Background Learning patient outcomes is recognized as crucial for ongoing refinement of clinical decision-making, but is often difficult in fragmented care with frequent handoffs. Data on resident habits of seeking outcome feedback after handoffs are lacking. Methods We performed a mixed-methods study including (1) an analysis of chart re-access rates after handoffs performed using access logs of the electronic health record (EHR); and (2) a web-based survey sent to internal medicine (IM) and emergency medicine (EM) residents about their habits of and barriers to learning the outcomes of patients after they have handed them off to other teams. Results Residents on ward rotations were often able to re-access charts of patients after handoffs, but those on EM or night admitting rotations did so <5% of the time. Among residents surveyed, only a minority stated that they frequently find out the outcomes of patients they have handed off, although learning outcomes was important to both their education and job satisfaction. Most were not satisfied with current systems of learning outcomes of patients after handoffs, citing too little time and lack of reliable patient tracking systems as the main barriers. Conclusions Despite perceived importance of learning outcomes after handoffs, residents cite difficulty with obtaining such information. Systematically providing feedback on patient outcomes would meet a recognized need among physicians in training.

T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.

The T and B cell repertoire make up the adaptive immune system and is mainly generated through somatic V(D)J gene recombination. Thus, the VJ gene usage may be a potential prognostic or predictive biomarker. However, analysis of the adaptive immune system is challenging due to the heterogeneity of the clonotypes that make up the repertoire. To address the heterogeneity of the T and B cell repertoire, we proposed a novel ensemble feature selection approach and customized statistical learning algorithm focusing on the VJ gene usage. We applied the proposed approach to T cell receptor sequences from recovered COVID-19 patients and healthy donors, as well as a group of lung cancer patients who received immunotherapy. Our approach identified distinct VJ genes used in the COVID-19 recovered patients comparing to the healthy donors and the VJ genes associated with the clinical response in the lung cancer patients. Simulation studies show that the ensemble feature selection approach outperformed other state-of-the-art feature selection methods based on both efficiency and accuracy. It consistently yielded higher stability and sensitivity with lower false discovery rates. When integrated with different classification methods, the ensemble feature selection approach had the best prediction accuracy. In conclusion, the proposed novel approach and the integration procedure is an effective feature selection technique to aid in correctly classifying different subtypes to better understand the signatures in the adaptive immune response associated with disease or the treatment in order to improve treatment strategies.

Exploring features associated with the clinical outcome of interest is a rapidly advancing area of research. However, with contemporary sequencing technologies capable of identifying over thousands of genes per sample, there is a challenge in constructing efficient prediction models that balance accuracy and resource utilization. To address this challenge, researchers have developed feature selection methods to enhance performance, reduce overfitting, and ensure resource efficiency. However, applying feature selection models to survival analysis, particularly in clinical datasets characterized by substantial censoring and limited sample sizes, introduces unique challenges. We propose a robust ensemble feature selection approach integrated with group Lasso to identify compelling features and evaluate its performance in predicting survival outcomes. Our approach consistently outperforms established models across various criteria through extensive simulations, demonstrating low false discovery rates, high sensitivity, and high stability. Furthermore, we applied the approach to a colorectal cancer dataset from The Cancer Genome Atlas, showcasing its effectiveness by generating a composite score based on the selected genes to correctly distinguish different subtypes of the patients. In summary, our proposed approach excels in selecting impactful features from high-dimensional data, yielding better outcomes compared to contemporary state-of-the-art models.

MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk. RESULTS: With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVARs performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVARs tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVARs G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools. AVAILABILITY AND IMPLEMENTATION: The TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sipuleucel-T is an autologous cellular immunotherapy, administered as three infusions, for metastatic castration-resistant prostate cancer (mCRPC). Sipuleucel-T induces T- and B-cell responses to prostatic acid phosphatase (PAP), correlating to improved survival. The long-term impact of sipuleucel-T on tumor antigen-specific immunologic memory remains unknown, in particular, B-cell responses, as measured by antigen-specific antibody responses and B-cell receptor (BCR) sequences. To evaluate whether sipuleucel-T could induce long-term immunologic memory, we examined circulating B-cell responses before and after sipuleucel-T treatment in two groups of patients with mCRPC: those who had previously received sipuleucel-T (treated; median, 8.9 years since the previous treatment) versus those who had not (naïve). Before re-treatment, previously treated patients exhibited persistent antibody responses as well as more focused and convergent BCR repertoires with distinct V(D)J gene usage compared with naïve patients. After re-treatment, previously treated patients maintained high-frequency clones and developed more convergent BCRs at earlier time points unlike naïve patients. With the first sipuleucel-T infusion specifically, previously treated patients had less shuffling within the 100 most abundant baseline clones. In contrast, naïve patients exhibited great BCR turnover with a continued influx of new B-cell clones. Social network analysis showed that previously treated patients had more highly organized B-cell repertoires, consistent with greater clonal maturation. Higher treatment-induced BCR clonality correlated with longer survival for naïve patients. These results demonstrated the capacity of sipuleucel-T to induce long-term immune memory and lasting changes to the B-cell repertoire.

Immune checkpoint inhibitors (ICI) are designed to activate exhausted tumor-reactive T cells thereby leading to tumor regression. Durvalumab, an ICI that binds to the programmed death ligand-1 (PD-L1) molecule, is approved as a consolidation therapy for treatment of patients with stage III, unresectable, non-small cell lung cancer (NSCLC). Immunophenotypic analysis of circulating immune cells revealed increases in circulating proliferating CD4 + and CD8 + T cells earlier after durvalumab treatment. To examine durvalumab's mechanism of action and identify potential predictive biomarkers, we assessed the circulating T cells phenotypes and TCR genes of 71 NSCLC patients receiving durvalumab enrolled in a Phase I trial (NCT01693562, September 14, 2012). Next-generation sequencing of TCR repertoire was performed on these NSCLC patients' peripheral blood samples at baseline and day 15. Though patients' TCR repertoire diversity showed mixed responses to the treatment, patients exhibiting increased diversity on day 15 attained significantly longer overall survival (OS) (median OS was not reached vs 17.2 months for those with decreased diversity, p = 0.015). We applied network analysis to assess convergent T cell clonotypes indicative of an antigen-driven immune response. Patients with larger TCR clusters had improved OS (median OS was not reached vs 13.1 months for patients with smaller TCR clusters, p = 0.013). Early TCR repertoire diversification after durvalumab therapy for NSCLC may be predictive of increased survival and provides a mechanistic basis for durvalumab pharmacodynamic activity.

Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.