Arrhythmogenic mechanisms in the isolated perfused hypokalaemic murine heart

AIM: Hypokalaemia is associated with a lethal form of ventricular tachycardia (VT), torsade de pointes, through pathophysiological mechanisms requiring clarification. METHODS: Left ventricular endocardial and epicardial monophasic action potentials were compared in isolated mouse hearts paced from the right ventricular epicardium perfused with hypokalaemic (3 and 4 mm [K(+)](o)) solutions. Corresponding K(+) currents were compared in whole-cell patch-clamped epicardial and endocardial myocytes. RESULTS: Hypokalaemia prolonged epicardial action potential durations (APD) from mean APD(90)s of 37.2 ± 1.7 ms (n = 7) to 58.4 ± 4.1 ms (n =7) and 66.7 ± 2.1 ms (n = 11) at 5.2, 4 and 3 mm [K(+)](o) respectively. Endocardial APD(90)s correspondingly increased from 51.6 ± 1.9 ms (n = 7) to 62.8 ± 2.8 ms (n = 7) and 62.9 ± 5.9 ms (n = 11) giving reductions in endocardial–epicardial differences, ΔAPD(90), from 14.4 ± 2.6 to 4.4 ± 5.0 and −3.4 ± 6.0 ms respectively. Early afterdepolarizations (EADs) occurred in epicardia in three of seven spontaneously beating hearts at 4 mm [K(+)](o) with triggered beats followed by episodes of non-sustained VT in nine of 11 preparations at 3 mm. Programmed electrical stimulation never induced arrhythmic events in preparations perfused with normokalemic solutions yet induced VT in two of seven and nine of 11 preparations at 4 and 3 mm [K(+)](o) respectively. Early outward K(+) current correspondingly fell from 73.46 ± 8.45 to 61.16±6.14 pA/pF in isolated epicardial but not endocardial myocytes (n = 9) (3 mm [K(+)](o)). CONCLUSIONS: Hypokalaemic mouse hearts recapitulate the clinical arrhythmogenic phenotype, demonstrating EADs and triggered beats that might initiate VT on the one hand and reduced transmural dispersion of repolarization reflected in ΔAPD(90) suggesting arrhythmogenic substrate on the other

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

Separation of early afterdepolarizations from arrhythmogenic substrate in the isolated perfused hypokalaemic murine heart through modifiers of calcium homeostasis

In human type 1 diabetes (T1D) and in its murine model, the major histocompatibility complex (MHC) class II molecules, human leukocyte antigens (HLA)-DQ and -DR and their murine orthologues, IA and IE, are the major genetic determinants. In this report, we have ranked HLA class II molecule-associated T1D risk in a two-sided gradient from very high to very low. Very low risk corresponded to dominant protection from T1D. We predicted the protein structure of DQ by using the published crystal structures of different allotypes of the murine orthologue of DQ, IA. We discovered marked similarities both within, and cross species between T1D protective class II molecules. Likewise, the T1D predisposing molecules showed conserved similarities that contrasted with the shared patterns observed between the protective molecules. We also found striking inter-isotypic conservation between protective DQ, IA allotypes and protective DR4 subtypes. The data provide evidence for a joint action of the class II peptide-binding pockets P1, P4 and P9 in disease susceptibility and resistance with a main role for P9 in DQ/IA and for P1 and P4 in DR/IE. Overall, these results suggest shared epitope(s) in the target autoantigen(s), and common pathways in human and murine T1D

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

Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants

Chronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry

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

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

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these \u201chidden responders\u201d may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines