The impact of long-term elevated CO2 on C and N retention in stable SOM pools

Elevated atmospheric CO2 frequently increases plant production and concomitant soil C inputs, which may cause additional soil C sequestration. However, whether the increase in plant production and additional soil C sequestration under elevated CO2 can be sustained in the long-term is unclear. One approach to study C-N interactions under elevated CO2 is provided by a theoretical framework that centers on the concept of progressive nitrogen limitation (PNL). The PNL concept hinges on the idea that N becomes less available with time under elevated CO2. One possible mechanism underlying this reduction in N availability is that N is retained in long-lived soil organic matter (SOM), thereby limiting plant production and the potential for soil C sequestration. The long-term nature of the PNL concept necessitates the testing of mechanisms in field experiments exposed to elevated CO2 over long periods of time. The impact of elevated CO2 and N-15 fertilization on L. perenne and T. repens monocultures has been studied in the Swiss FACE experiment for ten consecutive years. We applied a biological fractionation technique using long-term incubations with repetitive leaching to determine how elevated CO2 affects the accumulation of N and C into more stable SOM pools. Elevated CO2 significantly stimulated retention of fertilizer-N in the stable pools of the soils covered with L. perenne receiving low and high N fertilization rates by 18 and 22%, respectively, and by 45% in the soils covered by T. repens receiving the low N fertilization rate. However, elevated CO2 did not significantly increase stable soil C formation. The increase in N retention under elevated CO2 provides direct evidence that elevated CO2 increases stable N formation as proposed by the PNL concept. In the Swiss FACE experiment, however, plant production increased under elevated CO2, indicating that the additional N supply through fertilization prohibited PNL for plant production at this site. Therefore, it remains unresolved why elevated CO2 did not increase labile and stable C accumulation in these systems

Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data

The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. © 2011 Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands

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

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

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 tumor-infiltrating 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. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived \u201ccomputational stain\u201d developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles

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