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 unique serum IgG glycosylation signature predicts development of Crohn's disease and is associated with pathogenic antibodies to mannose glycan.

Inflammatory bowel disease (IBD) is characterized by chronic inflammation in the gut. There is growing evidence in Crohn's disease (CD) of the existence of a preclinical period characterized by immunological changes preceding symptom onset that starts years before diagnosis. Gaining insight into this preclinical phase will allow disease prediction and prevention. Analysis of preclinical serum samples, up to 6 years before IBD diagnosis (from the PREDICTS cohort), revealed the identification of a unique glycosylation signature on circulating antibodies (IgGs) characterized by lower galactosylation levels of the IgG fragment crystallizable (Fc) domain that remained stable until disease diagnosis. This specific IgG2 Fc glycan trait correlated with increased levels of antimicrobial antibodies, specifically anti-Saccharomyces cerevisiae (ASCA), pinpointing a glycome-ASCA hub detected in serum that predates by years the development of CD. Mechanistically, we demonstrated that this agalactosylated glycoform of ASCA IgG, detected in the preclinical phase, elicits a proinflammatory immune pathway through the activation and reprogramming of innate immune cells, such as dendritic cells and natural killer cells, via an FcγR-dependent mechanism, triggering NF-κB and CARD9 signaling and leading to inflammasome activation. This proinflammatory role of ASCA was demonstrated to be dependent on mannose glycan recognition and galactosylation levels in the IgG Fc domain. The pathogenic properties of (anti-mannose) ASCA IgG were validated in vivo. Adoptive transfer of antibodies to mannan (ASCA) to recipient wild-type mice resulted in increased susceptibility to intestinal inflammation that was recovered in recipient FcγR-deficient mice. Here we identify a glycosylation signature in circulating IgGs that precedes CD onset and pinpoint a specific glycome-ASCA pathway as a central player in the initiation of inflammation many years before CD diagnosis. This pathogenic glyco-hub may constitute a promising new serum biomarker for CD prediction and a potential target for disease prevention.

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.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma.

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

Immunology of COVID-19: Current State of the Science.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people worldwide, igniting an unprecedented effort from the scientific community to understand the biological underpinning of COVID19 pathophysiology. In this Review, we summarize the current state of knowledge of innate and adaptive immune responses elicited by SARS-CoV-2 infection and the immunological pathways that likely contribute to disease severity and death. We also discuss the rationale and clinical outcome of current therapeutic strategies as well as prospective clinical trials to prevent or treat SARS-CoV-2 infection.

Whole-brain resting-state connectivity underlying impaired inhibitory control during early versus longer-term abstinence in cocaine addiction.

Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD.

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.

ProNetView-ccRCC: A Web-Based Portal to Interactively Explore Clear Cell Renal Cell Carcinoma Proteogenomics Networks.

To better understand the molecular basis of cancer, the NCI's Clinical Proteomics Tumor Analysis Consortium (CPTAC) has been performing comprehensive large-scale proteogenomic characterizations of multiple cancer types. Gene and protein regulatory networks are subsequently being derived based on these proteogenomic profiles, which serve as tools to gain systems-level understanding of the molecular regulatory factories underlying these diseases. On the other hand, it remains a challenge to effectively visualize and navigate the resulting network models, which capture higher order structures in the proteogenomic profiles. There is a pressing need to have a new open community resource tool for intuitive visual exploration, interpretation, and communication of these gene/protein regulatory networks by the cancer research community. In this work, ProNetView-ccRCC (http://ccrcc.cptac-network-view.org/), an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data is introduced. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade.

High-depth whole genome sequencing of an Ashkenazi Jewish reference panel: enhancing sensitivity, accuracy, and imputation.

While increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. Here, we sequenced at full-depth (≥ 30×), across two platforms (Illumina X Ten and Complete Genomics, Inc.), a moderately large (n = 738) cohort of samples drawn from the Ashkenazi Jewish population. We developed a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population. Quality control (QC) thresholds for the Illumina X Ten platform were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. QC procedures also identified numerous regions that are poorly mapped using current reference or alternate assemblies. After stringent QC, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels, especially in the range of rare variants that may be most critical to further progress in mapping of complex phenotypes. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.

A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

PhosNetVis: a web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data.

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/.

Clonal Hematopoiesis of Indeterminate Potential in Crohn's Disease and Ulcerative Colitis.

Clonal hematopoiesis of indeterminate potential (CHIP) is the presence of somatic mutations in myeloid and lymphoid malignancy genes in the blood cells of individuals without a hematologic malignancy. Inflammation is hypothesized to be a key mediator in the progression of CHIP to hematologic malignancy and patients with CHIP have a high prevalence of inflammatory diseases. This study aimed to identify the prevalence and characteristics of CHIP in patients with inflammatory bowel disease (IBD). We analyzed whole exome sequencing data from 587 Crohn's disease (CD), 441 ulcerative colitis (UC), and 293 non-IBD controls to assess CHIP prevalence and used logistic regression to study associations with clinical outcomes. Older UC patients (age>45) harbored increased myeloid-CHIP mutations compared to younger patients (age≤45) ( p= 0.01). Lymphoid-CHIP was more prevalent in older IBD patients ( p =0.007). Young CD patients were found to have myeloid-CHIP with high-risk features. IBD patients with CHIP exhibited unique mutational profiles compared to controls. Steroid use was associated with increased CHIP ( p= 0.05), while anti-TNF therapy was associated with decreased myeloid-CHIP ( p= 0.03). Pathway enrichment analyses indicated overlap between CHIP genes, IBD phenotypes, and inflammatory pathways. Our findings underscore a connection between IBD and CHIP pathophysiology. Patients with IBD and CHIP had unique risk profiles especially among older UC patients and younger CD patients. These findings suggest distinct evolutionary pathways for CHIP in IBD and necessitate awareness among IBD providers and hematologists to identify patients potentially at risk for CHIP-related complications including malignancy, cardiovascular disease and acceleration of their inflammatory disease.

Designer high-density lipoprotein particles enhance endothelial barrier function and suppress inflammation.

High-density lipoprotein (HDL) nanoparticles promote endothelial cell (EC) function and suppress inflammation, but their utility in treating EC dysfunction has not been fully explored. Here, we describe a fusion protein named ApoA1-ApoM (A1M) consisting of apolipoprotein A1 (ApoA1), the principal structural protein of HDL that forms lipid nanoparticles, and ApoM, a chaperone for the bioactive lipid sphingosine 1-phosphate (S1P). A1M forms HDL-like particles, binds to S1P, and is signaling competent. Molecular dynamics simulations showed that the S1P-bound ApoM moiety in A1M efficiently activated EC surface receptors. Treatment of human umbilical vein ECs with A1M-S1P stimulated barrier function either alone or cooperatively with other barrier-enhancing molecules, including the stable prostacyclin analog iloprost, and suppressed cytokine-induced inflammation. A1M-S1P injection into mice during sterile inflammation suppressed neutrophil influx and inflammatory mediator secretion. Moreover, systemic A1M administration led to a sustained increase in circulating HDL-bound S1P and suppressed inflammation in a murine model of LPS-induced endotoxemia. We propose that A1M administration may enhance vascular endothelial barrier function, suppress cytokine storm, and promote resilience of the vascular endothelium.

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.

Germline rare deleterious variant load alters cancer risk, age of onset and tumor characteristics.

Recent studies show that rare, deleterious variants (RDVs) in certain genes are critical determinants of heritable cancer risk. To more comprehensively understand RDVs, we performed the largest-to-date germline variant calling analysis in a case-control setting for a multi-cancer association study from whole-exome sequencing data of 20,789 participants, split into discovery and validation cohorts. We confirm and extend known associations between cancer risk and germline RDVs in specific gene-sets, including DNA repair (OR = 1.50; p-value = 8.30e-07; 95% CI: 1.28-1.77), cancer predisposition (OR = 1.51; p-value = 4.58e-08; 95% CI: 1.30-1.75), and somatic cancer drivers (OR = 1.46; p-value = 4.04e-06; 95% CI: 1.24-1.72). Furthermore, personal RDV load in these gene-sets associated with increased risk, younger age of onset, increased M1 macrophages in tumor and, increased tumor mutational burden in specific cancers. Our findings can be used towards identifying high-risk individuals, who can then benefit from increased surveillance, earlier screening, and treatments that exploit their tumor characteristics, improving prognosis.

SCLC: Epidemiology, Risk Factors, Genetic Susceptibility, Molecular Pathology, Screening, and Early Detection.

We review research regarding the epidemiology, risk factors, genetic susceptibility, molecular pathology, and early detection of SCLC, a deadly tumor that accounts for 14% of lung cancers. We first summarize the changing incidences of SCLC globally and in the United States among males and females. We then review the established risk factor (i.e., tobacco smoking) and suspected nonsmoking-related risk factors for SCLC, and emphasize the importance of continued effort in tobacco control worldwide. Review of genetic susceptibility and molecular pathology suggests different molecular pathways in SCLC development compared with other types of lung cancer. Last, we comment on the limited utility of low-dose computed tomography screening in SCLC and on several promising blood-based molecular biomarkers as potential tools in SCLC early detection.

Sustained CD28 costimulation is required for self-renewal and differentiation of TCF-1+ PD-1+ CD8 T cells.

During persistent antigen stimulation, such as in chronic infections and cancer, CD8 T cells differentiate into a hypofunctional programmed death protein 1-positive (PD-1+) exhausted state. Exhausted CD8 T cell responses are maintained by precursors (Tpex) that express the transcription factor T cell factor 1 (TCF-1) and high levels of the costimulatory molecule CD28. Here, we demonstrate that sustained CD28 costimulation is required for maintenance of antiviral T cells during chronic infection. Low-level CD28 engagement preserved mitochondrial fitness and self-renewal of Tpex, whereas stronger CD28 signaling enhanced glycolysis and promoted Tpex differentiation into TCF-1neg exhausted CD8 T cells (Tex). Furthermore, enhanced differentiation by CD28 engagement did not reduce the Tpex pool. Together, these findings demonstrate that continuous CD28 engagement is needed to sustain PD-1+ CD8 T cells and suggest that increasing CD28 signaling promotes Tpex differentiation into more functional effector-like Tex, possibly without compromising long-term responses.