Noninvasive Urinary Metabolomic Profiling Identifies Diagnostic and Prognostic Markers in Lung Cancer

Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1,500 Da) were measured in urine collected from 469 patients with lung cancer and 536 population controls using unbiased liquid chromatography/mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further confirmed in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared with the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and N-acetylneuraminic acid (NANA) were each significantly (P < 0.00001) elevated in non-small cell lung cancer and associated with worse prognosis [HR = 1.81 (P = 0.0002), and 1.54 (P = 0.025), respectively]. Creatine riboside was the strongest classifier of lung cancer status in all and stage I-II cases, important for early detection, and also associated with worse prognosis in stage I-II lung cancer (HR = 1.71, P = 0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 to 0.99. Both metabolites were significantly (P < 0.03) enriched in tumor tissue compared with adjacent nontumor tissue (N = 48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis.

1H HR-MAS NMR Based Metabolic Profiling of Cells in Response to Treatment with a Hexacationic Ruthenium Metallaprism as Potential Anticancer Drug.

1H high resolution magic angle spinning (HR-MAS) NMR spectroscopy was applied in combination with multivariate statistical analyses to study the metabolic response of whole cells to the treatment with a hexacationic ruthenium metallaprism [1]6+ as potential anticancer drug. Human ovarian cancer cells (A2780), the corresponding cisplatin resistant cells (A2780cisR), and human embryonic kidney cells (HEK-293) were each incubated for 24 h and 72 h with [1]6+ and compared to untreated cells. Different responses were obtained depending on the cell type and incubation time. Most pronounced changes were found for lipids, choline containing compounds, glutamate and glutathione, nucleotide sugars, lactate, and some amino acids. Possible contributions of these metabolites to physiologic processes are discussed. The time-dependent metabolic response patterns suggest that A2780 cells on one hand and HEK-293 cells and A2780cisR cells on the other hand may follow different cell death pathways and exist in different temporal stages thereof.

Genome profiling of sterol synthesis shows convergent evolution in parasites and guides chemotherapeutic attack

Sterols are an essential class of lipids in eukaryotes, where they serve as structural components of membranes and play important roles as signaling molecules. Sterols are also of high pharmacological significance: cholesterol-lowering drugs are blockbusters in human health, and inhibitors of ergosterol biosynthesis are widely used as antifungals. Inhibitors of ergosterol synthesis are also being developed for Chagas's disease, caused by Trypanosoma cruzi. Here we develop an in silico pipeline to globally evaluate sterol metabolism and perform comparative genomics. We generate a library of hidden Markov model-based profiles for 42 sterol biosynthetic enzymes, which allows expressing the genomic makeup of a given species as a numerical vector. Hierarchical clustering of these vectors functionally groups eukaryote proteomes and reveals convergent evolution, in particular metabolic reduction in obligate endoparasites. We experimentally explore sterol metabolism by testing a set of sterol biosynthesis inhibitors against trypanosomatids, Plasmodium falciparum, Giardia, and mammalian cells, and by quantifying the expression levels of sterol biosynthetic genes during the different life stages of T. cruzi and Trypanosoma brucei. The phenotypic data correlate with genomic makeup for simvastatin, which showed activity against trypanosomatids. Other findings, such as the activity of terbinafine against Giardia, are not in agreement with the genotypic profile.

Molecular profiling of lung adenosquamous carcinoma: hybrid or genuine type?

Lung adenosquamous carcinoma is a particular subtype of non-small cell lung carcinoma that is defined by the coexistence of adenocarcinoma and squamous cell carcinoma components. The aim of this study was to assess the mutational profile in each component of 16 adenosquamous carcinoma samples from a Caucasian population by a combination of next generation sequencing using the cancer hotspot panel as well as the colon and lung cancer panel and FISH. Identified mutations were confirmed by Sanger sequencing of DNA from cancer cells of each component collected by Laser Capture microdissection. Mutations typical for adenocarcinoma as well as squamous cell carcinoma were identified. Driver mutations were predominantly in the trunk suggesting a monoclonal origin of adenosquamous carcinoma. Most remarkably, EGFR mutations and mutations in the PI3K signaling pathway, which accounted for 30% and 25% of tumors respectively, were more prevalent while KRAS mutations were less prevalent than expected for a Caucasian population. Surprisingly, expression of classifier miR-205 was intermediate between that of classical adenocarcinoma and squamous cell carcinoma suggesting that adenosquamous carcinoma is a transitional stage between these tumor types. The high prevalence of therapy-relevant targets opens new options of therapeutic intervention for adenosquamous carcinoma patients.

Profiling invasiveness in head and neck cancer: recent contributions of genomic and transcriptomic approaches.

High-throughput molecular profiling approaches have emerged as precious research tools in the field of head and neck translational oncology. Such approaches have identified and/or confirmed the role of several genes or pathways in the acquisition/maintenance of an invasive phenotype and the execution of cellular programs related to cell invasion. Recently published new-generation sequencing studies in head and neck squamous cell carcinoma (HNSCC) have unveiled prominent roles in carcinogenesis and cell invasion of mutations involving NOTCH1 and PI3K-patwhay components. Gene-expression profiling studies combined with systems biology approaches have allowed identifying and gaining further mechanistic understanding into pathways commonly enriched in invasive HNSCC. These pathways include antigen-presenting and leucocyte adhesion molecules, as well as genes involved in cell-extracellular matrix interactions. Here we review the major insights into invasiveness in head and neck cancer provided by high-throughput molecular profiling approaches.

Performance of gene-expression profiling test score variability to predict future clinical events in heart transplant recipients.

BACKGROUND A single non-invasive gene expression profiling (GEP) test (AlloMap®) is often used to discriminate if a heart transplant recipient is at a low risk of acute cellular rejection at time of testing. In a randomized trial, use of the test (a GEP score from 0-40) has been shown to be non-inferior to a routine endomyocardial biopsy for surveillance after heart transplantation in selected low-risk patients with respect to clinical outcomes. Recently, it was suggested that the within-patient variability of consecutive GEP scores may be used to independently predict future clinical events; however, future studies were recommended. Here we performed an analysis of an independent patient population to determine the prognostic utility of within-patient variability of GEP scores in predicting future clinical events. METHODS We defined the GEP score variability as the standard deviation of four GEP scores collected ≥315 days post-transplantation. Of the 737 patients from the Cardiac Allograft Rejection Gene Expression Observational (CARGO) II trial, 36 were assigned to the composite event group (death, re-transplantation or graft failure ≥315 days post-transplantation and within 3 years of the final GEP test) and 55 were assigned to the control group (non-event patients). In this case-controlled study, the performance of GEP score variability to predict future events was evaluated by the area under the receiver operator characteristics curve (AUC ROC). The negative predictive values (NPV) and positive predictive values (PPV) including 95 % confidence intervals (CI) of GEP score variability were calculated. RESULTS The estimated prevalence of events was 17 %. Events occurred at a median of 391 (inter-quartile range 376) days after the final GEP test. The GEP variability AUC ROC for the prediction of a composite event was 0.72 (95 % CI 0.6-0.8). The NPV for GEP score variability of 0.6 was 97 % (95 % CI 91.4-100.0); the PPV for GEP score variability of 1.5 was 35.4 % (95 % CI 13.5-75.8). CONCLUSION In heart transplant recipients, a GEP score variability may be used to predict the probability that a composite event will occur within 3 years after the last GEP score. TRIAL REGISTRATION Clinicaltrials.gov identifier NCT00761787.

Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse.

The adaptive response to extreme endurance exercise might involve transcriptional and translational regulation by microRNAs (miRNAs). Therefore, the objective of the present study was to perform an integrated analysis of the blood transcriptome and miRNome (using microarrays) in the horse before and after a 160 km endurance competition. A total of 2,453 differentially expressed genes and 167 differentially expressed microRNAs were identified when comparing pre- and post-ride samples. We used a hypergeometric test and its generalization to gain a better understanding of the biological functions regulated by the differentially expressed microRNA. In particular, 44 differentially expressed microRNAs putatively regulated a total of 351 depleted differentially expressed genes involved variously in glucose metabolism, fatty acid oxidation, mitochondrion biogenesis, and immune response pathways. In an independent validation set of animals, graphical Gaussian models confirmed that miR-21-5p, miR-181b-5p and miR-505-5p are candidate regulatory molecules for the adaptation to endurance exercise in the horse. To the best of our knowledge, the present study is the first to provide a comprehensive, integrated overview of the microRNA-mRNA co-regulation networks that may have a key role in controlling post-transcriptomic regulation during endurance exercise.

Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling

Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present NeuriteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturbation of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate different spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotemporal Rho GTPase signaling networks that regulate neurite outgrowth.