatal metal/metalloid exposures, individually and jointly, within a US-based cohort of infants born extremely preterm., Methods: We analyzed data from 269 participants in the US-based ELGAN cohort, which enrolled infants delivered at <28 weeks' gestation (2002-2004). Concentrations of 8 trace elements-including 4 non-essential and 4 essential-were measured using inductively coupled plasma tandem mass spectrometry in umbilical cord tissue, reflecting in utero fetal exposures. The infants were followed from birth to postnatal day 28 with bacterial blood culture results reported weekly to detect sepsis. Discrete-time hazard and quantile g-computation models were fit to estimate associations for individual trace elements and their mixtures with sepsis incidence., Results: Approximately 30% of the extremely preterm infants developed sepsis during the follow-up period (median follow-up: 2 weeks). After adjustment for potential confounders, no trace element was individually associated with sepsis risk. However, there was some evidence of a non-monotonic relationship for cadmium, with hazard ratios (HRs) for the second, third, and fourth (highest) quartiles being 1.13 (95% CI: 0.51-2.54), 1.94 (95% CI: 0.87-4.32), and 1.88 (95% CI: 0.90-3.93), respectively. The HRs for a quartile increase in concentrations of all 8 elements, all 4 non-essential elements, and all 4 essential elements were 0.92 (95% CI: 0.68-1.25), 1.19 (95% CI: 0.92-1.55), and 0.77 (95% CI: 0.57-1.06). Cadmium had the greatest positive contribution whereas arsenic, copper, and selenium had the greatest negative contributions to the mixture associations., Conclusions: We found some evidence that greater prenatal exposure to cadmium was associated with an increased the risk of bacterial sepsis in extremely preterm infants. However, this risk was counteracted by a combination of arsenic, copper, and selenium. Future studies are needed to confirm these findings and to evaluate the potential for nutritional interventions to prevent sepsis in high-risk infants., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Purpose: Improved risk stratification and predictive biomarkers of treatment response are needed for non-muscle-invasive bladder cancer (NMIBC). Here we assessed the clinical utility of targeted RNA and DNA molecular profiling in NMIBC., Experimental Design: Gene expression in NMIBC samples was profiled by NanoString nCounter, an RNA quantification platform, from two independent cohorts ( n = 28, n = 50); targeted panel sequencing was performed in a subgroup ( n = 50). Gene signatures were externally validated using two RNA sequencing datasets of NMIBC tumors ( n = 438, n = 73). Established molecular subtype classifiers and novel gene expression signatures were assessed for associations with clinicopathologic characteristics, somatic tumor mutations, and treatment outcomes., Results: Molecular subtypes distinguished between low-grade Ta tumors with FGFR3 mutations and overexpression (UROMOL-class 1) and tumors with more aggressive clinicopathologic characteristics (UROMOL-classes 2 and 3), which were significantly enriched with TERT promoter mutations. However, UROMOL subclasses were not associated with recurrence after bacillus Calmette-Guérin (BCG) immunotherapy in two independent cohorts. In contrast, a novel expression signature of an inflamed tumor microenvironment (TME) was associated with improved recurrence-free survival after BCG. Expression of immune checkpoint genes (PD-L1/PD-1/CTLA-4) was associated with an inflamed TME, but not with higher recurrence rates after BCG. FGFR3 mutations and overexpression were both associated with low immune signatures., Conclusions: Assessment of the immune TME, rather than molecular subtypes, is a promising predictive biomarker of BCG response. Modulating the TME in an immunologically "cold" tumor warrants further investigation. Integrated transcriptomic and exome sequencing should improve treatment selection in NMIBC., (©2021 The Authors; Published by the American Association for Cancer Research.)