Mutations in cancer-relevant genes are ubiquitous in histologically normal endometrial tissue.

OBJECTIVE: Endometrial cancer (EndoCA) is the most common gynecologic cancer and incidence and mortality rate continue to increase. Despite well-characterized knowledge of EndoCA-defining mutations, no effective diagnostic or screening tests exist. To lay the foundation for testing development, our study focused on defining the prevalence of somatic mutations present in non-cancerous uterine tissue. METHODS: We obtained ≥8 uterine samplings, including separate endometrial and myometrial layers, from each of 22 women undergoing hysterectomy for non-cancer conditions. We ultra-deep sequenced (>2000× coverage) samples using a 125 cancer-relevant gene panel. RESULTS: All women harbored complex mutation patterns. In total, 308 somatic mutations were identified with mutant allele frequencies ranging up to 96.0%. These encompassed 56 unique mutations from 24 genes. The majority of samples possessed predicted functional cancer mutations but curiously no growth advantage over non-functional mutations was detected. Functional mutations were enriched with increasing patient age (p = 0.045) and BMI (p = 0.0007) and in endometrial versus myometrial layers (68% vs 39%, p = 0.0002). Finally, while the somatic mutation landscape shared similar mutation prevalence in key TCGA-defined EndoCA genes, notably PIK3CA, significant differences were identified, including NOTCH1 (77% vs 10%), PTEN (9% vs 61%), TP53 (0% vs 37%) and CTNNB1 (0% vs 26%). CONCLUSIONS: An important caveat for future liquid biopsy/DNA-based cancer diagnostics is the repertoire of shared and distinct mutation profiles between histologically unremarkable and EndoCA tissues. The lack of selection pressure between functional and non-functional mutations in histologically unremarkable uterine tissue may offer a glimpse into an unrecognized EndoCA protective mechanism.

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.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Proteogenomic insights suggest druggable pathways in endometrial carcinoma.

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of ß-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

The most likely but largely ignored triggering factor for breast (or all) cancer invasion.

Breast cancer development and progression are believed to be a sequential process, from normal to hyperplastic, to in situ, and to invasive and metastatic stages. Given that over 90% of cancer deaths are caused by invasive and metastatic lesions, countless factors and multiple theories have been proposed as the triggering factor for the cascade of actions of cancer invasion. However, those factors and theories are largely based on the studies of cell lines or animal models. In addition, corresponding interventions based on these factors and theories have failed to reduce the incidence rate of invasive and metastatic lesions, suggesting that previous efforts may have failed to arm at the right target. Considering these facts and observations, we are proposing "A focal aberrant degeneration in the myoepithelial cell layer (MECL) as the most likely triggering factor for breast cancer invasion". Our hypothesis is based on our recent studies of breast and multiple other cancers. Our commentary provides the rationale, morphologic, immunohistochemical, and molecular data to support our hypotheses. As all epithelium-derived cancers share a very similar architecture, our hypothesis is likely to be applicable to invasion of all cancer types. We believe that human tissue-derived data may provide a more realistic roadmap to guide the clinic practice.

Reprogrammed Schwann Cells Organize into Dynamic Tracks that Promote Pancreatic Cancer Invasion.

Nerves are a component of the tumor microenvironment contributing to cancer progression, but the role of cells from nerves in facilitating cancer invasion remains poorly understood. Here we show that Schwann cells (SC) activated by cancer cells collectively function as tumor-activated Schwann cell tracks (TAST) that promote cancer cell migration and invasion. Nonmyelinating SCs form TASTs and have cell gene expression signatures that correlate with diminished survival in patients with pancreatic ductal adenocarcinoma. In TASTs, dynamic SCs form tracks that serve as cancer pathways and apply forces on cancer cells to enhance cancer motility. These SCs are activated by c-Jun, analogous to their reprogramming during nerve repair. This study reveals a mechanism of cancer cell invasion that co-opts a wound repair process and exploits the ability of SCs to collectively organize into tracks. These findings establish a novel paradigm of how cancer cells spread and reveal therapeutic opportunities. SIGNIFICANCE: How the tumor microenvironment participates in pancreatic cancer progression is not fully understood. Here, we show that SCs are activated by cancer cells and collectively organize into tracks that dynamically enable cancer invasion in a c-Jun-dependent manner. See related commentary by Amit and Maitra, p. 2240. This article is highlighted in the In This Issue feature, p. 2221.

Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer.

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) > 2 and ≥ pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p ≤ 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype.

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.

Post-anticoagulant D-dimer is a highly prognostic biomarker of COVID-19 mortality.

Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe coronavirus disease 2019 (COVID-19) illness. In this study, we determine whether changes in D-dimer levels after anticoagulation are independently predictive of in-hospital mortality. Adult patients hospitalised for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City (NY, USA). We studied the ability of post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and several laboratory tests. 1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalisation were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on mean D-dimer levels and trend following anticoagulation: 49% for the high mean-increase trend group; 27% for the high-decrease group; 21% for the low-increase group; and 9% for the low-decrease group (p<0.001). Using penalised logistic regression models to simultaneously analyse 67 clinical variables, the high increase (adjusted odds ratios (ORadj): 6.58, 95% CI 3.81-11.16), low increase (ORadj: 4.06, 95% CI 2.23-7.38) and high decrease (ORadj: 2.37; 95% CI 1.37-4.09) D-dimer groups (reference: low decrease group) had the highest odds for in-hospital mortality among all clinical features. Changes in D-dimer levels and trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and future studies of emerging treatments for severe COVID-19.

Proteogenomic and metabolomic characterization of human glioblastoma.

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.

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.

Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.

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.

ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies.

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, a prototype user-friendly web application called "ProTrack" is built with the CPTAC clear cell renal cell carcinoma (ccRCC) data set (http://ccrcc.cptac-data-view.org). Here the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data is described.

Proteogenomic Characterization of Endometrial Carcinoma.

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/ß-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.

Immune Escape in Prostate Cancer: Known and Predicted Mechanisms and Targets.

Complex immune evasion mechanisms and lack of biomarkers predicting responsiveness to immune checkpoint blockade therapies compromise immunotherapy's therapeutic efficacy for patients with prostate cancer. The authors review established and nominated immune evasion mechanisms in prostate cancer and discuss how the precise treatment strategies can be developed to improve efficacy of immunotherapy.

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.