The selection of comparators for randomized controlled trials of health-related behavioral interventions: recommendations of an NIH expert panel

Objectives: To provide recommendations for the selection of comparators for randomized controlled trials of health-related behavioral interventions. Study Design and Setting: The National Institutes of Health Office of Behavioral and Social Science Research convened an expert panel to critically review the literature on control or comparison groups for behavioral trials and to develop strategies for improving comparator choices and for resolving controversies and disagreements about comparators. Results: The panel developed a Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials. The model indicates that the optimal comparator is the one that best serves the primary purpose of the trial but that the optimal comparator's limitations and barriers to its use must also be taken into account. Conclusion: We developed best practice recommendations for the selection of comparators for health-related behavioral trials. Use of the Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials can improve the comparator selection process and help resolve disagreements about comparator choices

Observing Supermassive Black Holes across cosmic time: from phenomenology to physics

In the last decade, a combination of high sensitivity, high spatial resolution observations and of coordinated multi-wavelength surveys has revolutionized our view of extra-galactic black hole (BH) astrophysics. We now know that supermassive black holes reside in the nuclei of almost every galaxy, grow over cosmological times by accreting matter, interact and merge with each other, and in the process liberate enormous amounts of energy that influence dramatically the evolution of the surrounding gas and stars, providing a powerful self-regulatory mechanism for galaxy formation. The different energetic phenomena associated to growing black holes and Active Galactic Nuclei (AGN), their cosmological evolution and the observational techniques used to unveil them, are the subject of this chapter. In particular, I will focus my attention on the connection between the theory of high-energy astrophysical processes giving rise to the observed emission in AGN, the observable imprints they leave at different wavelengths, and the methods used to uncover them in a statistically robust way. I will show how such a combined effort of theorists and observers have led us to unveil most of the SMBH growth over a large fraction of the age of the Universe, but that nagging uncertainties remain, preventing us from fully understating the exact role of black holes in the complex process of galaxy and large-scale structure formation, assembly and evolution.Comment: 46 pages, 21 figures. This review article appears as a chapter in the book: "Astrophysical Black Holes", Haardt, F., Gorini, V., Moschella, U and Treves A. (Eds), 2015, Springer International Publishing AG, Cha

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

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

Floristic composition and community structure of epiphytic angiosperms in a terra firme forest in central Amazonia

This survey aimed to describe the floristic composition and structure of the epiphytic community occurring in a terra firme forest in the city of Coari, Brazil, in the Amazon region. Data collection was performed with a 1.5 ha plot method, with which upland, slope and lowland habitats were sampled. All angiosperm epiphytes and their host plants (diameter at breast height > 10 cm) were sampled. We recorded 3.528 individuals in 13 families, 48 genera and 164 species. Araceae was the most prevalent family with regard to the importance value and stood out in all related parameters, followed by Bromeliaceae, Cyclanthaceae and Orchidaceae. The species with the highest epiphytic importance values were Guzmania lingulata (L.) Mez. and Philodendron linnaei Kunth. The predominant life form was hemiepiphytic. Estimated floristic diversity was 3.2 (H'). The studied epiphytic community was distributed among 727 host plants belonging to 40 families, 123 genera and 324 species. One individual of Guarea convergens T.D. Penn. was the host with the highest richness and abundance of epiphytes. Stems/trunks of host plants were the most colonized segments, and the most favorable habitat for epiphytism was the lowlands, where 84.1% of species and 48.2% of epiphytic specimens were observed

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects

Pathogenic Germline Variants in 10,389 Adult Cancers

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer. A pan-cancer analysis identifies hundreds of predisposing germline variants

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

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing