Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

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.

Integrated glycoproteomic characterization of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC), a common form of RCC, is responsible for the high mortality rate of kidney cancer. Dysregulations of glycoproteins have been shown to associate with ccRCC. However, the molecular mechanism has not been well characterized. Here, a comprehensive glycoproteomic analysis is conducted using 103 tumors and 80 paired normal adjacent tissues. Altered glycosylation enzymes and corresponding protein glycosylation are observed, while two of the major ccRCC mutations, BAP1 and PBRM1, show distinct glycosylation profiles. Additionally, inter-tumor heterogeneity and cross-correlation between glycosylation and phosphorylation are observed. The relation of glycoproteomic features to genomic, transcriptomic, proteomic, and phosphoproteomic changes shows the role of glycosylation in ccRCC development with potential for therapeutic interventions. This study reports a large-scale tandem mass tag (TMT)-based quantitative glycoproteomic analysis of ccRCC that can serve as a valuable resource for the community.

Abstract 3127: Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) represent ~15-20% RCCs cases comprising nearly 20 different disease subtypes and a wide spectrum of clinical behavior from benign to highly aggressive course. Clinically, metastatic non-ccRCC patients, regardless of subtypes with distinct genomic aberrations, are all treated with the same standard of care therapies, underscoring the need for precision therapeutic strategies. Diagnostic challenges also exist as benign and malignant entities often display overlapping histomorphologies that current diagnostic cytokeratin markers cannot resolve. Therefore, identification of more reliable diagnostic and prognostic non-ccRCC biomarkers remains an unmet need in this field.

As part of the Clinical Tumor Analysis Consortium (CPTAC), we performed integrative analysis of multi-omics data including genomic next generation sequencing-based whole exome, whole genome, RNAseq, snRNAseq and mass spectrometry-based proteomics, post translational modifications (glycosylation and phosphorylation) and metabolomic profiles generated by CPTAC. The composition of the kidney tumor cohort (n=151) included 103 ccRCC, 15 oncocytomas, 13 papillary RCC (PRCC), 11 other rare tumors and 8 unclassified RCCs. Our multi-omic analysis revealed both unique and shared molecular features of RCC subtypes. We characterized proteogenomic, PTM and glycoproteome impact of genome instability (GI), a feature that is associated with poor prognosis in both ccRCC and non-ccRCC and affects 10-15% of cases. These analyses identified new prognostic signatures, outlier targetable kinase expression patterns, kinase-substrate relationships and differential protein glycosylation events. Glycoproteome analysis also revealed variation in cell-type specific marker expression among RCC subtypes such as FUT8 (core-fucosyltransferase) associated protein glycosylation in PRCC. Integrative analysis of snRNA-seq data predicted diverse tumor cell-of-origin and stratified RCC subtype specific proteogenomic signatures. Differential expression analysis revealed several novel diagnostic makers including MAPRE3, GPNMB, PIGR, SOSTDC1. These biomarkers were validated by IHC and their addition to existing panels results in improved diagnostic specificity. Metabolic characterization revealed RCC subtype-specific differences and increased oncometabolite SAICAR in oncocytomas that may have functional significance. The valuable proteogenomic data resource we generated contains several rare tumor types that are hard to obtain for proteogenomic characterization at the scale described here, and will certainly aid in future pan-RCC studies.

Citation Format: Ginny Xiaohe Li, Yi Hsiao, Lijun Chen, Rahul Mannan, Yuping Zhang, Francesca Petralia, Hanbyul Cho, Noshad Hosseini, Anna Calinawan, Yize Li, Shankara Anand, Aniket Dagar, Yifat Geffen, Felipe V. Leprevost, Anne Le, Sean Ponce, Michael Schnaubelt, Nataly Naser Al Deen, Wagma Caravan, Andrew Houston, Chandan Kumar-Sinha, Xiaoming Wang, Seema Chugh, Gilbert S. Omenn, Daniel W. Chan, Christopher Ricketts, Rohit Mehra, Arul Chinnaiyan, Li Ding, Marcin Cieslik, Hui Zhang, Saravana M. Dhanasekaran, Alexey I. Nesvizhskii. Comprehensive proteogenomic characterization of rare kidney tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3127.

Neoplastic cell enrichment of tumor tissues using coring and laser microdissection for proteomic and genomic analyses of pancreatic ductal adenocarcinoma

BACKGROUND

The identification of differentially expressed tumor-associated proteins and genomic alterations driving neoplasia is critical in the development of clinical assays to detect cancers and forms the foundation for understanding cancer biology. One of the challenges in the analysis of pancreatic ductal adenocarcinoma (PDAC) is the low neoplastic cellularity and heterogeneous composition of bulk tumors. To enrich neoplastic cells from bulk tumor tissue, coring, and laser microdissection (LMD) sampling techniques have been employed. In this study, we assessed the protein and KRAS mutation changes associated with samples obtained by these enrichment techniques and evaluated the fraction of neoplastic cells in PDAC for proteomic and genomic analyses.

METHODS

Three fresh frozen PDAC tumors and their tumor-matched normal adjacent tissues (NATs) were obtained from three sampling techniques using bulk, coring, and LMD; and analyzed by TMT-based quantitative proteomics. The protein profiles and characterizations of differentially expressed proteins in three sampling groups were determined. These three PDACs and samples of five additional PDACs obtained by the same three sampling techniques were also subjected to genomic analysis to characterize KRAS mutations.

RESULTS

The neoplastic cellularity of eight PDACs ranged from less than 10% to over 80% based on morphological review. Distinctive proteomic patterns and abundances of certain tumor-associated proteins were revealed when comparing the tumors and NATs by different sampling techniques. Coring and bulk tissues had comparable proteome profiles, while LMD samples had the most distinct proteome composition compared to bulk tissues. Further genomic analysis of bulk, cored, or LMD samples demonstrated that KRAS mutations were significantly enriched in LMD samples while coring was less effective in enriching for KRAS mutations when bulk tissues contained a relatively low neoplastic cellularity.

CONCLUSIONS

In addition to bulk tissues, samples from LMD and coring techniques can be used for proteogenomic studies. The greatest enrichment of neoplastic cellularity is obtained with the LMD technique.

Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.

OmicsOne: associate omics data with phenotypes in one-click

BACKGROUND

The rapid advancements of high throughput "omics" technologies have brought a massive amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset and the discovery of interesting genes, proteins, lipids, glycans, metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed for data analysis and visualization. However, it still lacks an efficient way to investigate the phenotypes with multiple omics data. Here, we present OmicsOne as an interactive web-based framework for rapid phenotype association analysis of multi-omic data by integrating quality control, statistical analysis, and interactive data visualization on 'one-click'.

MATERIALS AND METHODS

OmicsOne was applied on the previously published proteomic and glycoproteomic data sets of high-grade serous ovarian carcinoma (HGSOC) and the published proteome data set of lung squamous cell carcinoma (LSCC) to confirm its performance. The data was analyzed through six main functional modules implemented in OmicsOne: (1) phenotype profiling, (2) data preprocessing and quality control, (3) knowledge annotation, (4) phenotype associated features discovery, (5) correlation and regression model analysis for phenotype association analysis on individual features, and (6) enrichment analysis for phenotype association analysis on interested feature sets.

RESULTS

We developed an integrated software solution, OmicsOne, for the phenotype association analysis on multi-omics data sets. The application of OmicsOne on the public data set of ovarian cancer data showed that the software could confirm the previous observations consistently and discover new evidence for HNRNPU and a glycopeptide of HYOU1 as potential biomarkers for HGSOC data sets. The performance of OmicsOne was further demonstrated in the Tumor and NAT comparison study on the proteome data set of LSCC.

CONCLUSIONS

OmicsOne can effectively simplify data analysis and reveal the significant associations between phenotypes and potential biomarkers, including genes, proteins, and glycopeptides, in minutes to assist users to understand aberrant biological processes.

Abstract IA-003: Proteogenomic characterizations of pancreatic ductal adenocarcinoma

Pancreatic cancer is one of the deadliest cancers and the five-year survival rate is less than 10%. Pancreatic ductal adenocarcinoma (PDAC) represents more than 90% of all pancreatic malignancies, and is responsible for the majority of pancreatic cancer-related deaths. Towards understanding the underlying molecular alterations that drive PDAC oncogenesis and identify therapeutic targets for personalized treatments, we comprehensively characterized 140 pancreatic cancers and 67 normal adjacent tissues. To ensure robust, downstream analyses, tumor neoplastic cellularity was assessed via multiple, orthogonal strategies using molecular features, and verified via pathological estimation of tumor cellularity based on histological review to select tumors with sufficient tumor cellularity. We also included the analysis of 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole genome sequencing, whole exome sequencing, methylation, RNA-seq, and miRNA-seq were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. These characterizations revealed functional impacts of genomic and epigenomic alterations on proteins and protein modifications, delineated PDAC cell microenvironment compositions and the immune signatures for immunotherapy, also uncovered putative kinase inhibitors that could be tested for therapy. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

Citation Format: Liwei Cao, Chen Huang, Daniel Cui Zhou, Yingwei Hu, Mamie Lih, Sara R. Savage, Karsten Krug, David J. Clark, Michael Schnaubelt, Lijun Chen, Felipe da Veiga Leprevost, Rodrigo Vargas Eguez, Alexey I. Nesvizhskii, D.R. Mani, Gilbert S. Omenn, Emily S. Boja, Mehdi Mesri, Ana I. Robles, Henry Rodriguez, Oliver F. Bathe, Daniel W. Chan, Ralph H. Hruban, Li Ding, Bing Zhang, Hui Zhang, Clinical Proteomic Tumor Analysis Consortium. Proteogenomic characterizations of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr IA-003.

Proteogenomic characterization of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

Unbiased Proteomic and Phosphoproteomic Analysis Identifies Response Signatures and Novel Susceptibilities After Combined MEK and mTOR Inhibition in BRAFV600E Mutant Glioma

The mitogen-activated protein kinase pathway is one of the most frequently altered pathways in cancer. It is involved in the control of cell proliferation, invasion, and metabolism, and can cause resistance to therapy. A number of aggressive malignancies, including melanoma, colon cancer, and glioma, are driven by a constitutively activating missense mutation (V600E) in the v-Raf murine sarcoma viral oncogene homolog B (BRAF) component of the pathway. Mitogen-activated protein kinase kinase (MEK) inhibition is initially effective in targeting these cancers, but reflexive activation of mammalian target of rapamycin (mTOR) signaling contributes to frequent therapy resistance. We have previously demonstrated that combination treatment with the MEK inhibitor trametinib and the dual mammalian target of rapamycin complex 1/2 inhibitor TAK228 improves survival and decreases vascularization in a BRAFV600E mutant glioma model. To elucidate the mechanism of action of this combination therapy and understand the ensuing tumor response, we performed comprehensive unbiased proteomic and phosphoproteomic characterization of BRAFV600E mutant glioma xenografts after short-course treatment with trametinib and TAK228. We identified 13,313 proteins and 30,928 localized phosphosites, of which 12,526 proteins and 17,444 phosphosites were quantified across all samples (data available via ProteomeXchange; identifier PXD022329). We identified distinct response signatures for each monotherapy and combination therapy and validated that combination treatment inhibited activation of the mitogen-activated protein kinase and mTOR pathways. Combination therapy also increased apoptotic signaling, suppressed angiogenesis signaling, and broadly suppressed the activity of the cyclin-dependent kinases. In response to combination therapy, both epidermal growth factor receptor and class 1 histone deacetylase proteins were activated. This study reports a detailed (phospho)proteomic analysis of the response of BRAFV600E mutant glioma to combined MEK and mTOR pathway inhibition and identifies new targets for the development of rational combination therapies for BRAF-driven tumors.

Abstract 324: Unbiased proteomic and phosphoproteomic analysis identifies response signatures and novel susceptibilities after combined MEK and mTOR inhibition in BRAFV600E mutant glioma

The mitogen-activated protein kinase (MAPK) pathway is one of the most frequently altered pathways in cancer. It is involved in the control of cell proliferation, invasion, metabolism, and can cause resistance to therapy. A number of aggressive malignancies including melanoma, colon cancer, and glioma, are driven by a constitutively activating missense mutation (V600E) in the BRAF component of the pathway. MEK inhibition is initially effective in targeting these cancers, but reflexive activation of mTOR signaling contributes to frequent therapy resistance. We have previously demonstrated that combination treatment with the MEK inhibitor trametinib and the dual mTORC1/2 inhibitor TAK228 improves survival and decreases vascularization in a BRAFV600E mutant glioma model. To elucidate the mechanism of action of this combination therapy and understand the ensuing tumor response, we performed comprehensive unbiased proteomic and phosphoproteomic characterization of BRAFV600E mutant glioma xenografts after short-course treatment with trametinib and TAK228. We identified 13,313 proteins and 30,928 localized phosphosites, of which 12,526 proteins and 17,444 phosphosites were quantified across all samples (data available via ProteomeXchange; identifier PXD022329). We identified distinct response signatures for each monotherapy and combination therapy and validated that combination treatment inhibited activation of the MAPK and mTOR pathways. Combination therapy also increased apoptotic signaling, suppressed angiogenesis signaling, and broadly suppressed the activity of the cyclin-dependent kinases. In addition, combination therapy had a profound impact on cancer cell metabolic pathways, increasing the expression of proteins (and their activating phosphorylations) involved in glycolysis, the tricarboxylic acid (TCA) cycle, nucleotide biosynthesis, and DNA replication. In response to combination therapy, both receptor tyrosine kinase and histone deacetylase proteins were activated. This study reports a detailed (phospho)proteomic analysis of the response of BRAFV600E mutant glioma to combined MEK and mTOR pathway inhibition and identifies new targets for the development of rational combination therapies for aggressive BRAF-driven tumors.

Citation Format: Micah J. Maxwell, Antje Arnold, Heather Sweeney, Ljun Chen, Tung-Shing M. Lih, Michael Schnaubelt, Charles G. Eberhart, Jeffrey A. Rubens, Hui Zhang, David J. Clark, Eric H. Raabe. Unbiased proteomic and phosphoproteomic analysis identifies response signatures and novel susceptibilities after combined MEK and mTOR inhibition in BRAFV600E mutant glioma [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 324.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma

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.

Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potential roles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

Abstract 5118: Proteogenomics characterization of HPV-negative head and neck squamous cell carcinomas

Patients with head and neck squamous cell carcinomas (HNSCCs) are treated with surgery, radiation, chemotherapy, and limited targeted therapies. Compared to human papillomavirus (HPV)-positive HNSCCs, HPV-negative cases have worse treatment response and prognosis and represent an unmet clinical need. We performed comprehensive proteogenomic characterization of tumor specimens, matched normal adjacent tissues (NATs), and blood samples from 109 HPV-negative HNSCC patients. This cohort is dominated by tumors from oral cavity (45, 41%) and larynx (49, 45%). Somatic mutation and somatic copy number analyses validated previously reported genomic aberrations in HPV-negative HNSCC. Proteomics analysis linked p53 loss of heterozygosity to increased expression of EPCAM, a stemness marker. Additionally, FAT1 truncation mutations were associated with increased expression of proteins involved in keratinization, a key feature of SCC differentiation. Deletions of 3p and 9p led to the loss of genes encoding p16, chemokine receptors, and interferon/JAK/STAT signaling pathway proteins, whereas amplifications of 3q and 11q led to overexpression of proteins involved in cell proliferation and anti-apoptosis pathways. Comparative analysis of tumor and NAT proteomes and phosphoproteomes identified putative diagnostic biomarkers and druggable targets, and proteogenomic integration further identified putative neoantigens. Tumor site-specific characterization associated epigenetic silencing of neurofilaments with laryngeal but not oral cavity SCC. Protein targets of FDA approved or investigational drugs for HNSCC treatment showed high inter-tumor heterogeneity in their protein abundances. DNA copy number and RNA expression level were good surrogates of protein abundance for some targets, such as EGFR and PD-L1, but they failed to reflect protein levels or kinase activities for other targets, such as MMP9 and MTOR. Thus, there is a critical need for protein biomarker-driven treatment stratification. Deconvolution of bulk tumor gene expression data revealed an immune-hot subgroup and an immune-cold subgroup. Immune-hot tumors broadly overexpressed multiple immune checkpoints including PD-L1, IDO1, and CTLA4, underscoring the necessity of combination immune checkpoint inhibition to improve treatment efficacy. Immune-cold tumors were characterized by smoking, chromosomal instability, and activation of the CDK4/6-pRb axis, suggesting they could be targeted by CDK4/6 inhibitors. We also noted that EGFR-amplified tumors frequently harbor copy number aberrations of downstream signaling components of the EGFR pathway. This may explain the low response rate of EGFR-amplified tumors to EGFR inhibitors, and targeting multiple pathway components, including EGFR, PIK3CA and STAT3, may be required for these tumors. In summary, our integrative proteogenomic characterization revealed multiple novel insights into the pathogenesis and treatment of HPV-negative HNSCCs.

Citation Format: Chen Huang, Lijun Chen, Yize Li, Sara Savage, Michael Schnaubelt, Felipe V. Leprevost, Marcin Cieslik, Yongchao Dou, Bo Wen, Jonathan T. Lei, Kai Li, Eric Jaehnig, Zhiao Shi, Meenakshi Anurag, Jianbo Pan, Yingwei Hu, Rodrigo V. Eguez, David J. Clark, Matthew Wyczalkowski, Saravana M. Dhanasekaran, Chandan Kumar, Antonio Colaprico, Karsten Krug, Michael Gillette, D. R. Mani, Seungyeul Yoo, Jiayi Ji, Xiaoyu Song, Weiping Ma, Xi Steven Chen, Alex Pico, Nathan J. Edwards, Scott D. Jewell, Mathangi Thiagarajan, Emily S. Boja, Henry Rodriguez, Andrew Sikora, Pei Wang, Matthew Ellis, Gilbert S. Omenn, Li Ding, Alexey I. Nesvizhskii, Adel K. EI-Naggar, Daniel W. Chan, Hui Zhang, Bing Zhang, Clinical Proteomic Tumor Analysis Consortium. Proteogenomics characterization of HPV-negative head and neck squamous cell carcinomas [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5118.

Impact of Increased FUT8 Expression on the Extracellular Vesicle Proteome in Prostate Cancer Cells

Extracellular vesicles (EVs) are involved in intercellular communication, transporting proteins and nucleic acids to proximal and distal regions. There is evidence of glycosylation influencing protein routing into EVs; however, the impact of aberrant cellular glycotransferase expression on EV protein profiles has yet to be evaluated. In this study, we paired extracellular vesicle characterization and quantitative proteomics to determine the systemic impact of altered α(1,6)fucosyltranferase (FUT8) expression on prostate cancer-derived EVs. Our results showed that increased cellular expression of FUT8 could reduce the number of vesicles secreted by prostate cancer cells as well as increase the abundance of proteins associated with cell motility and prostate cancer metastasis. In addition, overexpression of FUT8 resulted in altered glycans on select EV-derived glycoproteins. This study presents the first evidence of altered cellular glycosylation impacting EV protein profiles and provides further rationale for exploring the functional role of glycosylation in EV biogenesis and biology.

Orthogonal Proteomic Platforms and Their Implications for the Stable Classification of High-Grade Serous Ovarian Cancer Subtypes

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) established a harmonized method for large-scale clinical proteomic studies. SWATH-MS, an instance of data-independent acquisition (DIA) proteomic methods, is an alternate proteomic approach. In this study, we used SWATH-MS to analyze remnant peptides from the original retrospective TCGA samples generated for the CPTAC ovarian cancer proteogenomic study. The SWATH-MS results recapitulated the confident identification of differentially expressed proteins in enriched pathways associated with the robust Mesenchymal high-grade serous ovarian cancer subtype and the homologous recombination deficient tumors. Hence, SWATH/DIA-MS presents a promising complementary or orthogonal alternative to the CPTAC proteomic workflow, with the advantages of simpler and faster workflows and lower sample consumption, albeit with shallower proteome coverage. In summary, both analytical methods are suitable to characterize clinical samples, providing proteomic workflow alternatives for cancer researchers depending on the context-specific goals of the studies.

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SWATH-MS and iTRAQ-DDA are used to classify 103 high-grade serous ovarian cancer

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SWATH-MS re-capitulates differentially expressed proteins in ovarian cancer subtypes

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SWATH-MS is a robust proteomic approach for large-scale clinical proteomic studies

Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability

In the absence of a dominant driving mutation other than uniformly present TP53 mutations, deeper understanding of the biology driving ovarian high-grade serous cancer (HGSC) requires analysis at a functional level, including post-translational modifications. Comprehensive proteogenomic and phosphoproteomic characterization of 83 prospectively collected ovarian HGSC and appropriate normal precursor tissue samples (fallopian tube) under strict control of ischemia time reveals pathways that significantly differentiate between HGSC and relevant normal tissues in the context of homologous repair deficiency (HRD) status. In addition to confirming key features of HGSC from previous studies, including a potential survival-associated signature and histone acetylation as a marker of HRD, deep phosphoproteomics provides insights regarding the potential role of proliferation-induced replication stress in promoting the characteristic chromosomal instability of HGSC and suggests potential therapeutic targets for use in precision medicine trials.

Comparison of ovarian cancer and normal precursors identifies key signaling pathways

Mitotic and cyclin-dependent kinases emerge as potential therapeutic targets

Previously identified hallmarks of homologous repair status and survival are confirmed

Replication stress appears to drive increased chromosomal instability

Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.

Glycans, Glycosite, and Intact Glycopeptide Analysis of N-Linked Glycoproteins Using Liquid Handling Systems

Aberrant glycosylation has been shown to associate with disease progression, and with glycoproteins representing the major protein component of biological fluids this makes them attractive targets for disease monitoring. Leveraging glycoproteomic analysis via mass spectrometry (MS) could provide the insight into the altered glycosylation patterns that relate to disease progression. However, investigation of large sample cohorts requires rapid, efficient, and highly reproducible sample preparation. To address the limitation, we developed a high-throughput method for characterizing glycans, glycosites, and intact glycopeptides (IGPs) derived from N-linked glycoproteins. We combined disparate peptide enrichment strategies (i.e., hydrophilic and hydrophobic) and a liquid handling platform allowing for a high throughput and rapid enrichment of IGP in a 96-well plate format. The C18/MAX-Tip workflow reduced sample processing time and facilitated the selective enrichment of IGPs from complex samples. Furthermore, our approach enabled the analysis of deglycosylated peptides and glycans from enriched IGPs following PNGase F digest. Following development and optimization of the C18/MAX-Tip methodology using the standard glycoprotein, fetuin, we investigated normal urine samples to obtain N-linked glycoprotein information. Together, our method enables a high-throughput enrichment of glycan, glycosites, and IGPs from biological samples.

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.

Simple Tip-Based Sample Processing Method for Urinary Proteomic Analysis

Mass spectrometry-based urinary proteomics is one of the most attractive strategies to discover proteins for diagnosis, prognosis, monitoring, or prediction of therapeutic responses of urological diseases involving the kidney, prostate, and bladder; however, interfering compounds found in urine necessitate sample preparation strategies that are currently not suitable for urinary proteomics in the clinical setting. Herein, we describe the C4-tip method, comprising a simple, automated strategy utilizing a reverse-phase resin tip-based format and "on-tip" digestion to examine the urine proteome. We first determined the optimal conditions for protein isolation and protease digestion on the C4-tip using the standard protein bovine fetuin. Next, we applied the C4-tip method to urinary proteomics, identifying a total of 813 protein groups using LC-MS/MS, with identified proteins from the C4-tip method displaying a similar distribution of gene ontology (GO) cellular component assignments compared to identified proteins from an ultrafiltration preparation method. Finally, we assessed the reproducibility of the C4-tip method, revealing a high Spearman correlation R-value for shared proteins identified across all tips. Together, we have shown the C4-tip method to be a simple, robust method for high-throughput analysis of the urinary proteome by mass spectrometry in the clinical setting.

Developing Workflow for Simultaneous Analyses of Phosphopeptides and Glycopeptides

Enrichment of modified peptides from global peptides is inevitable in mass spectrometric analysis protein modifications because of their importance in the study of cellular functions and low abundance in the global proteomic analysis. Recent advances in enrichment methods for modified peptides such as phosphopeptides and intact glycopeptides (IGPs) show that the methods for proteomic analyses of both protein modifications are robust. We have recently observed and reported a large number of IGPs from phosphoproteomic analysis using IMAC-based phosphopeptides enrichment procedure. To determine whether phosphorylated peptides could be specifically isolated from coenriched IGPs in IMAC experiments with different pH, IMAC procedures were performed at different pH conditions, and we found that the enrichment of phosphopeptides at pH 2.0 was the optimal condition for having the highest number of phosphopeptide identifications; however, coenrichment of phosphopeptides and glycopeptides was inevitable in the entire pH range. The hydrophilic enrichments of IGPs performed before or after IMAC enrichment were evaluated subsequently to determine the optimal workflow for simultaneous analyses of phosphopeptides and glycopeptides, and IMAC enrichment followed by hydrophilic enrichment was chosen as the optimized workflow. Applying the workflow to the TMT-labeled peptides from luminal and basal-like type of breast cancer patient-derived xenograft (PDX) models allowed quantitative analyses of phospho- and glycoproteomics with 17582 phosphopeptides and 3468 glycopeptides identified, and 1237 phosphopeptides and 236 glycopeptides showed significant expression differences between luminal and basal-like, respectively. This method allows simultaneous analyses of phosphoprotein and glycoprotein modifications, extending our understanding of roles of glycosylation and phosphorylation in biology and diseases.

Evaluation of NCI-7 Cell Line Panel as a Reference Material for Clinical Proteomics

Reference materials are vital to benchmarking the reproducibility of clinical tests and essential for monitoring laboratory performance for clinical proteomics. The reference material utilized for mass spectrometric analysis of the human proteome would ideally contain enough proteins to be suitably representative of the human proteome, as well as exhibit a stable protein composition in different batches of sample regeneration. Previously, The Clinical Proteomic Tumor Analysis Consortium (CPTAC) utilized a PDX-derived comparative reference (CompRef) materials for the longitudinal assessment of proteomic performance; however, inherent drawbacks of PDX-derived material, including extended time needed to grow tumors and high level of expertise needed, have resulted in efforts to identify a new source of CompRef material. In this study, we examined the utility of using a panel of seven cancer cell lines, NCI-7 Cell Line Panel, as a reference material for mass spectrometric analysis of human proteome. Our results showed that not only is the NCI-7 material suitable for benchmarking laboratory sample preparation methods, but also NCI-7 sample generation is highly reproducible at both the global and phosphoprotein levels. In addition, the predicted genomic and experimental coverage of the NCI-7 proteome suggests the NCI-7 material may also have applications as a universal standard proteomic reference.

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.