Identification of Racial Inequities in Access to Specialized Inpatient Heart Failure Care at an Academic Medical Center.

BACKGROUND: Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race. This study examined the relationship between race and admission service, and its effect on 30-day readmission and mortality Methods: We performed a retrospective cohort study from September 2008 to November 2017 at a single large urban academic referral center of all patients self-referred to the emergency department and admitted to either the cardiology or general medicine service with a principal diagnosis of HF, who self-identified as white, black, or Latinx. We used multivariable generalized estimating equation models to assess the relationship between race and admission to the cardiology service. We used Cox regression to assess the association between race, admission service, and 30-day readmission and mortality. RESULTS: Among 1967 unique patients (66.7% white, 23.6% black, and 9.7% Latinx), black and Latinx patients had lower rates of admission to the cardiology service than white patients (adjusted rate ratio, 0.91; 95% CI, 0.84-0.98, for black; adjusted rate ratio, 0.83; 95% CI, 0.72-0.97 for Latinx). Female sex and age >75 years were also independently associated with lower rates of admission to the cardiology service. Admission to the cardiology service was independently associated with decreased readmission within 30 days, independent of race. CONCLUSIONS: Black and Latinx patients were less likely to be admitted to cardiology for HF care. This inequity may, in part, drive racial inequities in HF outcomes.

Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data.

BACKGROUND: Transcription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes. RESULTS: Here, we developed a systematic pipeline to infer Condition-Specific Targets of human TF-TF complexes (called the CST pipeline) by integrating ChIP-seq data and TF motifs. In total, we predicted 2,392 TF complexes and 13,504 high-confidence or 127,994 low-confidence regulatory interactions amongst TF complexes and their target genes. We validated our predictions by (i) comparing predicted TF complexes to external TF complex databases, (ii) validating selected target genes of TF complexes using ChIP-qPCR and RT-PCR experiments, and (iii) analysing target genes of select TF complexes using gene ontology enrichment to demonstrate the accuracy of our work. Finally, the predicted results above were integrated and employed to construct a CST database. CONCLUSIONS: We built up a methodology to construct the CST database, which contributes to the analysis of transcriptional regulation and the identification of novel TF-TF complex formation in a certain condition. This database also allows users to visualize condition-specific TF regulatory networks through a user-friendly web interface.

iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data.

BACKGROUND: Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP's output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired. DESCRIPTION: To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server ( http://syslab3.nchu.edu.tw/iTAR/ ) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF. CONCLUSIONS: The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.

Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value.

UNLABELLED: Liposarcoma is the second most common form of sarcoma, which has been categorized into four molecular subtypes, which are associated with differential prognosis of patients. However, the transcriptional regulatory programs associated with distinct histologic and molecular subtypes of liposarcoma have not been investigated. This study uses integrative analyses to systematically define the transcriptional regulatory programs associated with liposarcoma. Likewise, computational methods are used to identify regulatory programs associated with different liposarcoma subtypes, as well as programs that are predictive of prognosis. Further analysis of curated gene sets was used to identify prognostic gene signatures. The integration of data from a variety of sources, including gene expression profiles, transcription factor-binding data from ChIP-Seq experiments, curated gene sets, and clinical information of patients, indicated discrete regulatory programs (e.g., controlled by E2F1 and E2F4), with significantly different regulatory activity in one or multiple subtypes of liposarcoma with respect to normal adipose tissue. These programs were also shown to be prognostic, wherein liposarcoma patients with higher E2F4 or E2F1 activity associated with unfavorable prognosis. A total of 259 gene sets were significantly associated with patient survival in liposarcoma, among which > 50% are involved in cell cycle and proliferation. IMPLICATIONS: These integrative analyses provide a general framework that can be applied to investigate the mechanism and predict prognosis of different cancer types.

Integrative analysis of breast cancer reveals prognostic haematopoietic activity and patient-specific immune response profiles.

Transcriptional programmes active in haematopoietic cells enable a variety of functions including dedifferentiation, innate immunity and adaptive immunity. Understanding how these programmes function in the context of cancer can provide valuable insights into host immune response, cancer severity and potential therapy response. Here we present a method that uses the transcriptomes of over 200 murine haematopoietic cells, to infer the lineage-specific haematopoietic activity present in human breast tumours. Correlating this activity with patient survival and tumour purity reveals that the transcriptional programmes of many cell types influence patient prognosis and are found in environments of high lymphocytic infiltration. Collectively, these results allow for a detailed and personalized assessment of the patient immune response to a tumour. When combined with routinely collected patient biopsy genomic data, this method can enable a richer understanding of the complex interplay between the host immune system and cancer.

Systematic analysis of hematopoietic gene expression profiles for prognostic prediction in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a hematopoietic disorder initiated by the leukemogenic transformation of myeloid cells into leukemia stem cells (LSCs). Preexisting gene expression programs in LSCs can be used to assess their transcriptional similarity to hematopoietic cell types. While this relationship has previously been examined on a small scale, an analysis that systematically investigates this relationship throughout the hematopoietic hierarchy has yet to be implemented. We developed an integrative approach to assess the similarity between AML patient tumor profiles and a collection of 232 murine hematopoietic gene expression profiles compiled by the Immunological Genome Project. The resulting lineage similarity scores (LSS) were correlated with patient survival to assess the relationship between hematopoietic similarity and patient prognosis. This analysis demonstrated that patient tumor similarity to immature hematopoietic cell types correlated with poor survival. As a proof of concept, we highlighted one cell type identified by our analysis, the short-term reconstituting stem cell, whose LSSs were significantly correlated with patient prognosis across multiple datasets, and showed distinct patterns in patients stratified by traditional clinical variables. Finally, we validated our use of murine profiles by demonstrating similar results when applying our method to human profiles.

E2F4 regulatory program predicts patient survival prognosis in breast cancer.

INTRODUCTION: Genetic and molecular signatures have been incorporated into cancer prognosis prediction and treatment decisions with good success over the past decade. Clinically, these signatures are usually used in early-stage cancers to evaluate whether they require adjuvant therapy following surgical resection. A molecular signature that is prognostic across more clinical contexts would be a useful addition to current signatures. METHODS: We defined a signature for the ubiquitous tissue factor, E2F4, based on its shared target genes in multiple tissues. These target genes were identified by chromatin immunoprecipitation sequencing (ChIP-seq) experiments using a probabilistic method. We then computationally calculated the regulatory activity score (RAS) of E2F4 in cancer tissues, and examined how E2F4 RAS correlates with patient survival. RESULTS: Genes in our E2F4 signature were 21-fold more likely to be correlated with breast cancer patient survival time compared to randomly selected genes. Using eight independent breast cancer datasets containing over 1,900 unique samples, we stratified patients into low and high E2F4 RAS groups. E2F4 activity stratification was highly predictive of patient outcome, and our results remained robust even when controlling for many factors including patient age, tumor size, grade, estrogen receptor (ER) status, lymph node (LN) status, whether the patient received adjuvant therapy, and the patient's other prognostic indices such as Adjuvant! and the Nottingham Prognostic Index scores. Furthermore, the fractions of samples with positive E2F4 RAS vary in different intrinsic breast cancer subtypes, consistent with the different survival profiles of these subtypes. CONCLUSIONS: We defined a prognostic signature, the E2F4 regulatory activity score, and showed it to be significantly predictive of patient outcome in breast cancer regardless of treatment status and the states of many other clinicopathological variables. It can be used in conjunction with other breast cancer classification methods such as Oncotype DX to improve clinical outcome prediction.