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Additional file 3. Peer review history.

Additional file 1: Supplementary Figures.

The Digital Imaging and Communication in Medicine (DICOM) specification is increasingly being adopted in digital pathology to promote data standardization and interoperability. Efficient conversion of proprietary file formats into the DICOM standard format is a key requirement for institutional adoption of DICOM, necessary to ensure compatibility with existing scanners, microscopes, and data archives. Here, we present a cloud computing architecture for DICOM conversion, leveraging an event-driven microservices framework hosted in a serverless computing environment in Google Cloud to enable efficient DICOM conversion at scales ranging from individual images to institutional-scale datasets. In our experiments, employing a microservices-based approach substantially reduced runtime to process a batch of images relative to parallel and serial processing. This work demonstrates the importance of designing scalable systems for enabling enterprise-level adoption of digital pathology workflows, and provides a blueprint for using a microservice architecture to enable efficient DICOM conversion.

Biomedical research centers can empower basic discovery and novel therapeutic strategies by leveraging their large-scale datasets from experiments and patients. This data, together with new technologies to create and analyze it, has ushered in an era of data-driven discovery which requires moving beyond the traditional individual, single-discipline investigator research model. This interdisciplinary niche is where computational biology thrives. It has matured over the past three decades and made major contributions to scientific knowledge and human health, yet researchers in the field often languish in career advancement, publication, and grant review. We propose solutions for individual scientists, institutions, journal publishers, funding agencies, and educators.

Additional file 2: Supplementary Figures. PDF file containing the raw results of all bioinformatics analysis. Figure S1. Cross-study of differential gene expression in PTEN-null vs PTEN-intact in ERG+ samples. Meta-analysis of HPFS/PHS and NH cohorts with Bayesian Hierarchical Model for DGE using XDE showing the top 25 most concordant differentially up- and down-regulated genes. PTEN status were based on IHC assays. Figure S2. PTEN expression levels stratified by CNV. Figure shows PTEN expression levels distribution by copy number variation (CNV), called by GISTIC algorithm. Figure S3. Correspondence-at-the-top (CAT) plot between TCGA CNV-based calls and the Bayesian Hierarchical Model approach (BHM). Agreement of genes ranked by t-statistics (TCGA) and average Bayesian Effect Size (BHM). Lines represent agreement between tested cohorts for PTEN-intact vs PTEN-null. Black-to-light grey shades represent the decreasing probability of agreeing by chance based on the hypergeometric distribution, with intervals ranging from 0.999999 (light grey) to 0.95 (dark grey). Lines outside this range represent agreement in different cohorts with a higher agreement than expected by chance. Figure S4. Expression of AC009478.1 is shown to be highly specific to PRAD, BLCA, to a lesser extent in UECA and BRCA. Figure shows raw expression values of SchLAP1 and AC009478.1 across cancer types. Pearson correlations and p-values are shown in red. Figure S5. Expression of FANTOM-CAT lncRNAs genes (top) and close coding genes (bottom) stratified by PTEN status. Significances based on t-test between PTEN-null and PTEN-intact using log2 CPM + 1 value. Significance cutoffs: * 0.05; **≤0.01; ***≤0.0001. Figure S6. Person correlation gene CATG00000038715 and CYP4F2 across cancer types. CATG00000038715 and CYP4F2 expression are shown to be highly correlated in PCa. Moreover, CATG00000038715 expression is shown to be highly specific to PCa. With exception of leukemia cells, none of the other tumors expressed high levels of CATG00000038715. Figure S7. Gene set enrichment for Androgen repressed genes. Gene set enrichment analysis of gene signature showing positive enrichment of genes repressed by dihydrotestosterone after 6 h of exposure obtained from Schaeffer et al. Enrichment for BHM-signature is shown in panel A and TCGA-signature in panel B.

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2., Genome Research, 30 (7), ISSN:1088-9051, ISSN:1549-5469

Chronic arsenic exposure is associated with the development of urothelial carcinoma of the bladder (UCB). To elucidate the contribution of arsenic exposure to urothelial cancer stem cell (CSC) generation, we established an in vitro stepwise malignant model transformed by chronically exposing human urothelial cells to arsenic. Using this model, we found that chronic arsenic exposure endows urothelial cells with malignant stemness properties including increased expression of stemness-related factors such as SOX2, sphere formation, self-renewal, invasion, and chemo-resistance. SOX2 was gradually and irreversibly overexpressed in line with acquired sphere-forming and self-renewal abilities. Following gene set enrichment analyses of arsenic-exposed and arsenic-unexposed cells, we found COX2 as an enriched gene for oncogenic signature. Mechanistically, arsenic-induced COX2/PGE2 increases SOX2 expression that eventually promotes malignant stem cell generation and repopulation. In urine samples from 90 subjects exposed to arsenic and 91 control subjects, we found a significant linear correlation between SOX2 and COX2 expression and the potential of SOX2 and COX2 expression as urinary markers to detect subjects exposed to arsenic. Furthermore, the combination marker yielded a high sensitivity for UCB detection in a separate cohort. Finally, our in vitro model exhibits basal-type molecular features, and dual inhibition of EGFR and COX2 attenuated stem cell enrichment more efficiently than an EGFR inhibitor alone. In conclusion, the COX2/PGE2-SOX2 axis promotes arsenic-induced malignant stem cell transformation. In addition, our findings indicate the possible use of SOX2 and COX2 expression as urinary markers for the risk stratification and detection of UCB.

Notch signaling can promote tumorigenesis in the nervous system and plays important roles in stem-like cancer cells. However, little is known about how Notch inhibition might alter tumor metabolism, particularly in lesions arising in the brain. The gamma-secretase inhibitor MRK003 was used to treat glioblastoma neurospheres, and they were subdivided into sensitive and insensitive groups in terms of canonical Notch target response. Global metabolomes were then examined using proton magnetic resonance spectroscopy, and changes in intracellular concentration of various metabolites identified which correlate with Notch inhibition. Reductions in glutamate were verified by oxidation-based colorimetric assays. Interestingly, the alkylating chemotherapeutic agent temozolomide, the mTOR-inhibitor MLN0128, and the WNT inhibitor LGK974 did not reduce glutamate levels, suggesting that changes to this metabolite might reflect specific downstream effects of Notch blockade in gliomas rather than general sequelae of tumor growth inhibition. Global and targeted expression analyses revealed that multiple genes important in glutamate homeostasis, including glutaminase, are dysregulated after Notch inhibition. Treatment with an allosteric inhibitor of glutaminase, compound 968, could slow glioblastoma growth, and Notch inhibition may act at least in part by regulating glutaminase and glutamate.

Distribution of assembled transcripts based on abundance (FPKM) and corresponding read density. The x-axis represents abundance threshold (FPKM) and y-axis represents read density. The red and green lines represent the Gaussian Mixture Model that was applied to assembled transcripts. The abundance threshold (represented by vertical dotted line) separates transcripts identified as true positives on the right (supported by more reads) from those identified as potential false positives on the left (supported by less reads). The two screenshots taken from genome browser demonstrates examples of high read density and low read density. (PDF 226 kb)

Strategy employed for determining potential RNA editing sites . The workflow that was followed to filter out false positive RNA-editing sites is shown. Examples of false positives due to mismapping of reads and uncalled SNPs is demonstrated with sequence alignment. Abbreviations (SNP: single nucleotide polymorphism; RDD: RNA-DNA difference; CNV: copy number variation). (PDF 65 kb)

Sequence variants identified in the genome and classification of SNPs in protein coding regions. (A) Total number of SNPs, insertions, deletions and substitutions identified from whole genome sequencing data. (B) Proportion of synonymous and non-synonymous SNPs identified in the coding region of the genome among non-synonymous SNPs (nsSNPs), proportion of conservative and non-conservative homozygous and heterozygous changes are shown. (C) nsSNP frequency across various molecular classes of genes. (PDF 68 kb)

Correlation of methylation and transcript levels of genes between memory and na誰ve CD4+ T cells. (A) Intersection of promoter methylation and mRNA expression data from na誰ve and memory T cells. Genes marked in red in the upper left quadrant showed significantly reduced expression in na誰ve CD4+ T cells due to promoter hypermethylation. Genes that showed the opposite trend are shown in the bottom right quadrant. (PDF 185 kb)

Correlation between miRNA read count, transcript, and protein abundance. (A) Protein abundance versus miRNA read count. The x axis for each circle represents all miRNAs identified with a given read count while the y axis corresponds to the average iBAQ value of the genes targeted by those miRNAs with the specified read count. The dashed red line represents the iBAQ value of genes that are not targeted by any miRNAs identified in this study and serves as a background, reference level of protein expression. The black line is a linear regression of iBAQ values versus the miRNA read count. (B) Transcript abundance versus miRNA read count. The x axis for each circle represents all miRNAs identified with a given read count while the y axis corresponds to the average FPKM value of the genes targeted by those miRNAs with the specified read count. The dashed red line represents the FPKM levels of genes that are not targeted by any miRNAs identified in this study and serves as a background, reference level of transcript abundance. The black line is a linear regression of FPKM values versus miRNA read count. There is no obvious correlation between FPKM values and miRNA read counts. (PDF 150 kb)

Concordance of this study with Komori et al. (A) Each blue dot represents the methylation level of a gene. On the x-axis are the methylation differences between memory and naïve cells measured by Komori et al. and on the y-axis are TSS200 methylation levels measured in this study. The r2 value is a pearson’s correlation coefficient between the two datasets. (B) Each blue dot represents the methylation level of a gene. On the x-axis are the methylation differences between memory and naïve cells where Komori et al. provided quantitative values and on the y-axis are the TSS200 methylation levels measured in this study. The r2 value is a pearson’s correlation coefficient between the two datasets. (C) Genes identified in Komori et al. as displaying inverse correlation between methylation levels and transcription are plotted. On the left axis and shown in blue are the log2 transformed fold change of transcript levels between memory and naïve cells measured by Komori et al. and on the right axis and shown in green are log2 transformed fold change of transcript levels between memory and naïve cells measured in this study. (D) The correlation between the log2 transformed fold changes of genes between memory and naïve cells measured in each study is shown. Each blue dot represents a gene and the r2 is a pearson’s correlation coefficient. (PDF 1410 kb)

Data generated on naĂŻve CD4+ T cells and memory CD4+ T cells. Basic metrics assaying the quality of generated data are provided for each dataset generated. (PDF 60 kb)

Purity of naĂŻve CD4+ T cells and memory CD4+ T cells. (A) FACS-plot showing the purity of naĂŻve CD4+ T cells. (B) FACS-plot showing the purity levels of memory CD4+ T cells. (PDF 71 kb)

Cell surface proteins of naĂŻve CD4+ T cells and their expression compared to resting memory CD4+ T cells (A) Cell surface proteins found in this study are compared against those found by Graessel et al. (B) iTRAQ measurements within this study are shown for cell surface proteins that Graessel et al. found to be increased in activated naĂŻve CD4+ T cells. (PDF 75 kb)

Background The 5-year cancer specific survival (CSS) for patients with muscle invasive urothelial carcinoma of the bladder (MIBC) treated with cystectomy alone is approximately 50%. Platinum based neoadjuvant chemotherapy (NAC) plus cystectomy results in a marginal 5-10% increase in 5-year CSS in MIBC. Interestingly, responders to NAC (

Example of a miRNA identified and the distribution of reads across a predicted hairpin structure. Putative novel miRNA identified from small RNA-Seq. Predicted stem loop structure, mature region and star regions are shown. (PDF 50 kb)

Correlation of methylation level at different gene features with transcript and protein expression levels. (A) Promoter methylation level and corresponding transcript abundance Methylation levels are represented by Beta values and transcript abundance is represented by FPKM values. (B) Scatterplots showing methylation levels at different gene features and transcript abundance. Methylation levels are represented on the x-axis and transcript abundance is represented on the y-axis. The red dot represents the mean methylation levels and transcript abundances. Spearmanâ s correlation coefficent is represented below each scatterplot. (C) Promoter methylation level and corresponding protein abundance (iBAQ values from Fig. 4). (D) Scatterplots showing methylation levels at different gene features and protein abundance. Methylation levels are represented on the x-axis and protein abundance is represented on the y-axis. The red dot represents the mean methylation levels and protein abundances. Spearmanâ s correlation coefficent is represented below each scatterplot. (PDF 1087 kb)

Comparison of a proteinâ s abundance versus its transcriptional abundance. Scatterplot of protein abundance versus transcript abundance is shown. Transcript abundance is represented as FPKM (log2) on the x-axis and protein abundance is represented as normalized iBAQ (log2) on the y-axis. (PDF 487 kb)

Sepsis is a major cause of mortality, and dysregulation of the immune response plays a central role in this syndrome. H2S, a recently discovered gaso-transmitter, is endogenously generated by many cell types, regulating a number of physiologic processes and pathophysiologic conditions. We report that H2S increased survival after experimental sepsis induced by cecal ligation and puncture (CLP) in mice. Exogenous H2S decreased the systemic inflammatory response, reduced apoptosis in the spleen, and accelerated bacterial eradication. We found that C/EBP homologous protein 10 (CHOP), a mediator of the endoplasmic reticulum stress response, was elevated in several organs after CLP, and its expression was inhibited by H2S treatment. Using CHOP-knockout (KO) mice, we demonstrated for the first time, to our knowledge, that genetic deletion of Chop increased survival after LPS injection or CLP. CHOP-KO mice displayed diminished splenic caspase-3 activation and apoptosis, decreased cytokine production, and augmented bacterial clearance. Furthermore, septic CHOP-KO mice treated with H2S showed no additive survival benefit compared with septic CHOP-KO mice. Finally, we showed that H2S inhibited CHOP expression in macrophages by a mechanism involving Nrf2 activation. In conclusion, our findings show a protective effect of H2S treatment afforded, at least partially, by inhibition of CHOP expression. The data reveal a major negative role for the transcription factor CHOP in overall survival during sepsis and suggest a new target for clinical intervention, as well potential strategies for treatment.

• To evaluate the cytotoxicity of dimeric naphthoquinones (BiQs) in prostate cancer cells. • To assess the interaction of dimeric naphthoquinones with common therapies including radiation and docetaxel.• The cytotoxicity of 12 different dimeric naphthoquinones was assessed in androgen-independent (PC-3, DU-145) and androgen-responsive (LNCaP, 22RV1) prostate cancer cell lines and in prostate epithelial cells (PrECs). • BiQ2 and BiQ11 were selected for determination of dose response, effects on colony formation and initial exploration into mechanism of action. • Synergistic effects with radiation and docetaxel were explored using colony-forming and MTT assays.• At concentrations of 15µM, BiQ2, BiQ3, BiQ11, BiQ12, and BiQ15 demonstrated cytotoxicity in all prostate cancer cell lines. • Treatment with BiQs limited the ability of prostate cancer cells to form colonies in clonogenic assays. • Exposure of prostate cancer to BiQs increased cellular reactive oxygen species (ROS), decreased ATP production, and promoted apoptosis. • BiQ cytotoxicity was independent of NADP(H):quinone oxidoreductase 1 (NQO1) activity in PrECs, PC-3 and 22RV1, but not DU-145 cells. • Exposure of prostate cancer cells to radiation before treatment with BiQs increased their activity allowing for inhibitory effects well below the IC(50) s of these compounds in PrECs. • Co-administration of BiQs with docetaxel had minimal additive effects.• Dimeric naphthoquinones represent a new class of compounds with prostate cancer cytotoxicity and synergistic effects with radiation. The cytotoxic effect of these agents is probably contributed to by the accumulation of ROS and mitochondrial dysfunction. • Further studies are warranted to better characterize this class of potential chemo-therapeutics.

Proteogenomic pipeline. Custom protein databases that were used for searching tandem mass spectrometry data for proteogenomic annotation. (PDF 59 kb)

Supplemental methods. (DOC 41 kb)

Supplemental methods. (DOC 41 kb)

Proteogenomic pipeline. Custom protein databases that were used for searching tandem mass spectrometry data for proteogenomic annotation. (PDF 59 kb)

The tumor suppressor DAB2IP contributes to modulate the network of information established between cancer cells and tumor microenvironment. Epigenetic and post-transcriptional inactivation of this protein is commonly observed in multiple human malignancies, and can potentially favor progression of tumors driven by a variety of genetic mutations. Performing a high-throughput screening of a large collection of human microRNA mimics, we identified miR-149-3p as a negative post-transcriptional modulator of DAB2IP. By efficiently downregulating DAB2IP, this miRNA enhances cancer cell motility and invasiveness, facilitating activation of NF-kB signaling and promoting expression of pro-inflammatory and pro-angiogenic factors. In addition, we found that miR-149-3p secreted by prostate cancer cells induces DAB2IP downregulation in recipient vascular endothelial cells, stimulating their proliferation and motility, thus potentially remodeling the tumor microenvironment. Finally, we found that inhibition of endogenous miR-149-3p restores DAB2IP activity and efficiently reduces tumor growth and dissemination of malignant cells. These observations suggest that miR-149-3p can promote cancer progression via coordinated inhibition of DAB2IP in tumor cells and in stromal cells.