Airway issues in anaplastic thyroid carcinoma

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Histidine‐functionalized diblock copolymer nanoparticles exhibit enhanced adsorption onto planar stainless steel

RAFT aqueous emulsion polymerization of isopropylideneglycerol monomethacrylate (IPGMA) is used to prepare a series of PGEO5MA46-PIPGMAy nanoparticles, where PGEO5MA is a hydrophilic methacrylic steric stabilizer block bearing pendent cis-diol groups. TEM studies confirm a spherical morphology while dynamic light scattering (DLS) analysis indicated that the z-average particle diameter can be adjusted by varying the target degree of polymerization for the core-forming PIPGMA block. Periodate oxidation is used to convert the cis-diol groups on PGEO5MA46-PIPGMA500 and PGEO5MA46-PIPGMA1000 nanoparticles into the analogous aldehyde-functionalized nanoparticles, which are then reacted with histidine via reductive amination. In each case, the extent of functionalization is more than 99% as determined by 1H NMR spectroscopy. Aqueous electrophoresis studies indicate that such derivatization converts initially neutral nanoparticles into zwitterionic nanoparticles with an isoelectric point at pH 7. DLS studies confirmed that such histidine-derivatized nanoparticles remain colloidally stable over a wide pH range. A quartz crystal microbalance is employed at 25°C to assess the adsorption of both the cis-diol- and histidine-functionalized nanoparticles onto planar stainless steel at pH 6. The histidine-bearing nanoparticles adsorb much more strongly than their cis-diol counterparts. For the highest adsorbed amount of 70.5 mg m–2, SEM indicates a fractional surface coverage of 0.23 for the adsorbed nanoparticles

Reverse sequence polymerization-induced self-assembly in aqueous media: a counter-intuitive approach to sterically-stabilized diblock copolymer nano-objects

Polymerization-induced self-assembly (PISA) is a powerful platform technology for the efficient synthesis of block copolymer nanoparticles in many types of solvents, including water. In PISA, a soluble precursor block is used to grow a second insoluble block, which leads to in situ self-assembly of the block copolymer chains. Thus, in the case of aqueous PISA, the water-soluble block is always prepared first because this confers steric stabilization. Herein, we challenge this paradigm by demonstrating that amphiphilic diblock copolymer chains can be prepared in water by preparing the hydrophobic block first via reversible addition–fragmentation chain transfer (RAFT) polymerization. This counter-intuitive reverse sequence PISA formulation utilizes an ionic RAFT agent to conduct the RAFT aqueous dispersion polymerization of 2-hydroxypropyl methacrylate (HPMA), which results in the formation of charge-stabilized PHPMA latex particles of ∼500 nm diameter. Initial attempts to chain-extend these hydrophobic PHPMA chains with water-miscible monomers such as glycerol monomethacrylate (GMA) were unsuccessful, with only uncontrolled free radical polymerization being observed in the aqueous phase. However, using a water-immiscible monomer such as isopropylideneglycerol methacrylate (IPGMA) enabled the synthesis of charge-stabilized PHPMA-PIPGMA latex particles. Subsequent acid hydrolysis of the PIPGMA block led to the in situ formation of sterically-stabilized PHPMA-PGMA diblock copolymer spheres. Alternatively, dissolution of the precursor PHPMA latex in a methanol/water binary mixture enables RAFT solution polymerization of water-miscible monomers such as GMA or N,N′-dimethylacrylamide (DMAC) to be achieved with good control. The resulting amphiphilic diblock copolymer chains then undergo self-assembly in aqueous solution after removal of the methanol co-solvent. Finally, this reverse sequence PISA protocol can also be applied to other vinyl monomers such as 2-methoxyethyl methacrylate (MOEMA) or diacetone acrylamide (DAAM), which significantly broadens its scope

Mucoadhesive pickering nanoemulsions via dynamic covalent chemistry

Hypothesis. Submicron oil droplets stabilized using aldehyde-functionalized nanoparticles should adhere to the primary amine groups present at the surface of sheep nasal mucosal tissue via Schiff base chemistry. Experiments. Well-defined sterically-stabilized diblock copolymer nanoparticles of 20 nm diameter were prepared in the form of concentrated aqueous dispersions via reversible addition-fragmentation chain transfer (RAFT) aqueous emulsion polymerization of 2,2,2-trifluoroethyl methacrylate (TFEMA) using a water-soluble methacrylic precursor bearing cis-diol groups. Some of these hydroxyl-functional nanoparticles were then selectively oxidized using an aqueous solution of sodium periodate to form a second batch of nanoparticles bearing pendent aldehyde groups within the steric stabilizer chains. Subjecting either hydroxyl- or aldehyde-functional nanoparticles to high-shear homogenization with a model oil (squalane) produced oil-in-water Pickering macroemulsions of 20–30 µm diameter. High-pressure microfluidization of such macroemulsions led to formation of the corresponding Pickering nanoemulsions with a mean droplet diameter of around 200 nm. Quartz crystal microbalance (QCM) experiments were used to examine adsorption of both nanoparticles and oil droplets onto a model planar substrate bearing primary amine groups, while a fluorescence microscopy-based mucoadhesion assay was developed to assess adsorption of the oil droplets onto sheep nasal mucosal tissue. Findings. Squalane droplets coated with aldehyde-functional nanoparticles adhered significantly more strongly to sheep nasal mucosal tissue than those coated with the corresponding hydroxyl-functional nanoparticles. This difference was attributed to the formation of surface imine bonds via Schiff base chemistry and was also observed for the two types of nanoparticles alone in QCM studies. Preliminary biocompatibility studies using planaria indicated only mild toxicity for these new mucoadhesive Pickering nanoemulsions, suggesting potential applications for the localized delivery of hydrophobic drugs

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

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

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

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