IMPORTANCE Broad-based genomic sequencing is being used more frequently for patients with advanced non-small cell lung cancer (NSCLC). However, little is known about the association between broad-based genomic sequencing and treatment selection or survival among patients with advanced NSCLC in a community oncology setting. OBJECTIVE To compare clinical outcomes between patients with advanced NSCLC who received broad-based genomic sequencing vs a control group of patients who received routine testing for EGFR mutations and/or ALK rearrangements alone. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of patients with chart-confirmed advanced NSCLC between January 1, 2011, and July 31, 2016, and who received care at 1 of 191 oncology practices across the United States using the Flatiron Health Database. Patients were diagnosed with stage IIIB/IV or unresectable nonsquamous NSCLC who received at least 1 line of antineoplastic treatment. EXPOSURES Receipt of either broad-based genomic sequencing or routine testing (EGFR and/or ALK only). Broad-based genomic sequencing included any multigene panel sequencing assay examining more than 30 genes prior to third-line treatment. MAIN OUTCOMES AND MEASURES Primary outcomes were 12-month mortality and overall survival from the start of first-line treatment. Secondary outcomes included frequency of genetic alterations and treatments received. RESULTS Among 5688 individuals with advanced NSCLC (median age, 67 years [interquartile range, 41-85], 63.6% white, 80% with a history of smoking); 875 (15.4%) received broad-based genomic sequencing and 4813 (84.6%) received routine testing. Among patients who received broad-based genomic sequencing, 4.5% received targeted treatment based on testing results, 9.8% received routine EGFR/ALK targeted treatment, and 85.1% received no targeted treatment. Unadjusted mortality rates at 12 months were 49.2% for patients undergoing broad-based genomic sequencing and 35.9% for patients undergoing routine testing. Using an instrumental variable analysis, there was no significant association between broad-based genomic sequencing and 12-month mortality (predicted probability of death at 12 months, 41.1% for broad-based genomic sequencing vs 44.4% for routine testing; difference −3.6% [95% CI, −18.4% to 11.1%]; P = .63). The results were consistent in the propensity score-matched survival analysis (42.0% vs 45.1%; hazard ratio, 0.92 [95% CI, 0.73 to 1.11]; P = .40) vs unmatched cohort (hazard ratio, 0.69 [95% CI, 0.62 to 0.77]; log-rank P < .001). CONCLUSIONS AND RELEVANCE Among patients with advanced non-small cell lung cancer receiving care in the community oncology setting, broad-based genomic sequencing directly informed treatment in a minority of patients and was not independently associated with better survival.

The Hi-C method is widely used to study the functional roles of the three-dimensional (3D) architecture of genomes. Here, we integrate Hi-C, whole-genome sequencing (WGS) and RNA-seq to study the 3D genome architecture of multiple myeloma (MM) and how it associates with genomic variation and gene expression. Our results show that Hi-C interaction matrices are biased by copy number variations (CNVs) and can be used to detect CNVs. Also, combining Hi-C and WGS data can improve the detection of translocations. We find that CNV breakpoints significantly overlap with topologically associating domain (TAD) boundaries. Compared to normal B cells, the numbers of TADs increases by 25% in MM, the average size of TADs is smaller, and about 20% of genomic regions switch their chromatin A/B compartment types. In summary, we report a 3D genome interaction map of aneuploid MM cells and reveal the relationship among CNVs, translocations, 3D genome reorganization, and gene expression regulation.

This retrospective matched cohort study describes 30 solid organ transplant (SOT) patients with Coronavirus Disease 2019 (COVID‐19) matched 1:2 to 60 non‐SOT patients (control group) based on age, body mass index (BMI), and comorbidities (hypertension and diabetes mellitus with hemoglobin A1c > 8.0%). The SOT group had a higher proportion of cardiovascular disease ( P < .05). During the index hospitalization, there were no significant differences with regard to disease severity or critical care needs (mechanical intubation, vasopressors, and renal replacement therapy). At 28 days, 4 (13%) patients died in the SOT group and 8 (13%) patients died in the control group ( P = 1.0). Nineteen patients received tocilizumab in the SOT group compared to 29 patients in the control group. Among these patients, interleukin‐6 (IL‐6) and soluble interleukin‐2 receptor (sIL2R) levels increased after tocilizumab and interleukin‐10 (IL‐10) levels decreased after tocilizumab. Overall, SOT patients had comparable mortality to non‐SOT patients, although numerically more SOT patients received tocilizumab (63% vs 48%) and steroids (37% vs 20%). Larger, multi‐center studies are needed to ascertain these findings. Lastly, the complex cytokine release syndrome in COVID‐19 remains an area of intense research and the analysis of key interleukin levels (IL‐6, IL‐10, and sIL2R) in this study contributes to the understanding of this process.

Motivation A number of computational methods have been proposed recently to profile tumor microenvironment (TME) from bulk RNA data, and they have proved useful for understanding microenvironment differences among therapeutic response groups. However, these methods are not able to account for tumor proportion nor variable mRNA levels across cell types. Results In this article, we propose a Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution (NITUMID) framework for TME profiling that addresses these limitations. It is designed to provide robust estimates of tumor and immune cells proportions simultaneously, while accommodating mRNA level differences across cell types. Through comprehensive simulations and real data analyses, we demonstrate that NITUMID not only can accurately estimate tumor fractions and cell types’ mRNA levels, which are currently unavailable in other methods; it also outperforms most existing deconvolution methods in regular cell type profiling accuracy. Moreover, we show that NITUMID can more effectively detect clinical and prognostic signals from gene expression profiles in tumor than other methods. Availability and implementation The algorithm is implemented in R. The source code can be downloaded at https://github.com/tdw1221/NITUMID. Supplementary information Supplementary data are available at Bioinformatics online.

We describe the cytokine profiles of a large cohort of hospitalized patients with moderate to critical COVID-19, focusing on IL-6, sIL2R, and IL-10 levels before and after receiving immune modulating therapies, namely, tocilizumab and glucocorticoids. We also discuss the possible roles of sIL2R and IL-10 as markers of ongoing immune dysregulation after IL-6 inhibition. We performed a retrospective chart review of adult patients admitted to a tertiary care center with moderate to critical SARS-CoV-2 infection. Disease severity was based on maximum oxygen requirement during hospital stay to maintain SpO2 > 93% (moderate, 0-3 L NC; severe, 4-6 L NC or non-rebreather; critical, HFNC, NIPPV, or MV). All patients were treated using the institution's treatment algorithm, which included consideration of tocilizumab for severe and critical disease. The most common cytokine elevations among all patients included IL-6, sIL2R, IFN-γ, and IL-10; patients who received tocilizumab had higher incidence of IL-6 and sIL2R elevations. Pre-tocilizumab IL-6 levels increased with disease severity (p = .0151). Both IL-6 and sIL2R levels significantly increased after administration of tocilizumab in all severity groups; IL-10 levels decreased in severe (p = .0203), but not moderate or critical, patients after they received tocilizumab. Cluster analysis revealed association between higher admission IL-6, sIL2R, and CRP levels and disease severity. Mean IL-6, sIL2R, and D-dimer were associated with mortality, and tocilizumab-treated patients with elevated IL-6, IL-10, and D-dimer were more likely to also receive glucocorticoids. Accessible clinical cytokine panels may be useful for monitoring response to treatment in COVID-19. The increase in sIL2R post-tocilizumab, despite administration of glucocorticoids, may indicate the need for combination therapy in order to modulate more than one hyperinflammatory pathway in COVID-19. We also discuss the role of cytokines as potential biomarkers for use of adjunct glucocorticoid therapy.Keywords COVID-19 . SARS-CoV-2 . cytokine release syndrome . interleukin-2 receptor (soluble) . interleukin-10 . tocilizumab . glucocorticoids . cytokine profile . cytokine panel

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. Here, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed substantial statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

A challenge in bulk gene differential expression analysis is to differentiate changes due to cell type-specific gene expression and cell type proportions. SCADIE is an iterative algorithm that simultaneously estimates cell type-specific gene expression profiles and cell type proportions, and performs cell type-specific differential expression analysis at the group level. Through its unique penalty and objective function, SCADIE more accurately identifies cell type-specific differentially expressed genes than existing methods, including those that may be missed from single cell RNA-Seq data. SCADIE has robust performance with respect to the choice of deconvolution methods and the sources and quality of input data.

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