Centipede venoms as a source of drug leads

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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

Surface passivation with a perfluoroalkane brush improves the precision of single-molecule measurements

Single-molecule imaging is invaluable for investigating the heterogeneous behavior and interactions of biological molecules. However, an impediment to precise sampling of single molecules is the irreversible adsorption of components onto the surfaces of cover glasses. This causes continuous changes in the concentrations of different molecules dissolved or suspended in the aqueous phase from the moment a sample is dispensed, which will shift, over time, the position of chemical equilibria between monomeric and multimeric components. Interferometric scattering microscopy (iSCAT) is a technique in the single-molecule toolkit that has the capability to detect unlabeled proteins and protein complexes both as they adsorb onto and desorb from a glass surface. Here, we examine the reversible and irreversible interactions between a number of different proteins and glass via analysis of the adsorption and desorption of protein at the single-molecule level. Furthermore, we present a method for surface passivation that virtually eliminates irreversible adsorption while still ensuring the residence time of molecules on surfaces is sufficient for detection of adsorption by iSCAT. By grafting high-density perfluoroalkane brushes on cover-glass surfaces, we observe approximately equal numbers of adsorption and desorption events for proteins at the measurement surface (±1%). The fluorous–aqueous interface also prevents the kinetic trapping of protein complexes and assists in establishing a thermodynamic equilibrium between monomeric and multimeric components. This surface passivation approach is valuable for in vitro single-molecule experiments using iSCAT microscopy because it allows for continuous monitoring of adsorption and desorption of protein without either a decline in detection events or a change in sample composition due to the irreversible binding of protein to surfaces

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

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

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

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 tumorinfiltrating 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

A New Platform for Single Molecule Measurements Using the Fluorous Effect

Irreversible adsorption of biomolecules onto imaging substrates is an impediment to expand the applications of single molecule techniques. Traditional polyethylene glycol (PEG) surfaces are only effective at low concentrations of analytes and their structure prevents their use for interferometric scattering (iSCAT) microscopy. We propose a new platform that virtually eliminates non-specific binding thanks to the omniphobicity of perfluorinated compounds, also known as the fluorous effect. Here, we showcase the anti-fouling properties of these substrates at a single molecule level through iSCAT measurements of a protein mixture. We believe these novel engineered substrates show great promise to study biomachinery processes requiring large analyte concentrations, where other passivation methods are not effective, through iSCAT microscopy and other single molecule techniques

2′- 19 F labelling of ribose in RNAs: a tool to analyse RNA/protein interactions by NMR in physiological conditions

Protein-RNA interactions are central to numerous cellular processes. In this work, we present an easy and straightforward NMR-based approach to determine the RNA binding site of RNA binding proteins and to evaluate the binding of pairs of proteins to a single-stranded RNA (ssRNA) under physiological conditions, in this case in nuclear extracts. By incorporation of a 19F atom on the ribose of different nucleotides along the ssRNA sequence, we show that, upon addition of an RNA binding protein, the intensity of the 19F NMR signal changes when the 19F atom is located near the protein binding site. Furthermore, we show that the addition of pairs of proteins to a ssRNA containing two 19F atoms at two different locations informs on their concurrent binding or competition. We demonstrate that such studies can be done in a nuclear extract that mimics the physiological environment in which these protein-ssRNA interactions occur. Finally, we demonstrate that a trifluoromethoxy group (-OCF3) incorporated in the 2′ribose position of ssRNA sequences increases the sensitivity of the NMR signal, leading to decreased measurement times, and reduces the issue of RNA degradation in cellular extracts