Pan-cancer proteogenomics characterization of tumor immunity.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

A proteogenomic portrait of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

ImmuneRegulation: a web-based tool for identifying human immune regulatory elements.

Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu.

An immunogenetic basis for lung cancer risk.

Cancer risk is influenced by inherited mutations, DNA replication errors, and environmental factors. However, the influence of genetic variation in immunosurveillance on cancer risk is not well understood. Leveraging population-level data from the UK Biobank and FinnGen, we show that heterozygosity at the human leukocyte antigen (HLA)-II loci is associated with reduced lung cancer risk in smokers. Fine-mapping implicated amino acid heterozygosity in the HLA-II peptide binding groove in reduced lung cancer risk, and single-cell analyses showed that smoking drives enrichment of proinflammatory lung macrophages and HLA-II+ epithelial cells. In lung cancer, widespread loss of HLA-II heterozygosity (LOH) favored loss of alleles with larger neopeptide repertoires. Thus, our findings nominate genetic variation in immunosurveillance as a critical risk factor for lung cancer.

Comparison of the hydration and diffusion of protons in perfluorosulfonic acid membranes with molecular dynamics simulations. scui@utk.edu.

Classical molecular dynamics (MD) simulations were performed to determine the hydrated morphology and hydronium ion diffusion coefficients in two different perfluorosulfonic acid (PFSA) membranes as functions of water content. The structural and transport properties of 1143 equivalent weight (EW) Nafion, with its relatively long perfluoroether side chains, are compared to the short-side-chain (SSC) PFSA ionomer at an EW of 977. The separation of the side chains was kept uniform in both ionomers consisting of -(CF 2) 15- units in the backbone, and the degree of hydration was varied from 5 to 20 weight % water. The MD simulations indicated that the distribution of water clusters is more dispersed in the SSC ionomer, which leads to a more connected water-channel network at the low water contents. This suggests that the SSC ionomer may be more inclined to form sample-spanning aqueous domains through which transport of water and protons may occur. The diffusion coefficients for both hydronium ions and water molecules were calculated at hydration levels of 4.4, 6.4, 9.6, and 12.8 H 2O/SO 3H for each ionomer. When compared to experimental proton diffusion coefficients, this suggests that as the water content is increased the contribution of proton hopping to the overall proton diffusion increases.

A molecular dynamics study of a nafion polyelectrolyte membrane and the aqueous phase structure for proton transport.

A molecular dynamics simulation study of hydrated Nafion at water contents ranging from 5 to 20 wt % was performed to examine the structure and dynamics of the hydrated polyelectrolyte system. The simulations show that the system forms segregated hydrophobic regions consisting primarily of the polymer backbone and hydrophilic regions with an inhomogeneous water distribution. We find that the water clustering strongly depends on the water content. At low water content, only isolated small water clusters are formed. As the water content increases, it becomes increasingly possible that a predominant majority of water molecules form a single cluster, suggesting that the hydrophilic regions become connected. We characterize the atomic structures formed within the system by various atomic pair correlation functions. The water structure factor shows a peak at q values corresponding to an intercluster distance about 2.5 nm and greater. With increasing water content, the distance moves to larger values, consistent with findings from scattering experiments. We find that the degree of solvation of hydronium ions by water molecules is a strong function of water content. At 5 wt %, a majority of the hydronium ions are hydrated by no more than two water molecules, prohibiting structural diffusion. As water content increases, the hydronium ions continue to become increasingly hydrated, resulting in structures capable of forming eigen ions, a necessary step in structural diffusion. Addressing the experimentally observed fact that conductivity in these membranes abruptly drops near 5 wt %, we find that both the local structure of the poorly hydrated hydronium ions and the disconnected nature of the global morphology of the water nanonetwork at low water content should contribute to poor conductivity.

A New Mixed All-Atom/Coarse-Grained Model: Application to Melittin Aggregation in Aqueous Solution.

We introduce a new mixed resolution, all-atom/coarse-grained approach (AACG), for modeling peptides in aqueous solution and apply it to characterizing the aggregation of melittin. All of the atoms in peptidic components are represented, while a single site is used for each water molecule. With the full flexibility of the peptide retained, our AACG method achieves speedups by a factor of 3-4 for CPU time reduction and another factor of roughly 7 for diffusion. An Ewald treatment permits the inclusion of long-range electrostatic interactions. These characteristics fit well with the requirements for studying peptide association and aggregation, where the system sizes and time scales require considerable computational resources with all-atom models. In particular, AACG is well suited for biologics since changes in peptide shape and long-range electrostatics may play an important role. The application of AACG to melittin, a 26-residue peptide with a well-known propensity to aggregate in solution, serves as an initial demonstration of this technology for studying peptide aggregation. We observed the formation of melittin aggregates during our simulations and characterized the time-evolution of aggregate size distribution, buried surface areas, and residue contacts. Key interactions including π-cation and π-stacking involving TRP19 were also examined. Our AACG simulations demonstrated a clear salt effect and a moderate temperature effect on aggregation and support the molten globule model of melittin aggregates. As a showcase, this work illustrates the useful role for AACG in investigations of peptide aggregation and its potential to guide formulation and design of biologics.

Toward a predictive understanding of water and charge transport in proton exchange membranes.

An analytical model for water and charge transport in highly acidic and highly confined systems such as proton exchange membranes of fuel cells is developed and compared to available experimental data. The model is based on observations from both experiment and multiscale simulation. The model accounts for three factors in the system including acidity, confinement, and connectivity. This model has its basis in the molecular-level mechanisms of water transport but has been coarse-grained to the extent that it can be expressed in an analytical form. The model uses the concentration of H(3)O(+) ion to characterize acidity, interfacial surface area per water molecule to characterize confinement, and percolation theory to describe connectivity. Several important results are presented. First, an integrated multiscale simulation approach including both molecular dynamics simulation and confined random walk theory is capable of quantitatively reproducing experimentally measured self-diffusivities of water in the perfluorinated sulfonic acid proton exchange membrane material, Nafion. The simulations, across a range of hydration conditions from minimally hydrated to fully saturated, have an average error for the self-diffusivity of water of 16% relative to experiment. Second, accounting for three factors-acidity, confinement, and connectivity-is necessary and sufficient to understand the self-diffusivity of water in proton exchange membranes. Third, an analytical model based on percolation theory is capable of quantitatively reproducing experimentally measured self-diffusivities of both water and charge in Nafion across a full range of hydration.

Applications of a general random-walk theory for confined diffusion.

A general random walk theory for diffusion in the presence of nanoscale confinement is developed and applied. The random-walk theory contains two parameters describing confinement: a cage size and a cage-to-cage hopping probability. The theory captures the correct nonlinear dependence of the mean square displacement (MSD) on observation time for intermediate times. Because of its simplicity, the theory also requires modest computational requirements and is thus able to simulate systems with very low diffusivities for sufficiently long time to reach the infinite-time-limit regime where the Einstein relation can be used to extract the self-diffusivity. The theory is applied to three practical cases in which the degree of order in confinement varies. The three systems include diffusion of (i) polyatomic molecules in metal organic frameworks, (ii) water in proton exchange membranes, and (iii) liquid and glassy iron. For all three cases, the comparison between theory and the results of molecular dynamics (MD) simulations indicates that the theory can describe the observed diffusion behavior with a small fraction of the computational expense. The confined-random-walk theory fit to the MSDs of very short MD simulations is capable of accurately reproducing the MSDs of much longer MD simulations. Furthermore, the values of the parameter for cage size correspond to the physical dimensions of the systems and the cage-to-cage hopping probability corresponds to the activation barrier for diffusion, indicating that the two parameters in the theory are not simply fitted values but correspond to real properties of the physical system.

A reactive molecular dynamics study of the thermal decomposition of perfluorodimethyl ether.

Classical reactive molecular dynamics (RMD) simulation is used to model the thermal decomposition of perfluorodimethyl ether (CF(3)OCF(3)), which is relevant as a simple molecule containing the necessary architectural elements to study the chemical stability of perfluoropolyether lubricants. The RMD algorithm employs nonreactive interaction potentials for the reactants and products. The reactivity is implemented through a coarse-grained simulation algorithm, incorporating elements from both the quantum and macroscopic descriptions of the reaction. The RMD scheme maps the quantum mechanically determined transition state onto a set of geometric triggers. When a configuration matching those triggers is found in the RMD simulation, the reaction instantaneously occurs. A brief, local equilibration process stabilizes the configuration, and the simulation continues. Using two geometric triggers, the RMD simulation can describe quantitatively the temperature dependence of the thermal decomposition of CF(3)OCF(3), when compared to the quantum mechanical standard.