Triton photodisintegration in three-dimensional approach

Two- and three- particles photodisintegration of the triton is investigated in a three-dimensional (3D) Faddeev approach. For this purpose the Jacobi momentum vectors for three particles system and spin-isospin quantum numbers of the individual nucleons are considered. Based on this picture the three-nucleon Faddeev integral equations with the two-nucleon interaction are formulated without employing the partial wave decomposition. The single nucleon current as well as $\pi-$ and $\rho-$ like exchange currents are used in an appropriate form to be employed in 3D approach. The exchange currents are derived from AV18 NN force. The two-body t-matrix, Deuteron and Triton wave functions are calculated in the 3D approach by using AV18 potential. Benchmarks are presented to compare the total cross section for the two- and three- particles photodisintegration in the range of $E_{\gamma}<30 MeV$. The 3D Faddeev approach shows promising results

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

Wnt signalling and cancer stem cells

[Abstract] Intracellular signalling mediated by secreted Wnt proteins is essential for the establishment of cell fates and proper tissue patterning during embryo development and for the regulation of tissue homeostasis and stem cell function in adult tissues. Aberrant activation of Wnt signalling pathways has been directly linked to the genesis of different tumours. Here, the components and molecular mechanisms implicated in the transduction of Wnt signal, along with important results supporting a central role for this signalling pathway in stem cell function regulation and carcinogenesis will be briefly reviewed.Ministerio de Ciencia e Innovación; SAF2008-0060

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

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

Comparing Presenting Clinical Features in 48 Children With Microscopic Polyangiitis to 183 Children Who Have Granulomatosis With Polyangiitis (Wegener&apos;s) : an ARChiVe Cohort Study

OBJECTIVE: To uniquely classify children with microscopic polyangiitis (MPA), to describe their demographic characteristics, presenting clinical features, and initial treatments in comparison to patients with granulomatosis with polyangiitis (Wegener's) (GPA). METHODS: The European Medicines Agency (EMA) classification algorithm was applied by computation to categorical data from patients recruited to the ARChiVe (A Registry for Childhood Vasculitis: e-entry) cohort, with the data censored to November 2015. The EMA algorithm was used to uniquely distinguish children with MPA from children with GPA, whose diagnoses had been classified according to both adult- and pediatric-specific criteria. Descriptive statistics were used for comparisons. RESULTS: In total, 231 of 440 patients (64% female) fulfilled the classification criteria for either MPA (n\u2009=\u200948) or GPA (n\u2009=\u2009183). The median time to diagnosis was 1.6 months in the MPA group and 2.1 months in the GPA group (ranging to 39 and 73 months, respectively). Patients with MPA were significantly younger than those with GPA (median age 11 years versus 14 years). Constitutional features were equally common between the groups. In patients with MPA compared to those with GPA, pulmonary manifestations were less frequent (44% versus 74%) and less severe (primarily, hemorrhage, requirement for supplemental oxygen, and pulmonary failure). Renal pathologic features were frequently found in both groups (75% of patients with MPA versus 83% of patients with GPA) but tended toward greater severity in those with MPA (primarily, nephrotic-range proteinuria, requirement for dialysis, and end-stage renal disease). Airway/eye involvement was absent among patients with MPA, because these GPA-defining features preclude a diagnosis of MPA within the EMA algorithm. Similar proportions of patients with MPA and those with GPA received combination therapy with corticosteroids plus cyclophosphamide (69% and 78%, respectively) or both drugs in combination with plasmapheresis (19% and 22%, respectively). Other treatments administered, ranging in decreasing frequency from 13% to 3%, were rituximab, methotrexate, azathioprine, and mycophenolate mofetil. CONCLUSION: Younger age at disease onset and, perhaps, both gastrointestinal manifestations and more severe kidney disease seem to characterize the clinical profile in children with MPA compared to those with GPA. Delay in diagnosis suggests that recognition of these systemic vasculitides is suboptimal. Compared with adults, initial treatment regimens in children were comparable, but the complete reversal of female-to-male disease prevalence ratios is a provocative finding

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias

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

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development. Comprehensive, integrated molecular analysis identifies molecular relationships across a large diverse set of human cancers, suggesting future directions for exploring clinical actionability in cancer treatment

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