[Association between congenital hypothyroidism and in-hospital adverse outcomes in very low birth weight infants]

Objective: To investigate the association between congenital hypothyroidism (CH) and the adverse outcomes during hospitalization in very low birth weight infants (VLBWI). Methods: This prospective, multicenter observational cohort study was conducted based on the data from the Sino-northern Neonatal Network (SNN). Data of 5 818 VLBWI with birth weight <1 500 g and gestational age between 24-<37 weeks that were admitted to the 37 neonatal intensive care units from January 1st, 2019 to December 31st, 2022 were collected and analyzed. Thyroid function was first screened at 7 to 10 days after birth, followed by weekly tests within the first 4 weeks, and retested at 36 weeks of corrected gestational age or before discharge. The VLBWI were assigned to the CH group or non-CH group. Chi-square test, Fisher exact probability method, Wilcoxon rank sum test, univariate and multivariate Logistic regression were used to analyze the relationship between CH and poor prognosis during hospitalization in VLBWI. Results: A total of 5 818 eligible VLBWI were enrolled, with 2 982 (51.3%) males and the gestational age of 30 (29, 31) weeks. The incidence of CH was 5.5% (319 VLBWI). Among the CH group, only 121 VLBWI (37.9%) were diagnosed at the first screening. Univariate Logistic regression analysis showed that CH was associated with increased incidence of extrauterine growth retardation (EUGR) (OR=1.31(1.04-1.64), P<0.05) and retinopathy of prematurity (ROP) of stage Ⅲ and above (OR=1.74(1.11-2.75), P<0.05). However, multivariate Logistic regression analysis showed no significant correlation between CH and EUGR, moderate to severe bronchopulmonary dysplasia, grade Ⅲ to Ⅳ intraventricular hemorrhage, neonatal necrotizing enterocolitis in stage Ⅱ or above, and ROP in stage Ⅲ or above (OR=1.04 (0.81-1.33), 0.79 (0.54-1.15), 1.15 (0.58-2.26), 1.43 (0.81-2.53), 1.12 (0.70-1.80), all P>0.05). Conclusion: There is no significant correlation between CH and in-hospital adverse outcomes, possibly due to timely diagnosis and active replacement therapy.

The miR408a-BBP-LAC3/CSD1 module regulates anthocyanin biosynthesis mediated by crosstalk between copper homeostasis and ROS homeostasis during light induction in Malus plants

INTRODUCTION

The expression of miR408 is affected by copper (Cu) conditions and positively regulates anthocyanin biosynthesis in Arabidopsis. However, the underlying mechanisms by which miR408 regulates anthocyanin biosynthesis mediated by Cu homeostasis and reactive oxygen species (ROS) homeostasis remain unclear in Malus plants.

OBJECTIVES

Our study aims to elucidate how miR408a and its target, basic blue protein (BBP) regulate Cu homeostasis and ROS homeostasis, and anthocyanin biosynthesis in Malus plants.

METHODS

The roles of miR408a and its target BBP in regulating anthocyanin biosynthesis, Cu homeostasis, and ROS homeostasis were mainly identified in Malus plants.

RESULTS

We found that the BBP protein interacted with the copper-binding proteins LAC3 (laccase) and CSD1 (Cu/Zn SOD superoxide dismutase), indicating a potential crosstalk between Cu homeostasis and ROS homeostasis might be mediated by miR408 to regulate the anthocyanin accumulation. Further studies showed that overexpressing miR408a or suppressing BBP transiently significantly increased the expression of genes related to Cu binding and Cu transport, leading to anthocyanin accumulation under light induction in apple fruit and Malus plantlets. Consistently, opposite results were obtained when repressing miR408a or overexpressing BBP. Moreover, light induction significantly increased the expression of miR408a, CSD1, and LAC3, but significantly reduced the BBP expression, resulting in increased Cu content and anthocyanin accumulation. Furthermore, excessive Cu significantly increased the anthocyanin accumulation, accompanied by reduced expression of miR408a and Cu transport genes, and upregulated expression of Cu binding proteins including BBP, LAC3, and CSD1 to maintain the Cu homeostasis and ROS homeostasis in Malus plantlets.

CONCLUSION

Our findings provide new insights into the mechanism by which the miR408a-BBP-LAC3/CSD1 module perceives light and Cu signals regulating Cu and ROS homeostasis, ultimately affecting anthocyanin biosynthesis in Malus plants.

Proteogenomic data and resources for pan-cancer analysis

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Estimating the causal effects of multiple intermittent treatments with application to COVID-19

To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset without the need to discretize or artificially align the measurement times. We further use machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatments on the composite of in-hospital mortality and ICU admission.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Power of Bayesian and Heuristic Tests to Detect Cross-Species Introgression with Reference to Gene Flow in the Tamias quadrivittatus Group of North American Chipmunks

In the past two decades, genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HYDE to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program BPP to re-analyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HYDE and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid-speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data. [Bayesian test; BPP; chipmunks; introgression; MSci; multispecies coalescent; Savage-Dickey density ratio.].

Metabolic engineering and mechanical investigation of enhanced plant autoluminescence

The fungal bioluminescence pathway (FBP) was identified from glowing fungi, which releases self-sustained visible green luminescence. However, weak bioluminescence limits the potential application of the bioluminescence system. Here, we screened and characterized a C3'H1 (4-coumaroyl shikimate/quinate 3'-hydroxylase) gene from Brassica napus, which efficiently converts p-coumaroyl shikimate to caffeic acid and hispidin. Simultaneous expression of BnC3'H1 and NPGA (null-pigment mutant in A. nidulans) produces more caffeic acid and hispidin as the natural precursor of luciferin and significantly intensifies the original fungal bioluminescence pathway (oFBP). Thus, we successfully created enhanced FBP (eFBP) plants emitting 3 × 10¹¹ photons/min/cm² , sufficient to illuminate its surroundings and visualize words clearly in the dark. The glowing plants provide sustainable and bio-renewable illumination for the naked eyes, and manifest distinct responses to diverse environmental conditions via caffeic acid biosynthesis pathway. Importantly, we revealed that the biosynthesis of caffeic acid and hispidin in eFBP plants derived from the sugar pathway, and the inhibitors of the energy production system significantly reduced the luminescence signal rapidly from eFBP plants, suggesting that the FBP system coupled with the luciferin metabolic flux functions in an energy-driven way. These findings lay the groundwork for genetically creating stronger eFBP plants and developing more powerful biological tools with the FBP system.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence-average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database.

A flexible approach for causal inference with multiple treatments and clustered survival outcomes

When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the R $$ \mathsf{R}\kern.15em $$ package riAFTBART $$ \mathsf{riAFTBART} $$ .

De Novo Ostomy Placement Is Associated with Increased Outpatient Opioid Use In Patients with Inflammatory Bowel Disease

BACKGROUND/AIMS

Opioid use is associated with poor outcomes in patients with inflammatory bowel disease (IBD). We aimed to identify novel factors associated with increased outpatient opioid (OPRx) use following IBD-related hospitalization.

METHODS

This was a retrospective cohort study of IBD patients ≥ 18 years old, hospitalized during 2018. The primary outcome was receiving ≥ 1(OPRx) in the year following index hospitalization (IH), excluding prescriptions written within 2 weeks of discharge. Secondary outcomes included having 1-2 vs ≥ 3 OPRx and rates of healthcare utilization. Univariate and multivariate analyses tested associations with OPRx.

RESULTS

Of 526 patients analyzed, 209 (40%) received at least 1 OPRx; with a median of 2 [1-3] OPRx. Presence or placement of ostomy at IH, exposure to opioids during IH, ulcerative colitis (UC), mental health comorbidities, admission for surgery and managed on the surgical service, and IBD surgery within 1 year prior to IH were associated with ≥ 1 OPRx on univariate analysis. On multivariable analysis, UC, ostomy placement during IH, anxiety, and inpatient opioid exposure were independently associated with ≥ 1 OPRx. A majority (> 70%) of both inpatient and outpatient opioid prescriptions were written by surgeons. Patients requiring ≥ 3 OPRx had the highest rates of unplanned IBD surgery (56% p = 0.04), all-cause repeat hospitalization (81%, p = 0.003), and IBD-related repeat hospitalization (77%, p = 0.007) in the year following IH.

CONCLUSIONS

A multimodal approach to pain management for IBD patients, as well as increased recognition that any patient with a de novo ostomy is at particular risk of opioid use, is needed.

Gestational Diabetes, Subsequent Type 2 Diabetes, and Food Security Status: National Health and Nutrition Examination Survey, 2007-2018

Introduction

Despite many studies linking various risk factors to the association between gestational diabetes and subsequent type 2 diabetes, little is known about how food insecurity affects their association. We aimed to assess how the association between gestational diabetes and subsequent type 2 diabetes varies by food security status among women in the US.

Methods

This study is a secondary data analysis of 9,505 US women aged 20 years or older who had at least 1 live birth; we used cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) from 2007 through 2018. The main outcome was a diagnosis of type 2 diabetes in the subsequent years after the first live birth. We used multivariable survey-weighted negative binomial regressions to examine whether the association between gestational diabetes and subsequent type 2 diabetes differed by food security status, with and without adjusting for health behavior factors.

Results

Gestational diabetes was significantly associated with subsequent type 2 diabetes (incidence rate ratio [IRR], 2.57; 95% CI, 2.45–2.69). The association between gestational diabetes and subsequent type 2 diabetes was significantly different by food security status (IRR, 2.34; 95% CI, 2.23–2.45 among food-secure women; IRR, 2.99; 95% CI, 2.70–3.28 among food-insecure women).

Conclusion

The association between gestational diabetes and subsequent type 2 diabetes differs significantly by food security status. Public health and health care practitioners should consider food security status when designing and implementing diabetes prevention interventions for women with a history of gestational diabetes.

Prospective Cohort Study to Investigate the Safety of Preoperative Tumor Necrosis Factor Inhibitor Exposure in Patients With Inflammatory Bowel Disease Undergoing Intra-abdominal Surgery

BACKGROUND & AIMS

Whether preoperative treatment of inflammatory bowel disease (IBD) with tumor necrosis factor inhibitors (TNFis) increases the risk of postoperative infectious complications remains controversial. The primary aim of this study was to determine whether preoperative exposure to TNFis is an independent risk factor for postoperative infectious complications within 30 days of surgery.

METHODS

We conducted a multicenter prospective observational study of patients with IBD undergoing intra-abdominal surgery across 17 sites from the Crohn's & Colitis Foundation Clinical Research Alliance. Infectious complications were categorized as surgical site infections (SSIs) or non-SSIs. Current TNFi exposure was defined as use within 12 weeks of surgery, and serum was collected for drug-level analyses. Multivariable models for occurrence of the primary outcome, any infection, or SSI were adjusted by predefined covariates (age, sex, preoperative steroid use, and disease type), baseline variables significantly associated (P < .05) with any infection or SSI separately, and TNFi exposure status. Exploratory models used TNFi exposure based on serum drug concentration.

RESULTS

A total of 947 patients were enrolled from September 2014 through June 2017. Current TNFi exposure was reported by 382 patients. Any infection (18.1% vs 20.2%, P = .469) and SSI (12.0% vs 12.6%, P = .889) rates were similar in patients currently exposed to TNFis and those unexposed. In multivariable analysis, current TNFi exposure was not associated with any infection (odds ratio, 1.050; 95% confidence interval, 0.716-1.535) or SSI (odds ratio, 1.249; 95% confidence interval, 0.793-1.960). Detectable TNFi drug concentration was not associated with any infection or SSI.

CONCLUSIONS

Preoperative TNFi exposure was not associated with postoperative infectious complications in a large prospective multicenter cohort.

Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning

Despite considerable progress, image captioning still suffers from the huge difference in quality between easy and hard examples, which is left unexploited in existing methods. To address this issue, we explore the hard example mining in image captioning, and propose a simple yet effective mechanism to instruct the model to pay more attention to hard examples, thereby improving the performance in both general and complex scenarios. We first propose a novel learning strategy, termed Metric-oriented Focal Mechanism (MFM), for hard example mining in image captioning. Differing from the existing strategies for classification tasks, MFM can adopt the generative metrics of image captioning to measure the difficulties of examples, and then up-weight the rewards of hard examples during training. To make MFM applicable to different datasets without tedious parameter tuning, we further introduce an adaptive reward metric called Effective CIDEr (ECIDEr), which considers the data distribution of easy and hard examples during reward estimation. Extensive experiments are conducted on the MS COCO benchmark, and the results show that while maintaining the performance on simple examples, MFM can significantly improve the quality of captions for hard examples. The ECIDEr-based MFM is equipped on the current SOTA method, e.g., DLCT (Luo et al., 2021), which outperforms all existing methods and achieves new state-of-the-art performance on both the off-line and on- line testing, i.e., 134.3 CIDEr for the off-line testing and 136.1 for the on- line testing of MSCOCO. To validate the generalization ability of ECIDEr-based MFM, we also apply it to another dataset, namely Flickr30k, and superior performance gains can also be obtained.

Prior Immunogenicity to Anti-TNF Biologics Is Not Associated with Increased Anti-drug Antibodies to Vedolizumab or Ustekinumab

INTRODUCTION

Patients with inflammatory bowel disease (IBD) on biologic therapy may lose response to anti-tumor necrosis factor agents (anti-TNFs) due to the development of anti-drug antibodies (ADAs). A history of anti-TNF ADA increases the risk of developing ADA to subsequent anti-TNFs; however, it is not known whether ADA to anti-TNFs increases the risk of ADA development to vedolizumab (VDZ) or ustekinumab (UST). We aimed to investigate whether prior history of ADA to anti-TNF increases the risk of ADA to VDZ and UST.

METHODS

We conducted a retrospective cohort study of patients at a tertiary care IBD center over the course of four years who had previous anti-TNF drug and ADA level data during maintenance treatment and subsequent VDZ or UST drug and antibody levels, all collected as standard of care. The primary outcome was the rate of ADA development to VDZ and UST in patients with and without prior anti-TNF immunogenicity. Descriptive statistics summarized the data and univariate tested associations.

RESULTS

Of the 152 IBD patients analyzed, 41 (27%) had a history of previous anti-TNF ADA with 22 (53.7%) having simultaneously undetectable anti-TNF drug levels. There was no significant difference in the rates of ustekinumab and vedolizumab ADA development between patients with prior ADA and patients without prior ADA (1/41 [2.7%] vs 1/111 [0.9%]; p = 0.54). There was also no difference in concomitant immunomodulator use with ustekinumab or vedolizumab initiation in patients with or without prior ADA (13/41 [31.7%] vs 31/111 [27.9%], p = 0.84). Neither patient who developed ADA to VDZ or UST was on concomitant immunomodulator at drug initiation, and both patients had detectable drug levels at the time of antibody detection.

CONCLUSIONS

We observed that prior immunogenicity to anti-TNF agents does not confer an increased risk of immunogenicity to ustekinumab or vedolizumab. Our data support the use of vedolizumab or ustekinumab as monotherapy for the treatment of IBD.

A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA

In the absence of a randomized experiment, a key assumption for drawing causal inference about treatment effects is the ignorable treatment assignment. Violations of the ignorability assumption may lead to biased treatment effect estimates. Sensitivity analysis helps gauge how causal conclusions will be altered in response to the potential magnitude of departure from the ignorability assumption. However, sensitivity analysis approaches for unmeasured confounding in the context of multiple treatments and binary outcomes are scarce. We propose a flexible Monte Carlo sensitivity analysis approach for causal inference in such settings. We first derive the general form of the bias introduced by unmeasured confounding, with emphasis on theoretical properties uniquely relevant to multiple treatments. We then propose methods to encode the impact of unmeasured confounding on potential outcomes and adjust the estimates of causal effects in which the presumed unmeasured confounding is removed. Our proposed methods embed nested multiple imputation within the Bayesian framework, which allow for seamless integration of the uncertainty about the values of the sensitivity parameters and the sampling variability, as well as use of the Bayesian Additive Regression Trees for modeling flexibility. Expansive simulations validate our methods and gain insight into sensitivity analysis with multiple treatments. We use the SEER-Medicare data to demonstrate sensitivity analysis using three treatments for early stage non-small cell lung cancer. The methods developed in this work are readily available in the R package SAMTx.

Determinants of Total End-of-Life Health Care Costs of Medicare Beneficiaries: A Quantile Regression Forests Analysis

BACKGROUND

To identify and rank the importance of key determinants of end-of-life (EOL) health care costs, and to understand how the key factors impact different percentiles of the distribution of health care costs.

METHOD

We applied a principled, machine learning-based variable selection algorithm, using Quantile Regression Forests, to identify key determinants for predicting the 10th (low), 50th (median), and 90th (high) quantiles of EOL health care costs, including costs paid for by Medicare, Medicaid, Medicare Health Maintenance Organizations (HMOs), private HMOs, and patient's out-of-pocket expenditures.

RESULTS

Our sample included 7 539 Medicare beneficiaries who died between 2002 and 2017. The 10th, 50th, and 90th quantiles of EOL health care cost are $5 244, $35 466, and $87 241, respectively. Regional characteristics, specifically, the EOL-Expenditure Index, a measure for regional variation in Medicare spending driven by physician practice, and the number of total specialists in the hospital referral region were the top 2 influential determinants for predicting the 50th and 90th quantiles of EOL costs but were not determinants of the 10th quantile. Black race and Hispanic ethnicity were associated with lower EOL health care costs among decedents with lower total EOL health care costs but were associated with higher costs among decedents with the highest total EOL health care costs.

CONCLUSIONS

Factors associated with EOL health care costs varied across different percentiles of the cost distribution. Regional characteristics and decedent race/ethnicity exemplified factors that did not impact EOL costs uniformly across its distribution, suggesting the need to use a "higher-resolution" analysis for examining the association between risk factors and health care costs.

NR2F1 is a barrier to dissemination of early stage breast cancer cells

Cancer cells can disseminate during very early and sometimes asymptomatic stages of tumor progression. Though biological barriers to tumorigenesis have been identified and characterized, the mechanisms that limit early dissemination remain largely unknown. We report here that the orphan nuclear receptor NR2F1/COUP-TF1 serves as a barrier to early dissemination. NR2F1 expression was decreased in patient ductal carcinoma in situ (DCIS) samples. High-resolution intravital imaging of HER2+ early stage cancer cells revealed that loss of function of NR2F1 increased in vivo dissemination and was accompanied by decreased E-cadherin expression, activation of WNT-dependent β-catenin signaling, disorganized laminin 5 deposition, and increased expression of EMT genes such as TWIST1, ZEB1, and PRRX1. Furthermore, downregulation of NR2F1 promoted a hybrid luminal/basal phenotype. NR2F1 expression was positively regulated by p38α signaling and repressed by HER2 and WNT4 pathways. Lastly, early cancer cells with NR2F1LOW/PRRX1HIGH staining were observed in DCIS samples. Together, these findings reveal the existence of an inhibitory mechanism of dissemination regulated by NR2F1 in early stage breast cancer cells.