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Background Observational studies have described racial differences in inflammatory bowel disease (IBD) genetics, clinical manifestations, and outcomes. Whether race impacts response to biologics in IBD is unclear. We conducted a post hoc analysis of phase 2 and 3 randomized clinical trials in ulcerative colitis to evaluate the effect of race on response to golimumab. Methods We analyzed pooled individual-level data from induction and maintenance trials of golimumab through the Yale Open Data Access Project. The primary outcome was clinical response. Secondary outcomes were clinical remission and endoscopic healing. Multivariable logistic regression was performed comparing White vs racial minority groups (Asian, Black, or other race), adjusting for potential confounders. Results There were 1006 participants in the induction (18% racial minority) and 783 participants in the maintenance (17% racial minority) trials. Compared with White participants, participants from racial minority groups had significantly lower clinical response (adjusted odds ratio [aOR], 0.43; 95% confidence interval [CI], 0.28-0.66), clinical remission (aOR, 0.41; 95% CI, 0.22-0.77), and endoscopic healing (aOR, 0.48; 95% CI, 0.31-0.74) at week 6. Participants from racial minority groups also had significantly lower clinical remission (aOR, 0.46; 95% CI, 0.28-0.74) and endoscopic healing (aOR, 0.63; 95% CI, 0.41-0.96) at week 30. There were no racial differences in placebo response rates. Conclusions Ulcerative colitis participants from racial minority groups were less likely to achieve clinical response, clinical remission, and endoscopic healing with golimumab compared with White participants in induction and maintenance trials. Further studies are needed to understand the impact of race on therapeutic response in IBD.

B cells, which are critical for intestinal homeostasis, remain understudied in ulcerative colitis (UC). In this study, we recruited three cohorts of patients with UC (primary cohort, n = 145; validation cohort 1, n = 664; and validation cohort 2, n = 143) to comprehensively define the landscape of B cells during UC-associated intestinal inflammation. Using single-cell RNA sequencing, single-cell IgH gene sequencing and protein-level validation, we mapped the compositional, transcriptional and clonotypic landscape of mucosal and circulating B cells. We found major perturbations within the mucosal B cell compartment, including an expansion of naive B cells and IgG(+) plasma cells with curtailed diversity and maturation. Furthermore, we isolated an auto-reactive plasma cell clone targeting integrin αvβ6 from inflamed UC intestines. We also identified a subset of intestinal CXCL13-expressing TFH-like T peripheral helper cells that were associated with the pathogenic B cell response. Finally, across all three cohorts, we confirmed that changes in intestinal humoral immunity are reflected in circulation by the expansion of gut-homing plasmablasts that correlates with disease activity and predicts disease complications. Our data demonstrate a highly dysregulated B cell response in UC and highlight a potential role of B cells in disease pathogenesis.

Targeting the α4β7-MAdCAM-1 axis with vedolizumab (VDZ) is a front-line therapeutic paradigm in ulcerative colitis (UC). However, mechanism(s) of action (MOA) of VDZ remain relatively undefined. Here, we examined three distinct cohorts of patients with UC (n=83, n=60, and n=21), to determine the effect of VDZ on the mucosal and peripheral immune system. Transcriptomic studies with protein level validation were used to study drug MOA using conventional and transgenic murine models. We found a significant decrease in colonic and ileal naïve B and T cells and circulating gut-homing plasmablasts (β7+) in VDZ-treated patients, pointing to gut-associated lymphoid tissue (GALT) targeting by VDZ. Murine Peyer’s patches (PP) demonstrated a significant loss cellularity associated with reduction in follicular B cells, including a unique population of epithelium-associated B cells, following anti-α4β7 antibody (mAb) administration. Photoconvertible (KikGR) mice unequivocally demonstrated impaired cellular entry into PPs in anti-α4β7 mAb treated mice. In VDZ-treated, but not anti-tumor necrosis factor-treated UC patients, lymphoid aggregate size was significantly reduced in treatment responders compared to non-responders, with an independent validation cohort further confirming these data. GALT targeting represents a novel MOA of α4β7-targeted therapies, with major implications for this therapeutic paradigm in UC, and for the development of new therapeutic strategies.

Tumor deconvolution is a reliable way to disentangle the diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA-seq) rather than protein levels in tumor cells. While gene expression is less expensive to measure, protein levels provide a more accurate view of immune markers. To facilitate the development as well as improve the reproducibility and reusability of multi-omic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic data sets. Decomprolute incorporates the large-scale multiomic data sets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. The platform consists of modular architecture and it comes with well-defined input and output formats at each module. As a result, it is robust and extendable easily with additional algorithms or analyses. The platform is available for access and use athttp://pnnl-compbio.github.io/decomprolute.MotivationTo provide a comprehensive platform for algorithm developers and researchers to benchmark and run tumor deconvolution algorithms on multiomic data.

Introduction The role of specific immune cell types within the tumor immune microenvironment in non-small cell lung cancer survival is unclear. The potential of these immune cells to become predictive biomarkers of prognosis, and to define subpopulations who will benefit of additional treatment is urgently needed. Methods Stage I to IIIA non-small cell lung cancer patients who underwent surgical resection were queried from the Cancer Genome Atlas; RNAseq data as well as clinical information was extracted. Sample-specific scores for different immune cells were computed via xCell. The association between each cell type and survival was assessed with Cox regression, both unadjusted and adjusted for sex, stage, smoking status, and tumor purity. Models were stratified by lung adenocarcinoma and lung squamous cell carcinoma. Results There were 383 lung adenocarcinoma and 328 lung squamous cell carcinoma samples, and 161 (42%) and 124 (38%) deaths respectively. There was no association between any immune cell infiltrations and survival in the combined unadjusted Cox regression model. After adjustment, the presence of CD8+ cytotoxic T cells (adjusted hazard ratio [HRajd]: 0.84; 95% confidence interval [CI]: 0.71-0.99; P=0.03), CD4+ helper T cells (HRajd: 0.79; 95% CI: 0.66-0.95; P=0.01) and CD20+ B cells (HRajd: 0.80; 95% CI: 0.66-0.97; P=0.02) were significant predictors of decreased risk of death. Conclusions This study shows that the adjustment for clinical characteristics is key when evaluating tumor immune infiltration and its association with cancer outcomes. Adjustment for confounding factors modified the prognostic significance of specific immune cell populations in early-stage surgically resected NSCLC cases; clinical attributes may have high relevance on immune infiltration composition.

Background Gender-based differences are reported in inflammatory bowel diseases (IBD) pathogenesis, but their impacts on IBD outcomes are not well known. We determined gender-based differences in response to treatment with tumor necrosis factor inhibitor (TNFi) therapies in individuals with ulcerative colitis (UC). Methods We used the Yale University Open Data Access (YODA) platform to abstract individual participant data from randomized clinical trials to study infliximab and golimumab as induction and maintenance therapies in moderately to severely active UC. Using multivariable logistic regression, we examined associations between gender and the endpoints of clinical remission, mucosal healing, and clinical response for each study individually and in a meta-analysis. Results Of 1639 patients included in induction trials (Program of Ulcerative Colitis Research Studies Utilizing an Investigational Treatment—Subcutaneous [PURSUIT-SC], active ulcerative colitis trials [ACT] 1 and 2) and 1280 patients included in maintenance trials (Program of Ulcerative Colitis Research Studies Utilizing an Investigational Treatment—Maintenance [PURSUIT-IM], ACT 1 and 2), 696 (42.5%) and 534 (41.7%) were women, respectively. In a meta-analysis of induction trials, the adjusted odds ratios (aORs) of clinical remission (aOR, 0.55; 95% CI, 0.31–0.97), mucosal healing (aOR, 0.47; 95% CI, 0.27–0.83), and clinical response (aOR, 0.51; 95% CI, 0.29–0.90) in the treatment arm and of clinical remission in the placebo arm (aOR, 0.34; 95% CI, 0.15–0.82) were lower in men compared to women. There were no differences in outcomes by gender in the treatment and placebo arms in the meta-analysis of maintenance trials. Conclusions Men are less likely to achieve clinical remission, mucosal healing, and clinical response compared to women during induction treatment with TNFi for UC, but not during the maintenance phase. Future studies delineating the mechanisms underlying these observations would be informative.

Background & Aims Biomarkers are needed to identify patients at risk for development of inflammatory bowel diseases. We aimed to identify serum biomarkers of Crohn's disease and ulcerative colitis that can be detected and quantified before diagnosis. Methods We obtained serum samples from patients archived before a diagnosis of Crohn's disease (n = 200) or ulcerative colitis (n = 199), as well as from 200 healthy individuals (controls), collected from 1998 through 2013 as part of the US Defense Medical Surveillance System. We measured levels of antibodies against microbes (anti–Saccharomyces cerevisiae IgA or IgG, anti–Escherichia coli outer membrane porin C, anti-CBir1, anti-flagellin 2, anti-flagellin X, and perinuclear anti-neutrophil cytoplasmic antibodies) and 1129 proteins in each sample. We then used functional principal component analysis to derive the time-varying trajectory for each marker, which then was used in a multivariate model to predict disease status. Predictive performances at different prediagnosis timepoints were evaluated using area under the receiver operating characteristic curves (AUROCs). Biological pathways that were up-regulated in serum from patients with Crohn's disease were identified based on changes in protein abundance at different time periods preceding diagnosis. Results We identified a panel of 51 protein biomarkers that were predictive of Crohn's disease within 5 years with an AUROC of 0.76 and a diagnosis within 1 year with an AUROC of 0.87. Based on the proteins included in the panel, imminent development of CD was associated with changes in the complement cascade, lysosomes, innate immune response, and glycosaminoglycan metabolism. Serum antibodies and proteins identified patients who received a diagnosis of ulcerative colitis within 5 years with an AUROC of only 0.56 and within 1 year with an AUROC of 0.72. Conclusions We identified a panel of serum antibodies and proteins that were predictive of patients who will receive a diagnosis of Crohn's disease within 5 years with high accuracy. By contrast we did not identify biomarkers associated with future diagnosis of ulcerative colitis.

Inflammatory bowel disease (IBD) is an immune-mediated disease of the intestinal tract, with complex pathophysiology involving genetic, environmental, microbiome, immunological and potentially other factors. Epidemiological data have provided important insights into risk factors associated with IBD, but are limited by confounding, biases and data quality, especially when pertaining to risk factors in early life. Multiomics platforms provide granular high-throughput data on numerous variables simultaneously and can be leveraged to characterize molecular pathways and risk factors for chronic diseases, such as IBD. Herein, we describe omics platforms that can advance our understanding of IBD risk factors and pathways, and available omics data on IBD and other relevant diseases. We highlight knowledge gaps and emphasize the importance of birth, at-risk and pre-diagnostic cohorts, and neonatal blood spots in omics analyses in IBD. Finally, we discuss network analysis, a powerful bioinformatics tool to assemble high-throughput data and derive clinical relevance.

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

// Stephanie Tuminello 1 , Rajwanth Veluswamy 1 , 2 , Wil Lieberman-Cribbin 1 , Sacha Gnjatic 2 , 3 , 4 , Francesca Petralia 5 , Pei Wang 5 , Raja Flores 6 and Emanuela Taioli 1 , 6 , 7 1 Institute for Translational Epidemiology and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA 2 Department of Medicine, Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA 3 Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA 4 Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA 5 Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA 6 Department of Thoracic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA 7 Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Correspondence to: Emanuela Taioli, email: Emanuela.taioli@mountsinai.org Keywords: NSCLC; immune contexture; tumor microenvironment; TILs Received: September 18, 2019 Accepted: December 05, 2019 Published: December 24, 2019 ABSTRACT Background: Early-stage non-small cell lung cancer (NSCLC) patients carry significant risk of recurrence post-surgery. In-depth characterization of the immune tumor microenvironment (TME) can have prognostic value. This study aimed to evaluate the association of individual immune cell types in the TME with clinical outcomes in surgically resected, early-stage NSCLC. Methods: We performed a systematic literature search of the National Library of Medicine database through November 2019, investigating predefined biomarkers (CD3+ T cells, CD4+ T helper cells, CD8+ cytotoxic T cells, CD20+ B cells, CD56+ & CD57+ Natural Killer (NK) cells, CD68+ Tissue Associated Macrophages (TAMS), FoxP3+ T regulatory cells, and Mast Cells (MC)), and their association with survival following PRISMA guidelines. Results: Studies that adjusted for important clinical covariates (such as stage and age) showed that higher levels of CD8+ cytotoxic T cells were associated with improved OS (HR = 0.68; 95% CI, 0.50–0.93) and DFS (HR = 0.60; 95% CI, 0.41–0.87), while increased CD20+ B cells (HR = 0.16; 95% CI, 0.04–0.64) and CD 56/57+ NK cells (HR = 0.50; 95% CI, 0.26–0.95) were associated with improved OS; lung cancers with increased FoxP3+ T regulatory cells (HR = 2.22; 95% CI, 1.47–3.34) had worse OS. Conclusions: Immune cell components of the TME have prognostic value in early-stage, surgically resected NSCLC, and may reveal which patients are more likely to need additional systemic treatment, including immunotherapy. Clinical covariates need to be considered when evaluating the prognostic value of immune cells in the TME.

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive phosphoproteomics and proteomics data for tumor and tumor-adjacent normal tissue across multiple cancer types. This dataset provides an unprecedented opportunity to systematically characterize pan-cancer kinase activities, which is essential for coupling tumor subtypes with kinase inhibitors as potential treatment. In this work, we performed Kinase Enrichment Analysis (KEA) using a CPTAC phosphoproteomics dataset to identify putative differences in kinase state between tumor and normal tissues within and across five types of cancer. We then implemented an interactive web-portal, the ProTrack Kinase Activity Portal (ProKAP), for querying, visualizing, and downloading the derived pan-cancer kinase activity scores together with the corresponding sample metadata, and protein and phosphoprotein expression profiles. To illustrate the usage of this digital resource, we analyzed the association between kinase activity scores and immune subtypes of clear cell renal cell carcinoma (ccRCC) derived from the CPTAC ccRCC study. We found multiple kinases, whose inhibition has been suggested to have therapeutic effect in other tumor types, are highly active in CD8+-enriched ccRCC tumors. The ProTrack Kinase Activity Portal (ProKAP) is available at: https://pancan-kea3.cptac-data-view.org.

Anti-granulocyte macrophage-colony stimulating factor autoantibodies (aGMAbs) are detected in patients with ileal Crohn's disease (CD). Their induction and mode of action during or before disease are not well understood. We aimed to investigate the underlying mechanisms associated with aGMAb induction, from functional orientation to recognized epitopes, for their impact on intestinal immune homeostasis and use as a predictive biomarker for complicated CD.We characterized using enzyme-linked immunosorbent assay naturally occurring aGMAbs in longitudinal serum samples from patients archived before the diagnosis of CD (n = 220) as well as from 400 healthy individuals (matched controls) as part of the US Defense Medical Surveillance System. We used biochemical, cellular, and transcriptional analysis to uncover a mechanism that governs the impaired immune balance in CD mucosa after diagnosis.Neutralizing aGMAbs were found to be specific for post-translational glycosylation on granulocyte macrophage-colony stimulating factor (GM-CSF), detectable years before diagnosis, and associated with complicated CD at presentation. Glycosylation of GM-CSF was altered in patients with CD, and aGMAb affected myeloid homeostasis and promoted group 1 innate lymphoid cells. Perturbations in immune homeostasis preceded the diagnosis in the serum of patients with CD presenting with aGMAb and were detectable in the noninflamed CD mucosa.Anti-GMAbs predict the diagnosis of complicated CD long before the diagnosis of disease, recognize uniquely glycosylated epitopes, and impair myeloid cell and innate lymphoid cell balance associated with altered intestinal immune homeostasis.

Characterizing the tumor microenvironment is crucial in order to improve responsiveness to immunotherapy and develop new therapeutic strategies. The fraction of different cell-types in the tumor microenvironment can be estimated based on transcriptomic profiling of bulk tumor data via deconvolution algorithms. One class of such algorithms, known as reference-based, rely on a reference signature containing gene expression data for various cell-types. The limitation of these methods is that such a signature is derived from the gene expression of pure cell-types, which might not be consistent with the transcriptomic profiling in solid tumors. On the other hand, reference-free methods usually require only a set of cell-specific markers to perform deconvolution; however, once the different components have been estimated from the data, their labeling can be problematic. To overcome these limitations, we propose BayesDeBulk - a new reference-free Bayesian method for bulk deconvolution based on gene expression data. Given a list of markers expressed in each cell-type (cell-specific markers), a repulsive prior is placed on the mean of gene expression in different cell-types to ensure that cell-specific markers are upregulated in a particular component. Contrary to existing reference-free methods, the labeling of different components is decided a priori through a repulsive prior. Furthermore, the advantage over reference-based algorithms is that the cell fractions as well as the gene expression of different cells are estimated from the data, simultaneously. Given its flexibility, BayesDeBulk can be utilized to perform bulk deconvolution beyond transcriptomic data, based on other data types such as proteomic profiles or the integration of both transcriptomic and proteomic profiles.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.

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.

Given that gastrointestinal (GI) symptoms are a prominent extrapulmonary manifestation of coronavirus disease 2019 (COVID-19), we investigated the impact of GI infection on disease pathogenesis in three large cohorts of patients in the United States and Europe. Unexpectedly, we observed that GI involvement was associated with a significant reduction in disease severity and mortality, with an accompanying reduction in key inflammatory proteins including IL-6, CXCL8, IL-17A and CCL28 in circulation. In a fourth cohort of COVID-19 patients in which GI biopsies were obtained, we identified severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) within small intestinal enterocytes for the first timein vivobut failed to obtain culturable virus. High dimensional analyses of GI tissues confirmed low levels of cellular inflammation in the GI lamina propria and an active downregulation of key inflammatory genes includingIFNG, CXCL8, CXCL2andIL1Bamong others. These data draw attention to organ-level heterogeneity in disease pathogenesis and highlight the role of the GI tract in attenuating SARS-CoV-2-associated inflammation with related mortality benefit.

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

SUMMARYImmunotherapy is becoming a mainstay in the treatment of NSCLC. While tumor mutational burden (TMB) has been shown to correlate with response to immunotherapy, little is known about the relation of the baseline immune response with the tumor genotype. Here, we profiled 35 early stage NSCLC lesions using multiscale single cell sequencing. Unsupervised clustering identified in a subset of patients a key cellular module consisting of PDCD1+ CXCL13+ activated T cells, IgG+ plasma cells, and SPP1+ macrophages, referred to as the lung cancer activation module (LCAMhi). Transcriptional data from two NSCLC cohorts confirmed a subset of patients with LCAMhi enrichment, which was independent of overall immune cell content. The LCAMhi module strongly correlated with TMB, expression of cancer testis antigens, and with TP53 mutations in smokers and non-smokers. These data establish LCAM as a key mode of immune cell activation associated with high tumor antigen load and driver mutations.

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/protein regulatory networks has subsequently been derived based on these proteogenomic profiles, which serve as useful tools to gain system-level understanding of the molecular regulatory factories underlying the 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, we introduce 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. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3,614 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.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we built a prototype user-friendly web application called “ProTrack” with the CPTAC clear cell renal cell carcinoma (ccRCC) data set (http://ccrcc.cptac-data-view.org). Here we describe the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data.Statement of SignificanceThe CPTAC initiative (https://proteomics.cancer.gov/) has generated multi-omics data for multiple cancer types to understand the proteogenomic aberrations of these malignancies. Collectively this effort has so far produced a large data resource for the research community, including high-throughput profiles for proteome, phosphoproteome, whole exome, whole genome, transcriptome, and DNA methylome. To make this valuable data-resource useful to the larger research community, there is a pressing need for development of user-friendly, readily accessible, and easily shared analytic and visualization tools for aligning multi-omics data and exploring alterations in key cancer genes, to drive and support new biological hypotheses. To bridge this gap, we have developed CPTAC ProTrack, an interactive web application which uses a multilayered, client-server architecture in order to deliver an interactive web experience to any user of a modern web-browser. This tool is intentionally designed accessible for researchers, biologists, and clinicians who are interested in multi-omic data without any need to code.

Even though cancer is driven by genomic alterations, the chain functions causing this disease are largely carried out by proteins. Proteins are also typically targeted in treatment. However, proteomes are harder and more expensive to measure than genomes and transcriptomes. Thus, it would be very valuable to accurately estimate protein levels using other omics data. To catalise developments of solutions to this problem, and to answer fundamental questions about transcriptional and translational control, we leveraged the power of crowdsourcing via a collaborative competition: The NCI-CPTAC DREAM Proteogenomics Challenge. The best performance for predicting protein and phosphorylation levels was achieved by an ensemble of models including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types and, for phosphorylation prediction, phosphosite proximity. Proteins from metabolic pathways were the best predicted, whereas complex proteins were the least well predicted. However, the performance even of the best performing model was modest, suggesting that the level for many proteins are strongly regulated through translational control and degradation. From the best-performing model, we identified common predictors, which are predictive of survival outcome. Our results shed light on the potential application of computational models to large scale proteogenomic characterization of cancer in order to better understand signaling dysregulation mechanisms in the disease.

Immunotherapy is a mainstay of non-small cell lung cancer (NSCLC) management. While tumor mutational burden (TMB) correlates with response to immunotherapy, little is known about the relationship between the baseline immune response and tumor genotype. Using single-cell RNA sequencing, we profiled 361,929 cells from 35 early-stage NSCLC lesions. We identified a cellular module consisting of PDCD1(+)CXCL13(+) activated T cells, IgG(+) plasma cells, and SPP1(+) macrophages, referred to as the lung cancer activation module (LCAM(hi)). We confirmed LCAM(hi) enrichment in multiple NSCLC cohorts, and paired CITE-seq established an antibody panel to identify LCAM(hi) lesions. LCAM presence was found to be independent of overall immune cell content and correlated with TMB, cancer testis antigens, and TP53 mutations. High baseline LCAM scores correlated with enhanced NSCLC response to immunotherapy even in patients with above median TMB, suggesting that immune cell composition, while correlated with TMB, may be a nonredundant biomarker of response to immunotherapy.

Background Exosomes and other extracellular vesicles (EVs) have emerged as an important mechanism of cell-to-cell communication. However, previous studies either did not fully resolve what genetic materials were shuttled by exosomes or only focused on a specific set of miRNAs and mRNAs. A more systematic method is required to identify the genetic materials that are potentially transferred during cell-to-cell communication through EVs in an unbiased manner. Results In this work, we present a novel next generation of sequencing (NGS) based approach to identify EV mediated mRNA exchanges between co-cultured adipocyte and macrophage cells. We performed molecular and genomic profiling and jointly considered data from RNA sequencing (RNA-seq) and genotyping to track the “sequence varying mRNAs” transferred between cells. We identified 8 mRNAs being transferred from macrophages to adipocytes and 21 mRNAs being transferred in the opposite direction. These mRNAs represented biological functions including extracellular matrix, cell adhesion, glycoprotein, and signal peptides. Conclusions Our study sheds new light on EV mediated RNA communications between adipocyte and macrophage cells, which may play a significant role in developing insulin resistance in diabetic patients. This work establishes a new method that is applicable to examining genetic material exchanges in many cellular systems and has the potential to be extended to in vivo studies as well. Electronic supplementary material The online version of this article (10.1186/s12864-017-4359-1) contains supplementary material, which is available to authorized users.

Background Observational studies suggest non-white patients with inflammatory bowel disease (IBD) have worse clinical outcomes.1 There are limited data on whether race impacts response to biologic therapy. We therefore aimed to evaluate the efficacy of the tumor necrosis factor (TNF) antagonist golimumab comparing white to non-white participants, using individual participant level data from phase 2/3 randomized clinical trials of TNF antagonist therapy in ulcerative colitis (UC). Methods We conducted a pooled analysis of individual-level data from the induction and maintenance trials of golimumab in UC accessible through Yale University Open Data Access Project (YODA). There were insufficient non-white participants in infliximab studies accessible through YODA, precluding meaningful analysis. We analyzed patients in the placebo and treatment arms separately. Our primary outcome was clinical response and secondary outcomes were clinical remission and endoscopic healing according to clinical trial definitions. We compared white and non-white (defined as Black, Asian, or Other race) participants using multivariable logistic regression a priori adjusting for age, sex, treatment group, baseline Mayo score, immunomodulator and corticosteroid use. Effect estimates were expressed as adjusted odds ratios (aOR) and 95% confidence intervals (95% CI). Results A total of 1,006 participants were included in the induction trial (PURSUIT-SC; 18% non-white) and 783 participants in the maintenance trial (PURSUIT-M; 17% non-white). Non-white participants had significantly lower odds of week 6 clinical response (aOR 0.43, 95%CI 0.28–0.66), clinical remission (aOR 0.41, 95%CI 0.22–0.77) and endoscopic remission (aOR 0.48, 95%CI 0.30–0.74) compared to white participants (Figure). Non-white participants also had a lower adjusted odds of week 30 clinical response (aOR 0.64, 95%CI 0.40–1.01), clinical remission (aOR 0.45, 95%CI 0.28–0.74), and endoscopic remission (aOR 0.62, 95%CI 0.41–0.96). By week 54, the lower odds of these outcomes among non-whites were no longer statistically significant (Figure). Conclusion Non-white UC patients were less likely to achieve clinical response, clinical remission, and endoscopic healing with golimumab compared to white patients in these clinical trials. Further studies are needed to understand these differences and whether they are observed with other drugs or outside the context of clinical trials. Reference

Background There are increasing data on sex-based differences in IBD epidemiology, phenotype and outcomes. The purpose of this study was to characterize sex-based differences in response to tumor necrosis factor inhibitor (TNFi) therapies in patients with ulcerative colitis (UC). Methods We conducted a pooled analysis of individual-level data from randomized clinical trials (RCTs) on induction therapy with infliximab and golimumab, accessed through the Yale University Open Data Access platform. We analyzed patients in the treatment and placebo arms separately. Using multivariable logistic regression modeling, we compared the primary outcome of clinical remission, and secondary outcomes of clinical response and mucosal healing, according to clinical trial definitions, between male and female patients with UC at week 6 (golimumab) or week 8 (infliximab) of induction therapy. Effect estimates were expressed as adjusted odds ratios (aOR) and 95% confidence intervals (CI). Results We included 1639 patients (696, 42.5% were women, Table) from two trials using infliximab (ACT-1 and 2) and one trial using golimumab (PURSUIT). Male patients were older than female patients in the treatment arm of the overall cohort [mean age (standard deviation) 31.31 (16.53) years and 29.34 (15.19) years, respectively). All other baseline characteristics, such as sex, race, Mayo score at screening, and concomitant systemic corticosteroid and immunomodulator use were comparable. Compared to female patients, male patients were significantly less likely to achieve clinical remission in both treatment and placebo arms (aOR 0.55, 95% CI 0.31, 0.97 and 0.34, 95% CI 0.15, 0.82, respectively, Figure). Compared to female patients, male patients were significantly less likely to achieve mucosal healing and clinical response in the treatment arm (aOR 0.47, 95% CI 0.27, 0.83 and 0.51, 95% CI 0.29, 0.90, respectively), but there was no significant difference in these secondary outcomes by sex in the placebo arm. Conclusion Based on this analysis of pooled data from RCTs, we demonstrate that clinical remission, response and mucosal healing with TNFi induction therapies are less likely in male patients compared to female patients with UC. Clinical remission is also less likely in male patients on placebo. These finding may lead to further research on sex-based differences in treatment outcomes, including mechanistic studies.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem cell-like features. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation. We used machine learning approaches to extract epigenetic, transcriptomic, and proteomic features from human stem cells and applied the computed result to index the Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumor samples by their proteogenomic hallmarks of stemness. Leveraging the resource generated by Human Induced Pluripotent Stem Cells Consortium (HipSci), we extracted stemness signatures from the coherent epigenetic, transcriptomic, and proteomic datasets, using one-class logistic regression machine learning algorithm. The newly obtained stemness scores based on the DNA methylation and gene expression are significantly more robust compared to our previous published work based on the dataset obtained from a smaller number of samples with only genomics data. The stemness score computed by machine learning algorithms using proteomic datasets is novel and original. The obtained proteomic score is able to classify stem cells and non-stem cell classes. Indexing of CPTAC tumors with proteomic stemness score brought us with previously unappreciated findings. We have used the stemness scores computed using gene expression, DNA methylation, and proteins and their modifications to interrogate the coherent proteogenomic CPTAC datasets. The initial analysis of over 2000 tumor samples obtained from twelve types of primary carcinomas of breast, ovary, lung, kidney, uterus, brain (pediatric and adult), head and neck, liver, stomach, colon, and pancreas has confirmed our previously published results. Importantly, our original proteomic-based score brought the analyses to a novel dimension, far beyond previously described results. The initial findings of our work identified proteins and phospho-proteins as active nodes of signaling pathways and transcriptional networks that drive aggressiveness of the primary tumors that cause resistance to existing therapies. Our results indicate that cancer stemness is associated with the engagement of developmental pathways shaping tumor plasticity. Progressive oncogenic de-differentiation of cancer cells impacts tumor microenvironment and is associated with “cold” tumors thus impairing anti-tumor immune response and limiting the efficacy of immunotherapies. Hallmarks of cancer stemness correlate with increased tumor pathology grade and clinical stage and results in worse survival of the cancer patients across analyzed tumor types. Targeting identified proteins and cellular mechanisms that drive lethal phenotype of de-differentiated tumors with existing or novel drugs may pave the way for clinical development of effective cures for cancer patients. Citation Format: Maciej Wiznerowicz, Antono Colaprico, Erik Storrs, Francesca Petralia, Iga Kołodziejczak, Felipe da Veiga Leprevost, Weiping Ma, Daniel Cui Zhou, Bo Wen, Alexander Lazar, Pietro Pugliese, Michele Ceccarelli, Bożena Kamińska, Jan Lubiński, Alexey Nesvizhskii, Bing Zhang, Henry Rodriguez, Ana L. Robles, Mehdi Mesri. Pan-cancer stemness defined by CPTAC proteogenomics guides personalized therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3113.

Glioblastoma (GBM) is the most aggressive nervous system cancer, with median survival under 2 years. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. We performed an integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs. We identified key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation as well as potential targets for EGFR-, TP53- and RB1-altered tumors. We detected immune subtypes, driven by the presence of distinct immune cell populations using bulk omics, validated by snRNA-seq, and they were correlated with specific expression and histone acetylation patterns. Acetylation of histone H2B in classical-like and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identified specific lipid distributions across subtypes and distinct global metabolic changes in IDH mutated tumors. By comparing the adult GBM proteomics to the adolescent and young adult (AYA) GBM cohort from the HOPE study, we found downregulated IDH1 expression and up-regulated expression of genes in the NADH dehydrogenase family, including NDUFB1, and NDUFB3 among others, which may be related to high IDH1 mutation frequency in AYA. This work highlights biological relationships which could potentially aid GBM patient stratifications for more effective treatments. Citation Format: Liang-Bo Wang, Alla Karpova, Marina A. Gritsenko, Jennifer E. Kyle, Song Cao, Yize Li, Dmitry Rykunov, Antonio Colaprico, Joseph Rothstein, Runyu Hong, Vasileios Stathias, MacIntosh Cornwell, Francesca Petralia, Richard D. Smith, Antonio Iavarone, Milan G. Chheda, Jill S. Barnholtz-Sloan, Karin D. Rodland, Tao Liu, Li Ding, Clinical Proteomic Tumor Analysis Consortium. Proteogenomic and metabolomic characterization of human glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2170.

Background & Aims Given that gastrointestinal (GI) symptoms are a prominent extrapulmonary manifestation of COVID-19, we investigated intestinal infection with SARS-CoV-2, its effect on pathogenesis, and clinical significance. Methods Human intestinal biopsy tissues were obtained from patients with COVID-19 (n = 19) and uninfected control individuals (n = 10) for microscopic examination, cytometry by time of flight analyses, and RNA sequencing. Additionally, disease severity and mortality were examined in patients with and without GI symptoms in 2 large, independent cohorts of hospitalized patients in the United States (N = 634) and Europe (N = 287) using multivariate logistic regressions. Results COVID-19 case patients and control individuals in the biopsy cohort were comparable for age, sex, rates of hospitalization, and relevant comorbid conditions. SARS-CoV-2 was detected in small intestinal epithelial cells by immunofluorescence staining or electron microscopy in 15 of 17 patients studied. High-dimensional analyses of GI tissues showed low levels of inflammation, including down-regulation of key inflammatory genes including IFNG, CXCL8, CXCL2, and IL1B and reduced frequencies of proinflammatory dendritic cells compared with control individuals. Consistent with these findings, we found a significant reduction in disease severity and mortality in patients presenting with GI symptoms that was independent of sex, age, and comorbid illnesses and despite similar nasopharyngeal SARS-CoV-2 viral loads. Furthermore, there was reduced levels of key inflammatory proteins in circulation in patients with GI symptoms. Conclusions These data highlight the absence of a proinflammatory response in the GI tract despite detection of SARS-CoV-2. In parallel, reduced mortality in patients with COVID-19 presenting with GI symptoms was observed. A potential role of the GI tract in attenuating SARS-CoV-2–associated inflammation needs to be further examined.

Motivation Integrative approaches characterizing the interactions among different types of biological molecules have been demonstrated to be useful for revealing informative biological mechanisms. One such example is the interaction between microRNA (miRNA) and messenger RNA (mRNA), whose deregulation may be sensitive to environmental insult leading to altered phenotypes. The goal of this work is to develop an effective data integration method to characterize deregulation between miRNA and mRNA due to environmental toxicant exposures. We will use data from an animal experiment designed to investigate the effect of low-dose environmental chemical exposure on normal mammary gland development in rats to motivate and evaluate the proposed method. Results We propose a new network approach—integrative Joint Random Forest (iJRF), which characterizes the regulatory system between miRNAs and mRNAs using a network model. iJRF is designed to work under the high-dimension low-sample-size regime, and can borrow information across different treatment conditions to achieve more accurate network inference. It also effectively takes into account prior information of miRNA–mRNA regulatory relationships from existing databases. When iJRF is applied to the data from the environmental chemical exposure study, we detected a few important miRNAs that regulated a large number of mRNAs in the control group but not in the exposed groups, suggesting the disruption of miRNA activity due to chemical exposure. Effects of chemical exposure on two affected miRNAs were further validated using breast cancer human cell lines. Availability and implementation R package iJRF is available at CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND & AIMS: We investigated the effects of inducing deep remission in patients with early Crohn's disease (CD). METHODS: We collected follow-up data from 122 patients (mean age, 31.2 ± 11.3 y) with early, moderate to severe CD (median duration, 0.2 years; interquartile range, 0.1-0.5) who participated in the Effect of Tight Control Management on CD (CALM) study, at 31 sites, representing 50% of the original CALM patient population. Fifty percent of patients (n = 61) were randomly assigned to a tight control strategy (increased therapy based on fecal level of calprotectin, serum level of C-reactive protein, and symptoms), and 50% were assigned to conventional management. We categorized patients as those who were vs were not in deep remission (CD endoscopic index of severity scores below 4, with no deep ulcerations or steroid treatment, for 8 or more weeks) at the end of the follow-up period (median, 3.02 years; range, 0.05-6.26 years). The primary outcome was a composite of major adverse outcomes that indicate CD progression during the follow-up period: new internal fistulas or abscesses, strictures, perianal fistulas or abscesses, or hospitalization or surgery for CD. Kaplan-Meier and penalized Cox regression with bootstrapping were used to compare composite rates between patients who achieved or did not achieve remission at the end of the follow-up period. RESULTS: Major adverse outcomes were reported for 34 patients (27.9%) during the follow-up period. Significantly fewer patients in deep remission at the end of the CALM study had major adverse outcomes during the follow-up period (P = .01). When we adjusted for potential confounders, deep remission (adjusted hazard ratio, 0.19; 95% confidence interval, 0.07-0.31) was significantly associated with a lower risk of major adverse outcome. CONCLUSIONS: In an analysis of follow-up data from the CALM study, we associated induction of deep remission in early, moderate to severe CD with decreased risk of disease progression over a median time of 3 years, regardless of tight control or conventional management strategy. ispartof: GASTROENTEROLOGY vol:159 issue:1 pages:139-147 ispartof: location:United States status: published

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

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.

We performed a comprehensive proteogenomic analysis across seven childhood brain tumors for a deeper understanding of their functional biology. Whole genome sequencing, RNAseq, quantitative proteomic profiling and phosphoproteomics were performed on 219 fresh frozen tumor samples representing the histologic diagnoses of: low grade astrocytoma (93), ependymoma (32), high grade astrocytoma (26), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16) and atypical teratoid rhabdoid tumor (12). Unsupervised clustering analysis based on proteomics data reveals eight clusters with distinct protein profiles and pathway activities. While some clusters coincide with histologic diagnoses, a couple of clusters appear to be a mixture of different diagnoses, including one cluster consisting of “aggressive” tumors characterized by poor survival and high stemness scores. By integrating proteomic data with RNAseq and WGS data, we characterize the impact of mutations (H3K27M, BRAFV600E, BRAF fusion) and CNVs upon the proteome across various diagnoses. Multiomics based kinase-substrate association analysis and co-expression network analysis reveal targetable active kinase networks within these tumors. Proteomic data reveals unique biology associated with H3K27M mutation status in HGG and BRAF aberrations in LGG. Characterization of the tumor microenvironment through deconvolution analyses based on multi-omics data reveals 5 distinct tumor clusters associated with different populations of infiltrating immune cells and the relative activity of the immune system based upon the expression of pro-inflammation or immunosuppressive markers. This study reports the first large-scale deep comprehensive proteogenomic analysis crossing traditional histologic boundaries to uncover foundational pediatric brain tumor biology including functional insight that helps drive translational efforts.

Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. Here we describe quantitative mass-spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers, of which 77 provided high-quality data. Integrated analyses provided insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. Interrogation of the 5q trans-effects against the Library of Integrated Network-based Cellular Signatures, connected loss of CETN3 and SKP1 to elevated expression of epidermal growth factor receptor (EGFR), and SKP1 loss also to increased SRC tyrosine kinase. Global proteomic data confirmed a stromal-enriched group of proteins in addition to basal and luminal clusters, and pathway analysis of the phosphoproteome identified a G-protein-coupled receptor cluster that was not readily identified at the mRNA level. In addition to ERBB2, other amplicon-associated highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We demonstrate that proteogenomic analysis of breast cancer elucidates the functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets.

We focus on characterizing common and different coexpression patterns among RNAs and proteins in breast cancer tumors. To address this problem, we introduce Joint Random Forest (JRF), a novel nonparametric algorithm to simultaneously estimate multiple coexpression networks by effectively borrowing information across protein and gene expression data. The performance of JRF was evaluated through extensive simulation studies using different network topologies and data distribution functions. Advantages of JRF over other algorithms that estimate class-specific networks separately were observed across all simulation settings. JRF also outperformed a competing method based on Gaussian graphic models. We then applied JRF to simultaneously construct gene and protein coexpression networks based on protein and RNAseq data from CPTAC-TCGA breast cancer study. We identified interesting common and differential coexpression patterns among genes and proteins. This information can help to cast light on the potential disease mechanisms of breast cancer.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

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.

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.