An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Retardation of arsenic transport through a Pleistocene aquifer

Groundwater drawn daily from shallow alluvial sands by millions of wells over large areas of south and southeast Asia exposes an estimated population of over a hundred million people to toxic levels of arsenic1. Holocene aquifers are the source of widespread arsenic poisoning across the region2, 3. In contrast, Pleistocene sands deposited in this region more than 12,000 years ago mostly do not host groundwater with high levels of arsenic. Pleistocene aquifers are increasingly used as a safe source of drinking water4 and it is therefore important to understand under what conditions low levels of arsenic can be maintained. Here we reconstruct the initial phase of contamination of a Pleistocene aquifer near Hanoi, Vietnam. We demonstrate that changes in groundwater flow conditions and the redox state of the aquifer sands induced by groundwater pumping caused the lateral intrusion of arsenic contamination more than 120 metres from a Holocene aquifer into a previously uncontaminated Pleistocene aquifer. We also find that arsenic adsorbs onto the aquifer sands and that there is a 16–20-fold retardation in the extent of the contamination relative to the reconstructed lateral movement of groundwater over the same period. Our findings suggest that arsenic contamination of Pleistocene aquifers in south and southeast Asia as a consequence of increasing levels of groundwater pumping may have been delayed by the retardation of arsenic transport.National Science Foundation (U.S.) (NSF grant EAR09-11557)Swiss Agency for Development and Cooperation (Grant NAFOSTED 105-09-59-09 to CETASD, the Centre for Environmental Technology and Sustainable Development (Vietnam))National Institute of Environmental Health Sciences (NIEHS grant P42 ES010349)National Institute of Environmental Health Sciences (NIEHS grant P42 ES016454

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Combination Antifungal Therapy for Cryptococcal Meningitis

Background Combination antifungal therapy (amphotericin B deoxycholate and flucytosine) is the recommended treatment for cryptococcal meningitis but has not been shown to reduce mortality, as compared with amphotericin B alone. We performed a randomized, controlled trial to determine whether combining flucytosine or high-dose fluconazole with high-dose amphotericin B improved survival at 14 and 70 days. Methods We conducted a randomized, three-group, open-label trial of induction therapy for cryptococcal meningitis in patients with human immunodeficiency virus infection. All patients received amphotericin B at a dose of 1 mg per kilogram of body weight per day; patients in group 1 were treated for 4 weeks, and those in groups 2 and 3 for 2 weeks. Patients in group 2 concurrently received flucytosine at a dose of 100 mg per kilogram per day for 2 weeks, and those in group 3 concurrently received fluconazole at a dose of 400 mg twice daily for 2 weeks. Results A total of 299 patients were enrolled. Fewer deaths occurred by days 14 and 70 among patients receiving amphotericin B and flucytosine than among those receiving amphotericin B alone (15 vs. 25 deaths by day 14; hazard ratio, 0.57; 95% confidence interval [CI], 0.30 to 1.08; unadjusted P=0.08; and 30 vs. 44 deaths by day 70; hazard ratio, 0.61; 95% CI, 0.39 to 0.97; unadjusted P=0.04). Combination therapy with fluconazole had no significant effect on survival, as compared with monotherapy (hazard ratio for death by 14 days, 0.78; 95% CI, 0.44 to 1.41; P=0.42; hazard ratio for death by 70 days, 0.71; 95% CI, 0.45 to 1.11; P=0.13). Amphotericin B plus flucytosine was associated with significantly increased rates of yeast clearance from cerebrospinal fluid (−0.42 log10 colony-forming units [CFU] per milliliter per day vs. −0.31 and −0.32 log10 CFU per milliliter per day in groups 1 and 3, respectively; P<0.001 for both comparisons). Rates of adverse events were similar in all groups, although neutropenia was more frequent in patients receiving a combination therapy. Conclusions Amphotericin B plus flucytosine, as compared with amphotericin B alone, is associated with improved survival among patients with cryptococcal meningitis. A survival benefit of amphotericin B plus fluconazole was not found

A new kinetic model for biogas production from co-digestion by batch mode

Coming out from the growth kinetics, the Gompertz model has been developed and considered as the best one for simulating the biogas production from anaerobic digestion. However, the model has failed to describe the starting point of the process, and no-sense of lag phase constant has been pointed out. Thus, the goal of this study is to develop a new kinetic model of biogas production with meaningful constants that can alternate the Gompertz model. The kinetic constants of the model were determined by applying the least squares fitting method for experimental data. The experimental data were taken from running seven batch reactors of co-digestion of vegetable, sludge and horse manure under 37oC, pH of 6.7, and total solids of 2.5%. The result of the high coefficient of determination (0.9611-0.9906) demonstrated that the new biogas production kinetic model was feasible to simulate the biogas generation process. This finding has opened a new choice that can deal with simulation of the biogas production. Moreover, co-digestion of vegetable, horse manure, and sludge was also evaluated under strong attention. The biogas potential was in the range of 183-648 Nml/g-VS with the best carbon-to-nitrogen ratio of 16. Vegetable waste played a major role in producing the biogas yield while horse manure and sludge contributed to balancing nutrient of the digestion process. Also, the strong correlation between carbon-to-nitrogen ratio and kinetic constants confirmed that the carbon-to-nitrogen ratio was the key factor that influenced biogas generation

Kinetics of carbon dioxide, methane and hydrolysis in co-digestion of food and vegetable wastes

Kinetic models which can express the behaviors of hydrolysis and biogas generation more precisely than the conventional models were developed. The developed models were evaluated based on the experimental data of six batch reactors. Anaerobic digestion test was co-digestion of food and vegetable waste with inoculating horse dung by 15% of the total wet weight, at the temperature of 37oC. For hydrolysis, the modified model was developed from an original first-order kinetic model. The modified first-order kinetic model was proved to be better than the original one with the hydrolysis rate constant in the range of 0.22-0.34/day and hydrolyzable rate of 0.80 to 0.84. Kinetics of carbon dioxide and methane were developed from a current potential model. The comparison between experimental data and modeling values had the high correlation of determination (0.9918-0.9998) and low root mean square errors (0.08-4.51) indicating the feasibility of the developed model. In which, the evolution of methane showed the rate constant in the range of 0.031-0.039/day. The carbon dioxide from fermentation accounted for 12-44% of the total observed carbon dioxide. Thus, separation of fermentation and methanogenesis by various reactors may reduce the price of methane enrichment significantly. There was a lag time between methanogenesis and fermentation in reactors (λ = 7-11 days). Also, the biogas yield was in the range of 431.6-596.9 Nml/g-VS with the CH4 concentration of 56.2-67.5%. The best methane yield (393.7 Nml/g-VS) was in a reactor with food waste to the vegetable waste ratio by 1.8:1 (wet basis) and C/N ratio by 25.4

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing