Differential transcript usage analysis incorporating quantification uncertainty via compositional measurement error regression modeling.

Differential transcript usage (DTU) occurs when the relative expression of multiple transcripts arising from the same gene changes between different conditions. Existing approaches to detect DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. Here we propose a new method, CompDTU, that uses compositional regression to model the relative abundance proportions of each transcript that are of interest in DTU analyses. This procedure leverages fast matrix-based computations that make it ideally suited for DTU analysis with larger sample sizes. This method also allows for the testing of and adjustment for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty in the expression estimates for each transcript in RNA-seq data. We extend our CompDTU method to incorporate quantification uncertainty leveraging common output from RNA-seq expression quantification tool in a novel method CompDTUme. Through several power analyses, we show that CompDTU has excellent sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty, while also maintaining favorable speed and scalability. We motivate our methods using data from the Cancer Genome Atlas Breast Invasive Carcinoma data set, specifically using RNA-seq data from primary tumors for 740 patients with breast cancer. We show greatly reduced computation time from our new methods as well as the ability to detect several novel genes with significant DTU across different breast cancer subtypes.

Tumor Immunogenomic Features Determine Outcomes in Patients with Metastatic Colorectal Cancer Treated with Standard-of-Care Combinations of Bevacizumab and Cetuximab.

PURPOSE: CALGB/SWOG 80405 was a randomized phase III trial in first-line patients with metastatic colorectal cancer treated with bevacizumab, cetuximab, or both, plus chemotherapy. We tested the effect of tumor immune features on overall survival (OS). EXPERIMENTAL DESIGN: Primary tumors (N = 554) were profiled by RNA sequencing. Immune signatures of macrophages, lymphocytes, TGFß, IFNγ, wound healing, and cytotoxicity were measured. CIBERSORTx scores of naive and memory B cells, plasma cells, CD8+ T cells, resting and activated memory CD4+ T cells, M0 and M2 macrophages, and activated mast cells were measured. RESULTS: Increased M2 macrophage score [HR, 6.30; 95% confidence interval (CI), 3.0-12.15] and TGFß signature expression (HR, 1.35; 95% CI, 1.05-1.77) were associated with shorter OS. Increased scores of plasma cells (HR, 0.55; 95% CI, 0.38-0.87) and activated memory CD4+ T cells (HR, 0.34; 95% CI, 0.16-0.65) were associated with longer OS. Using optimal cutoffs from these four features, patients were categorized as having either 4, 3, 2, or 0-1 beneficial features associated with longer OS, and the median (95% CI) OS decreased from 42.5 (35.8-47.8) to 31.0 (28.8-34.4), 25.2 (20.6-27.9), and 17.7 (13.5-20.4) months respectively (P = 3.48e-11). CONCLUSIONS: New immune features can be further evaluated to improve patient response. They provide the rationale for more effective immunotherapy strategies.

Chronic E-Cigarette Exposure Alters the Human Bronchial Epithelial Proteome.

RATIONALE: E-cigarettes vaporize propylene glycol/vegetable glycerin (PG/VG), nicotine, and flavorings. However, the long-term health effects of exposing lungs to vaped e-liquids are unknown. OBJECTIVES: To determine the effects of chronic vaping on pulmonary epithelia. METHODS: We performed research bronchoscopies on healthy nonsmokers, cigarette smokers, and e-cigarette users (vapers) and obtained bronchial brush biopsies and lavage samples from these subjects for proteomic investigation. We further employed in vitro and murine exposure models to support our human findings. MEASUREMENTS AND MAIN RESULTS: Visual inspection by bronchoscopy revealed that vaper airways appeared friable and erythematous. Epithelial cells from biopsy samples revealed approximately 300 proteins that were differentially expressed in smoker and vaper airways, with only 78 proteins being commonly altered in both groups and 113 uniquely altered in vapers. For example, CYP1B1 (cytochrome P450 family 1 subfamily B member 1), MUC5AC (mucin 5 AC), and MUC4 levels were increased in vapers. Aerosolized PG/VG alone significantly increased MUC5AC protein in human airway epithelial cultures and in murine nasal epithelia in vivo. We also found that e-liquids rapidly entered cells and that PG/VG reduced membrane fluidity and impaired protein diffusion. CONCLUSIONS: We conclude that chronic vaping exerts marked biological effects on the lung and that these effects may in part be mediated by the PG/VG base. These changes are likely not harmless and may have clinical implications for the development of chronic lung disease. Further studies will be required to determine the full extent of vaping on the lung.