aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph.

Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.

Characteristics of Lung Cancer Patients With Asymptomatic or Undiagnosed SARS-CoV-2 Infections.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be spread by individuals unaware they are infected. Such dissemination has heightened ramifications in cancer patients, who may need to visit healthcare facilities frequently, be exposed to immune-compromising therapies, and face greater morbidity from coronavirus disease 2019 (COVID-19). We determined characteristics of (1) asymptomatic, clinically diagnosed, and (2) serologically documented but clinically undiagnosed SARS-CoV-2 infection among individuals with lung cancer. PATIENTS AND METHODS: In a multicenter registry, individuals with lung cancer (regardless of prior SARS-CoV-2 vaccination or documented infection) underwent collection of clinical data and serial blood samples, which were tested for antinucleocapsid protein antibody (anti-N Ab) or IgG (N) levels. We used multivariable logistic regression models to investigate clinical characteristics associated with the presence or absence of symptoms and the presence or absence of a clinical diagnosis among patients with their first SARS-CoV-2 infection. RESULTS: Among patients with serologic evidence or clinically documented SARS-CoV-2 infection, 80/142 (56%) had no reported symptoms at their first infection, and 61/149 (40%) were never diagnosed. Asymptomatic infection was more common among older individuals and earlier-stage lung cancer. In multivariable analysis, non-white individuals with SARS-CoV-2 serologic positivity were 70% less likely ever to be clinically diagnosed (P = .002). CONCLUSIONS: In a multicenter lung cancer population, a substantial proportion of SARS-CoV-2 infections had no associated symptoms or were never clinically diagnosed. Because such cases appear to occur more frequently in populations that may face greater COVID-19-associated morbidity, measures to limit disease spread and severity remain critical.

Proportional Hazards Violations in Phase 3 Cancer Clinical Trials: A Potential Source of Trial Misinterpretation.

BACKGROUND: Survival analyses of novel agents with long-term responders often exhibit differential hazard rates over time. Such proportional hazards violations (PHVs) may reduce the power of the log-rank test and lead to misinterpretation of trial results. We aimed to characterize the incidence and study attributes associated with PHVs in phase 3 oncology trials and assess the utility of restricted mean survival time (RMST) and MaxCombo as additional analyses. METHODS: Clinicaltrials.gov and PubMed were searched to identify 2-arm, randomized, phase 3 superiority-design cancer trials with time-to-event primary endpoints and published results through 2020. Patient-level data were reconstructed from published Kaplan-Meier curves. PHVs were assessed using Schoenfeld residuals. RESULTS: Three hundred fifty-seven Kaplan-Meier comparisons across 341 trials were analyzed, encompassing 292,831 enrolled patients. PHVs were identified in 85/357 (23.8%; 95%CI 19.7%, 28.5%) comparisons. In multivariable analysis, non-OS endpoints (odds ratio [OR] 2.16 [95%CI 1.21, 3.87]; P=.009) were associated with higher odds of PHVs, and immunotherapy comparisons (OR 1.94 [95%CI 0.98, 3.86]; P=.058) were weakly suggestive of higher odds of PHVs. Few trials with PHVs (25/85, 29.4%) pre-specified a statistical plan to account for PHVs. Fourteen trials with PHVs exhibited discordant statistical signals with RMST or MaxCombo, of which ten (71%) reported negative results. CONCLUSION: PHVs are common across therapy types, and attempts to account for PHVs in statistical design are lacking despite the potential for results exhibiting non-proportional hazards to be misinterpreted.

Time-Dependent Effects of Clinical Interventions on SARS-CoV-2 Immunity in Patients with Lung Cancer.

In patients with lung cancer (LC), understanding factors that impact the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike antibody (SAb) titers over time is critical, but challenging, due to evolving treatments, infections, vaccinations, and health status. The objective was to develop a time-dependent regression model elucidating individual contributions of factors influencing SAb levels in LC patients using a prospective, longitudinal, multi-institutional cohort study initiated in January 2021. The study evaluated 296 LC patients-median age 69; 55% female; 50% stage IV. Blood samples were collected every three months to measure SAb levels using FDA-approved ELISA. Asymptomatic and unreported infections were documented through measurement of anti-nucleocapsid Ab levels (Meso Scale Discovery). Associations between clinical characteristics and titers were evaluated using a time-dependent linear regression model with a generalized estimating equation (GEE), considering time-independent variables (age, sex, ethnicity, smoking history, histology, and stage) and time-dependent variables (booster vaccinations, SARS-CoV-2 infections, cancer treatment, steroid use, and influenza vaccination). Significant time-dependent effects increasing titer levels were observed for prior SARS-CoV-2 infection (p < 0.001) and vaccination/boosters (p < 0.001). Steroid use (p = 0.043) and chemotherapy (p = 0.033) reduced titer levels. Influenza vaccination was associated with increased SAb levels (p < 0.001), independent of SARS-CoV-2 vaccine boosters. Prior smoking significantly decreased titers in females (p = 0.001). Age showed no association with titers. This GEE-based linear regression model unveiled the nuanced impact of multiple variables on patient anti-spike Ab levels over time. After controlling for the major influences of vaccine and SARS-CoV-2 infections, chemotherapy and steroid use were found to have negatively affected titers. Smoking in females significantly decreased titers. Surprisingly, influenza vaccinations were also significantly associated, likely indirectly, with improved SARS-CoV-2 titers.

Telephone-Based Guideline-Directed Medical Therapy Optimization in Navajo Nation: The Hózhó Randomized Clinical Trial.

Importance: Underutilization of guideline-directed medical therapy for heart failure with reduced ejection fraction is a major cause of poor outcomes. For many American Indian patients receiving care through the Indian Health Service, access to care, especially cardiology care, is limited, contributing to poor uptake of recommended therapy. Objective: To examine whether a telehealth model in which guideline-directed medical therapy is initiated and titrated over the phone with remote telemonitoring using a home blood pressure cuff improves guideline-directed medical therapy use (eg, drug classes and dosage) in patients with heart failure with reduced ejection fraction in Navajo Nation. Design, Setting, and Participants: The Heart Failure Optimization at Home to Improve Outcomes (Hózhó) randomized clinical trial was a stepped-wedge, pragmatic comparative effectiveness trial conducted from February to August 2023. Patients 18 years and older with a diagnosis of heart failure with reduced ejection fraction receiving care at 2 Indian Health Service facilities in rural Navajo Nation (defined as having primary care physician with 1 clinical visit and 1 prescription filled in the last 12 months) were enrolled. Patients were randomized to the telehealth care model or usual care in a stepped-wedge fashion, with 5 time points (30-day intervals) until all patients crossed over into the intervention. Data analyses were completed in January 2024. Intervention: A phone-based telehealth model in which guideline-directed medical therapy is initiated and titrated at home, using remote telemonitoring with a home blood pressure cuff. Main Outcomes and Measures: The primary outcome was an increase in the number of guideline-directed classes of drugs filled from the pharmacy at 30 days postrandomization. Results: Of 103 enrolled American Indian patients, 42 (40.8%) were female, and the median (IQR) age was 65 (53-77) years. The median (IQR) left ventricular ejection fraction was 32% (24%-36%). The primary outcome occurred significantly more in the intervention group (66.2% vs 13.1%), thus increasing uptake of guideline-directed classes of drugs by 53% (odds ratio, 12.99; 95% CI, 6.87-24.53; P < .001). The number of patients needed to receive the telehealth intervention to result in an increase of guideline-directed drug classes was 1.88. Conclusions and Relevance: In this heart failure trial in Navajo Nation, a telephone-based strategy of remote initiation and titration for outpatients with heart failure with reduced ejection fraction led to improved rates of guideline-directed medical therapy at 30 days compared with usual care. This low-cost strategy could be expanded to other rural settings where access to care is limited. Trial Registration: ClinicalTrials.gov Identifier: NCT05792085.

EVPsort: An Atlas of Small ncRNA Profiling and Sorting in Extracellular Vesicles and Particles.

Extracellular vesicles and particles (EVPs) play a crucial role in mediating cell-to-cell communication by transporting various molecular cargos, with small non-coding RNAs (ncRNAs) holding particular significance. A thorough investigation into the abundance and sorting mechanisms of ncRNA within EVPs is imperative for advancing their clinical applications. We have developed EVPsort, which not only provides an extensive overview of ncRNA profiling in 3,162 samples across various biofluids, cell lines, and disease contexts but also seamlessly integrates 19 external databases and tools. This integration encompasses information on associations between ncRNAs and RNA-binding proteins (RBPs), motifs, targets, pathways, diseases, and drugs. With its rich resources and powerful analysis tools, EVPsort extends its profiling capabilities to investigate ncRNA sorting, identify relevant RBPs and motifs, and assess functional implications. EVPsort stands as a pioneering database dedicated to comprehensively addressing both the abundance and sorting of ncRNA within EVPs. It is freely accessible at https://bioinfo.vanderbilt.edu/evpsort/.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

A Phase 1/2 Study to Evaluate the Safety and Activity of Nivolumab in Combination With Vorolanib, a Vascular Endothelial Growth Factor Tyrosine Kinase Inhibitor, in Patients With Refractory Thoracic Tumors.

Introduction: Targeting the tumor microenvironment may enhance response to immunotherapy (immune checkpoint inhibitors) and improve outcomes for patients. This study tested the safety and efficacy of vorolanib, a novel tyrosine kinase inhibitor of vascular endothelial growth factor, platelet-derived growth factor, and c-KIT, in combination with programmed cell death protein 1 blockade using nivolumab for refractory thoracic malignancies. Methods: This single-arm multicenter study enrolled patients with extensive-stage SCLC, thymic carcinoma, and NSCLC, either naive or had progressed on previous chemotherapy or immune checkpoint inhibitors (either primary or acquired resistance). The primary objective of phase 1 was to determine the maximum tolerated dose, and the primary end point for each dose-expansion cohort was the objective response rate. Results: A total of 88 patients were enrolled in phase 1 (n = 11) and dose expansion (n = 77) cohorts. Transaminitis was dose-limiting and expansion proceeded with oral vorolanib 200 mg daily combined with intravenous nivolumab 240 mg every 2 weeks. The objective response rate per cohort were as follows: NSCLC naive 33% (five of 15, 95% confidence interval [CI]: 13%-60%), NSCLC primary refractory 5.9% (one of 17, 95% CI: 0%-17.6%), NSCLC acquired resistance 11.1% (two of 18, 95% CI: 0%-27.8%); SCLC 0% (zero of 18), and thymic carcinoma 11% (one of nine, 95% CI: 0%-33%). Disease control rate ranged from 11.1% in SCLC (two of 18, 0%-27.8%) to 66.7 % in thymic carcinoma (six of nine, 95% CI: 33.3%-100%). The most common adverse events were fatigue (32%), aspartate transaminase (27%) and alanine transaminase elevation (25%), and diarrhea (19%). Transaminitis was more common in patients with thymic carcinoma than other tumors. Conclusions: Vorolanib plus nivolumab had a manageable safety profile and may have clinical benefits in various thoracic malignancies. The disease control rate in thymic malignancies warrants further assessment.

FindAdapt: A python package for fast and accurate adapter detection in small RNA sequencing.

Adapter trimming is an essential step for analyzing small RNA sequencing data, where reads are generally longer than target RNAs ranging from 18 to 30 bp. Most adapter trimming tools require adapter information as input. However, adapter information is hard to access, specified incorrectly, or not provided with publicly available datasets, hampering their reproducibility and reusability. Manual identification of adapter patterns from raw reads is labor-intensive and error-prone. Moreover, the use of randomized adapters to reduce ligation biases during library preparation makes adapter detection even more challenging. Here, we present FindAdapt, a Python package for fast and accurate detection of adapter patterns without relying on prior information. We demonstrated that FindAdapt was far superior to existing approaches. It identified adapters successfully in 180 simulation datasets with diverse read structures and 3,184 real datasets covering a variety of commercial and customized small RNA library preparation kits. FindAdapt is stand-alone software that can be easily integrated into small RNA sequencing analysis pipelines.

scKWARN: Kernel-weighted-average robust normalization for single-cell RNA-seq data.

MOTIVATION: Single-cell RNA-seq normalization is an essential step to correct unwanted biases caused by sequencing depth, capture efficiency, dropout, and other technical factors. Existing normalization methods primarily reduce biases arising from sequencing depth by modeling count-depth relationship and/or assuming a specific distribution for read counts. However, these methods may lead to over or under-correction due to presence of technical biases beyond sequencing depth and the restrictive assumption on models and distributions. RESULTS: We present scKWARN, a Kernel Weighted Average Robust Normalization designed to correct known or hidden technical confounders without assuming specific data distributions or count-depth relationships. scKWARN generates a pseudo expression profile for each cell by borrowing information from its fuzzy technical neighbors through a kernel smoother. It then compares this profile against the reference derived from cells with the same bimodality patterns to determine the normalization factor. As demonstrated in both simulated and real datasets, scKWARN outperforms existing methods in removing a variety of technical biases while preserving true biological heterogeneity. AVAILABILITY AND IMPLEMENTATION: scKWARN is freely available at https://github.com/cyhsuTN/scKWARN.

Atezolizumab in Combination With Carboplatin and Survival Outcomes in Patients With Metastatic Triple-Negative Breast Cancer: The TBCRC 043 Phase 2 Randomized Clinical Trial.

Importance: Agents targeting programmed death ligand 1 (PD-L1) have demonstrated efficacy in triple-negative breast cancer (TNBC) when combined with chemotherapy and are now the standard of care in patients with PD-L1-positive metastatic disease. In contrast to microtubule-targeting agents, the effect of combining platinum compounds with programmed cell death 1 (PD-1)/PD-L1 immunotherapy has not been extensively determined. Objective: To evaluate the efficacy of atezolizumab with carboplatin in patients with metastatic TNBC. Design, Setting, and Participants: This phase 2 randomized clinical trial was conducted in 6 centers from August 2017 to June 2021. Interventions: Patients with metastatic TNBC were randomized to receive carboplatin area under the curve (AUC) 6 alone or with atezolizumab, 1200 mg, every 3 weeks until disease progression or unacceptable toxic effects with a 3-year duration of follow-up. Main Outcome and Measures: The primary end point was investigator-assessed progression-free survival (PFS). Secondary end points included overall response rate (ORR), clinical benefit rate (CBR), and overall survival (OS). Other objectives included correlation of response with tumor PD-L1 levels, tumor-infiltrating lymphocytes (TILs), tumor DNA- and RNA-sequenced biomarkers, TNBC subtyping, and multiplex analyses of immune markers. Results: All 106 patients with metastatic TNBC who were enrolled were female with a mean (range) age of 55 (27-79) years, of which 12 (19%) identified as African American/Black, 1 (1%) as Asian, 73 (69%) as White, and 11 (10%) as unknown. Patients were randomized and received either carboplatin (n = 50) or carboplatin and atezolizumab (n = 56). The combination improved PFS (hazard ratio [HR], 0.66; 95% CI, 0.44-1.01; P = .05) from a median of 2.2 to 4.1 months, increased ORR from 8.0% (95% CI, 3.2%-18.8%) to 30.4% (95% CI, 19.9%-43.3%), increased CBR at 6 months from 18.0% (95% CI, 9.8%-30.1%) to 37.5% (95% CI, 26.0%-50.6%), and improved OS (HR, 0.60; 95% CI, 0.37-0.96; P = .03) from a median of 8.6 to 12.6 months. Subgroup analysis showed PD-L1-positive tumors did not benefit more from adding atezolizumab (HR, 0.62; 95% CI, 0.23-1.65; P = .35). Patients with high TILs (HR, 0.12; 95% CI, 0.30-0.50), high mutation burden (HR, 0.50; 95% CI, 0.23-1.06), and prior chemotherapy (HR, 0.59; 95% CI, 0.36-0.95) received greater benefit on the combination. Patients with obesity and patients with more than 125 mg/dL on-treatment blood glucose levels were associated with better PFS (HR, 0.35; 95% CI, 0.10-1.80) on the combination. TNBC subtypes benefited from adding atezolizumab, except the luminal androgen receptor subtype. Conclusions and Relevance: In this randomized clinical trial, the addition of atezolizumab to carboplatin significantly improved survival of patients with metastatic TNBC regardless of PD-L1 status. Further, lower risk of disease progression was associated with increased TILs, higher mutation burden, obesity, and uncontrolled blood glucose levels. Trial Registration: ClinicalTrials.gov Identifier: NCT03206203.

A Large-Scale Meta-Analysis Reveals Positive Feedback between Macrophages and T Cells That Sensitizes Tumors to Immunotherapy.

Although considerable efforts have been dedicated to identifying predictive signatures for immune checkpoint inhibitor (ICI) treatment response, current biomarkers suffer from poor generalizability and reproducibility across different studies and cancer types. The integration of large-scale multiomics studies holds great promise for discovering robust biomarkers and shedding light on the mechanisms of immune resistance. In this study, we conducted the most extensive meta-analysis involving 3,037 ICI-treated patients with genetic and/or transcriptomics profiles across 14 types of solid tumor. The comprehensive analysis uncovered both known and novel reliable signatures associated with ICI treatment outcomes. The signatures included tumor mutational burden (TMB), IFNG and PDCD1 expression, and notably, interactions between macrophages and T cells driving their activation and recruitment. Independent data from single-cell RNA sequencing and dynamic transcriptomic profiles during the ICI treatment provided further evidence that enhanced cross-talk between macrophages and T cells contributes to ICI response. A multivariable model based on eight nonredundant signatures significantly outperformed existing models in five independent validation datasets representing various cancer types. Collectively, this study discovered biomarkers predicting ICI response that highlight the contribution of immune cell networks to immunotherapy efficacy and could help guide patient treatment. SIGNIFICANCE: Identification of robust immunogenomic connections, particularly macrophage T-cell interactions, in a large-scale pan-cancer meta-analysis and development of a predictive model for immunotherapy response that outperformed existing models could facilitate clinical decision-making.

NCI10066: a Phase 1/2 study of olaparib in combination with ramucirumab in previously treated metastatic gastric and gastroesophageal junction adenocarcinoma.

BACKGROUND: Our preclinical work revealed tumour hypoxia induces homologous recombination deficiency (HRD), increasing sensitivity to Poly (ADP-ribose) polymerase inhibitors. We aimed to induce tumour hypoxia with ramucirumab thereby sensitising tumours to olaparib. PATIENTS AND METHODS: This multi-institution single-arm Phase 1/2 trial enrolled patients with metastatic gastroesophageal adenocarcinoma refractory to ≥1 systemic treatment. In dose escalation, olaparib was evaluated at escalating dose levels with ramucirumab 8 mg/kg day 1 in 14-day cycles. The primary endpoint of Phase 1 was the recommended Phase 2 dose (RP2D), and in Phase 2 the primary endpoint was the overall response rate (ORR). RESULTS: Fifty-one patients received ramucirumab and olaparib. The RP2D was olaparib 300 mg twice daily with ramucirumab 8 mg/kg. In evaluable patients at the RP2D the ORR was 6/43 (14%) (95% CI 4.7-25.6). The median progression-free survival (PFS) was 2.8 months (95% CI 2.3-4.2) and median overall survival (OS) was 7.3 months (95% CI 5.7-13.0). Non-statistically significant improvements in PFS and OS were observed for patients with tumours with mutations in HRD genes. CONCLUSIONS: Olaparib and ramucirumab is well-tolerated with efficacy that exceeds historical controls with ramucirumab single agent for gastric cancer in a heavily pre-treated patient population.

Identification and Characterization of Avoidable Hospital Admissions in Patients With Lung Cancer.

BACKGROUND: More than 50% of patients with lung cancer are admitted to the hospital while receiving treatment, which is a burden to patients and the healthcare system. This study characterizes the risk factors and outcomes of patients with lung cancer who were admitted to the hospital. METHODS: A multidisciplinary oncology care team conducted a retrospective medical record review of patients with lung cancer admitted in 2018. Demographics, disease and admission characteristics, and end-of-life care utilization were recorded. Following a multidisciplinary consensus review process, admissions were determined to be either "avoidable" or "unavoidable." Generalized estimating equation logistic regression models assessed risks and outcomes associated with avoidable admissions. RESULTS: In all, 319 admissions for 188 patients with a median age of 66 years (IQR, 59-74 years) were included. Cancer-related symptoms accounted for 65% of hospitalizations. Common causes of unavoidable hospitalizations were unexpected disease progression causing symptoms, chronic obstructive pulmonary disease exacerbation, and infection. Of the 47 hospitalizations identified as avoidable (15%), the median overall survival was 1.6 months compared with 9.7 months (hazard ratio, 2.07; 95% CI, 1.34-3.19; P<.001) for unavoidable hospitalizations. Significant reasons for avoidable admissions included cancer-related pain (P=.02), hypervolemia (P=.03), patient desire to initiate hospice services (P=.01), and errors in medication reconciliation or distribution (P<.001). Errors in medication management caused 26% of the avoidable hospitalizations. Of admissions in patients receiving immunotherapy (n=102) or targeted therapy (n=44), 9% were due to adverse effects of treatment. Patients receiving immunotherapy and targeted therapy were at similar risk of avoidable hospitalizations compared with patients not receiving treatment (P=.3 and P=.1, respectively). CONCLUSIONS: We found that 15% of hospitalizations among patients with lung cancer were potentially avoidable. Uncontrolled symptoms, delayed implementation of end-of-life care, and errors in medication reconciliation were associated with avoidable inpatient admissions. Symptom management tools, palliative care integration, and medication reconciliations may mitigate hospitalization risk.

A Specialized Epithelial Cell Type Regulating Mucosal Immunity and Driving Human Crohn's Disease.

Crohn's disease (CD) is a complex chronic inflammatory disorder that may affect any part of gastrointestinal tract with extra-intestinal manifestations and associated immune dysregulation. To characterize heterogeneity in CD, we profiled single-cell transcriptomics of 170 samples from 65 CD patients and 18 non-inflammatory bowel disease (IBD) controls in both the terminal ileum (TI) and ascending colon (AC). Analysis of 202,359 cells identified a novel epithelial cell type in both TI and AC, featuring high expression of LCN2, NOS2, and DUOX2, and thus is named LND. LND cells, confirmed by high-resolution in-situ RNA imaging, were rarely found in non-IBD controls, but expanded significantly in active CD. Compared to other epithelial cells, genes defining LND cells were enriched in antimicrobial response and immunoregulation. Moreover, multiplexed protein imaging demonstrated that LND cell abundance was associated with immune infiltration. Cross-talk between LND and immune cells was explored by ligand-receptor interactions and further evidenced by their spatial colocalization. LND cells showed significant enrichment of expression specificity of IBD/CD susceptibility genes, revealing its role in immunopathogenesis of CD. Investigating lineage relationships of epithelial cells detected two LND cell subpopulations with different origins and developmental potential, early and late LND. The ratio of the late to early LND cells was related to anti-TNF response. These findings emphasize the pathogenic role of the specialized LND cell type in both Crohn's ileitis and Crohn's colitis and identify novel biomarkers associated with disease activity and treatment response.

Longitudinal nucleocapsid antibody testing reveals undocumented SARS-CoV-2 infections in patients with lung cancer.

Patients diagnosed with lung cancer (LC) exhibit increased susceptibility to SARS-CoV-2 infection. Rodilla et al. monitor the levels of plasma anti-nucleocapsid antibodies within a cohort of fully vaccinated LC patients and reveal that the actual infection rate is nearly twice the documented rate, indicating a significant prevalence of unreported cases.

Single-cell and Spatial Transcriptomics Clustering with an Optimized Adaptive K-Nearest Neighbor Graph.

Single-cell and spatial transcriptomics have been widely used to characterize cellular landscape in complex tissues. To understand cellular heterogeneity, one essential step is to define cell types through unsupervised clustering. While typical clustering methods have difficulty in identifying rare cell types, approaches specifically tailored to detect rare cell types gain their ability at the cost of poorer performance for grouping abundant ones. Here, we developed aKNNO, a method to identify abundant and rare cell types simultaneously based on an adaptive k-nearest neighbor graph with optimization. Benchmarked on 38 simulated and 20 single-cell and spatial transcriptomics datasets, aKNNO identified both abundant and rare cell types accurately. Without sacrificing performance for clustering abundant cell types, aKNNO discovered known and novel rare cell types that those typical and even specifically tailored methods failed to detect. aKNNO, using transcriptome alone, stereotyped fine-grained anatomical structures more precisely than those integrative approaches combining expression with spatial locations and histology image.

scDemultiplex: An iterative beta-binomial model-based method for accurate demultiplexing with hashtag oligos.

Single-cell sequencing have been widely used to characterize cellular heterogeneity. Sample multiplexing where multiple samples are pooled together for single-cell experiments, attracts wide attention due to its benefits of increasing capacity, reducing costs, and minimizing batch effects. To analyze multiplexed data, the first crucial step is to demultiplex, the process of assigning cells to individual samples. Inaccurate demultiplexing will create false cell types and result in misleading characterization. We propose scDemultiplex, which models hashtag oligo (HTO) counts with beta-binomial distribution and uses an iterative strategy for further refinement. Compared with seven existing demultiplexing approaches, scDemultiplex achieved great performance in both high-quality and low-quality data. Additionally, scDemultiplex can be combined with other approaches to improve their performance.

Systemic Anticancer Therapy and Thromboembolic Outcomes in Hospitalized Patients With Cancer and COVID-19.

Importance: Systematic data on the association between anticancer therapies and thromboembolic events (TEEs) in patients with COVID-19 are lacking. Objective: To assess the association between anticancer therapy exposure within 3 months prior to COVID-19 and TEEs following COVID-19 diagnosis in patients with cancer. Design, Setting, and Participants: This registry-based retrospective cohort study included patients who were hospitalized and had active cancer and laboratory-confirmed SARS-CoV-2 infection. Data were accrued from March 2020 to December 2021 and analyzed from December 2021 to October 2022. Exposure: Treatments of interest (TOIs) (endocrine therapy, vascular endothelial growth factor inhibitors/tyrosine kinase inhibitors [VEGFis/TKIs], immunomodulators [IMiDs], immune checkpoint inhibitors [ICIs], chemotherapy) vs reference (no systemic therapy) in 3 months prior to COVID-19. Main Outcomes and Measures: Main outcomes were (1) venous thromboembolism (VTE) and (2) arterial thromboembolism (ATE). Secondary outcome was severity of COVID-19 (rates of intensive care unit admission, mechanical ventilation, 30-day all-cause mortality following TEEs in TOI vs reference group) at 30-day follow-up. Results: Of 4988 hospitalized patients with cancer (median [IQR] age, 69 [59-78] years; 2608 [52%] male), 1869 had received 1 or more TOIs. Incidence of VTE was higher in all TOI groups: endocrine therapy, 7%; VEGFis/TKIs, 10%; IMiDs, 8%; ICIs, 12%; and chemotherapy, 10%, compared with patients not receiving systemic therapies (6%). In multivariable log-binomial regression analyses, relative risk of VTE (adjusted risk ratio [aRR], 1.33; 95% CI, 1.04-1.69) but not ATE (aRR, 0.81; 95% CI, 0.56-1.16) was significantly higher in those exposed to all TOIs pooled together vs those with no exposure. Among individual drugs, ICIs were significantly associated with VTE (aRR, 1.45; 95% CI, 1.01-2.07). Also noted were significant associations between VTE and active and progressing cancer (aRR, 1.43; 95% CI, 1.01-2.03), history of VTE (aRR, 3.10; 95% CI, 2.38-4.04), and high-risk site of cancer (aRR, 1.42; 95% CI, 1.14-1.75). Black patients had a higher risk of TEEs (aRR, 1.24; 95% CI, 1.03-1.50) than White patients. Patients with TEEs had high intensive care unit admission (46%) and mechanical ventilation (31%) rates. Relative risk of death in patients with TEEs was higher in those exposed to TOIs vs not (aRR, 1.12; 95% CI, 0.91-1.38) and was significantly associated with poor performance status (aRR, 1.77; 95% CI, 1.30-2.40) and active/progressing cancer (aRR, 1.55; 95% CI, 1.13-2.13). Conclusions and Relevance: In this cohort study, relative risk of developing VTE was high among patients receiving TOIs and varied by the type of therapy, underlying risk factors, and demographics, such as race and ethnicity. These findings highlight the need for close monitoring and perhaps personalized thromboprophylaxis to prevent morbidity and mortality associated with COVID-19-related thromboembolism in patients with cancer.

Systematic Assessment of Small RNA Profiling in Human Extracellular Vesicles.

MOTIVATION: Extracellular vesicles (EVs) are produced and released by most cells and are now recognized to play a role in intercellular communication through the delivery of molecular cargo, including proteins, lipids, and RNA. Small RNA sequencing (small RNA-seq) has been widely used to characterize the small RNA content in EVs. However, there is a lack of a systematic assessment of the quality, technical biases, RNA composition, and RNA biotypes enrichment for small RNA profiling of EVs across cell types, biofluids, and conditions. METHODS: We collected and reanalyzed small RNA-seq datasets for 2756 samples from 83 studies involving 55 with EVs only and 28 with both EVs and matched donor cells. We assessed their quality by the total number of reads after adapter trimming, the overall alignment rate to the host and non-host genomes, and the proportional abundance of total small RNA and specific biotypes, such as miRNA, tRNA, rRNA, and Y RNA. RESULTS: We found that EV extraction methods varied in their reproducibility in isolating small RNAs, with effects on small RNA composition. Comparing proportional abundances of RNA biotypes between EVs and matched donor cells, we discovered that rRNA and tRNA fragments were relatively enriched, but miRNAs and snoRNA were depleted in EVs. Except for the export of eight miRNAs being context-independent, the selective release of most miRNAs into EVs was study-specific. CONCLUSION: This work guides quality control and the selection of EV isolation methods and enhances the interpretation of small RNA contents and preferential loading in EVs.