DiNAMIC.Duo: detecting somatic DNA copy number differences without a normal reference.

MOTIVATION: Somatic DNA copy number alterations (CNAs) arise in tumor tissue because of underlying genomic instability. Recurrent CNAs that occur in the same genomic region across multiple independent samples are of interest to researchers because they may contain genes that contribute to the cancer phenotype. However, differences in copy number states between cancers are also commonly of interest, for example when comparing tumors with distinct morphologies in the same anatomic location. Current methodologies are limited by their inability to perform direct comparisons of CNAs between tumor cohorts, and thus they cannot formally assess the statistical significance of observed copy number differences or identify regions of the genome where these differences occur. RESULTS: We introduce the DiNAMIC.Duo R package that can be used to identify recurrent CNAs in a single cohort or recurrent copy number differences between two cohorts, including when neither cohort is copy neutral. The package utilizes Python scripts for computational efficiency and provides functionality for producing figures and summary output files. AVAILABILITY AND IMPLEMENTATION: The DiNAMIC.Duo R package is available from CRAN at https://cran.r-project.org/web/packages/DiNAMIC.Duo/index.html. This article uses publicly available data from the Broad Institute TCGA Genome Data Analysis Center, https://doi.org/10.7908/C11G0KM9. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Associations Between Prenatal Vitamin D and Placental Gene Expression.

Background: Vitamin D is a hormone regulating gene transcription. Prenatal vitamin D has been linked to immune and vascular function in the placenta, a key organ of pregnancy. To date, studies of vitamin D and placental gene expression have focused on a limited number of candidate genes. Transcriptome-wide RNA sequencing can provide a more complete representation of the placental effects of vitamin D. Objective: We investigated the association between prenatal vitamin D levels and placental gene expression in a large, prospective pregnancy cohort. Methods: Participants were recruited in Shelby County, Tennessee in the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Vitamin D level (plasma total 25-hydroxyvitatmin D, [25(OH)D]) was measured at mid-pregnancy (16-28 weeks' gestation) and delivery. Placenta samples were collected at birth. RNA was isolated and sequenced. We identified differentially expressed genes (DEGs) using adjusted linear regression models. We also conducted weighted gene co-expression network analysis (WGCNA). Results: The median 25(OH)D of participants was 21.8 ng/mL at mid-pregnancy (N=774, IQR: 15.4-26.5 ng/mL) and 23.6 ng/mL at delivery (N=753, IQR: 16.8-29.1 ng/mL). Placental expression of 25 DEGs was associated with 25(OH)D at mid-pregnancy, but no DEG was associated with 25(OH)D at delivery. DEGs were related to energy metabolism, cytoskeletal function, and RNA transcription. Using WGCNA, we identified 2 gene modules whose expression was associated with 25(OH)D at mid-pregnancy and 1 module associated with 25(OH)D at delivery. These modules were enriched for genes related to mitochondrial and cytoskeletal function, and were regulated by transcription factors including ARNT2, BHLHE40, FOSL2, JUND, and NFKB1. Conclusions: Our results indicate that 25(OH)D during mid-pregnancy, but not at delivery, is associated with placental gene expression at birth. Future research is needed to investigate a potential role of vitamin D in programming placental mitochondrial metabolism, intracellular transport, and transcriptional regulation during pregnancy.

Matrix Linear Models for connecting metabolite composition to individual characteristics.

High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

Social Disparities of Pain and Pain Intensity Among Women Diagnosed With Early Stage Breast Cancer.

BACKGROUND: Breast cancer is one of the most commonly diagnosed cancers among women in the United States and pain is the most common side effect of breast cancer and its treatment. Yet, the relationships between social determinants of pain and pain experience/intensity remain under-investigated. We examined the associations between social determinants of pain both at the individual level and the neighborhood level to understand how social conditions are associated with pain perception among early stage breast cancer patients. METHODS: We conducted integrated statistical analysis of 1,191 women with early stage breast cancer treated at a large cancer center in Memphis, Tennessee. Combining electronic health records, patient-reported data and census data regarding residential address at the time of first diagnosis, we evaluated the relationships between social determinants and pain perception. Pain responses were self-reported by a patient as a numerical rating scale score at the patient's initial diagnosis and follow-up clinical visits. We implemented two sets of statistical analyses of the zero-inflated Poisson model and estimated the associations between neighborhood poverty prevalence and breast cancer pain intensity. After adjustment for demographic characteristics, cancer stage, and chemotherapy, pain perception was significantly associated with poverty and blight level of the neighborhood. RESULTS: Among women living in the highest-poverty areas, the odds of reporting pain were 2.48 times higher than those in the lowest-poverty area. Women living in the highest-blight area had 5.43 times higher odds of reporting pain than those in the lowest-blight area. Neighborhood-level social determinants were significantly associated with pain intensity among women diagnosed with early-stage breast cancer. CONCLUSIONS: Distressed neighborhood conditions are significantly associated with higher pain perception. Breast cancer patients living in socio-economically disadvantaged neighborhoods and in poor environmental conditions reported higher pain severity compared to patients from less distressed neighborhoods. Therefore, post-diagnosis pain treatment design needs to be tailored to the social determinants of the breast cancer patients.

The anatomy of COVID-19 comorbidity networks among hospitalized Korean patients.

OBJECTIVES: We aimed to examine how comorbidities were associated with outcomes (illness severity or death) among hospitalized patients with coronavirus disease 2019 (COVID-19). METHODS: Data were provided by the National Medical Center of the Korea Disease Control and Prevention Agency. These data included the clinical and epidemiological information of all patients hospitalized with COVID-19 who were discharged on or before April 30, 2020 in Korea. We conducted comorbidity network and multinomial logistic regression analyses to identify risk factors associated with COVID-19 disease severity and mortality. The outcome variable was the clinical severity score (CSS), categorized as mild (oxygen treatment not needed), severe (oxygen treatment needed), or death. RESULTS: In total, 5,771 patients were included. In the fully adjusted model, chronic kidney disease (CKD) (odds ratio [OR], 2.58; 95% confidence interval [CI], 1.19 to 5.61) and chronic obstructive pulmonary disease (COPD) (OR, 3.19; 95% CI, 1.35 to 7.52) were significantly associated with disease severity. CKD (OR, 5.35; 95% CI, 2.00 to 14.31), heart failure (HF) (OR, 3.15; 95% CI, 1.22 to 8.15), malignancy (OR, 3.38; 95% CI, 1.59 to 7.17), dementia (OR, 2.62; 95% CI, 1.45 to 4.72), and diabetes mellitus (OR, 2.26; 95% CI, 1.46 to 3.49) were associated with an increased risk of death. Asthma and hypertension showed statistically insignificant associations with an increased risk of death. CONCLUSIONS: Underlying diseases contribute differently to the severity of COVID-19. To efficiently allocate limited medical resources, underlying comorbidities should be closely monitored, particularly CKD, COPD, and HF.

SCISSOR: a framework for identifying structural changes in RNA transcripts.

High-throughput sequencing protocols such as RNA-seq have made it possible to interrogate the sequence, structure and abundance of RNA transcripts at higher resolution than previous microarray and other molecular techniques. While many computational tools have been proposed for identifying mRNA variation through differential splicing/alternative exon usage, challenges in its analysis remain. Here, we propose a framework for unbiased and robust discovery of aberrant RNA transcript structures using short read sequencing data based on shape changes in an RNA-seq coverage profile. Shape changes in selecting sample outliers in RNA-seq, SCISSOR, is a series of procedures for transforming and normalizing base-level RNA sequencing coverage data in a transcript independent manner, followed by a statistical framework for its analysis ( https://github.com/hyochoi/SCISSOR ). The resulting high dimensional object is amenable to unsupervised screening of structural alterations across RNA-seq cohorts with nearly no assumption on the mutational mechanisms underlying abnormalities. This enables SCISSOR to independently recapture known variants such as splice site mutations in tumor suppressor genes as well as novel variants that are previously unrecognized or difficult to identify by any existing methods including recurrent alternate transcription start sites and recurrent complex deletions in 3' UTRs.

Integrative Analysis of miRNAs Identifies Clinically Relevant Epithelial and Stromal Subtypes of Head and Neck Squamous Cell Carcinoma.

PURPOSE: The objective of this study is to characterize the role of miRNAs in the classification of head and neck squamous cell carcinoma (HNSCC). EXPERIMENTAL DESIGN: Here, we analyzed 562 HNSCC samples, 88 from a novel cohort and 474 from The Cancer Genome Atlas, using miRNA microarray and miRNA sequencing, respectively. Using an integrative correlations method followed by miRNA expression-based hierarchical clustering, we validated miRNA clusters across cohorts. Evaluation of clusters by logistic regression and gene ontology approaches revealed subtype-based clinical and biological characteristics. RESULTS: We identified two independently validated and statistically significant (P < 0.01) tumor subtypes and named them "epithelial" and "stromal" based on associations with functional target gene ontology relating to differing stages of epithelial cell differentiation. miRNA-based subtypes were correlated with individual gene expression targets based on miRNA seed sequences, as well as with miRNA families and clusters including the miR-17 and miR-200 families. These correlated genes defined pathways relevant to normal squamous cell function and pathophysiology. miRNA clusters statistically associated with differential mutation patterns including higher proportions of TP53 mutations in the stromal class and higher NSD1 and HRAS mutation frequencies in the epithelial class. miRNA classes correlated with previously reported gene expression subtypes, clinical characteristics, and clinical outcomes in a multivariate Cox proportional hazards model with stromal patients demonstrating worse prognoses (HR, 1.5646; P = 0.006). CONCLUSIONS: We report a reproducible classification of HNSCC based on miRNA that associates with known pathologically altered pathways and mutations of squamous tumors and is clinically relevant.

Immune checkpoint blockade reprograms systemic immune landscape and tumor microenvironment in obesity-associated breast cancer.

Immune checkpoint blockade (ICB) has improved outcomes in some cancers. A major limitation of ICB is that most patients fail to respond, which is partly attributable to immunosuppression. Obesity appears to improve immune checkpoint therapies in some cancers, but impacts on breast cancer (BC) remain unknown. In lean and obese mice, tumor progression and immune reprogramming were quantified in BC tumors treated with anti-programmed death-1 (PD-1) or control. Obesity augments tumor incidence and progression. Anti-PD-1 induces regression in lean mice and potently abrogates progression in obese mice. BC primes systemic immunity to be highly responsive to obesity, leading to greater immunosuppression, which may explain greater anti-PD-1 efficacy. Anti-PD-1 significantly reinvigorates antitumor immunity despite persistent obesity. Laminin subunit beta-2 (Lamb2), downregulated by anti-PD-1, significantly predicts patient survival. Lastly, a microbial signature associated with anti-PD-1 efficacy is identified. Thus, anti-PD-1 is highly efficacious in obese mice by reinvigorating durable antitumor immunity. VIDEO ABSTRACT.

Whole-genome characterization of lung adenocarcinomas lacking the RTK/RAS/RAF pathway.

RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(-) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(-) cases are required to understand this important LUAD subset.