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Antibody mediated strategies for protein biomarker detection are common, but may limit discovery. We hypothesized that the use of antibody-free proteomics is feasible for detecting protein biomarkers in plasma of patients sustaining major trauma. A subset of subjects with major trauma from a prospective observational trial were analyzed. Patients were assigned to one of four groups based on their presenting Abbreviated Injury Severity Score (AIS). Sensitive, antibody-free selective reaction monitoring (SRM) mass spectrometry (MS), with spiked-in isotopically labeled synthetic peptides, was used for targeted protein quantification of a panel of 10 prospective targets. An overall tiered sensitivity analytical approach was used for peptide detection and quantification based upon plasma immunoaffinity depletion and PRISM fractionation. Forty-four patients were included in the analysis, of which 82% were men with a mean age of 50 (±19) years. Half had isolated head injury (n = 22), with the remaining patients experiencing multiple injuries or polytrauma (n = 14), isolated body injury (n = 2), or minor injury (n = 6). Peptides from 3 proteins (vascular adhesion molecule 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], and matrix metalloproteinase 9 [MMP9]) were detected and quantified in non-depleted processed plasma. Peptides from 2 proteins (angiopoietin 2 [Ang2] and plasminogen activator inhibitor-1 [PAI1]) were detected and quantification in depleted plasma, whereas the remaining 5 of the 10 prospective targets were undetected. VCAM1 (p = 0.02) and MMP9 (p = 0.03) were significantly upregulated in in the major trauma groups (1–3) versus mild injury group (4), whereas the others were not. There were no differences in protein expression between patients with traumatic brain injury (TBI; groups 1 and 2) versus those without TBI (groups 3 and 4). We detected non-specific upregulation of proteins reflecting blood–brain barrier breakdown in severely injured patients, indicating label-free MS techniques are feasible and may be informative.

Background: Approximately 85% of the U.S. military active duty population is male and less than 50 years of age, with elevated levels of known risk factors for oropharyngeal squamous cell carcinoma (OPSCC), including smoking, excessive use of alcohol, and greater numbers of sexual partners, and elevated prevalence of human papilloma virus (HPV). Given the recent rise in incidence of OPSCC related to the HPV, the Department of Defense Serum Repository provides an unparalleled resource for longitudinal studies of OPSCC in the military for the identification of early detection biomarkers. Methods: We identified 175 patients diagnosed with OPSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature. Results: Using a Random Forest machine learning approach, we derived a 13-protein signature that distinguishes cases versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The AUC for the derived Random Forest classifier was 0.90. Conclusions: This 13-protein classifier is highly promising for detection of OPSCC prior to overt symptoms. Impact: Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification.

Summary 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.

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and downstream costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Here, we demonstrate the capability of internal standard triggered-parallel reaction monitoring (IS-PRM) to prioritize candidate biomarkers for validation studies. A 5,176-plex assay coupling immunodepletion and fractionation with IS-PRM was developed and implemented in human plasma to quantify peptides representing 1,314 breast cancer biomarker candidates. Compared to prior approaches using data-dependent analysis, IS-PRM showed improved sensitivity (912 vs 295 proteins quantified) and precision (CV 0.1 vs 0.27) enabling rank-ordering of candidate biomarkers for validation studies. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Heterogeneity in composition is inherent in all cell populations, even those containing a single cell type. Single-cell proteomics characterization of cell heterogeneity is currently achieved by antibody-based technologies, which are limited by the availability of high-quality antibodies. Herein we report a simple, easily implemented, mass spectrometry (MS)- based targeted proteomics approach, termed cLC–SRM (carrier-assisted liquid chromatography coupled to selected reaction monitoring), for reliable multiplexed quantification of proteins in low numbers of mammalian cells. We combine a new single-tube digestion protocol to process low numbers of cells with minimal loss together with sensitive LC–SRM for protein quantification. This single-tube protocol builds upon trifluoroethanol digestion and further minimizes sample losses by tube pretreatment and the addition of carrier proteins. We also optimized the denaturing temperature and trypsin concentration to significantly improve digestion efficiency. cLC–SRM was demonstrated to have sufficient sensitivity for reproducible detection of most epidermal growth factor receptor (EGFR) pathway proteins expressed at levels ≥30 000 and ≥3000 copies per cell for 10 and 100 mammalian cells, respectively. Thus, cLC–SRM enables reliable quantification of low to moderately abundant proteins in less than 100 cells and could be broadly useful for multiplexed quantification of important proteins in small subpopulations of cells or in size-limited clinical samples. Further improvements of this method could eventually enable targeted single-cell proteomics when combined with either SRM or other emerging ultrasensitive MS detection.

Our study details the stepwise evolution of gilteritinib resistance in FLT3-mutated acute myeloid leukemia (AML). Early resistance is mediated by the bone marrow microenvironment, which protects residual leukemia cells. Removing these supportive extrinsic ligands drives evolution of late, intrinsic resistance. Whole exome sequencing, CRISPR/Cas, metabolomics, proteomics, and pharmacologic approaches were used to mechanistically define both early and late resistance. Early resistant cells undergo metabolic reprogramming, grow more slowly, and are dependent upon Aurora kinase B (AURKB). Late resistant cells are characterized byexpansion of pre-existing NRAS mutant subclones and continued metabolic reprogramming. Our model closely mirrors the timing and mutations of AML patients treated with gilteritinib.Pharmacological inhibition of AURKB resensitized both early resistant cell cultures and primary leukemia cells from gilteritinib-treated AML patients. These findings support a combinatorial strategy to target early resistant AML cells with AURKB inhibitors and gilteritinib before the expansion of pre-existing resistance mutations occurs.

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.

Extracellular vesicles (EVs) are released by nearly all cell types as part of normal cell physiology, transporting biological cargo, including nucleic acids and proteins, across the cell membrane. In pathological states such as cancer, EV-derived cargo may mirror the altered state of the cell of origin. Exosomes are the smaller, 50–150 nanometer-sized EVs released from fusion of multivesicular endosomes with the plasma membrane. Exosomes play important roles in cell-cell communication and participate in multiple cancer processes, including invasion and metastasis. Therefore, proteomic analysis of exosomes is a promising approach to discover potential cancer biomarkers, even though it is still at an early stage. Herein, we critically review the advances in exosome isolation methods and their compatibility with mass spectrometry (MS)-based proteomic analysis, as well as studies of exosomes in pathogenesis and progression of prostate and bladder cancer, two common urologic cancers whose incidence rates continue to rise annually. As urological tumors, both urine and blood samples are feasible for noninvasive or minimally invasive analysis. A better understanding of the biological cargo and functions of exosomes via high-throughput proteomics will help provide new insights into complex alterations in cancer and provide potential therapeutic targets and personalized treatment for patients.

SUMMARY Despite wide use of anti-vascular endothelial growth factor (VEGF) therapy for many solid cancers, most individuals become resistant to this therapy, leading to disease progression. Therefore, new biomarkers and strategies for blocking adaptive resistance of cancer to anti-VEGF therapy are needed. As described here, we demonstrate that cancer-derived small extracellular vesicles package increasing quantities of VEGF and other factors in response to anti-VEGF therapy. The packaging process of VEGF into small extracellular vesicles (EVs) is mediated by the tetraspanin CD63. Furthermore, small EV-VEGF (eVEGF) is not accessible to anti-VEGF antibodies and can trigger intracrine VEGF signaling in endothelial cells. eVEGF promotes angiogenesis and enhances tumor growth despite bevacizumab treatment. These data demonstrate a mechanism where VEGF is partitioned into small EVs and promotes tumor angiogenesis and progression. These findings have clinical implications for biomarkers and therapeutic strategies for ovarian cancer., Graphical abstract, In brief Ma et.al report that cancer-cell-derived small EVs contain increasing amounts of VEGF (eVEGF) and contribute to resistance to anti-VEGF therapy (AVT). CD63 is a potential mediator that regulates packaging of VEGF into small EVs. eVEGF can trigger intracrine VEGF signaling in endothelial cells and promote angiogenesis despite AVT.

Glioblastoma (GBM) is the most aggressive nervous system cancer, with median survival under 2 years. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. We performed an integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs. We identified key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation as well as potential targets for EGFR-, TP53- and RB1-altered tumors. We detected immune subtypes, driven by the presence of distinct immune cell populations using bulk omics, validated by single nulcei RNA sequencing (snRNA-seq), and they were correlated with specific expression and histone acetylation patterns. Acetylation of histone H2B in classical-like and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identified specific lipid distributions across subtypes and distinct global metabolic changes in IDH mutated tumors. This work highlights biological relationships which could potentially aid GBM patient stratifications for more effective treatments.

Introduction: Breast cancer (BC) is the second most common cause of cancer death among women in the USA. Recent proteogenomic studies of human BC have identified many potential therapeutic biomarkers for the most common types of BC. Here, we strive to comprehensively understand the proteogenomic landscape of difficult-to-treat breast cancer (DTBC) tumors. Methods: 118 primary breast tumors, with a focus on DTBC patients, were selected from the IRB-approved protocol. The study cohort included 30 triple negative, 16 HER2+, 16 Luminal A, 39 Luminal B1, and 17 Luminal B2 selected by immunohistochemistry. Breast tumors were embedded in OCT (Optimum Cutting Temperature) and processed by laser microdissection (LMD) to remove non-tumorous tissue. DNA, RNA, and protein were simultaneously extracted from each tumor using the illustra triplePrep kit. Paired-end RNA sequencing and whole genome sequencing (WGS) were performed for 118 and 100 tumors, respectively, using the Illumina HiSeq platform. Quantitative global proteomics and phosphoproteomic analyses were performed on 113 and 50 tumors, respectively, using isobaric TMT 6-plex labeling with the “universal reference” strategy. Results: Unlike other BC cohorts, our WGS cohort had an almost equal number of Estrogen(ER)+ (55.6%) and ER- (44.4%) cases, which gave us a nominally higher percentage TP53 (57%) and lower PIK3CA (32%) non-silent somatic mutation. Similar to previous reports, GATA3 was mutated in 13% of only ER+ cases. Unlike a recent report on proteomics clustering of bulk-processed tumors, our data show that a stromal-enriched cluster was not mixed with all subtypes but a majority of Luminal A (LumA) subtype, probably because LMD excluded stromal components in other tumor subtypes. Consensus clustering of proteomics data separated intrinsic Her2 patients with outcome differences. Her2 patients associated with the Basal-enriched proteome cluster had better survival than those associated with the LumA-enriched proteome cluster. Previously reported marker PGK1, a predictor for poor survival in BC, was upregulated in Her2 tumors associated with the LumA-enriched proteome cluster. The clustering of phosphoproteomics data separated intrinsic Basal patients into groups with and without relapse. We identified 3 and 18 genes with upregulated kinase activities in the relapse-free and relapsed Basal patients, respectively. In conclusion, this study provides a high-quality proteogenomic resource for DTBC investigation and identifies potential molecular markers for predicting outcomes for patients with less responsive tumors. The views expressed in this abstract are solely of the authors and do not reflect the official policy of the Departments of Army/Navy/Air Force, DoD, USUHS, HJF. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Praveen-Kumar Raj-Kumar, Tao Liu, Lori A. Sturtz, Albert J. Kovatich, Marina A. Gritsenko, Vladislav A. Petyuk, Brenda Deyarmin, Jianfang Liu, Anupama Praveen-Kumar, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Leonid Kvecher, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Karin D. Rodland, Craig D. Shriver, Zhaoyu Li, Hai Hu. Proteogenomics of laser microdissected difficult-to-treat breast cancers identifies Basal and Her2 patients associated with outcome differences [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 2172.

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

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.

Biomarkers for effective early diagnosis and prognosis of prostate cancer are still lacking. Multiplexed assays for cancer-associated proteins could be useful for identifying biomarkers for cancer detection and stratification. Herein, we report the development of sensitive targeted mass spectrometry assays for simultaneous quantification of 10 prostate cancer-associated proteins in urine. The diagnostic utility of these markers was evaluated with an initial cohort of 20 clinical urine samples. Individual marker concentration was normalized against the measured urinary prostate-specific antigen level as a reference of prostate-specific secretion. The areas under the receiver-operating characteristic curves for the 10 proteins ranged from 0.75 for CXL14 to 0.87 for CEAM5. Furthermore, MMP9 level was found to be significantly higher in patients with high Gleason scores, suggesting a potential of MMP9 as a marker for risk level assessment. Taken together, our work illustrated the feasibility of accurate multiplexed measurements of low-abundance cancer-associated proteins in urine and provided a viable path forward for preclinical verification of candidate biomarkers for prostate cancer.

Background: The treatment of acute myeloid leukemia (AML) patients is a formidable challenge, due to disease heterogeneity and the ability to acquire secondary mutations in response to treatment. As a result, persistence and expansion of residual AML clones prevents complete remission. Emerging evidence suggests that extrinsic factors from the microenvironment protect AML cells from therapy, promote disease progression and drug resistance. Previous work from our lab highlights that higher levels of proinflammatory cytokines in the microenvironment, promote AML progression. This emphasizes the rationale to implement targeted therapies that block extrinsic signaling along with tumor intrinsic changes to overcome drug resistance in AML. Methods and Results: To identify factors and pathways that promote drug sensitivity and resistance in AML, we quantified the levels of 41 cytokines and growth factors from the plasma of 350 newly diagnosed AML patient samples using a multiplex luminex assay. The same cohort were analyzed for drug response to 122 small molecule inhibitors using an ex vivo functional drug sensitivity assay. These data were integrated with the gene expression and reactome pathway data to identify differentially regulated pathways and markers for drug response. Data integration revealed that protein synthesis pathways were significantly enriched in venetoclax, (Bcl-2 inhibitor) resistant AML samples, whereas, cytokine and immune pathways such as aberrant activation of monocyte chemoattractant protein-1 (MCP-1; p= 4.9e-07) were correlated with resistance to trametinib, a MEK inhibitor. In contrast, trametinib sensitivity was associated with activation of IL-1 and TLR signaling, suggesting specific extrinsic pathways may drive specific drug response. As a proof-of-concept, we dissected the role of MCP-1 activation in mediating trametinib resistance in AML. For this, we treated primary AML cells with MCP-1 in the presence of trametinib that led to a 4-fold increase in IC50 (0.4 vs 2.4 µM). To model drug response in vitro, we generated trametinib resistant AML cell lines MOLM13 and MV4;11 (FLT3-ITD mutated) and OCI-AML2 (DNMT3A mutated) by culturing cells in trametinib continuously over 4 months. All the trametinib resistant AML cell lines showed 2-10-fold increase in MCP-1 levels in whole cell lysate and in the conditioned media compared to the parental cells. Further, a long-term exposure of AML cells to MCP-1 in the presence of trametinib conferred a growth advantage with a 4-fold increase in cell numbers in comparison to the cells cultured with trametinib alone (n=4/condition). To delineate the molecular pathways driven by MCP-1 in trametinib resistance, we performed global phosphoproteomics using AML cell lines. We identified that MCP-1 activated various pro-survival pathways such as ERK, JNK, SRC, P70, and PKC kinases as well as cell cycle regulatory proteins as early as 5 mins in trametinib resistant cells. Targeting MCP-1 receptor-CCR2 by pharmacological inhibition, significantly reduced viability of trametinib resistant AML cells by 3-fold. The treatment with CCR2 inhibitor or trametinib individually reduced p-JNK levels by 3-fold in AML cells. A combined treatment with CCR2 inhibitor and trametinib, showed a 7-fold reduction in p-JNK levels. These data reinforced the role of MCP-1 in conferring trametinib resistance and that blockade of MCP-1 signaling re-sensitizes AML cells to trametinib. Conclusion: We show that MCP-1 augments trametinib resistance in AML by triggering novel signaling pathways. These effects can be reversed by blockade of MCP-1-CCR2 axis. Such combination treatment strategy incorporating extrinsic pathways together with the intrinsic ones would aid in re-sensitizing AML cells to therapy and represent an attractive new strategy. Our functional screen identifies that these extrinsic signals modulating drug response are unique to specific targeted therapy and should be considered when designing new therapies and clinical trials. We also created a large resource with 350 primary AML samples correlating microenvironment factors and gene expression with response to 122 small molecules, many of those which are FDA approved. This resource will help identify new drug response pathways in the context of microenvironment to design novel treatment regimens for preclinical and clinical testing and to improve AML outcomes. Disclosures Tyner: Syros: Research Funding; Agios: Research Funding; Incyte: Research Funding; Seattle Genetics: Research Funding; Takeda: Research Funding; Petra: Research Funding; Janssen: Research Funding; Constellation: Research Funding; Aptose: Research Funding; Genentech: Research Funding; Gilead: Research Funding; Array: Research Funding; AstraZeneca: Research Funding.

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.

In acute myeloid leukemia (AML), activating mutations in FLT3 are the most common genetic abnormality. Multiple FLT3 inhibitors have been developed, including the FDA-approved inhibitor gilteritinib. However, AML patients only respond to gilteritinib for approximately 6 months due to the emergence of drug resistance. While gilteritinib eliminates blasts in peripheral circulation, residual blasts in the bone marrow microenvironment are protected by cytokines and growth factors. Persistence of these residual cells represents early resistance to treatment. How these cells adapt to survive in the marrow microenvironment remains unclear, but over time resistant subclones resume growth and lead to relapsed disease. At relapse, many patients have intrinsic resistance mutations, what we term late resistance. In this study, we used a stepwise model that charts the temporal evolution of early to late gilteritinib resistance. To recapitulate early resistance, we cultured the FLT3-ITD+ AML cell lines, MOLM-14 and MV4;11, with exogenous microenvironmental ligands that allow cells to become resistant to gilteritinib in a ligand-dependent manner. After 7 weeks, all cultures with ligand resumed growth (early resistance), whereas cells without ligand never resumed growth. Following ligand withdrawal, the cells become transiently sensitive to gilteritinib but resumed growth after 2 months (late resistance). We comprehensively analyzed early and late resistance by integrating whole exome sequencing, CRISPR/Cas9 screening, proteomics, metabolomics, and small-molecule inhibitor screening. Early resistance is characterized by slowly dividing cells and metabolic reprogramming, particularly with respect to lipid metabolism. Early resistant cultures also became uniquely dependent on Aurora kinase B (AURKB) for survival. We then validated these findings in primary AML cells from patients (N=11) treated with gilteritinib and found that early resistant cells demonstrated reduced cell cycle and alterations in lipid metabolism. Gene expression analysis of sequential stromal cell samples from AML patients (N=13) pre- and post gilteritinib treatment showed an increase in lipid metabolism following gilteritinib treatment, indicating that the microenvironment is also dynamic and in crosstalk with neighboring AML cells. Primary early resistant AML cells also became dependent on AURKB signaling, and were exquisitely sensitive to the combination of AURKB inhibitors and gilteritinib. In contrast, late resistance is driven by an expansion of pre-existing NRAS mutant subclones, consistent with the resistance profile of AML patients on gilteritinib. Metabolic reprogramming continued to evolve in late resistance with further dependence upon lipid metabolism. Our study provides mechanistic understanding of how the marrow microenvironment contributes to extrinsic early resistance, which then leads to late intrinsic resistance. We also define a unique vulnerability to AURKB inhibitors in early resistance that may thwart the expansion of late resistant NRAS subclones. Citation Format: Sunil K. Joshi, Tamilla Nechiporuk, Daniel Bottomly, Paul Piehowski, Julie A. Reisz, Janét Pittsenbarger, Andy Kaempf, Sara J.C. Gosline, Yi-Ting Wang, Tao Liu, Cristina E. Tognon, Angelo D’Alessandro, Jeffrey W. Tyner, Shannon K. McWeeney, Karin D. Rodland, Brian J. Druker, Elie Traer. The AML microenvironment catalyzes a step-wise evolution to gilteritinib resistance [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT022.

Cancer is among the major public health problems worldwide, representing the leading cause of morbidity and mortality in industrialized countries [...]

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.

SUMMARY The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this “proteogenomics” approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases.Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy., In Brief Breast cancer is a highly heterogeneous disease with variable outcomes and subtype-driven treatment approaches, making precision medicine a considerable challenge. Proteogenomic analyses of 122 primary breast cancers provide insights into clinically relevant biology, including cell cycle dysregulation, tumor immunogenicity, aberrant metabolism, and heterogeneity in therapeutic target expression., Graphical Abstract

Endometrial cancer (EC) is the sixth most common cancer in women globally and is one of the few cancers for which mortality is increasing; to address the need to develop new methods for the diagnosis and treatment of this disease, we performed a comprehensive proteogenomic characterization of 95 endometrial carcinomas. We prospectively collected 83 endometrioid and 12 serous tumors, along with 49 normal tissue samples, for multi-omic characterization. Each sample underwent whole exome sequencing, whole genome sequencing, total RNA and miRNA sequencing, and DNA methylation array analyses. In addition, we evaluated all samples for protein, phosphorylation, and acetylation levels using an integrated, isobaric tandem mass tag labeling–based quantitative proteomics workflow. This dataset, publicly available through the Genomic Data Commons, CPTAC data portal, and other sources, provides a valuable resource for researchers and clinicians. Our analyses of these tumors revealed, among other things, a potential role for circRNAs in the epithelial-mesenchymal transition and new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. We found possible new consequences of perturbations to the Wnt/β-catenin pathway, particularly CTNNB1 hotspot mutations; moreover, from the genome-wide acetylation survey, we found potential regulatory mechanisms linking Wnt signaling and histone acetylation. Phosphorylation data analysis suggests an elevated DNA damage response in serous tumors, which we hypothesize may be targetable by certain FDA-approved drugs. We found that TP53 hotspot mutations play unique trans-acting roles as compared to the same mutations in colon and ovarian cancers. 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; of particular translational interest, we identified a subset of patients who, despite having high amounts of microsatellite instability, exhibit biomarkers which suggest that immunotherapy would be an ineffective treatment for their disease. By integrating global measurements of protein abundance, phosphorylation, and acetylation with next-generation genomics and transcriptomics, we have provided novel insights into biological processes underlying the development of endometrial cancers and generated strong hypotheses for new approaches to personalized treatment of individuals with endometrial cancer. Citation Format: Emily A. Kawaler, Yongchao Dou, Daniel Cui Zhou, Marina A. Gritsenko, Karin D. Rodland, Li Ding, Bing Zhang, Tao Liu, David Fenyö, Clinical Proteomic Tumor Analysis Consortium. Proteogenomic characterization of endometrial carcinoma [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 6580.

Background: Acute myeloid leukemia (AML) is a heterogeneous disease with a dismal 5 year survival rate below 30%. Current AML therapies have provided little improvement in achieving complete remission. This is attributed to the development of drug resistance and extrinsic factors from the microenvironment that promote AML progression. Previously, our lab demonstrated that proinflammatory cytokines from the bone marrow microenvironment such as IL-1, promote AML progression. This emphasizes the rationale to design and implement targeted therapies to block extrinsic pathways coupled with intrinsic pathways conferring drug resistance in AML. Methods: To identify factors that promote drug sensitivity and resistance in AML, we used a cohort of 350 AML patients with various genetic subtypes. We quantified levels of 41 growth factors in plasma samples using a multiplex luminex assay. The same cohort of AML samples were analyzed for drug response to 130 small molecule inhibitors in in vitro functional drug sensitivity assay, for RNA-seq gene expression, and whole exome sequencing. The data was integrated to identify differential pathways and markers for drug response. To model drug response in vitro we created a series of drug resistant AML cell lines by culturing these cells in specific drugs long-term. Results: Pathway analysis integrating drug sensitivity, transcriptome, and cytokine expression data identified distinct differentially regulated pathways for various inhibitors. We found that protein synthesis pathways were significantly enriched in sorafenib (FLT-3 inhibitor) resistant samples whereas cytokine and immune pathways (such as TLR and MCP1 signaling) were correlated with trametinib (MEK inhibitor) and venetoclax (Bcl-2 inhibitor) resistance. Specifically, venetoclax resistance correlated with augmented levels of cytokines such as IL-1 and IP-10 and growth factors such as PDGF. Further, trametinib resistance in AML samples is correlated with the upregulation of CCL11, MCP1, and TGFα but a downregulation of CCL22 levels. Accordingly, using AML cell line models, we observed an increase in MCP1 and TGFα at the transcript and secreted protein levels in trametinib resistant cell lines. We demonstrated that MCP1 stimulation activates survival pathways by activating pERK and pJNK signaling, thus reducing apoptosis in AML cells. Targeting MCP1 in combination with trametinib reverses these effects, thus offering a novel therapeutic approach to overcome drug resistance. Conclusion and perspectives: Our data suggest that distinct extrinsic pathways may regulate the response to specific targeted therapy, thus providing a strong basis to design new treatment regimens. Combination treatment incorporating extrinsic pathways would aid in sensitizing AML cells to therapy. Our study offers an integrated approach and a resource to identify such pathways for a large number of tested drugs, and narrows down functionally important pathways associated with drug response. Citation Format: Rucha V. Modak, Alisa Damnernsawad, Ted Laderas, Guanming Wu, Jeffery W. Tyner, Karin D. Rodland, Shannon K. McWeeney, Anupriya Agarwal. An integrated approach reveals novel extrinsic pathways and cytokine signatures associated with drug response in acute myeloid leukemia [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 5942.

SUMMARY 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., Graphical Abstract, In Brief McDermott et al. present the proteogenomic analysis of prospectively collected ovarian high-grade serous cancer samples and appropriate normal precursor samples under tight ischemic control. They identify tumor-associated signaling pathways and mitotic and cyclin-dependent kinases as key oncogenic drivers potentially related to chromosomal instability.

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.

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis &ge, 1-year post-RP, (ii) biochemical recurrence &ge, 1-year post-RP, and (iii) no progression after &ge, 10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

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

SUMMARY 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., Graphical Abstract, In Brief Comprehensive proteogenomic characterization in 103 treatment-naive clear cell renal cell carcinoma patient samples highlights tumor-specific alterations at the proteomic level that are unrevealed by transcriptomic profiling and proposes a revised subtyping scheme based on integrated omics analysis.

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Each year, thousands of patients are at risk of cerebral ischemic injury, due to iatrogenic responses to surgical procedures. Prophylactic treatment of these patients as standard care could minimize potential neurological complications. We have shown that protection of brain tissue, in a non-human primate model of cerebral ischemic injury, is possible through pharmacological preconditioning using the immune activator D192935. We postulate that preconditioning with D192935 results in neuroprotective reprogramming that is evident in the brain following experimentally induced cerebral ischemia. We performed quantitative proteomic analysis of cerebral spinal fluid (CSF) collected post-stroke from our previously published efficacy study to determine whether CSF protein profiles correlated with induced protection. Four groups of animals were examined: naive animals (no treatment or stroke); animals treated with vehicle prior to stroke; D192935 treated and stroked animals, further delineated into two groups, ones that were protected (small infarcts) and those that were not protected (large infarcts). We found that distinct protein clusters defined the protected and non-protected animal groups, with a 16-member cluster of proteins induced exclusively in D192935 protected animals. Seventy percent of the proteins induced in the protected animals have functions that would enhance neuroprotection and tissue repair, including several members associated with M2 macrophages, a macrophage phenotype shown to contribute to neuroprotection and repair during ischemic injury. These studies highlight the translational importance of CSF biomarkers in defining mechanism and monitoring responses to treatment in development of stroke therapeutics.

Renal micropuncture and renal 2-photon imaging are seminal techniques in renal physiology. However, micropuncture is limited by dependence on conventional microscopy to surface nephron features, and 2-photon studies are limited in that interventions can only be assessed at the organ, rather than the nephron level. In particular, micropuncture studies of the glomeruli of mice have been challenged by the paucity of surface glomeruli in mice. To address this limitation in order to pursue studies of aspirate from Bowman's space in mouse physiologic models, we developed 2-photon glomerular micropuncture. We present a novel surgical preparation that allows lateral access to the kidney while preserving the required vertical imaging column for 2-photon microscopy. Administration of high molecular weight fluorescein isothiocyanate (FITC)-dextran is used to render the renal vasculature and therefore glomeruli visible for 2-photon imaging. A quantum dot-coated pipette is then introduced under stereotactic guidance to a glomerulus selected from the several to many which may be visualized within the imaging window. In this protocol, we provide details of the preparation, materials, and methods necessary to carry out the procedure. This technique facilitates previously-impossible physiologic study of the kidney, including recovery of filtrate from Bowman's space and all segments of the nephron within the imaging depth limit, about 100 µm below the renal capsule. Pressure, charge and flow may all be measured using the introduced pipette. Here, we provide representative data from liquid chromatography/mass spectrometry performed on aspirate from Bowman's space. We expect this technique to have wide applicability in renal physiologic investigation.

Acute cardiorenal syndrome (CRS-1) is a morbid complication of acute cardiovascular disease. Heart-to-kidney signals transmitted by "cardiorenal connectors" have been postulated, but investigation into CRS-1 has been limited by technical limitations and a paucity of models. To address these limitations, we developed a translational model of CRS-1, cardiac arrest and cardiopulmonary resuscitation (CA/CPR), and now report findings from nanoscale mass spectrometry proteomic exploration of glomerular filtrate 2 hours after CA/CPR or sham procedure. Filtrate acquisition was confirmed by imaging, molecular weight and charge distribution, and exclusion of protein specific to surrounding cells. Filtration of proteins specific to the heart was detected following CA/CPR and confirmed with mass spectrometry performed using urine collections from mice with deficient tubular endocytosis. Cardiac LIM protein was a CA/CPR-specific filtrate component. Cardiac arrest induced plasma release of cardiac LIM protein in mice and critically ill human cardiac arrest survivors, and administration of recombinant cardiac LIM protein to mice altered renal function. These findings demonstrate that glomerular filtrate is accessible to nanoscale proteomics and elucidate the population of proteins filtered 2 hours after CA/CPR. The identification of cardiac-specific proteins in renal filtrate suggests a novel signaling mechanism in CRS-1. We expect these findings to advance understanding of CRS-1.

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of the tumor peptidome has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the tumors peptidome information in cancer research have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification, and the prevalence of confounding factors and biases associated with sample handling and processing. To address this need, we have recently developed an effective and robust analytical platform as well as a novel informatics approach for comprehensive analyses of tissue peptidomes. The ability of this new peptidomics pipeline for high-throughput, comprehensive, and quantitative peptidomics analysis, as well as the suitability of clinical ovarian tumor samples with postexcision delay limited to less than 60min before freezing for peptidomics analysis, has been demonstrated. These initial analyses set a stage for further determination of molecular details and functional significance of the peptidomic activities in ovarian cancer.

Breaking down the silos between disciplines to accelerate the pace of cancer research is a key paradigm for the Cancer Moonshot. Molecular analyses of cancer biology have tended to segregate between a focus on nucleic acids – DNA, RNA and their modifications – and a focus on proteins and protein function. Proteogenomics represents a fusion of those two approaches, leveraging the strengths of each to provide a more integrated vision of the flow of information from DNA to RNA to protein and eventually function at the molecular level. Proteogenomic studies have been incorporated into multiple activities associated with the cancer moonshot, demonstrating substantial added value. Innovative study designs integrating genomic, transcriptomic and proteomic data, particularly those using clinically relevant samples and involving clinical trials, are poised to provide new insights regarding cancer risk, progression, and response to therapy.

There is an unmet technical challenge for mass spectrometry (MS)-based proteomic analysis of single mammalian cells. Quantitative proteomic analysis of single cells has been previously achieved by antibody-based immunoassays but is limited by the availability of high-quality antibodies. Herein we report a facile targeted MS-based proteomics method, termed cPRISM-SRM (carrier-assisted high-pressure, high-resolution separations with intelligent selection and multiplexing coupled to selected reaction monitoring), for reliable analysis of low numbers of mammalian cells. The method capitalizes on using “carrier protein” to assist processing of low numbers of cells with minimal loss, high-resolution PRISM separation for target peptide enrichment, and sensitive SRM for protein quantification. We have demonstrated that cPRISM-SRM has sufficient sensitivity to quantify proteins expressed at ≥200,000 copies per cell at the single-cell level and ≥3000 copies per cell in 100 mammalian cells. We envision that with further improvement cPRISM-SRM has the potential to move toward targeted MS-based single-cell proteomics.

Interleukin-1 receptor-associated kinase 1 (IRAK1), an essential mediator of innate immunity and inflammatory responses, is constitutively active in multiple cancers. We evaluated the role of IRAK1 in acute myeloid leukemia (AML) and assessed the inhibitory activity of multikinase inhibitor pacritinib on IRAK1 in AML. We demonstrated that IRAK1 is overexpressed in AML and provides a survival signal to AML cells. Genetic knockdown of IRAK1 in primary AML samples and xenograft model showed a significant reduction in leukemia burden. Kinase profiling indicated pacritinib has potent inhibitory activity against IRAK1. Computational modeling combined with site-directed mutagenesis demonstrated high-affinity binding of pacritinib to the IRAK1 kinase domain. Pacritinib exposure reduced IRAK1 phosphorylation in AML cells. A higher percentage of primary AML samples showed robust sensitivity to pacritinib, which inhibits FLT3, JAK2, and IRAK1, relative to FLT3 inhibitor quizartinib or JAK1/2 inhibitor ruxolitinib, demonstrating the importance of IRAK1 inhibition. Pacritinib inhibited the growth of AML cells harboring a variety of genetic abnormalities not limited to FLT3 and JAK2. Pacritinib treatment reduced AML progenitors in vitro and the leukemia burden in AML xenograft model. Overall, IRAK1 contributes to the survival of leukemic cells, and the suppression of IRAK1 may be beneficial among heterogeneous AML subtypes.

Background Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ~10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for early detection, diagnosis, prognosis or targeted therapy of prostate cancer. Moreover, the SPOP mutation sites are distributed in a relatively short region with multiple lysine residues, posing significant challenges for bottom-up proteomics analysis of the SPOP mutations. Methods To address this issue, PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry assays have been developed for quantifying wild-type SPOP protein and 11 prostate cancer-derived SPOP mutations. Results Despite inherent limitations due to amino acid sequence constraints, all the PRISM-SRM assays developed using Arg-C digestion showed a linear dynamic range of at least two orders of magnitude, with limits of quantification ranged from 0.1 to 1 fmol/μg of total protein in the cell lysate. Applying these SRM assays to analyze HEK293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations; however, at present, it is unknown if this also affects the biological activity of the SPOP protein. Conclusions In summary, PRISM-SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, providing significant potential in biomarker development for prostate cancer. Electronic supplementary material The online version of this article (doi:10.1186/s12967-017-1276-7) contains supplementary material, which is available to authorized users.

Mass spectrometry (MS) based targeted proteomic methods such as selected reaction monitoring (SRM) are emerging as a promising tool for verification of candidate proteins in biological and biomedical applications. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute has investigated the standardization and analytical validation of the SRM assays and demonstrated robust analytical performance on different instruments across different laboratories. An Assay Portal has also been established by CPTAC to provide the research community a resource consisting of large sets of targeted MS-based assays, and a depository to share assays publicly. Herein, we report the development of 98 SRM assays that have been thoroughly characterized according to the CPTAC Assay Characterization Guidance Document; 37 of these passed all five experimental tests. The assays cover 70 proteins previously identified at the protein level in ovarian tumors. The experiments, methods and results for characterizing these SRM assays for their MS response, repeatability, selectivity, stability, and endogenous detection are described in detail. Data are available via PeptideAtlas, Panorama and the CPTAC Assay Portal.

Protein modification by O-linked β-N-acetylglucosamine (O-GlcNAc) is emerging as an important factor in the pathogenesis of sporadic Alzheimer's disease (AD); however, detailed molecular characterization of this important protein post-translational modification at the proteome level has been highly challenging, owing to its low stoichiometry and labile nature. Herein, we report the most comprehensive, quantitative proteomics analysis for protein O-GlcNAcylation in postmortem human brain tissues with and without AD by the use of isobaric tandem mass tag labelling, chemoenzymatic photocleavage enrichment, and liquid chromatography coupled to mass spectrometry. A total of 1850 O-GlcNAc peptides covering 1094 O-GlcNAcylation sites were identified from 530 proteins in the human brain. One hundred and thirty-one O-GlcNAc peptides covering 81 proteins were altered in AD brains as compared with controls (q

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches.

A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population who ultimately develops lung cancer. Previously we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at-risk for lung cancer. Proteins were identified by shotgun proteomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and selected candidate biomarkers of risk were validated in an independent sample cohort by parallel reaction monitoring. The purpose of the current study is to validate, in independent cohorts from Vanderbilt University Medical Center, a selected list of 54 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM) at the Pacific Northwest National Laboratory. We tested proteins from bronchial brushings and bronchial biopsies collected from individuals at low risk (n=10) and high risk (n=10) based on the Tammemagi risk stratification model as well as from patients with lung cancer (n=10). Multiplexed LC-SRM assays were developed for all 54 proteins and the protein concentrations were determined by LC-SRM using heavy isotope-labeled peptides for 44 and 49 proteins in the brushings and biopsy samples, respectively. Pairwise comparisons by Wilcoxon rank sum test demonstrated significant overexpression, in the high-risk group compared to the low-risk group, of ALDH3A1 and AKR1B10 in both brushings and biopsies, and of SFN/YWHAS only in the biopsies. Significant overexpression of ALDH1A1, ALDH3A1, AKR1B10, ANXA1, CTNNB1, G6PD, LGALS7B, ALOX15, PGK1, SFN/YWHAS, and UBA1 were observed in the lung cancer group compared to the low-risk group in the brushings only. LGALS7B was also overexpressed in the brushings of high risk compared to lung cancer group. In conclusion, these SRM results revealed promise of selected candidate proteins to stratify the at-risk population for lung cancer. Next, our validation efforts will test the promising candidates in second, larger independent cohort; methods with increased sensitivity such as PRISM (high-pressure, high-resolution separations with intelligent selection and multiplexing)-SRM for analysis of the previously undetected low abundant protein candidates and carrier-assisted SRM approaches for analysis of very small-sized samples. Ultimately, we hope to deliver a signature of risk that may provide the basis for lung cancer risk assessment and possibly novel future prevention strategies. This work is supported by UO1CA152662. Citation Format: SM Jamshedur Rahman, Yuqian Gao, Athena A. Schepmoes, Yi-Ting Wang, Tujin Shi, Sheau-Chiann Chen, Heidi Chen, Karin D. Rodland, Tao Liu, Pierre P. Massion. Validation of a proteomic signature of lung cancer risk from bronchial brushings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1620.

We prospectively collected matched tumor specimens, adjacent non-tumor tissues, and blood samples from 110 colon cancer patients and analyzed the samples using seven omics platforms, including whole-exome sequencing, copy number arrays, RNA-Seq, miRNA-Seq, label-free global proteomics, isobaric tandem mass tag (TMT) labeling-based global proteomics, and TMT-based phosphoproteomics. Comparative proteomic and phosphoproteomic analysis of paired tumor and adjacent normal samples produced the first comprehensive catalogue of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers and drug targets. These cancer-associated proteins and phosphosites had very little overlap with known cancer genes in the Cancer Gene Census, providing a novel information layer to our knowledge about colon cancer. One notable finding in differential proteome analysis is the identification of several cancer/testis antigens that were recurrently over-expressed in tumors compared to adjacent normal tissue, including IGF2BP3 (51%), SPAG1 (14%), and ATAD2 (8%). Through integrative analysis of the whole-exome sequencing, RNA-Seq, and proteomics data, we further predicted personalized neoantigens for 38% of the patients. In total, we found proteomics-supported neoantigens or cancer/testis antigens for 78% of the tumors in this cohort, demonstrating the potential of proteogenomics in identifying tumor antigens for cancer vaccine development. Proteomics data complemented somatic copy number analysis results and showed that multiple somatic copy number deletion events converge to repress the endocytosis pathway, suggesting its tumor suppressor role in colon cancer. In addition to reinforcing or complementing genomic findings, proteogenomic integration may also contradict genomics data-based inferences and lead to unexpected discoveries and therapeutic opportunities. Proteomics data identified SOX9 as an oncogene in colon cancer, whereas it was predicted to be a tumor suppressor based on somatic mutation data in the TCGA study. Phosphoproteomics data revealed a dual role of Rb phosphorylation in promoting proliferation and repressing apoptosis in colon cancer, clarifying the long-standing puzzle of colon cancer-specific amplification of this tumor suppressor and highlighting a unique opportunity for targeting Rb phosphorylation in colon cancer. Microsatellite instability status has been approved by the FDA as a biomarker for selecting patients for checkpoint inhibitor therapy in colorectal and other solid tumors. However, many MSI-high tumors fail to respond to checkpoint inhibition. Our proteogenomic analysis identified a subtype-specific association between increased glycolysis and decreased CD8 T cell infiltration in MSI-high colon tumors, suggesting glycolysis as a target for overcoming immune evasion in this MSI-H tumors. We make the primary and processed datasets available in publicly accessible data repositories and portals to allow broad use of these datasets for new biological discoveries and therapeutic hypothesis generation. Citation Format: Bing Zhang, Suhas Vasaikar, Chen Huang, Xiaojing Wang, Vladislav A. Petyuk, Sara R. Savage, Bo Wen, Yongchao Dou, Yun Zhang, Zhiao Shi, Osama A. Arshad, Marina A. Gritsenko, Lisa J. Zimmerman, Jason E. McDermott, Therese R. Clauss, Ronald J. Moore, Rui Zhao, Matthew E. Monroe, Yi-Ting Wang, Matthew C. Chambers, Robbert J. Slebos, Ken S. Lau, Qianxing Mo, Li Ding, Matthew Ellis, Mathangi Thiagarajan, Christopher R. Kinsinger, Henry Rodriguez, Richard D. Smith, Karin D. Rodland, Daniel C. Liebler, Tao Liu, CPTAC Investigators. Proteogenomic characterization of human colon cancer reveals new therapeutic opportunities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-006.

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

Anterior gradient 2 (AGR2) is a secreted, cancer-associated protein in many types of epithelial cancer cells. We developed a highly sensitive targeted mass spectrometric assay for quantification of AGR2 in urine and serum. Digested peptides from clinical samples were processed by PRISM (high pressure and high resolution separations coupled with intelligent selection and multiplexing), which incorporates high pH reversed-phase liquid chromatography (LC) separations to fractionate and select target fractions for follow-on LC-selected reaction monitoring (LC-SRM) analyses. The PRISM-SRM assay for AGR2 showed a reproducibility of

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process.Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme.Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection.Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort.Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

Various genetic mutations associated with cancer are known to alter cell signaling, but it is not clear whether they dysregulate signaling pathways by altering the abundance of pathway proteins. Using a combination of RNA sequencing and ultrasensitive targeted proteomics, we defined the primary components—16 core proteins and 10 feedback regulators—of the epidermal growth factor receptor (EGFR)–mitogen-activated protein kinase (MAPK) pathway in normal human mammary epithelial cells and then quantified their absolute abundance across a panel of normal and breast cancer cell lines as well as fibroblasts. We found that core pathway proteins were present at very similar concentrations across all cell types, with a variance similar to that of proteins previously shown to display conserved abundances across species. In contrast, EGFR and transcriptionally controlled feedback regulators were present at highly variable concentrations. The absolute abundance of most core proteins was between 50,000 and 70,000 copies per cell, but the adaptors SOS1, SOS2, and GAB1 were found at far lower amounts (2000 to 5000 copies per cell). MAPK signaling showed saturation in all cells between 3000 and 10,000 occupied EGFRs, consistent with the idea that adaptors limit signaling. Our results suggest that the relative stoichiometry of core MAPK pathway proteins is very similar across different cell types, with cell-specific differences mostly restricted to variable amounts of feedback regulators and receptors. The low abundance of adaptors relative to EGFR could be responsible for previous observations that only a fraction of total cell surface EGFR is capable of rapid endocytosis, high-affinity binding, and mitogenic signaling.

SummaryEven though hyperthermia is a promising treatment for cancer, the relationship between specific temperatures and clinical benefits and predictors of sensitivity of cancer to hyperthermia is poorly understood. Ovarian and uterine tumors have diverse hyperthermia sensitivities. Integrative analyses of the specific gene signatures and the differences in response to hyperthermia between hyperthermia-sensitive and -resistant cancer cells identified CTGF as a key regulator of sensitivity. CTGF silencing sensitized resistant cells to hyperthermia. CTGF small interfering RNA (siRNA) treatment also sensitized resistant cancers to localized hyperthermia induced by copper sulfide nanoparticles and near-infrared laser in orthotopic ovarian cancer models. CTGF silencing aggravated energy stress induced by hyperthermia and enhanced apoptosis of hyperthermia-resistant cancers.

INTRODUCTION: The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. AREAS COVERED: In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. EXPERT OPINION: Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.