Nitrogen Production in Starburst Galaxies Detected by GALEX

We investigate the production of nitrogen in star-forming galaxies with ultraviolet (UV) radiation detected by the Galaxy Evolution Explorer Satellite (GALEX). We use a sample of 8745 GALEX emission-line galaxies matched to the Sloan Digital Sky Survey (SDSS) spectroscopic sample. We derive both gas-phase oxygen and nitrogen abundances for the sample and apply stellar population synthesis models to derive stellar masses and star formation histories of the galaxies. We compare oxygen abundances derived using three different diagnostics. We derive the specific star formation rates of the galaxies by modeling the seven-band GALEX+SDSS photometry. We find that galaxies that have log (SFR/M_*) ≳ − 10.0 typically have values of log (N/O) ~ 0.05 dex less than galaxies with log (SFR/M_*) ≾ − 10.0 and similar oxygen abundances

UV Star Formation Rates in the Local Universe

We measure star formation rates of ~50,000 optically-selected galaxies in the local universe (z~0.1), spanning a range from gas-rich dwarfs to massive ellipticals. We obtain dust-corrected SFRs by fitting the GALEX (UV) and SDSS (optical) photometry to a library of population synthesis models that include dust attenuation. For star-forming galaxies, our UV-based SFRs compare remarkably well with those derived from SDSS H alpha. Deviations from perfect agreement between these two methods are due to differences in the dust attenuation estimates. In contrast to H alpha, UV provides reliable SFRs for galaxies with weak or no H alpha emission, and where H alpha is contaminated with an emission from an AGN. We use full-SED SFRs to calibrate a simple prescription that uses GALEX UV magnitudes to produce good SFRs for normal star-forming galaxies. The specific SFR is considered as a function of stellar mass for (1) star-forming galaxies with no AGN, (2) those hosting an AGN, and for (3) galaxies without H alpha emission. We find that the three have distinct star formation histories, with AGN lying intermediate between the star-forming and the quiescent galaxies. Normal star forming galaxies (without an AGN) lie on a relatively narrow linear sequence. Remarkably, galaxies hosting a strong AGN appear to represent the massive continuation of this sequence. Weak AGN, while also massive, have lower SFR, sometimes extending to the realm of quiescent galaxies. We propose an evolutionary sequence for massive galaxies that smoothly connects normal star-forming galaxies to quiescent (red sequence) galaxies via strong and weak AGN. We confirm that some galaxies with no H alpha emission show signs of SF in the UV. We derive a UV-based cosmic SFR density at z=0.1 with smaller total error than previous measurements (abridged).Comment: Accepted for publication in ApJ (Special GALEX Supplement issue - Dec 2007). v2: Typo in Eq. 2 correcte

The UV-Optical Color Dependence of Galaxy Clustering in the Local Universe

We measure the UV-optical color dependence of galaxy clustering in the local universe. Using the clean separation of the red and blue sequences made possible by the NUV - r color-magnitude diagram, we segregate the galaxies into red, blue and intermediate "green" classes. We explore the clustering as a function of this segregation by removing the dependence on luminosity and by excluding edge-on galaxies as a means of a non-model dependent veto of highly extincted galaxies. We find that \xi (r_p, \pi) for both red and green galaxies shows strong redshift space distortion on small scales -- the "finger-of-God" effect, with green galaxies having a lower amplitude than is seen for the red sequence, and the blue sequence showing almost no distortion. On large scales, \xi (r_p, \pi) for all three samples show the effect of large-scale streaming from coherent infall. On scales 1 Mpc/h < r_p < 10 Mpc/h, the projected auto-correlation function w_p(r_p) for red and green galaxies fits a power-law with slope \gamma ~ 1.93 and amplitude r_0 ~ 7.5 and 5.3, compared with \gamma ~ 1.75 and r_0 ~ 3.9 Mpc/h for blue sequence galaxies. Compared to the clustering of a fiducial L* galaxy, the red, green, and blue have a relative bias of 1.5, 1.1, and 0.9 respectively. The w_p(r_p) for blue galaxies display an increase in convexity at ~ 1 Mpc/h, with an excess of large scale clustering. Our results suggest that the majority of blue galaxies are likely central galaxies in less massive halos, while red and green galaxies have larger satellite fractions, and preferentially reside in virialized structures. If blue sequence galaxies migrate to the red sequence via processes like mergers or quenching that take them through the green valley, such a transformation may be accompanied by a change in environment in addition to any change in luminosity and color.Comment: accepted by MNRA

Ly{\alpha} Emission from High Redshift Sources in COSMOS

We investigate spectroscopically measured Ly{\alpha} equivalent widths and escape fractions of 244 sources of which 95 are Lyman Break Galaxies (LBGs) and 106 Lyman Alpha Emitters (LAEs) at z~4.2, z~4.8, and z~5.6 selected from intermediate and narrow band observations. The sources were selected from the Cosmic Evolution Survey (COSMOS), and observed with the DEIMOS spectrograph. We find that the distribution of equivalent widths shows no evolution with redshift for both the LBG selected sources and the intermediate/narrowband LAEs. We also find that the Ly{\alpha} escape fraction of intermediate and narrow band LAEs is on average higher and has a larger variation than the escape fraction of LBG selected sources. The escape fraction does not show a dependence with redshift. Similar to what has been found for LAEs at low redshifts, the sources with the highest extinctions show the lowest escape fractions. The range of escape fractions increases with decreasing extinction. This is evidence that the dust extinction is the most important factor affecting the escape of Ly{\alpha} photons, but at low extinctions other factors such as HI covering fraction and gas kinematics can be just as effective at inhibiting the escape of Ly{\alpha} photons.Comment: accepted to Ap

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

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

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

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

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

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy