A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Noninvasive Detection of Nonalcoholic Steatohepatitis Using Clinical Markers and Circulating Levels of Lipids and Metabolites.

BACKGROUND & AIMS: Use of targeted mass spectrometry (MS)-based methods is increasing in clinical chemistry laboratories. We investigate whether MS-based profiling of plasma improves noninvasive risk estimates of nonalcoholic steatohepatitis (NASH) compared with routinely available clinical parameters and patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotype at rs738409. METHODS: We used MS-based analytic platforms to measure levels of lipids and metabolites in blood samples from 318 subjects who underwent a liver biopsy because of suspected NASH. The subjects were divided randomly into estimation (n = 223) and validation (n = 95) groups to build and validate the model. Gibbs sampling and stepwise logistic regression, which fulfilled the Bayesian information criterion, were used for variable selection and modeling. RESULTS: Features of the metabolic syndrome and the variant in PNPLA3 encoding I148M were significantly more common among subjects with than without NASH. We developed a model to identify subjects with NASH based on clinical data and PNPLA3 genotype (NASH Clin Score), which included aspartate aminotransferase (AST), fasting insulin, and PNPLA3 genotype. This model identified subjects with NASH with an area under the receiver operating characteristic of 0.778 (95% confidence interval, 0.709-0.846). We then used backward stepwise logistic regression analyses of variables from the NASH Clin Score and MS-based factors associated with NASH to develop the NASH ClinLipMet Score. This included glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6, AST, and fasting insulin, along with PNPLA3 genotype. It identified patients with NASH with an area under the receiver operating characteristic of 0.866 (95% confidence interval, 0.820-0.913). The NASH ClinLipMet score identified patients with NASH with significantly higher accuracy than the NASH Clin Score or MS-based profiling alone. CONCLUSIONS: A score based on MS (glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6) and knowledge of AST, fasting insulin, and PNPLA3 genotype is significantly better than a score based on clinical or metabolic profiles alone in determining the risk of NASH.

Accumulated Metabolites of Hydroxybutyric Acid Serve as Diagnostic and Prognostic Biomarkers of Ovarian High-Grade Serous Carcinomas.

Ovarian cancer is a heterogeneous disease of low prevalence, but poor survival. Early diagnosis is critical for survival, but it is often challenging because the symptoms of ovarian cancer are subtle and become apparent only during advanced stages of the disease. Therefore, the identification of robust biomarkers of early disease is a clinical priority. Metabolomic profiling is an emerging diagnostic tool enabling the detection of biomarkers reflecting alterations in tumor metabolism, a hallmark of cancer. In this study, we performed metabolomic profiling of serum and tumor tissue from 158 patients with high-grade serous ovarian cancer (HGSOC) and 100 control patients with benign or non-neoplastic lesions. We report metabolites of hydroxybutyric acid (HBA) as novel diagnostic and prognostic biomarkers associated with tumor burden and patient survival. The accumulation of HBA metabolites caused by HGSOC was also associated with reduced expression of succinic semialdehyde dehydrogenase (encoded by ALDH5A1), and with the presence of an epithelial-to-mesenchymal transition gene signature, implying a role for these metabolic alterations in cancer cell migration and invasion. In conclusion, our findings represent the first comprehensive metabolomics analysis in HGSOC and propose a new set of metabolites as biomarkers of disease with diagnostic and prognostic capabilities.

From drug response profiling to target addiction scoring in cancer cell models.

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package.

Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids.

AIMS/HYPOTHESIS: We examined whether analysis of lipids by ultra-performance liquid chromatography (UPLC) coupled to MS allows the development of a laboratory test for non-alcoholic fatty-liver disease (NAFLD), and how a lipid-profile biomarker compares with the prediction of NAFLD and liver-fat content based on routinely available clinical and laboratory data. METHODS: We analysed the concentrations of molecular lipids by UPLC-MS in blood samples of 679 well-characterised individuals in whom liver-fat content was measured using proton magnetic resonance spectroscopy ((1)H-MRS) or liver biopsy. The participants were divided into biomarker-discovery (n = 287) and validation (n = 392) groups to build and validate the diagnostic models, respectively. RESULTS: Individuals with NAFLD had increased triacylglycerols with low carbon number and double-bond content while lysophosphatidylcholines and ether phospholipids were diminished in those with NAFLD. A serum-lipid signature comprising three molecular lipids ('lipid triplet') was developed to estimate the percentage of liver fat. It had a sensitivity of 69.1% and specificity of 73.8% when applied for diagnosis of NAFLD in the validation series. The usefulness of the lipid triplet was demonstrated in a weight-loss intervention study. CONCLUSIONS/INTERPRETATION: The liver-fat-biomarker signature based on molecular lipids may provide a non-invasive tool to diagnose NAFLD, in addition to highlighting lipid molecular pathways involved in the disease.

Characterization of microbial metabolism of Syrah grape products in an in vitro colon model using targeted and non-targeted analytical approaches.

PURPOSE: Syrah red grapes are used in the production of tannin-rich red wines. Tannins are high molecular weight molecules, proanthocyanidins (PAs), and poorly absorbed in the upper intestine. In this study, gut microbial metabolism of Syrah grape phenolic compounds was investigated. METHODS: Syrah grape pericarp was subjected to an enzymatic in vitro digestion model, and red wine and grape skin PA fraction were prepared. Microbial conversion was screened using an in vitro colon model with faecal microbiota, by measurement of short-chain fatty acids by gas chromatography (GC) and microbial phenolic metabolites using GC with mass detection (GC-MS). Red wine metabolites were further profiled using two-dimensional GC mass spectrometry (GCxGC-TOFMS). In addition, the effect of PA structure and dose on conversion efficiency was investigated by GC-MS. RESULTS: Red wine exhibited a higher degree of C1-C3 phenolic acid formation than PA fraction or grape pericarp powders. Hydroxyphenyl valeric acid (flavanols and PAs as precursors) and 3,5-dimethoxy-4-hydroxybenzoic acid (anthocyanin as a precursor) were identified from the red wine metabolite profile. In the absence of native grape pericarp or red wine matrix, the isolated PAs were found to be effective in the dose-dependent inhibition of microbial conversions and short-chain fatty acid formation. CONCLUSIONS: Metabolite profiling was complementary to targeted analysis. The identified metabolites had biological relevance, because the structures of the metabolites resembled fragments of their grape phenolic precursors or were in agreement with literature data.