A mesocosm tool to optically study phytoplankton dynamics

The accuracy of remote sensing algorithms for phytoplankton biomass and physiology is difficult to test under natural conditions due to rapid changes in physical and biological forcings and the practical inability to manipulate nutrient conditions and phytoplankton composition in the sea. Therefore, an indoor mesocosm was designed to examine the optical properties of phytoplankton under controlled and manipulated conditions of irradiance, temperature, turbulence, and nutrient availability. Equipped with hyperspectral radiometers and bottom irradiance meters, it is shown that under semi-natural environmental conditions biogeochemically relevant species as Emiliania huxleyi and Phaeocystis globosa can be grown with good precision (+/- 20%) between duplicate mesocosms and between duplicate sensors (< 5% deviation). The accuracy of chlorophyll estimates by absorption, using an Integrating Cavity Absorption Meter, and fluorescence using water-leaving radiance was 74% to 80%, respectively, as it was negatively influenced by changes in phytoplankton physiology. Biomass detection was limitedto 1 to 2 mu g chlorophyll/L with an apparent linearity to 50 mu g chlorophyll/L. Estimates of the quantum efficiency of fluorescence (phi approximate to 0.01) were comparable to real-world estimates derived from satellite observations. It is concluded that the mesocosms adequately simulate natural conditions with sufficient accuracy and precision and that they offer an important tool in validating assumptions and hypotheses underlying remote sensing algorithms and models

Prevalence of c-KIT Mutations in Gonadoblastoma and Dysgerminomas of Patients with Disorders of Sex Development (DSD) and Ovarian Dysgerminomas

Activating c-KIT mutations (exons 11 and 17) are found in 10-40% of testicular seminomas, the majority being missense point mutations (codon 816). Malignant ovarian dysgerminomas represent ~3% of all ovarian cancers in Western countries, resembling testicular seminomas, regarding chromosomal aberrations and c-KIT mutations. DSD patients with specific Y-sequences have an increased risk for Type II Germ Cell Tumor/Cancer, with gonadoblastoma as precursor progressing to dysgerminoma. Here we present analysis of c-KIT exon 8, 9, 11, 13 and 17, and PDGFRA exon 12, 14 and 18 by conventional sequencing together with mutational analysis of c-KIT codon 816 by a sensitive and specific LightCycler melting curve analysis, confirmed by sequencing. The results are combined with data on TSPY and OCT3/4 expression in a series of 16 DSD patients presenting with gonadoblastoma and dysgerminoma and 15 patients presenting pure ovarian dysgerminomas without DSD. c-KIT codon 816 mutations were detected in five out of the total of 31 cases (all found in pure ovarian dysgerminomas). A synonymous SNP (rs 5578615) was detected in two patients, one DSD patient (with bilateral disease) and one patient with dysgerminoma. Next to these, three codon N822K mutations were detected in the group of 15 pure ovarian dysgerminomas. In total activating c-KIT mutations were found in 53% of ovarian dysgerminomas without DSD. In the group of 16 DSD cases a N505I and D820E mutation was found in a single tumor of a patient with gonadoblastoma and dysgerminoma. No PDGFRA mutations were found. Positive OCT3/4 staining was present in all gonadoblastomas and dysgerminomas investigated, TSPY expression was only seen in the gonadoblastoma/dysgerminoma lesions of the 16 DSD patients. This data supports the existence of two distinct but parallel pathways in the development of dysgerminoma, in which mutational status of c-KIT might parallel the presence of TSPY

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

A genome-wide RNAi screen in mouse embryonic stem cells identifies Mp1 as a key mediator of differentiation

Knockdown of the scaffolding protein Mek binding protein 1 (Mp1) in mouse embryonic stem cells suppresses differentiation but not proliferation, and more invasive human germ cell tumors express lower amounts of Mp1

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

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

The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Renal cell carcinoma(RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis

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

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN. We present a computational study determining the frequency and extent of alterations of the MYC network across the 33 human cancers of TCGA. These data, together with MYC, positively correlated pathways as well as mutually exclusive cancer genes, will be a resource for understanding MYC-driven cancers and designing of therapeutics