In vitro assessment of inflammatory skin potential of poly(methyl methacrylate) at non-cytotoxic concentrations.

Poly(methyl methacrylate) (PMMA) is considered an attractive substrate material for fabricating wearable skin sensors such as fitness bands and microfluidic devices. Despite its widespread use, inflammatory and allergic responses have been attributed to the use of this material. Therefore, the main objective of this study was to obtain a comprehensive understanding of potential biological effects triggered by PMMA at non-cytotoxic concentrations using in vitro models of NIH3T3 fibroblasts and reconstructed human epidermis (RhE). It was hypothesized that concentrations that do not reduce cell viability are sufficient to activate pathways of inflammatory processes in the skin. The study included cytotoxicity, cell metabolism, cytokine quantification, histopathological, and gene expression analyses. The NIH3T3 cell line was used as a testbed for screening cell toxicity levels associated with the concentration of PMMA with different molecular weights (MWs) (i.e., MW ~5,000 and ~15,000 g/mol). The lower MW of PMMA had a half-maximal inhibitory concentration (IC50 ) value of 5.7 mg/cm2 , indicating greater detrimental effects than the higher MW (IC50 = 14.0 mg/cm2 ). Non-cytotoxic concentrations of 3.0 mg/cm2 for MW ~15,000 g/mol and 0.9 mg/cm2 for MW ~5,000 g/mol) induced negative metabolic changes in NIH3T3 cells. Cell viability was severely reduced to 7% after the exposure to degradation by-products generated after thermal and photodegradation degradation of PMMA. PMMA at non-cytotoxic concentrations still induced overexpression of pro-inflammatory cytokines, chemokines, and growth factors (IL1B, CXCL10, CCL5, IL1R1, IL7, IL17A, VEGFA, FGF2, IFNG, IL15) on the RhE model. The inflammatory response was also supported by histopathological and gene expression analyses of PMMA-treated RhE, indicating tissue damage and gene overexpression. Results suggested that non-cytotoxic concentrations of PMMA (3.0 to 5.6 mg/cm2 for MW ~15,000 g/mol and 0.9 to 2.1 mg/cm2 for MW ~5,000 g/mol) were sufficient to negatively alter NIH3T3 cells metabolism and activate inflammatory events in the RhE skin.

Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.

Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T-cell process, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, design-of-experiment-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from early in-culture media assessment that were highly predictive of the end-product CD4/CD8 ratio and total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+). Our results demonstrate a broadly applicable platform tool to predict end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing.

Collagen I Fibrous Substrates Modulate the Proliferation and Secretome of Estrogen Receptor-Positive Breast Tumor Cells in a Hormone-Restricted Microenvironment.

The fibril orientation of type I collagen has been shown to contribute to tumor invasion and metabolic changes. Yet, there is limited information about its impact on tumor cells' behavior in a restrictive growth environment. Restrictive growth environments are generated by the inhibition of a proliferation stimulus during therapy or as an inflammatory response to suppress tumor expansion. In this study, the impact of a type I collagen matrix orientation and fibrous architecture on cell proliferation and response to estrogen receptor (ER) therapy were examined using estrogen-dependent breast tumor cells (MCF-7 and T-47D) cultured in a hormone-restricted environment. The use of hormone-free culture media, as well as pharmacological inhibitors of ER, Tamoxifen, and Fulvestrant, were investigated as hormone restrictive conditions. Examination of cultures at 72 h showed that tumor cell proliferation was significantly stimulated (1.8-fold) in the absence of hormones on collagen fibrous substrates, but not on polycaprolactone fibrous substrates of equivalent orientation. ER inhibitors did not suppress cell proliferation on collagen fibrous substrates. The examination of reporter cells for ER signaling showed a lack of activity, thus confirming a shift toward an ER-independent proliferation mechanism. Examination of two selective inhibitors of α2ß1 and α1ß1 integrins showed that cell proliferation is suppressed in the presence of the α2ß1 integrin inhibitor only, thereby indicating that the observed changes in tumor cell behavior are caused by a combination of integrin signaling and/or an intrinsic structural motif that is uniquely present in the collagen fibrils. Adjacent coculture studies on collagen substrates showed that tumor cells on collagen can stimulate the proliferation of cells on tissue culture plastic through soluble factors. The magnitude of this effect correlated with the increased surface anisotropy of the substrate. This sensing in fibril orientation was further supported by a differential expression pattern of secreted proteins that were identified on random and aligned orientation substrates. Overall, this study shows a new role for electrospun collagen I fibrous substrates by supporting a shift toward an ER-independent tumor cell proliferation mechanism in ER+ breast tumor cells.

Mesenchymal Cells Support the Oncogenicity and Therapeutic Response of the Hedgehog Pathway in Triple-Negative Breast Cancer.

The paracrine interaction between tumor cells and adjacent stroma has been associated with the oncogenic activity of the Hedgehog (Hh) pathway in triple-negative breast tumors. The present study developed a model of paracrine Hh signaling and examined the impact of mesenchymal cell sources and culture modalities in the oncogenicity of the Hh pathway in breast tumor cells. Studies consisted of tumor cell monocultures and co-cultures with cancer-associated and normal fibroblasts, tumor cells that undergo epithelial-mesenchymal transition (EMT), or adipose-derived mesenchymal stem cells (ADMSCs). Hh ligand and pathway inhibitors, GANT61 and NVP-LDE225 (NVP), were evaluated in both cell cultures and a mouse xenograft model. Results in monocultures show that tumor cell viability and Hh transcriptional activity were not affected by Hh inhibitors. In co-cultures, down-regulation of GLI1, SMO, and PTCH1 in the stroma correlated with reduced tumor growth rates in xenografted tumors and cell cultures, confirming a paracrine interaction. Fibroblasts and EMT cells supported Hh transcriptional activity and enhanced tumor cell growth. Mixed and adjacent culture modalities indicate that tumor growth is supported via fibroblast-secreted soluble factors, whereas enriched tumor stemness requires close proximity between tumor and fibroblasts. Overall this study provides a tumor-mesenchymal model of Hh signaling and highlights the therapeutic value of mesenchymal cells in the oncogenic activity of the Hh pathway.

Hedgehog-mesenchyme gene signature identifies bi-modal prognosis in luminal and basal breast cancer sub-types.

Hedgehog signaling (Hh) has been shown to be hyper-activated in several cancers. However, active Hh signaling can promote or inhibit tumor growth; thus identification of markers beyond main canonical Hh target genes is needed to improve patient selection and clinical outcome in response to Hh inhibitors. Cancer-associated fibroblasts (CAFs) have been linked with tumor progression and beneficial response to Hh inhibitors. Thus, we hypothesized that genes associated with Hh-activated CAFs can be used for stratification of tumors that will benefit from Hh inhibitors. In this work, we evaluated a 15-gene fingerprint that combines Hh and mesenchymal genes associated with CAF phenotype to profile breast cancer sub-types based on gene expression patterns among clustered groups. About 3800 cancer samples were evaluated using random forest models and linear discriminant analysis to sort breast cancer by subtypes and therapeutic approach. The results showed that the Hh-mesenchyme gene fingerprint has a highly sensitive and differential expression pattern among basal and luminal A sub-groups. Basal samples with high levels of Hh target genes had better prognosis than luminal A samples. Luminal A samples with a tendency towards Hh signaling suppression had higher overall and disease-free survival rates particularly if deprived of hormone therapy. Hh transcriptional repressor GLI3 and signaling activator SMO were the top 2 genes for discriminating among samples with active Hh signaling in human breast cancer subtypes and Hh-inhibitor resistant tumors. Caveolin-1 (CAV1), a gene with low expression in CAFs, shows strong correlation with active Hh signaling and discrimination among survival curves in luminal A patients with active or inactive Hh signaling. Our data suggest that CAV1 is an important gene for monitoring Hh inhibition in tumors and support further stratification by hormone therapy status prior to use of Hh inhibitors.

Biphasic components of sarcomatoid clear cell renal cell carcinomas are molecularly similar to each other, but distinct from, non-sarcomatoid renal carcinomas.

Sarcomatoid transformation, wherein an epithelioid carcinomatous tumour component coexists with a sarcomatoid histology, is a predictor of poor prognosis in clear cell renal cell carcinoma. Our understanding of sarcomatoid change has been hindered by the lack of molecular examination. Thus, we sought to characterize molecularly the biphasic epithelioid and sarcomatoid components of sarcomatoid clear cell renal cell carcinoma and compare them to non-sarcomatoid clear cell renal cell carcinoma. We examined the transcriptome of the epithelioid and sarcomatoid components of advanced stage sarcomatoid clear cell renal cell carcinoma (n=43) and non-sarcomatoid clear cell renal cell carcinoma (n=37) from independent discovery and validation cohorts using the cDNA microarray and RNA-seq platforms. We analyzed DNA copy number profiles, generated using SNP arrays, from patients with sarcomatoid clear cell renal cell carcinoma (n=10) and advanced non-sarcomatoid clear cell renal cell carcinoma (n=155). The epithelioid and sarcomatoid components of sarcomatoid clear cell renal cell carcinoma had similar gene expression and DNA copy number signatures that were, however, distinct from those of high-grade, high-stage non-sarcomatoid clear cell renal cell carcinoma. Prognostic clear cell renal cell carcinoma gene expression profiles were shared by the biphasic components of sarcomatoid clear cell renal cell carcinoma and the sarcomatoid component showed a partial epithelial-to-mesenchymal transition signature. Our genome-scale microarray-based transcript data were validated in an independent set of sarcomatoid and non-sarcomatoid clear cell renal cell carcinomas using RNA-seq. Sarcomatoid clear cell renal cell carcinoma is molecularly distinct from non-sarcomatoid clear cell renal cell carcinoma, with its genetic programming largely shared by its biphasic morphological components. These data explain why a low percentage of sarcomatoid histology augurs a poor prognosis; suggest the need to modify the pathological grading system and introduce the potential for candidate biomarkers to detect sarcomatoid change preoperatively without specifically sampling the histological sarcomatoid component.

PRADA: pipeline for RNA sequencing data analysis.

SUMMARY: Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/prada/ CONTACT: gadgetz@broadinstitute.org or rverhaak@mdanderson.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inferring tumour purity and stromal and immune cell admixture from expression data.

Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe 'Estimation of STromal and Immune cells in MAlignant Tumours using Expression data' (ESTIMATE)--a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/.

A survey of intragenic breakpoints in glioblastoma identifies a distinct subset associated with poor survival.

With the advent of high-throughput sequencing technologies, much progress has been made in the identification of somatic structural rearrangements in cancer genomes. However, characterization of the complex alterations and their associated mechanisms remains inadequate. Here, we report a comprehensive analysis of whole-genome sequencing and DNA copy number data sets from The Cancer Genome Atlas to relate chromosomal alterations to imbalances in DNA dosage and describe the landscape of intragenic breakpoints in glioblastoma multiforme (GBM). Gene length, guanine-cytosine (GC) content, and local presence of a copy number alteration were closely associated with breakpoint susceptibility. A dense pattern of repeated focal amplifications involving the murine double minute 2 (MDM2)/cyclin-dependent kinase 4 (CDK4) oncogenes and associated with poor survival was identified in 5% of GBMs. Gene fusions and rearrangements were detected concomitant within the breakpoint-enriched region. At the gene level, we noted recurrent breakpoints in genes such as apoptosis regulator FAF1. Structural alterations of the FAF1 gene disrupted expression and led to protein depletion. Restoration of the FAF1 protein in glioma cell lines significantly increased the FAS-mediated apoptosis response. Our study uncovered a previously underappreciated genomic mechanism of gene deregulation that can confer growth advantages on tumor cells and may generate cancer-specific vulnerabilities in subsets of GBM.

A statistical framework for multiparameter analysis at the single-cell level.

Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barrett's esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.

Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: missing value imputation using temporal datasets.

Despite significant improvements in recent years, proteomic datasets currently available still suffer from large number of missing values. Integrative analyses based upon incomplete proteomic and transcriptomic datasets could seriously bias the biological interpretation. In this study, we applied a non-linear data-driven stochastic gradient boosted trees (GBT) model to impute missing proteomic values using a temporal transcriptomic and proteomic dataset of Shewanella oneidensis. In this dataset, genes' expression was measured after the cells were exposed to 1 mM potassium chromate for 5, 30, 60, and 90 min, while protein abundance was measured for 45 and 90 min. With the ultimate objective to impute protein values for experimentally undetected samples at 45 and 90 min, we applied a serial set of algorithms to capture relationships between temporal gene and protein expression. This work follows four main steps: (1) a quality control step for gene expression reliability, (2) mRNA imputation, (3) protein prediction, and (4) validation. Initially, an S control chart approach is performed on gene expression replicates to remove unwanted variability. Then, we focused on the missing measurements of gene expression through a nonlinear Smoothing Splines Curve Fitting. This method identifies temporal relationships among transcriptomic data at different time points and enables imputation of mRNA abundance at 45 min. After mRNA imputation was validated by biological constrains (i.e. operons), we used a data-driven GBT model to impute protein abundance for the proteins experimentally undetected in the 45 and 90 min samples, based on relevant predictors such as temporal mRNA gene expression data and cellular functional roles. The imputed protein values were validated using biological constraints such as operon and pathway information through a permutation test to investigate whether dispersion measures are indeed smaller for known biological groups than for any set of random genes. Finally, we demonstrated that such missing value imputation improved characterization of the temporal response of S. oneidensis to chromate.

Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins.

MOTIVATION: Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems. RESULTS: In this study, we present a non-linear data-driven model to predict abundance for undetected proteins using two independent datasets of cognate transcriptomic and proteomic data collected from Desulfovibrio vulgaris. We use stochastic gradient boosted trees (GBT) to uncover possible non-linear relationships between transcriptomic and proteomic data, and to predict protein abundance for the proteins not experimentally detected based on relevant predictors such as mRNA abundance, cellular role, molecular weight, sequence length, protein length, guanine-cytosine (GC) content and triple codon counts. Initially, we constructed a GBT model using all possible variables to assess their relative importance and characterize the behavior of the predictive model. A strong plateau effect in the regions of high mRNA values and sparse data occurred in this model. Hence, we removed genes in those areas based on thresholds estimated from the partial dependency plots where this behavior was captured. At this stage, only the strongest predictors of protein abundance were retained to reduce the complexity of the GBT model. After removing genes in the plateau region, mRNA abundance, main cellular functional categories and few triple codon counts emerged as the top-ranked predictors of protein abundance. We then created a new tuned GBT model using the five most significant predictors. The construction of our non-linear model consists of a set of serial regression trees models with implicit strength in variable selection. The model provides variable relative importance measures using as a criterion mean square error. The results showed that coefficients of determination for our nonlinear models ranged from 0.393 to 0.582 in both datasets, providing better results than linear regression used in the past. We evaluated the validity of this non-linear model using biological information of operons, regulons and pathways, and the results demonstrated that the coefficients of variation of estimated protein abundance values within operons, regulons or pathways are indeed smaller than those for random groups of proteins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.