The cytokine Meteorin-like inhibits anti-tumor CD8+ T cell responses by disrupting mitochondrial function.

Tumor-infiltrating lymphocyte (TIL) hypofunction contributes to the progression of advanced cancers and is a frequent target of immunotherapy. Emerging evidence indicates that metabolic insufficiency drives T cell hypofunction during tonic stimulation, but the signals that initiate metabolic reprogramming in this context are largely unknown. Here, we found that Meteorin-like (METRNL), a metabolically active cytokine secreted by immune cells in the tumor microenvironment (TME), induced bioenergetic failure of CD8+ T cells. METRNL was secreted by CD8+ T cells during repeated stimulation and acted via both autocrine and paracrine signaling. Mechanistically, METRNL increased E2F-peroxisome proliferator-activated receptor delta (PPARδ) activity, causing mitochondrial depolarization and decreased oxidative phosphorylation, which triggered a compensatory bioenergetic shift to glycolysis. Metrnl ablation or downregulation improved the metabolic fitness of CD8+ T cells and enhanced tumor control in several tumor models, demonstrating the translational potential of targeting the METRNL-E2F-PPARδ pathway to support bioenergetic fitness of CD8+ TILs.

TP53 structure-function relationships in metastatic castrate-sensitive prostate cancer and the impact of APR-246 treatment.

PURPOSE: Despite well-informed work in several malignancies, the phenotypic effects of TP53 mutations in metastatic castration-sensitive prostate cancer (mCSPC) progression and metastasis are not clear. We characterized the structure-function and clinical impact of TP53 mutations in mCSPC. PATIENTS AND METHODS: We performed an international retrospective review of men with mCSPC who underwent next-generation sequencing and were stratified according to TP53 mutational status and metastatic burden. Clinical outcomes included radiographic progression-free survival (rPFS) and overall survival (OS) evaluated with Kaplan-Meier and multivariable Cox regression. We also utilized isogenic cancer cell lines to assess the effect of TP53 mutations and APR-246 treatment on migration, invasion, colony formation in vitro, and tumor growth in vivo. Preclinical experimental observations were compared using t-tests and ANOVA. RESULTS: Dominant-negative (DN) TP53 mutations were enriched in patients with synchronous (vs. metachronous) (20.7% vs. 6.3%, p < 0.01) and polymetastatic (vs. oligometastatic) (14.4% vs. 7.9%, p < 0.01) disease. On multivariable analysis, DN mutations were associated with worse rPFS (hazards ratio [HR] = 1.97, 95% confidence interval [CI]: 1.31-2.98) and overall survival [OS] (HR = 2.05, 95% CI: 1.14-3.68) compared to TP53 wild type (WT). In vitro, 22Rv1 TP53 R175H cells possessed stronger migration, invasion, colony formation ability, and cellular movement pathway enrichment in RNA sequencing analysis compared to 22Rv1 TP53 WT cells. Treatment with APR-246 reversed the effects of TP53 mutations in vitro and inhibited 22Rv1 TP53 R175H tumor growth in vivo in a dosage-dependent manner. CONCLUSIONS: DN TP53 mutations correlated with worse prognosis in prostate cancer patients and higher metastatic potential, which could be counteracted by APR-246 treatment suggesting a potential future therapeutic avenue.

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously.

MOTIVATION: Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. RESULTS: Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. AVAILABILITY AND IMPLEMENTATION: The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.

Concomitant assessment of PD-1 and CD56 expression identifies subsets of resting cord blood Vδ2 T cells with disparate cytotoxic potential.

Vγ9Vδ2 T lymphocytes are programmed for broad antimicrobial responses with rapid production of Th1 cytokines even before birth, and thus thought to play key roles against pathogens in infants. The process regulating Vδ2 cell acquisition of cytotoxic potential shortly after birth remains understudied. We observed that perforin production in cord blood Vδ2 cells correlates with phenotypes defined by the concomitant assessment of PD-1 and CD56. Bulk RNA sequencing of sorted Vδ2 cell fractions indicated that transcripts related to cytotoxic activity and NK function are enriched in the subset with the highest proportion of perforin+ cells. Among differentially expressed transcripts, IRF8, previously linked to CD8 T cell effector differentiation and NK maturation, has the potential to mediate Vδ2 cell differentiation towards cytotoxic effectors. Our current and past results support the hypothesis that distinct mechanisms regulate Vδ2 cell cytotoxic function before and after birth, possibly linked to different levels of microbial exposure.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Comparative transcriptomics of primary cells in vertebrates.

Gene expression profiles in homologous tissues have been observed to be different between species, which may be due to differences between species in the gene expression program in each cell type, but may also reflect differences in cell type composition of each tissue in different species. Here, we compare expression profiles in matching primary cells in human, mouse, rat, dog, and chicken using Cap Analysis Gene Expression (CAGE) and short RNA (sRNA) sequencing data from FANTOM5. While we find that expression profiles of orthologous genes in different species are highly correlated across cell types, in each cell type many genes were differentially expressed between species. Expression of genes with products involved in transcription, RNA processing, and transcriptional regulation was more likely to be conserved, while expression of genes encoding proteins involved in intercellular communication was more likely to have diverged during evolution. Conservation of expression correlated positively with the evolutionary age of genes, suggesting that divergence in expression levels of genes critical for cell function was restricted during evolution. Motif activity analysis showed that both promoters and enhancers are activated by the same transcription factors in different species. An analysis of expression levels of mature miRNAs and of primary miRNAs identified by CAGE revealed that evolutionary old miRNAs are more likely to have conserved expression patterns than young miRNAs. We conclude that key aspects of the regulatory network are conserved, while differential expression of genes involved in cell-to-cell communication may contribute greatly to phenotypic differences between species.

Recounting the FANTOM CAGE-Associated Transcriptome.

Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes, including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, inclusive of over 109,000 coding and noncoding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue-specific transcription profiles for distinct classes of coding and noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identifying novel noncoding genes potentially involved in tumor pathogenesis and progression, and (c) confirm the prognostic value for several enhancer lncRNAs expression in cancer. Our resource is instrumental for the systematic molecular characterization of lncRNA by the FANTOM6 Consortium. In conclusion, comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs.

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer.

BACKGROUND: While the efficacy of neoadjuvant chemotherapy (NACT) in treating triple-negative breast cancer (TNBC) is generally accepted, not all patients derive benefit from this preoperative treatment. Presently, there are no validated biomarkers to predict the NACT response, and previous attempts to develop predictive classifiers based on gene expression data have not demonstrated clinical utility. However, predictive models incorporating biological constraints have shown increased robustness and improved performance compared to agnostic classifiers. METHODS: We used the preoperative transcriptomic profiles from 298 patients with TNBC to train and test a rank-based classifier, k-top scoring pairs, to predict whether the patient will have pathological complete response (pCR) or residual disease (RD) following NACT. To reduce overfitting and enhance the signature's interpretability, we constrained the training process to genes involved in the Notch signaling pathway. Subsequently, we evaluated the signature performance on two independent cohorts with 75 and 71 patients. Finally, we assessed the prognostic value of the signature by examining its association with relapse-free survival (RFS) using Kaplan‒Meier (KM) survival estimates and a multivariate Cox proportional hazards model. RESULTS: The final signature consists of five gene pairs, whose relative ordering can be predictive of the NACT response. The signature has a robust performance at predicting pCR in TNBC patients with an area under the ROC curve (AUC) of 0.76 and 0.85 in the first and second testing cohorts, respectively, outperforming other gene signatures developed for the same purpose. Additionally, the signature was significantly associated with RFS in an independent TNBC patient cohort even after adjusting for T stage, patient age at the time of diagnosis, type of breast surgery, and menopausal status. CONCLUSION: We introduce a robust gene signature to predict pathological complete response (pCR) in patients with TNBC. This signature applies easily interpretable, rank-based decision rules to genes regulated by the Notch signaling pathway, a known determinant in breast cancer chemoresistance. The robust predictive and prognostic performance of the signature make it a strong candidate for clinical implementation, aiding in the stratification of TNBC patients undergoing NACT.

A robust and interpretable gene signature for predicting the lymph node status of primary T1/T2 oral cavity squamous cell carcinoma.

Oral cavity squamous cell carcinoma (OSCC) affects more than 30 000 individuals in the United States annually, with smoking and alcohol consumption being the main risk factors. Management of early-stage tumors usually includes surgical resection followed by postoperative radiotherapy in certain cases. The cervical lymph nodes (LNs) are the most common site for local metastasis, and elective neck dissection is usually performed if the primary tumor thickness is greater than 3.5 mm. However, postoperative histological examination often reveals that many patients with early-stage disease are negative for neck nodal metastasis, posing a pressing need for improved risk stratification to either avoid overtreatment or prevent the disease progression. To this end, we aimed to identify a primary tumor gene signature that can accurately predict cervical LN metastasis in patients with early-stage OSCC. Using gene expression profiles from 189 samples, we trained K-top scoring pairs models and identified six gene pairs that can distinguish primary tumors with nodal metastasis from those without metastasis. The signature was further validated on an independent cohort of 35 patients using real-time polymerase chain reaction (PCR) in which it achieved an area under the receiver operating characteristic (ROC) curve and accuracy of 90% and 91%, respectively. These results indicate that such signature holds promise as a quick and cost effective method for detecting patients at high risk of developing cervical LN metastasis, and may be potentially used to guide the neck treatment regimen in early-stage OSCC.

Donor-derived acute myeloid leukemia in solid organ transplantation.

We report the transmission of acute myeloid leukemia (AML) undetected at donation from a deceased organ donor to two kidneys and one liver recipients. We reviewed the medical records, and performed molecular analyses and whole exome sequencing (WES) to ascertain AML donor origin and its molecular evolution. The liver recipient was diagnosed 11 months after transplantation and died from complications 2 months later. The two kidney recipients (R1 and R2) were diagnosed 19 and 20 months after transplantation and both received treatment for leukemia. R1 died of complications 11 months after diagnosis, while R2 went into complete remission for 44 months, before relapsing. R2 died 10 months later of complications from allogenic bone marrow transplantation. Microsatellite analysis demonstrated donor chimerism in circulating cells from both kidney recipients. Targeted molecular analyses and medical records revealed NPM1 mutation present in the donor and recipients, while FLT3 was mutated only in R1. These findings were confirmed by WES, which revealed additional founder and clonal mutations, and HLA genomic loss in R2. In conclusion, we report the first in-depth genomic analysis of AML transmission following solid organ transplantation, revealing distinct clonal evolution, and providing a potential molecular explanation for tumor escape.

Efficient representations of tumor diversity with paired DNA-RNA aberrations.

Cancer cells display massive dysregulation of key regulatory pathways due to now well-catalogued mutations and other DNA-related aberrations. Moreover, enormous heterogeneity has been commonly observed in the identity, frequency and location of these aberrations across individuals with the same cancer type or subtype, and this variation naturally propagates to the transcriptome, resulting in myriad types of dysregulated gene expression programs. Many have argued that a more integrative and quantitative analysis of heterogeneity of DNA and RNA molecular profiles may be necessary for designing more systematic explorations of alternative therapies and improving predictive accuracy. We introduce a representation of multi-omics profiles which is sufficiently rich to account for observed heterogeneity and support the construction of quantitative, integrated, metrics of variation. Starting from the network of interactions existing in Reactome, we build a library of "paired DNA-RNA aberrations" that represent prototypical and recurrent patterns of dysregulation in cancer; each two-gene "Source-Target Pair" (STP) consists of a "source" regulatory gene and a "target" gene whose expression is plausibly "controlled" by the source gene. The STP is then "aberrant" in a joint DNA-RNA profile if the source gene is DNA-aberrant (e.g., mutated, deleted, or duplicated), and the downstream target gene is "RNA-aberrant", meaning its expression level is outside the normal, baseline range. With M STPs, each sample profile has exactly one of the 2M possible configurations. We concentrate on subsets of STPs, and the corresponding reduced configurations, by selecting tissue-dependent minimal coverings, defined as the smallest family of STPs with the property that every sample in the considered population displays at least one aberrant STP within that family. These minimal coverings can be computed with integer programming. Given such a covering, a natural measure of cross-sample diversity is the extent to which the particular aberrant STPs composing a covering vary from sample to sample; this variability is captured by the entropy of the distribution over configurations. We apply this program to data from TCGA for six distinct tumor types (breast, prostate, lung, colon, liver, and kidney cancer). This enables an efficient simplification of the complex landscape observed in cancer populations, resulting in the identification of novel signatures of molecular alterations which are not detected with frequency-based criteria. Estimates of cancer heterogeneity across tumor phenotypes reveals a stable pattern: entropy increases with disease severity. This framework is then well-suited to accommodate the expanding complexity of cancer genomes and epigenomes emerging from large consortia projects.

Transcriptional landscape of PTEN loss in primary prostate cancer.

BACKGROUND: PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa. METHODS: Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas. RESULTS: The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression. CONCLUSION: We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

Digitizing omics profiles by divergence from a baseline.

Data collected from omics technologies have revealed pervasive heterogeneity and stochasticity of molecular states within and between phenotypes. A prominent example of such heterogeneity occurs between genome-wide mRNA, microRNA, and methylation profiles from one individual tumor to another, even within a cancer subtype. However, current methods in bioinformatics, such as detecting differentially expressed genes or CpG sites, are population-based and therefore do not effectively model intersample diversity. Here we introduce a unified theory to quantify sample-level heterogeneity that is applicable to a single omics profile. Specifically, we simplify an omics profile to a digital representation based on the omics profiles from a set of samples from a reference or baseline population (e.g., normal tissues). The state of any subprofile (e.g., expression vector for a subset of genes) is said to be "divergent" if it lies outside the estimated support of the baseline distribution and is consequently interpreted as "dysregulated" relative to that baseline. We focus on two cases: single features (e.g., individual genes) and distinguished subsets (e.g., regulatory pathways). Notably, since the divergence analysis is at the individual sample level, dysregulation can be analyzed probabilistically; for example, one can estimate the probability that a gene or pathway is divergent in some population. Finally, the reduction in complexity facilitates a more "personalized" and biologically interpretable analysis of variation, as illustrated by experiments involving tissue characterization, disease detection and progression, and disease-pathway associations.

Genetic Analysis of Small Well-differentiated Pancreatic Neuroendocrine Tumors Identifies Subgroups With Differing Risks of Liver Metastases.

OBJECTIVE: The aim of this study was to investigate the key molecular alterations in small primary pancreatic neuroendocrine tumors (PanNETs) associated with the development of liver metastases. BACKGROUND: Well-differentiated PanNETs with small size are typically indolent; however, a limited subset metastasize to the liver. METHODS: A total of 87 small primary PanNETs (<3 cm), including 32 metastatic cases and 55 nonmetastatic cases after a 5-year follow-up, were immunolabeled for DAXX/ATRX and analyzed for alternative lengthening of telomeres (ALT) by Fluorescence In Situ Hybridization. A subset of these cases, 24 that metastasized and 24 that did not metastasize, were assessed by targeted next-generation sequencing and whole-genome copy number variation. RESULTS: In the entire cohort, high Ki-67 (OR 1.369; 95% CI 1.121-1.673; P = 0.002), N-stage (OR 4.568; 95% CI 1.458-14.312; P = 0.009), and ALT-positivity (OR 3.486; 95% CI 1.093-11.115; P = 0.035) were independently associated with liver metastases. In the subset assessed by next-generation sequencing and copy number variation analysis, 3 molecular subtypes with differing risks of liver metastases were identified. Group 1 (n = 15; 73% metastasized) was characterized by recurrent chromosomal gains, CN-LOH, DAXX mutations, and ALT-positivity. Group 2 (n = 19; 42% metastasized, including 5 G1 tumors) was characterized by limited copy number alterations and mutations. Group 3 (n = 14; 35% metastasized) were defined by chromosome 11 loss. CONCLUSIONS: We identified genomic patterns of small PanNETs associated with a different risk for liver metastases. Molecular alterations, such as DAXX mutations, chromosomal gains, and ALT, are associated with an increased risk of metastasis in small PanNETs. Therefore, targeted sequencing and/or ALT analysis may help in the clinical decisions for these small PanNETs.

PTEN Loss with ERG Negative Status is Associated with Lethal Disease after Radical Prostatectomy.

PURPOSE: Few groups have investigated the combined effects of PTEN loss and ERG expression on the outcomes of metastasis of or death from prostate cancer in surgically treated patients. We examined the association of PTEN/ERG status with lethal prostate cancer in patients treated with radical prostatectomy. MATERIALS AND METHODS: Included in analysis were 791 patients with clinically localized prostate cancer treated with radical prostatectomy at a single institution. Genetically validated immunohistochemistry assays for PTEN and ERG were performed on tissue microarrays. Multivariable Cox proportional hazard models were used to assess the association of PTEN/ERG status with lethal prostate cancer (defined as metastasis or prostate cancer specific death), adjusting for patient age, race, pathological grade and stage, and surgical margin status. RESULTS: Median followup in the cohort was 12.8 years. Of 791 cases 203 (25%) demonstrated PTEN loss and 330 of 776 (43%) were ERG positive. On multivariable analysis PTEN loss (HR 1.9, 95% CI 1.2-3.0, p=0.012) but not ERG expression (HR 0.6, 95% CI 0.4-1.1, p=0.11) was associated with an increased risk of lethal prostate cancer. The association of PTEN loss with lethal disease only remained among men with ERG negative tumors (HR 2.3, 95% CI 1.3-4.1, p=0.005) and not ERG positive tumors (HR 1.1, 95% CI 0.6-2.1, p=0.81). CONCLUSIONS: PTEN loss is associated with an increased risk of lethal prostate cancer after radical prostatectomy and this risk is most pronounced in the subgroup of patients with ERG negative tumors. This work corroborates the use of PTEN and ERG status for risk stratification in surgically treated patients.

Genome-Wide Somatic Copy Number Alterations and Mutations in High-Grade Pancreatic Intraepithelial Neoplasia.

To obtain a better understanding of the genetic alterations of high-grade pancreatic intraepithelial neoplasia (HG-PanIN), we performed whole-genome copy number analysis by using single nucleotide polymorphism microarrays and targeted next-generation sequencing of 11 microdissected HG-PanIN and two low-grade PanIN lesions associated with HG-PanIN. HG-PanIN mutation profiles were compared with those of their associated invasive pancreatic ductal adenocarcinoma. All PanIN lesions harbored somatic KRAS mutations. The most common copy number losses in the HG-PanIN were at the CDKN2A (9p21), TP53 (17p13), and SMAD4 (18q21) loci. Chromosomal losses in HG-PanIN were also found at 6p25-p24, 6q11-q27, 12q24, and 17q23-q24. Biallelic inactivation of CDKN2A and TP53 was detected in five of eight and in three of eight evaluable PanIN lesions, respectively. None of the HG-PanIN lesions had SMAD4 mutations or homozygous deletion. Copy number gains were noted at the MYC (8q24) and CCNE1 (19q12) loci and at 1q25-q31. Four HG-PanINs and one low-grade PanIN harbored chromothripsis-like regions. Five of seven pancreatic ductal adenocarcinomas evaluated had additional mutations that were not found in their associated HG-PanIN. HG-PanIN harbors widespread copy number alterations and commonly shows evidence of biallelic inactivation of CDKN2A and TP53 but not SMAD4. Chromothripsis events contribute to the copy number alterations of HG-PanIN.

Expression of HIV-1 matrix protein p17 and association with B-cell lymphoma in HIV-1 transgenic mice.

HIV-1 infection is associated with increased risk for B-cell lymphomas. How HIV infection promotes the development of lymphoma is unclear, but it may involve chronic B-cell activation, inflammation, and/or impaired immunity, possibly leading to a loss of control of oncogenic viruses and reduced tumor immunosurveillance. We hypothesized that HIV structural proteins may contribute to lymphomagenesis directly, because they can persist long term in lymph nodes in the absence of viral replication. The HIV-1 transgenic mouse Tg26 carries a noninfectious HIV-1 provirus lacking part of the gag-pol region, thus constituting a model for studying the effects of viral products in pathogenesis. Approximately 15% of Tg26 mice spontaneously develop leukemia/lymphoma. We investigated which viral proteins are associated with the development of leukemia/lymphoma in the Tg26 mouse model, and performed microarray analysis on RNA from spleen and lymph nodes to identify potential mechanisms of lymphomagenesis. Of the viral proteins examined, only expression of HIV-1 matrix protein p17 was associated with leukemia/lymphoma development and was highly expressed in bone marrow before disease. The tumor cells resembled pro-B cells, and were CD19+IgM-IgD-CD93+CD43+CD21-CD23-VpreB+CXCR4+ Consistent with the pro-B-cell stage of B-cell development, microarray analysis revealed enrichment of transcripts, including Rag1, Rag2, CD93, Vpreb1, Vpreb3, and Igll1 We confirmed RAG1 expression in Tg26 tumors, and hypothesized that HIV-1 matrix protein p17 may directly induce RAG1 in B cells. Stimulation of human activated B cells with p17 enhanced RAG1 expression in three of seven donors, suggesting that intracellular signaling by p17 may lead to genomic instability and transformation.

Arsenic promotes the COX2/PGE2-SOX2 axis to increase the malignant stemness properties of urothelial cells.

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 chemoresistance. 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.

PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF.

SUMMARY: Non-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g. time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel patternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with patternMarkers requires whole-genome data. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. Additionally, a manual version of the GWCoGAPS algorithm contains analytic and visualization tools including patternMatcher, a Shiny web application. The decomposition in the manual pipeline can be replaced with any NMF algorithm, for further generalization of the software. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTEx data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data. AVAILABILITY AND IMPLEMENTATION: PatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license. CONTACT: gsteinobrien@jhmi.edu or ccolantu@jhmi.edu or ejfertig@jhmi.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Only three driver gene mutations are required for the development of lung and colorectal cancers.

Cancer arises through the sequential accumulation of mutations in oncogenes and tumor suppressor genes. However, how many such mutations are required for a normal human cell to progress to an advanced cancer? The best estimates for this number have been provided by mathematical models based on the relation between age and incidence. For example, the classic studies of Nordling [Nordling CO (1953) Br J Cancer 7(1):68-72] and Armitage and Doll [Armitage P, Doll R (1954) Br J Cancer 8(1):1-12] suggest that six or seven sequential mutations are required. Here, we describe a different approach to derive this estimate that combines conventional epidemiologic studies with genome-wide sequencing data: incidence data for different groups of patients with the same cancer type were compared with respect to their somatic mutation rates. In two well-documented cancer types (lung and colon adenocarcinomas), we find that only three sequential mutations are required to develop cancer. This conclusion deepens our understanding of the process of carcinogenesis and has important implications for the design of future cancer genome-sequencing efforts.