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.

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.

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.

The role of CTNNB1 mutations and matrix metalloproteinases (MMPs) in anti-angiogenesis treatment of endometrial carcinoma.

OBJECTIVE: Treatment options and associated biomarkers for advanced and recurrent disease are limited. Endometrial cancers (ECs) with CTNNB1 exon 3 mutations appear to have preferential response to bevacizumab, an anti-angiogenesis treatment, though the mechanism of action is unknown. We aim to identify mediators of bevacizumab-responsive endometrial cancers. METHODS: We analyzed RNA expression from TCGA and protein expression from CPTAC to identify likely targets for ß-catenin overactivity. We then transiently and stably overexpressed ß-catenin in EC cells to confirm the results suggested by our in silico analysis. We performed corroborative experiments by silencing CTNNB1 in mutated cell lines to demonstrate functional specificity. We implanted transduced cells into xenograft models to study microvessel density. RESULTS: CTNNB1-mutated ECs were associated with increased ß-catenin and MMP7 protein abundance (P < 0.001), but not VEGF-A protein abundance. Overexpressing ß-catenin in EC cells did not increase VEGF-A abundance but did increase expression and secretion of MMP7 (P < 0.03). Silencing CTNNB1 in CTNNB1-mutated cells decreased MMP7 gene expression in EC (P < 0.0001). Microvessel density was not increased. CONCLUSIONS: These data provide a mechanistic understanding for bevacizumab-response in CTNNB1-mutated ECs demonstrated in GOG-86P. We hypothesize that overexpressed and secreted MMP7 potentially digests VEGFR-1, releasing VEGF-A, and increasing its availability. These activities may drive the formation of permeable vessels, which contributes to tumor progression, metastasis, and immune suppression. This mechanism is unique to EC and advocates for further clinical trials evaluating this treatment-related biomarker.

Proteogenomics connects somatic mutations to signalling in breast cancer.

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.

BlackSheep: A Bioconductor and Bioconda Package for Differential Extreme Value Analysis.

Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep, a package for nonparametric description and differential analysis of genome-wide data, available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/blacksheepr.html) and Bioconda (https://bioconda.github.io/recipes/blksheep/README.html). BlackSheep is a complementary tool to other differential expression analysis methods, which is particularly useful when analyzing small subgroups in a larger cohort.

Quantitative Mass Spectrometry to Interrogate Proteomic Heterogeneity in Metastatic Lung Adenocarcinoma and Validate a Novel Somatic Mutation CDK12-G879V.

Lung cancer is the leading cause of cancer death in both men and women. Tumor heterogeneity is an impediment to targeted treatment of all cancers, including lung cancer. Here, we sought to characterize tumor proteome and phosphoproteome changes by longitudinal, prospective collection of tumor tissue from an exceptional responder lung adenocarcinoma patient who survived with metastatic lung adenocarcinoma for over seven years while undergoing HER2-directed therapy in combination with chemotherapy. We employed "Super-SILAC" and TMT labeling strategies to quantify the proteome and phosphoproteome of a lung metastatic site and eight distinct metastatic progressive lymph nodes collected during these seven years, including five lymph nodes procured at autopsy. We identified specific signaling networks enriched in lung compared with the lymph node metastatic sites. We correlated the changes in protein abundance with changes in copy number alteration (CNA) and transcript expression. ERBB2/HER2 protein expression was higher in lung, consistent with a higher degree of ERBB2 amplification in lung compared with the lymph node metastatic sites. To further interrogate the mass spectrometry data, a patient-specific database was built by incorporating all the somatic and germline variants identified by whole genome sequencing (WGS) of genomic DNA from the lung, one lymph node metastatic site and blood. An extensive validation pipeline was built to confirm variant peptides. We validated 360 spectra corresponding to 55 germline and 6 somatic variant peptides. Targeted MRM assays revealed two novel variant somatic peptides, CDK12-G879V and FASN-R1439Q, expressed in lung and lymph node metastatic sites, respectively. The CDK12-G879V mutation likely results in a nonfunctional CDK12 kinase and chemotherapy susceptibility in lung metastatic sites. Knockdown of CDK12 in lung adenocarcinoma cells increased chemotherapy sensitivity which was rescued by wild type, but not CDK12-G879V expression, consistent with the complete resolution of the lung metastatic sites in this patient.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching.

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.

Blockade of IL1ß and PD1 with Combination Chemotherapy Reduces Systemic Myeloid Suppression in Metastatic Pancreatic Cancer with Heterogeneous Effects in the Tumor.

Innate inflammation promotes tumor development, although the role of innate inflammatory cytokines in established human tumors is unclear. Herein, we report clinical and translational results from a phase Ib trial testing whether IL1ß blockade in human pancreatic cancer would alleviate myeloid immunosuppression and reveal antitumor T-cell responses to PD1 blockade. Patients with treatment-naïve advanced pancreatic ductal adenocarcinoma (n = 10) were treated with canakinumab, a high-affinity monoclonal human antiinterleukin-1ß (IL1ß), the PD1 blocking antibody spartalizumab, and gemcitabine/n(ab)paclitaxel. Analysis of paired peripheral blood from patients in the trial versus patients receiving multiagent chemotherapy showed a modest increase in HLA-DR+CD38+ activated CD8+ T cells and a decrease in circulating monocytic myeloid-derived suppressor cells (MDSC) by flow cytometry for patients in the trial but not in controls. Similarly, we used patient serum to differentiate monocytic MDSCs in vitro and showed that functional inhibition of T-cell proliferation was reduced when using on-treatment serum samples from patients in the trial but not when using serum from patients treated with chemotherapy alone. Within the tumor, we observed few changes in suppressive myeloid-cell populations or activated T cells as assessed by single-cell transcriptional profiling or multiplex immunofluorescence, although increases in CD8+ T cells suggest that improvements in the tumor immune microenvironment might be revealed by a larger study. Overall, the data indicate that exposure to PD1 and IL1ß blockade induced a modest reactivation of peripheral CD8+ T cells and decreased circulating monocytic MDSCs; however, these changes did not lead to similarly uniform alterations in the tumor microenvironment.

Impact of comprehensive family history and genetic analysis in the multidisciplinary pancreatic tumor clinic setting.

BACKGROUND: Genetic testing is recommended for all pancreatic ductal adenocarcinoma (PDAC) patients. Prior research demonstrates that multidisciplinary pancreatic cancer clinics (MDPCs) improve treatment- and survival-related outcomes for PDAC patients. However, limited information exists regarding the utility of integrated genetics in the MDPC setting. We hypothesized that incorporating genetics in an MDPC serving both PDAC patients and high-risk individuals (HRI) could: (1) improve compliance with guideline-based genetic testing for PDAC patients, and (2) optimize HRI identification and PDAC surveillance participation to improve early detection and survival. METHODS: Demographics, genetic testing results, and pedigrees were reviewed for PDAC patients and HRI at one institution over 45 months. Genetic testing analyzed 16 PDAC-associated genes at minimum. RESULTS: Overall, 969 MDPC subjects were evaluated during the study period; another 56 PDAC patients were seen outside the MDPC. Among 425 MDPC PDAC patients, 333 (78.4%) completed genetic testing; 29 (8.7%) carried a PDAC-related pathogenic germline variant (PGV). Additionally, 32 (9.6%) met familial pancreatic cancer (FPC) criteria. These PDAC patients had 191 relatives eligible for surveillance or genetic testing. Only 2/56 (3.6%) non-MDPC PDAC patients completed genetic testing (p < 0.01). Among 544 HRI, 253 (46.5%) had a known PGV or a designation of FPC, and were eligible for surveillance at baseline; of the remainder, 15/291 (5.2%) were eligible following genetic testing and PGV identification. CONCLUSION: Integrating genetics into the multidisciplinary setting significantly improved genetic testing compliance by reducing logistical barriers for PDAC patients, and clarified cancer risks for their relatives while conserving clinical resources. Overall, we identified 206 individuals newly eligible for surveillance or genetic testing (191 relatives of MDPC PDAC patients, and 15 HRI from this cohort), enabling continuity of care for PDAC patients and at-risk relatives in one clinic.

POLQ inhibition elicits an immune response in homologous recombination-deficient pancreatic adenocarcinoma via cGAS/STING signaling.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that harbors mutations in homologous recombination-repair (HR-repair) proteins in 20%-25% of cases. Defects in HR impart a specific vulnerability to poly ADP ribose polymerase inhibitors and platinum-containing chemotherapy in tumor cells. However, not all patients who receive these therapies respond, and many who initially respond ultimately develop resistance. Inactivation of the HR pathway is associated with the overexpression of polymerase theta (Polθ, or POLQ). This key enzyme regulates the microhomology-mediated end-joining (MMEJ) pathway of double-strand break (DSB) repair. Using human and murine HR-deficient PDAC models, we found that POLQ knockdown is synthetically lethal in combination with mutations in HR genes such as BRCA1 and BRCA2 and the DNA damage repair gene ATM. Further, POLQ knockdown enhances cytosolic micronuclei formation and activates signaling of cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING), leading to enhanced infiltration of activated CD8+ T cells in BRCA2-deficient PDAC tumors in vivo. Overall, POLQ, a key mediator in the MMEJ pathway, is critical for DSB repair in BRCA2-deficient PDAC. Its inhibition represents a synthetic lethal approach to blocking tumor growth while concurrently activating the cGAS-STING signaling pathway to enhance tumor immune infiltration, highlighting what we believe to be a new role for POLQ in the tumor immune environment.

Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment.

The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and its role in response to therapy is lacking. Here, we perform single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we find a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibit similar transcriptional responses to chemotherapy and do not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observe decreased ligand-receptor interactions in treated samples, particularly between TIGIT on CD8 + T cells and its receptor on cancer cells, and identify TIGIT as the major inhibitory checkpoint molecule of CD8 + T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.

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.

Mass Spectrometry-Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers.

Activation of PI3K signaling is frequently observed in triple-negative breast cancer (TNBC), yet PI3K inhibitors have shown limited clinical activity. To investigate intrinsic and adaptive mechanisms of resistance, we analyzed a panel of patient-derived xenograft models of TNBC with varying responsiveness to buparlisib, a pan-PI3K inhibitor. In a subset of patient-derived xenografts, resistance was associated with incomplete inhibition of PI3K signaling and upregulated MAPK/MEK signaling in response to buparlisib. Outlier phosphoproteome and kinome analyses identified novel candidates functionally important to buparlisib resistance, including NEK9 and MAP2K4. Knockdown of NEK9 or MAP2K4 reduced both baseline and feedback MAPK/MEK signaling and showed synthetic lethality with buparlisib in vitro A complex in/del frameshift in PIK3CA decreased sensitivity to buparlisib via NEK9/MAP2K4-dependent mechanisms. In summary, our study supports a role for NEK9 and MAP2K4 in mediating buparlisib resistance and demonstrates the value of unbiased omic analyses in uncovering resistance mechanisms to targeted therapy.Significance: Integrative phosphoproteogenomic analysis is used to determine intrinsic resistance mechanisms of triple-negative breast tumors to PI3K inhibition. Cancer Res; 78(10); 2732-46. ©2018 AACR.

Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.

Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.

Microbiome analyses of pacific white shrimp (Litopenaeus vannamei) collected from disparate geographical locations.

In this study, the tail muscle microbiota of pacific white shrimp (Litopenaeus vannamei) sourced from five countries across Central and South America and Southeast Asia were determined and compared. The genomic DNA was sequenced at around 10 × coverage for each geographical location and was assembled de novo for comparative analysis. The assembled sequences for all the lines were classified based on their similarity to the sequences in the public database. We found that there is high correlation among the microbiota of shrimp from disparate regions, as well as the presence of some DNA from bacteria known to cause food poisoning in humans. Sequencing data has been deposited at NCBI-SRA database and can be found under the BioProject ID PRJNA282154.

Learning to predict post-hospitalization VTE risk from EHR data.

We consider the task of predicting which patients are most at risk for post-hospitalization venothromboembolism (VTE) using information automatically elicited from an EHR. Given a set of cases and controls, we use machine-learning methods to induce models for making these predictions. Our empirical evaluation of this approach offers a number of interesting and important conclusions. We identify several risk factors for VTE that were not previously recognized. We show that machine-learning methods are able to induce models that identify high-risk patients with accuracy that exceeds previously developed scoring models for VTE. Additionally, we show that, even without having prior knowledge about relevant risk factors, we are able to learn accurate models for this task.