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Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

Abstract Crohn’s disease (CD) is a complex chronic inflammatory disorder with both gastrointestinal and extra-intestinal manifestations associated immune dysregulation. Analyzing 202,359 cells from 170 specimens across 83 patients, we identify a distinct epithelial cell type in both terminal ileum and ascending colon (hereon as ‘LND’) with high expression of LCN2, NOS2, and DUOX2 and genes related to antimicrobial response and immunoregulation. LND cells, confirmed by in-situ RNA and protein imaging, are rare in non-IBD controls but expand in active CD, and actively interact with immune cells and specifically express IBD/CD susceptibility genes, suggesting a possible function in CD immunopathogenesis. Furthermore, we discover early and late LND subpopulations with different origins and developmental potential. A higher ratio of late-to-early LND cells correlates with better response to anti-TNF treatment. Our findings thus suggest a potential pathogenic role for LND cells in both Crohn’s ileitis and colitis.

Abstract 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.

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.

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.

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.

Abstract Background Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we presented an approach to preview power reductions and to estimate sample sizes required to achieve the desired power when treatment switching occurs in the intention-to-treat analysis. Methods We proposed a simulation-based approach and developed an R package to perform power and sample sizes estimation in clinical trials with treatment switching. Results We simulated a number of randomized trials incorporating treatment switching and investigated the impact of the relative effectiveness of the experimental treatment to the control, the switching probability, the switching time, and the deviation between the assumed and the real distributions for the survival time on power reductions and sample sizes estimation. The switching probability and the switching time are key determinants for significant power decreasing and thus sample sizes surging to maintain the desired power. The sample sizes required in randomized trials absence of treatment switching vary from around four-fifths to one-seventh of the sample sizes required in randomized trials allowing treatment switching as the switching probability increases. The power reductions and sample sizes increase with the decrease of switching time. Conclusions The simulation-based approach not only provides a preview for power declining but also calculates the required sample size to achieve an expected power in the intention-to-treat analysis when treatment switching occurs. It will provide researchers and clinicians with useful information before randomized controlled trials are conducted.

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.

Summary: Background: Breakthrough SARS-CoV-2 infections following vaccination against COVID-19 are of international concern. Patients with cancer have been observed to have worse outcomes associated with COVID-19 during the pandemic. We sought to evaluate the clinical characteristics and outcomes of patients with cancer who developed breakthrough SARS-CoV-2 infections after 2 or 3 doses of mRNA vaccines. Methods: We evaluated the clinical characteristics of patients with cancer who developed breakthrough infections using data from the multi-institutional COVID-19 and Cancer Consortium (CCC19; NCT04354701). Analysis was restricted to patients with laboratory-confirmed SARS-CoV-2 diagnosed in 2021 or 2022, to allow for a contemporary unvaccinated control population; potential differences were evaluated using a multivariable logistic regression model after inverse probability of treatment weighting to adjust for potential baseline confounding variables. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) are reported. The primary endpoint was 30-day mortality, with key secondary endpoints of hospitalization and ICU and/or mechanical ventilation (ICU/MV). Findings: The analysis included 2486 patients, of which 564 and 385 had received 2 or 3 doses of an mRNA vaccine prior to infection, respectively. Hematologic malignancies and recent receipt of systemic anti-neoplastic therapy were more frequent among vaccinated patients. Vaccination was associated with improved outcomes: in the primary analysis, 2 doses (aOR: 0.62, 95% CI: 0.44–0.88) and 3 doses (aOR: 0.20, 95% CI: 0.11–0.36) were associated with decreased 30-day mortality. There were similar findings for the key secondary endpoints of ICU/MV (aOR: 0.60, 95% CI: 0.45–0.82 and 0.37, 95% CI: 0.24–0.58) and hospitalization (aOR: 0.60, 95% CI: 0.48–0.75 and 0.35, 95% CI: 0.26–0.46) for 2 and 3 doses, respectively. Importantly, Black patients had higher rates of hospitalization (aOR: 1.47, 95% CI: 1.12–1.92), and Hispanic patients presented with higher rates of ICU/MV (aOR: 1.61, 95% CI: 1.06–2.44). Interpretation: Vaccination against COVID-19, especially with additional doses, is a fundamental strategy in the prevention of adverse outcomes including death, among patients with cancer. Funding: This study was partly supported by grants from the National Cancer Institute grant number P30 CA068485 to C-YH, YS, SM, JLW; T32-CA236621 and P30-CA046592 to C.R.F; CTSA 2UL1TR001425-05A1 to TMW-D; ACS/FHI Real-World Data Impact Award, P50 MD017341-01, R21 CA242044-01A1, Susan G. Komen Leadership Grant Hunt to MKA. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH).

Background Whether influenza vaccination (FV) is associated with the severity of immune-related adverse events (IRAEs) in patients with advanced thoracic cancer on immune checkpoint inhibitors (ICIs) is not fully understood. Methods Patients enrolled in this retrospective cohort study were identified from the Vanderbilt BioVU database and their medical records were reviewed. Patients with advanced thoracic cancer who received FV within 3 months prior to or during their ICI treatment period were enrolled in the FV-positive cohort and those who did not were enrolled in the FV-negative cohort. The primary objective was to detect whether FV is associated with decreased IRAE severity. The secondary objectives were to evaluate whether FV is associated with a decreased risk for grade 3–5 IRAEs and better survival times. Multivariable ordinal logistic regression was used for the primary analysis. Results A total of 142 and 105 patients were enrolled in the FV-positive and FV-negative cohorts, respectively. There was no statistically significant difference in patient demographics or cumulative incidences of IRAEs between the two cohorts. In the primary analysis, FV was inversely associated with the severity of IRAEs (OR 0.63; p=0.046). In the secondary analysis, FV was associated with a decreased risk for grade 3–5 IRAEs (OR 0.42; p=0.005). Multivariable Cox regression showed that FV was not associated with survival times. Conclusions Our study showed that FV does not increase toxicity for patients with advanced thoracic cancer on ICIs and is associated with a decreased risk for grade 3–5 IRAEs. No statistically significant survival differences were found between patients with and without FV.

Abstract Ductal carcinoma in situ (DCIS) is the most common type of pre-invasive breast cancer diagnosed in women. Because the majority of DCIS cases are unlikely to progress to invasive breast cancer, many women are over-treated for DCIS. By understanding the molecular basis of early stage breast cancer progression, we may identify better prognostic factors and design treatments tailored specifically to the predicted outcome of DCIS. Chemokines are small soluble molecules with complex roles in inflammation and cancer progression. Previously, we demonstrated that CCL2/CCR2 chemokine signaling in breast cancer cell lines regulated growth and invasion through p42/44MAPK and SMAD3 dependent mechanisms. Here, we sought to determine the clinical and functional relevance of CCL2/CCR2 signaling proteins to DCIS progression. Through immunostaining analysis of DCIS and IDC tissues, we show that expression of CCL2, CCR2, phospho-SMAD3 and phospho-p42/44MAPK correlate with IDC. Using PDX models and an immortalized hDCIS.01 breast epithelial cell line, we show that breast epithelial cells with high CCR2 and high CCL2 levels form invasive breast lesions that express phospho-SMAD3 and phospho-p42/44MAPK. These studies demonstrate that increased CCL2/CCR2 signaling in breast tissues is associated with DCIS progression, and could be a signature to predict the likelihood of DCIS progression to IDC.

Era+ breast cancer patients often develop resistance to endocrine therapy. Here, the authors show that FGFR1 amplification is a resistance mechanism to CDK4/6 inhibitor and endocrine therapy and that combined treatment with FGFR, CDK4/6, and anti-estrogens is a potential therapeutic strategy in Era+ breast cancer tumors.

Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.

We previously found that short-term treatment (week 8 to 12 after injury) with high-dose angiotensin receptor blocker (ARB) induced the regression of existing glomerulosclerosis in 5/6 nephrectomy rats. We therefore assessed the effects of long-term intervention with ARB vs. nonspecific antihypertensives in this study. Adult rats underwent 5/6 nephrectomy and renal biopsy 8 weeks later. The rats were then divided into three groups with equivalent renal function and glomerular sclerosis and treated with high-dose losartan (ARB), nonspecific antihypertensive triple-therapy (TRX), or left untreated (Control) until week 30. We found that blood pressure, serum creatinine levels, and glomerulosclerosis were lower at sacrifice in ARB and TRX vs. Control. Only ARB reduced proteinuria and maintained the density of WT-1-positive podocytes. Glomerular tufts showed more double-positive cells for CD44, a marker of activated parietal epithelial cells, and synaptopodin after ARB vs. TRX or Control. ARB treatment reduced aldosterone levels. ARB-treated rats had significantly improved survival when compared with TRX or Control. We conclude that both long-term ARB and triple-therapy ameliorate progression, but do not sustain the regression of glomerulosclerosis. ARB resulted in the superior preservation of podocyte integrity and decreased proteinuria and aldosterone, linked to increased survival in the uremic environment.

Abstract Background Enhancers are distal cis-regulatory elements that control gene expression. Despite an increasing appreciation of the importance of enhancers in cellular function and disease, our knowledge of enhancer-regulated transcription is very limited. Nascent RNA sequencing technologies, such as global nuclear run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), not only provide a direct and reliable measurement of enhancer activity, but also allow for quantifying transcription of enhancers and target genes simultaneously, making these technologies extremely useful for exploring enhancer-mediated regulation. Results Nascent RNA sequencing analysis (NRSA) provides a comprehensive view of enhancer-mediated gene regulation. NRSA not only outperforms existing methods for enhancer identification, but also enables annotation and quantification of active enhancers, and prediction of their target genes. Furthermore, NRSA identifies functionally important enhancers by integrating 1) nascent transcriptional changes in enhancers and their target genes and 2) binding profiles from regulator(s) of interest. Applied to wildtype and histone deacetylase 3 (Hdac3) knockout mouse livers, NRSA showed that HDAC3 regulates RNA polymerase recruitment through both proximal (promoter) and distal (enhancer) regulatory elements. Integrating ChIP-seq with PRO-seq data, NRSA prioritized enhancers based on their potential contribution to mediating HDAC3 regulation. Conclusions NRSA will greatly facilitate the usage of nascent RNA sequencing techniques and accelerate the study of enhancer-mediated regulation.

Abstract Background One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/. Conclusions RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

MicroRNAs (miRNAs) are small endogenous non-coding RNAs that play important roles in regulating gene expression. Most miRNAs are located within or close to genes (host). miRNAs and their host genes have either coordinated or independent transcription. We performed a comprehensive investigation on co-transcriptional patterns of miRNAs and host genes based on 4707 patients across 21 cancer types. We found that only 11.6% of miRNA-host pairs were co-transcribed consistently and strongly across cancer types. Most miRNA-host pairs showed a strong coexpression only in some specific cancer types, demonstrating a high heterogenous pattern. For two particular types of intergenic miRNAs, readthrough and divergent miRNAs, readthrough miRNAs showed higher coexpression with their host genes than divergent ones. miRNAs located within non-coding genes had tighter co-transcription with their hosts than those located within protein-coding genes, especially exonic and junction miRNAs. A few precursor miRNAs changed their dominate form between 5′ and 3′ strands in different cancer types, including miR-486, miR-99b, let-7e, miR-125a, let-7g, miR-339, miR-26a, miR-16, and miR-218, whereas only two miRNAs with multiple host genes switched their co-transcriptional partner in different cancer types (miR-219a-1 with SLC39A7/HSD17B8 and miR-3615 with RAB37/SLC9A3R1). miRNAs generated from distinct precursors (such as miR-125b from miR-125b-1 or miR-125b-2) were more likely to have cancer-dependent main contributors. miRNAs and hosts were less co-expressed in KIRC than other cancer types, possibly due to its frequent VHL mutations. Our findings shed new light on miRNA biogenesis and cancer diagnosis and treatments.

Abstract Background High throughput sequencing technology enables the both the human genome and transcriptome to be screened at the single nucleotide resolution. Tools have been developed to infer single nucleotide variants (SNVs) from both DNA and RNA sequencing data. To evaluate how much difference can be expected between DNA and RNA sequencing data, and among tissue sources, we designed a study to examine the single nucleotide difference among five sources of high throughput sequencing data generated from the same individual, including exome sequencing from blood, tumor and adjacent normal tissue, and RNAseq from tumor and adjacent normal tissue. Results Through careful quality control and analysis of the SNVs, we found little difference between DNA-DNA pairs (1%–2%). However, between DNA-RNA pairs, SNV differences ranged anywhere from 10% to 20%. Conclusions Only a small portion of these differences can be explained by RNA editing. Instead, the majority of the DNA-RNA differences should be attributed to technical errors from sequencing and post-processing of RNAseq data. Our analysis results suggest that SNV detection using RNAseq is subject to high false positive rates.

Background: For Chinese patients with hepatocellular carcinoma (HCC), surgical resection is the most important treatment to achieve long-term survival for patients with an early-stage tumor, and yet the prognosis after surgery is diverse. We aimed to construct a scoring system (Shanghai Score) for individualized prognosis estimation and adjuvant treatment evaluation. Methods: A multivariate Cox proportional hazards model was constructed based on 4166 HCC patients undergoing resection during 2001–2008 at Zhongshan Hospital. Age, hepatitis B surface antigen, hepatitis B e antigen, partial thromboplastin time, total bilirubin, alkaline phosphatase, γ-glutamyltransferase, α-fetoprotein, tumor size, cirrhosis, vascular invasion, differentiation, encapsulation, and tumor number were finally retained by a backward step-down selection process with the Akaike information criterion. The Harrell's concordance index (C-index) was used to measure model performance. Shanghai Score is calculated by summing the products of the 14 variable values times each variable's corresponding regression coefficient. Totally 1978 patients from Zhongshan Hospital undergoing resection during 2009–2012, 808 patients from Eastern Hepatobiliary Surgery Hospital during 2008–2010, and 244 patients from Tianjin Medical University Cancer Hospital during 2010–2011 were enrolled as external validation cohorts. Shanghai Score was also implied in evaluating adjuvant treatment choices based on propensity score matching analysis. Results: Shanghai Score showed good calibration and discrimination in postsurgical HCC patients. The bootstrap-corrected C-index (confidence interval [CI]) was 0.74 for overall survival (OS) and 0.68 for recurrence-free survival (RFS) in derivation cohort (4166 patients), and in the three independent validation cohorts, the CI s for OS ranged 0.70–0.72 and that for RFS ranged 0.63–0.68. Furthermore, Shanghai Score provided evaluation for adjuvant treatment choices (transcatheter arterial chemoembolization or interferon-α). The identified subset of patients at low risk could be ideal candidates for curative surgery, and subsets of patients at moderate or high risk could be recommended with possible adjuvant therapies after surgery. Finally, a web server with individualized outcome prediction and treatment recommendation was constructed. Conclusions: Based on the largest cohort up to date, we established Shanghai Score – an individualized outcome prediction system specifically designed for Chinese HCC patients after surgery. The Shanghai Score web server provides an easily accessible tool to stratify the prognosis of patients undergoing liver resection for HCC.

To comprehensively study extracellular small RNAs (sRNA) by sequencing (sRNA-seq), we developed a novel pipeline to overcome current limitations in analysis entitled, “Tools for Integrative Genome analysis of Extracellular sRNAs (TIGER)”. To demonstrate the power of this tool, sRNA-seq was performed on mouse lipoproteins, bile, urine and livers. A key advance for the TIGER pipeline is the ability to analyse both host and non-host sRNAs at genomic, parent RNA and individual fragment levels. TIGER was able to identify approximately 60% of sRNAs on lipoproteins and >85% of sRNAs in liver, bile and urine, a significant advance compared to existing software. Moreover, TIGER facilitated the comparison of lipoprotein sRNA signatures to disparate sample types at each level using hierarchical clustering, correlations, beta-dispersions, principal coordinate analysis and permutational multivariate analysis of variance. TIGER analysis was also used to quantify distinct features of exRNAs, including 5ʹ miRNA variants, 3ʹ miRNA non-templated additions and parent RNA positional coverage. Results suggest that the majority of sRNAs on lipoproteins are non-host sRNAs derived from bacterial sources in the microbiome and environment, specifically rRNA-derived sRNAs from Proteobacteria. Collectively, TIGER facilitated novel discoveries of lipoprotein and biofluid sRNAs and has tremendous applicability for the field of extracellular RNA.

The nature and anatomic location of the protective memory CD8+ T cell subset induced by intranasal vaccination remain poorly understood. We developed a vaccination model to assess the anatomic location of protective memory CD8+ T cells and their role in lower airway infections. Memory CD8+ T cells elicited by local intranasal, but not systemic, vaccination with an engineered non-replicative CD8+ T cell-targeted antigen confer enhanced protection to a lethal respiratory viral challenge. This protection depends on a distinct CXCR3LO resident memory CD8+ T (Trm) cell population that preferentially localizes to the pulmonary interstitium. Because they are positioned close to the mucosa, where infection occurs, interstitial Trm cells act before inflammation can recruit circulating memory CD8+ T cells into the lung tissue. This results in a local protective immune response as early as 1 day post-infection. Hence, vaccine strategies that induce lung interstitial Trm cells may confer better protection against respiratory pathogens.

We report that p73 is expressed in multiciliated cells (MCCs), is required for MCC differentiation, and directly regulates transcriptional modulators of multiciliogenesis. Loss of ciliary biogenesis provides a unifying mechanism for many phenotypes observed in p73 knockout mice including hydrocephalus; hippocampal dysgenesis; sterility; and chronic inflammation/infection of lung, middle ear, and sinus. Through p73 and p63 ChIP-seq using murine tracheal cells, we identified over 100 putative p73 target genes that regulate MCC differentiation and homeostasis. We validated Foxj1, a transcriptional regulator of multiciliogenesis, and many other cilia-associated genes as direct target genes of p73 and p63. We show p73 and p63 are co-expressed in a subset of basal cells and suggest that p73 marks these cells for MCC differentiation. In summary, p73 is essential for MCC differentiation, functions as a critical regulator of a transcriptome required for MCC differentiation, and, like p63, has an essential role in development of tissues.

Immunotherapy is used to treat melanoma, however patient responses vary widely highlighting the need for factors that can predict therapeutic success. Here, the authors show that MHC-II molecules expressed by tumour cells are positively correlated with a good response to therapy and overall patient survival.

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.

High throughput genomic assays empower us to study the entire human genome in short time with reasonable cost. Formalin fixed-paraffin-embedded (FFPE) tissue processing remains the most economical approach for longitudinal tissue specimen storage. Therefore, the ability to apply high throughput genomic applications to FFPE specimens can expand clinical assays and discovery. Many studies have measured the accuracy and repeatability of data generated from FFPE specimens using high throughput genomic assays. Together, these studies demonstrate feasibility and provide crucial guidance for future studies using FFPE specimens. Here, we summarize the findings of these studies and discuss the limitations of high throughput data generated from FFPE specimens across several platforms that include microarray, high throughput sequencing, and NanoString.

Triple-negative breast cancer (TNBC) is a heterogeneous disease that can be classified into distinct molecular subtypes by gene expression profiling. Considered a difficult-to-treat cancer, a fraction of TNBC patients benefit significantly from neoadjuvant chemotherapy and have far better overall survival. Outside of BRCA1/2 mutation status, biomarkers do not exist to identify patients most likely to respond to current chemotherapy; and, to date, no FDA-approved targeted therapies are available for TNBC patients. Previously, we developed an approach to identify six molecular subtypes TNBC (TNBCtype), with each subtype displaying unique ontologies and differential response to standard-of-care chemotherapy. Given the complexity of the varying histological landscape of tumor specimens, we used histopathological quantification and laser-capture microdissection to determine that transcripts in the previously described immunomodulatory (IM) and mesenchymal stem-like (MSL) subtypes were contributed from infiltrating lymphocytes and tumor-associated stromal cells, respectively. Therefore, we refined TNBC molecular subtypes from six (TNBCtype) into four (TNBCtype-4) tumor-specific subtypes (BL1, BL2, M and LAR) and demonstrate differences in diagnosis age, grade, local and distant disease progression and histopathology. Using five publicly available, neoadjuvant chemotherapy breast cancer gene expression datasets, we retrospectively evaluated chemotherapy response of over 300 TNBC patients from pretreatment biopsies subtyped using either the intrinsic (PAM50) or TNBCtype approaches. Combined analysis of TNBC patients demonstrated that TNBC subtypes significantly differ in response to similar neoadjuvant chemotherapy with 41% of BL1 patients achieving a pathological complete response compared to 18% for BL2 and 29% for LAR with 95% confidence intervals (CIs; [33, 51], [9, 28], [17, 41], respectively). Collectively, we provide pre-clinical data that could inform clinical trials designed to test the hypothesis that improved outcomes can be achieved for TNBC patients, if selection and combination of existing chemotherapies is directed by knowledge of molecular TNBC subtypes.

The recently-introduced Orbitrap Fusion mass spectrometry permits various types of MS2 acquisition methods. To date, these different MS2 strategies and the optimal data interpretation approach for each have not been adequately evaluated. This study comprehensively investigated the four MS2 strategies: HCD-OT (higher-energy-collisional-dissociation with Orbitrap detection), HCD-IT (HCD with ion trap, IT), CID-IT (collision-induced-dissociation with IT) and CID-OT on Orbitrap Fusion. To achieve extensive comparison and identify the optimal data interpretation method for each technique, several search engines (SEQUEST and Mascot) and post-processing methods (score-based, PeptideProphet, and Percolator) were assessed for all techniques for the analysis of a human cell proteome. It was found that divergent conclusions could be made from the same dataset when different data interpretation approaches were used and therefore requiring a relatively fair comparison among techniques. Percolator was chosen for comparison of techniques because it performs the best among all search engines and MS2 strategies. For the analysis of human cell proteome using individual MS2 strategies, the highest number of identifications was achieved by HCD-OT, followed by HCD-IT and CID-IT. Based on these results, we concluded that a relatively fair platform for data interpretation is necessary to avoid divergent conclusions from the same dataset, and HCD-OT and HCD-IT may be preferable for protein/peptide identification using Orbitrap Fusion.

Identification of correlation pattern and signal pathway among biomarkers in patients has become increasingly interesting for its potential values in diagnosis, treatment and prognosis. EGFR and p-AKT signaling in osteosarcoma (OS) patients were analyzed for its relationship with cancer cell proliferation maker, Ki-67, using causal procedures and statistical tests. A total of 69 patients were collected who present to Vanderbilt University Medical Center with newly diagnosed, previously untreated osteosarcomas during the clinical study period 1994 through 2003. Tissue microarrays were constructed for EGFR, p-AKT and Ki-67. The mediation model was constructed with structural equation model (SEM) for the causal analysis of the three biomarkers in osteosarcoma patients. The results suggested a mediating effect of p-AKT for the causal relationship between EGFR and Ki-67. The study also found significant associations between EGFR and Ki-67 (p = 0.002), EGFR and p-AKT (p = 0.027), and p-AKT and Ki-67 controlling EGFR (p = 0.004). After the impact of EGFR on Ki-67 was accounted for by p-AKT, the relation between EGFR and Ki-67 was no longer signifi cant (p = 0.381). The mediating effect was confirmed with Sobel test (p

Systems biology is an approach to comprehensively study complex interactions within a biological system. Most published systems vaccinology studies have utilized whole blood or peripheral blood mononuclear cells (PBMC) to monitor the immune response after vaccination. Because human blood is comprised of multiple hematopoietic cell types, the potential for masking responses of under-represented cell populations is increased when analyzing whole blood or PBMC. To investigate the contribution of individual cell types to the immune response after vaccination, we established a rapid and efficient method to purify human T and B cells, natural killer (NK) cells, myeloid dendritic cells (mDC), monocytes, and neutrophils from fresh venous blood. Purified cells were fractionated and processed in a single day. RNA-Seq and quantitative shotgun proteomics were performed to determine expression profiles for each cell type prior to and after inactivated seasonal influenza vaccination. Our results show that transcriptomic and proteomic profiles generated from purified immune cells differ significantly from PBMC. Differential expression analysis for each immune cell type also shows unique transcriptomic and proteomic expression profiles as well as changing biological networks at early time points after vaccination. This cell type-specific information provides a more comprehensive approach to monitor vaccine responses.

In breast and colon cancers, transforming growth factor (TGIF)-β signaling initially has an antineoplastic effect, inhibiting tumor growth, but eventually exerts a proneoplastic effect, increasing motility and cancer spread. In prostate cancer, studies using human samples have correlated the loss of the TGIF-β type II receptor (TβRll) with higher tumor grade. To determine the effect of an inhibited TGIF-β pathway on prostate cancer, we bred transgenic mice expressing the tumorigenic SV40 large T antigen in the prostate with transgenic mice expressing a dominant negative TβRII mutant (DNIIR) in the prostate. Transgene(s) and TGIF-β expression were identified in the prostate and decreased protein levels of plasminogen activator inhibitor type I, as a marker for TGIF-β signaling, correlated with expression of the DNIIR. Although the sizes of the neoplastic prostates were not enlarged, increased amounts of metastasis were observed in mice expressing both transgenes compared to age-matched control mice expressing only the large T antigen transgene. Our study demonstrates for the first time that a disruption of TGIF-β signaling in prostate cancer plays a causal role in promoting tumor metastasis.

RNAseq and microarray methods are frequently used to measure gene expression level. While similar in purpose, there are fundamental differences between the two technologies. Here, we present the largest comparative study between microarray and RNAseq methods to date using The Cancer Genome Atlas (TCGA) data. We found high correlations between expression data obtained from the Affymetrix one-channel microarray and RNAseq (Spearman correlations coefficients of ∼0.8). We also observed that the low abundance genes had poorer correlations between microarray and RNAseq data than high abundance genes. As expected, due to measurement and normalization differences, Agilent two-channel microarray and RNAseq data were poorly correlated (Spearman correlations coefficients of only ∼0.2). By examining the differentially expressed genes between tumor and normal samples we observed reasonable concordance in directionality between Agilent two-channel microarray and RNAseq data, although a small group of genes were found to have expression changes reported in opposite directions using these two technologies. Overall, RNAseq produces comparable results to microarray technologies in term of expression profiling. The RNAseq normalization methods RPKM and RSEM produce similar results on the gene level and reasonably concordant results on the exon level. Longer exons tended to have better concordance between the two normalization methods than shorter exons.

Angiogenesis, the recruitment and re-configuration of pre-existing vasculature, is essential for tumor growth and metastasis. Increased tumor vascularization often correlates with poor patient outcomes in a broad spectrum of carcinomas. We identified four jointed box 1 (FJX1) as a candidate regulator of tumor angiogenesis in colorectal cancer. FJX1 mRNA and protein are upregulated in human colorectal tumor epithelium as compared with normal epithelium and colorectal adenomas, and high expression of FJX1 is associated with poor patient prognosis. FJX1 mRNA expression in colorectal cancer tissues is significantly correlated with changes in known angiogenesis genes. Augmented expression of FJX1 in colon cancer cells promotes growth of xenografts in athymic mice and is associated with increased tumor cell proliferation and vascularization. Furthermore, FJX1 null mice develop significantly fewer colonic polyps than wild-type littermates after combined dextran sodium sulfate (DSS) and azoxymethane (AOM) treatment. In vitro, conditioned media from FJX1 expressing cells promoted endothelial cell capillary tube formation in a HIF1-α dependent manner. Taken together our results support the conclusion that FJX1 is a novel regulator of tumor progression, due in part, to its effect on tumor vascularization.

Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.

Next-generation sequencing (NGS) technology has provided researchers with opportunities to study the genome in unprecedented detail. In particular, NGS is applied to disease association studies. Unlike genotyping chips, NGS is not limited to a fixed set of SNPs. Prices for NGS are now comparable to the SNP chip, although for large studies the cost can be substantial. Pooling techniques are often used to reduce the overall cost of large-scale studies. In this study, we designed a rigorous simulation model to test the practicability of estimating allele frequency from pooled sequencing data. We took crucial factors into consideration, including pool size, overall depth, average depth per sample, pooling variation, and sampling variation. We used real data to demonstrate and measure reference allele preference in DNAseq data and implemented this bias in our simulation model. We found that pooled sequencing data can introduce high levels of relative error rate (defined as error rate divided by targeted allele frequency) and that the error rate is more severe for low minor allele frequency SNPs than for high minor allele frequency SNPs. In order to overcome the error introduced by pooling, we recommend a large pool size and high average depth per sample.

Despite available demographic data on the factors that contribute to breast cancer mortality in large population datasets, local patterns are often overlooked. Such local information could provide a valuable metric by which regional community health resources can be allocated to reduce breast cancer mortality. We used national and statewide datasets to assess geographical distribution of breast cancer mortality rates and known risk factors influencing breast cancer mortality in middle Tennessee. Each county in middle Tennessee, and each ZIP code within metropolitan Davidson County, was scored for risk factor prevalence and assigned quartile scores that were used as a metric to identify geographic areas of need. While breast cancer mortality often correlated with age and incidence, geographic areas were identified in which breast cancer mortality rates did not correlate with age and incidence, but correlated with additional risk factors, such as mammography screening and socioeconomic status. Geographical variability in specific risk factors was evident, demonstrating the utility of this approach to identify local areas of risk. This method revealed local patterns in breast cancer mortality that might otherwise be overlooked in a more broadly based analysis. Our data suggest that understanding the geographic distribution of breast cancer mortality, and the distribution of risk factors that contribute to breast cancer mortality, will not only identify communities with the greatest need of support, but will identify the types of resources that would provide the most benefit to reduce breast cancer mortality in the community.

VAPB (VAMP- associated protein B) is an ER protein that regulates multiple biological functions. Although aberrant expression of VAPB is associated with breast cancer, its function in tumor cells is poorly understood. In this report, we provide evidence that VAPB regulates breast tumor cell proliferation and AKT activation. VAPB protein expression is elevated in primary and metastatic tumor specimens, and VAPB mRNA expression levels correlated negatively with patient survival in two large breast tumor datasets. Overexpression of VAPB in mammary epithelial cells increased cell growth, whereas VAPB knockdown in tumor cells inhibited cell proliferation in vitro and suppressed tumor growth in orthotopic mammary gland allografts. The growth regulation of mammary tumor cells controlled by VAPB appears to be mediated, at least in part, by modulation of AKT activity. Overexpression of VAPB in MCF10A-HER2 cells enhances phosphorylation of AKT. In contrast, knockdown of VAPB in MMTV-Neu tumor cells inhibited pAKT levels. Pharmacological inhibition of AKT significantly reduced three-dimensional spheroid growth induced by VAPB. Collectively, the genetic, functional and mechanistic analyses suggest a role of VAPB in tumor promotion in human breast cancer.

IntroductionYes-associated protein (YAP) is considered an oncogene found amplified in multiple tumors, including head and neck squamous cell carcinoma (HNSCC). However, the role for YAP expression in HNSCC is not understood. Based on the central role of YAP in the hippo pathway, we tested if YAP was associated with the stage of HNSCC progression and metastatic potential.MethodsTo determine the expression of YAP in human benign and HNSCC tissue specimens, immunohistochemical analyses were performed in whole tissue samples and tissue microarrays. The expression of YAP in tissues of microarray was first associated with clinic-pathologic factors and results verified in samples from whole tissue sections. To investigate the role of YAP and p63 in regulating HNSCC epithelial to mesenchymal transition, epithelial and mesenchymal markers were assayed in Fadu and SCC-25 cells, HNSCC cells with endogenously elevated YAP expression and siRNA-mediated expression knockdown.ResultsAnalysis of human HNSCC tissues suggested YAP expression was elevated in tumors compared to benign tissues and specifically localized at the tumor invasive front (p value < 0.05). But, indexed YAP expression was lower with greater tumor grade (p value = 0.02). In contrast, p63 expression was primarily elevated in high-grade tumors. Interestingly, both YAP and p63 was strongly expressed at the tumor invasive front and in metastatic HNSCC. Strikingly, we demonstrated YAP expression in the primary HNSCC tumor was associated with nodal metastasis in univariate analysis (p value = 0.02). However, the knockdown of YAP in Fadu and SCC-25 cell lines was not associated with changes in epithelial to mesenchymal transdifferentiation or p63 expression.ConclusionTogether, YAP expression, in combination with p63 can facilitate identification of HNSCC tumors from hyperplastic and benign tissues and the metastatic function of YAP in HNSCC may not be a result of epithelia to mesenchymal transdifferentiation.

To examine the effects of Roux-en-Y gastric bypass (RYGB) surgery with and without laparoscopic removal of omental fat (omentectomy) on the temporal gene expression profiles of skeletal muscle.Previously reported were the whole-body metabolic effects of a randomized, single-blinded study in patients receiving RYGB surgery stratified to receive or not receive omentectomy. In this follow up study we report on changes in skeletal muscle gene expression in a subset of 21 patients, for whom biopsies were collected preoperatively and at either 6 months or 12 months postoperatively.RNA isolated from skeletal muscle biopsies of 21 subjects (8 without omentectomy and 13 with omentectomy) taken before RYGB or at 6 and 12 months postoperatively were subjected to gene expression profiling via Exon 1.0 S/T Array and Taqman Low Density Array. Robust Multichip Analysis and gene enrichment data analysis revealed 84 genes with at least a 4-fold expression difference after surgery. At 6 and 12 months the RYGB with omentectomy group displayed a greater reduction in the expression of genes associated with skeletal muscle inflammation (ANKRD1, CDR1, CH25H, CXCL2, CX3CR1, IL8, LBP, NFIL3, SELE, SOCS3, TNFAIP3, and ZFP36) relative to the RYGB non-omentectomy group. Expressions of IL6 and CCL2 were decreased at all postoperative time points. There was differential expression of genes driving protein turnover (IGFN1, FBXW10) in both groups over time and increased expression of PAAF1 in the non-omentectomy group at 12 months. Evidence for the activation of skeletal muscle satellite cells was inferred from the up-regulation of HOXC10. The elevated post-operative expression of 22 small nucleolar RNAs and the decreased expression of the transcription factors JUNB, FOS, FOSB, ATF3 MYC, EGR1 as well as the orphan nuclear receptors NR4A1, NR4A2, NR4A3 suggest dramatic reorganizations at both the cellular and genetic levels.These data indicate that RYGB reduces skeletal muscle inflammation, and removal of omental fat further amplifies this response.ClinicalTrials.gov NCT00212160.

Pre-clinical studies provide compelling evidence that Eph family receptor tyrosine kinases (RTKs) and ligands promote cancer growth, neovascularization, invasion, and metastasis. Tumor suppressive roles have also been reported for the receptors, however, creating a potential barrier for clinical application. Determining how these observations relate to clinical outcome is a crucial step for translating the biological and mechanistic data into new molecularly targeted therapies. We investigated eph and ephrin expression in human breast cancer relative to endpoints of overall and/or recurrence-free survival in large microarray datasets. We also investigated protein expression in commercial human breast tissue microarrays (TMA) and Stage I prognostic TMAs linked to recurrence outcome data. We found significant correlations between ephA2, ephA4, ephA7, ephB4, and ephB6 and overall and/or recurrence-free survival in large microarray datasets. Protein expression in TMAs supported these trends. While observed no correlation between ephrin ligand expression and clinical outcome in microarray datasets, ephrin-A1 and EphA2 protein co-expression was significantly associated with recurrence in Stage I prognostic breast cancer TMAs. Our data suggest that several Eph family members are clinically relevant and tractable targets for intervention in human breast cancer. Moreover, profiling Eph receptor expression patterns in the context of relevant ligands and in the context of stage may be valuable in terms of diagnostics and treatment.

Expansion of Gr-1+/CD11b+ myeloid derived suppressor cells (MDSCs) is governed by the presence of increasingly metastatic, malignant primary tumors. Metastasis, not the primary tumor, is often the cause of mortality. This study sought to fully characterize the MDSC proteome in response to metastatic and non-metastatic mammary tumors using label-free mass spectrometry shotgun proteomics in a mouse model with tumor cell lines, 67NR and 4T1, derived from the same tumor. 67NR cells form only primary mammary tumors, whereas 4T1 cells readily metastasize to the lungs, lymph nodes, and blood. Overall analysis identified a total of 2825 protein groups with a 0.78% false discovery rate. Of the 2814 true identifications, 43 proteins were exclusive to the 67NR group, 153 were exclusive to the 4T1 group, and 2618 were shared. Among the shared cohort, 26 proteins were increased and 31 were decreased in the metastatic 4T1 cohort compared to non-metastatic 67NR controls after filtering. MDSCs selectively express proteins involved in the γ-glutamyl transferase, glutathione synthase pathways, CREB transcription factor signaling, and other pathways involved in platelet aggregation, as well as lipid and amino acid metabolism, in response to highly metastatic 4T1 tumors. Cell cycle regulation dominated protein pathways and ontological groups of the 67NR non-metastatic group. Not only does this study provide a starting point to identify potential biomarkers of metastasis expressed by MDSCs; it identifies critical pathways that are unique to non-metastatic and metastatic conditions. Therapeutic interventions aimed at these pathways in MDSC may offer a new route to control malignancy and metastasis.

Breast cancer often metastasizes to bone causing osteolytic bone resorption which releases active TGFβ. Because TGFβ favors progression of breast cancer metastasis to bone, we hypothesized that treatment using anti-TGFβ antibody may reduce tumor burden and rescue tumor-associated bone loss in metastatic breast cancer. In this study we have tested the efficacy of an anti-TGFβ antibody 1D11 preventing breast cancer bone metastasis. We have used two preclinical breast cancer bone metastasis models, in which either human breast cancer cells or murine mammary tumor cells were injected in host mice via left cardiac ventricle. Using several in vivo, in vitro and ex vivo assays, we have demonstrated that anti-TGFβ antibody treatment have significantly reduced tumor burden in the bone along with a statistically significant threefold reduction in osteolytic lesion number and tenfold reduction in osteolytic lesion area. A decrease in osteoclast numbers (p = 0.027) in vivo and osteoclastogenesis ex vivo were also observed. Most importantly, in tumor-bearing mice, anti-TGFβ treatment resulted in a twofold increase in bone volume (p

We hypothesized that chronic inhibition of epidermal growth factor receptor (EGFR) by cetuximab, a monoclonal anti-EGFR antibody, induces up-regulation of its ligands resulting in resistance and that microRNAs (miRs) play an important role in the ligand regulation in head and neck squamous cell carcinoma (HNSCC).Genome-wide changes in gene and miR expression were determined in cetuximab-sensitive cell line, SCC1, and its resistant derivative 1Cc8 using DNA microarrays and RT-PCR. The effects of differentially expressed EGFR ligands and miRs were examined by MTS, colony formation, ELISA, and western blot assays. Heparin-binding EGF-like growth factor (HB-EGF) and its regulator, miR-212, were differentially expressed with statistical significance when SCC1 and 1Cc8 were compared for gene and miR expression. Stimulation with HB-EGF induced cetuximab resistance in sensitive cell lines. Inhibition of HB-EGF and the addition of miR-212 mimic induced cetuximab sensitivity in resistant cell lines. MicroRNA-212 and HB-EGF expression were inversely correlated in an additional 33 HNSCC and keratinocyte cell lines. Six tumors and 46 plasma samples from HNSCC patients were examined for HB-EGF levels. HB-EGF plasma levels were lower in newly diagnosed HNSCC patients when compared to patients with recurrent disease.Increased expression of HB-EGF due to down-regulation of miR-212 is a possible mechanism of cetuximab resistance. The combination of EGFR ligand inhibitors or miR modulators with cetuximab may improve the clinical outcome of cetuximab therapy in HNSCC.

Inflammation is associated with most diseases, which makes understanding the mechanisms of inflammation vitally important.Here, we demonstrate a critical function of interleukin-32beta (IL-32beta) in vascular inflammation. IL-32beta is present in tissues from humans, but is absent in rodents. We found that the gene is highly expressed in endothelial cells. Three isoforms of IL-32, named IL-32alpha, beta, and epsilon, were cloned from human endothelial cells, with IL-32beta being the major isoform. Pro-inflammatory cytokines (TNFalpha and IL-1beta) induced IL-32beta expression through NF-kappaB. Conversely, IL-32beta propagated vascular inflammation via induction of vascular cell adhesion molecules and inflammatory cytokines. Accordingly, IL-32beta increased adhesion of inflammatory cells to activated endothelial cells, a paramount process in inflammation. These results illustrate a positive feedback regulation that intensifies and prolongs inflammation. Importantly, endothelial/hematopoietic expression of IL-32beta in transgenic mice elevated inflammation and worsened sepsis. This was demonstrated by significant elevation of leukocyte infiltration and serum levels of TNFalpha and IL-1beta, increased vascular permeability and lung damage, and accelerated animal death. Together, our results reveal an important function of IL-32 in vascular inflammation and sepsis development.Our results reveal an important function of IL-32 in vascular inflammation and sepsis development.

Promoter hypermethylation coupled with loss of heterozygosity at the same locus results in loss of gene function in many tumor cells. The "rules" governing which genes are methylated during the pathogenesis of individual cancers, how specific methylation profiles are initially established, or what determines tumor type-specific methylation are unknown. However, DNA methylation markers that are highly specific and sensitive for common tumors would be useful for the early detection of cancer, and those required for the malignant phenotype would identify pathways important as therapeutic targets.In an effort to identify new cancer-specific methylation markers, we employed a high-throughput global expression profiling approach in lung cancer cells. We identified 132 genes that have 5' CpG islands, are induced from undetectable levels by 5-aza-2'-deoxycytidine in multiple non-small cell lung cancer cell lines, and are expressed in immortalized human bronchial epithelial cells. As expected, these genes were also expressed in normal lung, but often not in companion primary lung cancers. Methylation analysis of a subset (45/132) of these promoter regions in primary lung cancer (n = 20) and adjacent nonmalignant tissue (n = 20) showed that 31 genes had acquired methylation in the tumors, but did not show methylation in normal lung or peripheral blood cells. We studied the eight most frequently and specifically methylated genes from our lung cancer dataset in breast cancer (n = 37), colon cancer (n = 24), and prostate cancer (n = 24) along with counterpart nonmalignant tissues. We found that seven loci were frequently methylated in both breast and lung cancers, with four showing extensive methylation in all four epithelial tumors.By using a systematic biological screen we identified multiple genes that are methylated with high penetrance in primary lung, breast, colon, and prostate cancers. The cross-tumor methylation pattern we observed for these novel markers suggests that we have identified a partial promoter hypermethylation signature for these common malignancies. These data suggest that while tumors in different tissues vary substantially with respect to gene expression, there may be commonalities in their promoter methylation profiles that represent targets for early detection screening or therapeutic intervention.