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Leveraging biased signaling of G protein-coupled receptors has been proposed as a promising strategy for the development of drugs with higher specificity. However, the consequences of selectively targeting G protein- or β-arrestin-mediated signaling on cellular functions are not comprehensively understood. In this study, we utilized phosphoproteomics to gain a systematic overview of signaling induced by the four biased and balanced dopamine D2 receptor (D

Coiled-coil-helix-coiled-coil-helix domain-containing 2, a mitochondrial protein, encoded by CHCHD2 is located at chromosome 7p11.2 and proximal to the EGFR gene. Here, bioinformatic analyses revealed that CHCHD2 is consistently coamplified with EGFR in non–small cell lung carcinoma (NSCLC). In addition, CHCHD2 and EGFR protein expression levels were positively correlated and upregulated relative to normal lung in NSCLC tumor-derived xenografts. Knockdown of CHCHD2 expression in NSCLC cells attenuated cell proliferation, migration, and mitochondrial respiration. CHCHD2 protein–protein interactions were assessed by the complementary approaches of affinity purification mass spectrometry and in vivo proximity ligation. The CHCHD2 interactome includes the apparent hub proteins C1QBP (a mitochondrial protein) and YBX1 (an oncogenic transcription factor), and an overlapping set of hub-associated proteins implicated in cell regulation.Implications: CHCHD2 influences mitochondrial and nuclear functions and contributes to the cancer phenotype associated with 7p11.2 amplification in NSCLC. Mol Cancer Res; 13(7); 1119–29. ©2015 AACR.

Figure S2. Analysis of ECAR in NSCLC cell lines

Table S5. CHCHD2 interacting proteins identified by BioID

Driven by the lack of targeted therapies, triple-negative breast cancers (TNBCs) have the worst overall survival of all breast cancer subtypes. Considering that cell surface proteins are favorable drug targets and are predominantly glycosylated, glycoproteome profiling has significant potential to facilitate the identification of much-needed drug targets for TNBCs. Here, we performed

Cancer metabolism adapts the metabolic network of its tissue of origin. However, breast cancer is not a disease of a single origin. Multiple epithelial populations serve as the culprit cell of origin for specific breast cancer subtypes, yet our knowledge of the metabolic network of normal mammary epithelial cells is limited. Using a multi-omic approach, here we identify the diverse metabolic programmes operating in normal mammary populations. The proteomes of basal, luminal progenitor and mature luminal cell populations revealed enrichment of glycolysis in basal cells and of oxidative phosphorylation in luminal progenitors. Single-cell transcriptomes corroborated lineage-specific metabolic identities and additional intra-lineage heterogeneity. Mitochondrial form and function differed across lineages, with clonogenicity correlating with mitochondrial activity. Targeting oxidative phosphorylation and glycolysis with inhibitors exposed lineage-rooted metabolic vulnerabilities of mammary progenitors. Bioinformatics indicated breast cancer subtypes retain metabolic features of their putative cell of origin. Thus, lineage-rooted metabolic identities of normal mammary cells may underlie breast cancer metabolic heterogeneity and targeting these vulnerabilities could advance breast cancer therapy.

Resistance to genotoxic therapies and tumor recurrence are hallmarks of glioblastoma (GBM), an aggressive brain tumor. Here, we explore the functional drivers of post-treatment recurrent GBM. By conducting genome-wide CRISPR-Cas9 screens in patient-derived GBM models, we uncover distinct genetic dependencies in recurrent tumor cells that were absent in their patient-matched primary predecessors, accompanied by increased mutational burden and differential transcript and protein expression. These analyses map a multilayered genetic response to drive tumor recurrence, identifying protein tyrosine phosphatase 4A2 (PTP4A2) as a novel modulator of self-renewal, proliferation and tumorigenicity at GBM recurrence. Mechanistically, genetic perturbation or small molecule inhibition of PTP4A2 represses axon guidance activity through a dephosphorylation axis with roundabout guidance receptor 1 (ROBO1), exploiting a functional dependency on ROBO signaling. Importantly, engineered anti-ROBO1 single-domain antibodies also mimic the effects of PTP4A2 inhibition. We conclude that functional reprogramming drives tumorigenicity and dependence on a multi-targetable PTP4A2-ROBO1 signaling axis at GBM recurrence.

Glioblastoma (GBM) is characterized by extensive cellular and genetic heterogeneity. Its initial presentation as primary disease (pGBM) has been subject to exhaustive molecular and cellular profiling. By contrast, our understanding of how GBM evolves to evade the selective pressure of therapy is starkly limited. The proteomic landscape of recurrent GBM (rGBM), which is refractory to most treatments used for pGBM, are poorly known. We, therefore, quantified the transcriptome and proteome of 134 patient-derived pGBM and rGBM samples, including 40 matched pGBM-rGBM pairs. GBM subtypes transition from pGBM to rGBM towards a preferentially mesenchymal state at recurrence, consistent with the increasingly invasive nature of rGBM. We identified immune regulatory/suppressive genes as important drivers of rGBM and in particular 2-5-oligoadenylate synthase 2 (OAS2) as an essential gene in recurrent disease. Our data identify a new class of therapeutic targets that emerge from the adaptive response of pGBM to therapy, emerging specifically in recurrent disease and may provide new therapeutic opportunities absent at pGBM diagnosis.

Axons are complex subcellular compartments that are extremely long in relation to cell bodies, especially in peripheral nerves. Many processes are required and regulated during axon injury, including anterograde and retrograde transport, glia-to-axon macromolecular transfer, and local axonal protein synthesis. Many in vitro omics approaches have been used to gain insight into these processes, but few have been applied in vivo. Here we adapted the osmotic ex vivo axoplasm isolation method and analyzed the adult rat sciatic-nerve-extruded axoplasm by label-free quantitative proteomics before and after injury. 2087 proteins groups were detected in the axoplasm, revealing translation machinery and microtubule-associated proteins as the most overrepresented biological processes. Ribosomal proteins (73) were detected in the uninjured axoplasm and increased their levels after injury but not within whole sciatic nerves. Meta-analysis showed that detected ribosomal proteins were present in in vitro axonal proteomes. Because local protein synthesis is important for protein localization, we were interested in detecting the most abundant newly synthesized axonal proteins in vivo. With an MS/MS-BONCAT approach, we detected 42 newly synthesized protein groups. Overall, our work indicates that proteomics profiling is useful for local axonal interrogation and suggests that ribosomal proteins may play an important role, especially during injury.

Triple negative breast cancer (TNBC) stands out due to its aggressive course and high metastatic rates. No consistent protein biomarker has been described for TNBC, resulting in a scarcity of adjuvant therapies for the afflicted patients. Hence, there is an urgent need to determine novel TNBC-associated proteins for the development of new therapeutics and the identification of novel molecular mechanisms driving this malignancy. Cell surface proteins represent attractive targets for novel therapies, due to their easily accessible localization and their involvement in essential signaling pathways. Objectives: We aimed to uncover novel TNBC-associated cell surface proteins involved in carcinogenesis and metastasis for the development of novel targeted therapeutics. Rationale: Taking advantage of the fact that cell surface proteins are often N-glycosylated, we employed hydrazide chemistry to isolate N-glycopeptides. The glycoproteome of six immortalized TNBC cell lines and five ‘healthy’ controls (HC) (the immortalized cell line MCF10A and four patient-derived human mammary epithelial cell lines) was analyzed using LC-MS/MS and label-free quantification. A data mining strategy was employed to select candidates of interest. We knocked-down (k.d.) the top five candidates of interest (using siRNA technology) and evaluated cell growth using growth-curve analysis. Plexin B3 (PLXNB3) was selected for subsequent functional validations. The effects of PLXNB3 k.d. (using siRNA and CRISPR technology) on cancer cell growth, apoptosis and adhesion were interrogated using western blotting and standard cell biology protocols.Results: The N-glycoproteomics approach led to the identification of 1044 glycoproteins, with over 70% described as plasma membrane/secreted proteins. Gene Ontology analysis revealed that the TNBC-associated glycoproteome was enriched in biological processes such as: mesenchyme development, MAPK signaling, positive regulation of proliferation and regulation of neuron projection development. Candidates of interest were selected from our list by focusing on those plasma membrane proteins that were enriched in TNBC cells compared to HC according to our label-free quantified proteomics data, and that showed limited expression in healthy tissues according to the Human Protein Atlas. We further restricted the list of candidates to those proteins whose k.d. impaired cancer cell growth, with little effect on the HC. PLXNB3, an understudied protein typically expressed in healthy neuronal tissues, was selected for in depth functional analysis. Elevated mRNA PLXNB3 levels in breast cancer patients is associated with poorer overall survival compared to low mRNA expression (Integrated TCGA Pan-Cancer Clinical Data Resource, 2018). PLXNB3 k.d. (using siRNA and CRISPR technologies) impaired TNBC cell growth (in both adherent and spheroid cultures) and was associated with elevated levels of cleaved-caspase 3 and cleaved-caspase 7 compared to scrambled controls. Furthermore, PLXNB3 k.d. negatively impacted TNBC cell adhesion to extracellular matrixes compared to scrambled controls. Subsequent tests will evaluate PLXNB3’s role in tumor growth and metastasis in vivo, using orthotopic tumor models in immunocompromised mice. Citation Format: Laura Kuhlmann, Ankit Sinha, Vladimir Ignatchenko, Andrew Macklin, Lydia Liu, Meinusha Govindarajan, Amanda Khoo, Salvador Mejia-Guerrero, Jennifer Cruickshank, Hal Berman, Thomas Kislinger. Identification of cell surface proteins in triple negative breast cancer for the development of novel targeted therapies [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS18-31.

BackgroundChordomas are rare, clinically aggressive tumors with a median survival of 6-7 years and a high rate of disease progression despite maximal surgery and radiotherapy. Given the limited options available to prevent and treat progression and relatively poor prognosis, there remains an urgent need for the development of novel therapies to improve clinical outcomes. Cell-surface proteins are attractive therapeutic targets due to their accessible subcellular localization.MethodsHere, we used a proteomics approach to identify novel chordoma-specific cell-surface protein markers. Four established chordoma cell lines were analyzed by quantitative proteomics using a comprehensive differential ultracentrifugation organellar fractionation approach. A subtractive proteomics strategy was applied to select proteins that are plasma membrane enriched. Using commercially available antibodies, the expression profiles of these cell-surface proteins were validated across chordoma cell lines, patient surgical tissue samples, and normal tissue lysates via Western blotting. The candidates were further validated by immunohistochemical analysis in a 25-patient tissue cohort. Finally, the essentiality of these candidates for in vitro chordoma growth was evaluated.ResultsMass spectrometry-based proteomics identified 120 high-confidence cell-surface proteins in four established chordoma cell line models. Systematic data integration prioritized two chordoma-specific cell surface proteins for further interrogation. Immunohistochemistry in a richly annotated cohort of chordoma tumor tissues revealed that PLA2R1 and SLC6A12 are broadly expressed in chordoma patient samples. Higher expression of PLA2R1 correlated with poor prognosis whereas SLC6A12 expression was significantly enriched in skull-base chordomas compared to those arising in the spine. Using a siRNA-mediated knockdown of PLA2R1, we demonstrated significant inhibition of cell growth and colony-forming ability, suggesting these proteins play an essential role in chordoma biology.ConclusionWe have comprehensively elucidated the proteome of four established chordoma cell lines. Subtractive proteomics and integrative data mining revealed novel cell-surface proteins required for chordoma cell survival and associated with clinical parameters in a small chordoma tissue sample cohort.

Medulloblastoma (MB) is the most common type of malignant pediatric brain cancer. Current standard of care (SOC) involves maximal safe resection and neuraxis radiotherapy and chemotherapy in individuals older than 3 years. To date, these cytotoxic SOC combined with craniospinal irradiation led to devastating neurocognitive and developmental deficits impacting quality of life for pediatric patients. The biological heterogeneity of MB is highlighted by the existence of four distinct molecular subgroups (WNT, SHH, Group 3, and Group 4). Group 3 and Group 4 have the poorest patient outcomes because of their aggressive, metastatic nature, and so often remain treatment refractory to SOC. Group 3 has a poor prognosis due to its high incidence of leptomeningeal spread and an overall survival rate of less than 50%. The cytotoxic nature and lack of response in specific subtypes to SOC underscores the urgent need for developing and translating novel treatment options including immunotherapies. In our earlier work, we have developed a therapy-adapted patient derived xenograft (PDX) model of the Group 3 MB as the tumor cells undergoes therapy in vitro and in vivo. N-glycocapture surfaceome profiling of the MB cells through this PDX model identified Integrin α5 (ITGA5) as one of the most differentially expressed targets found at recurrence when compared to engraftment and untreated timepoints. Through shRNA knockdown and small molecule inhibition, we identify ITGA5 expression marks a MB cell subpopulation with increased self-renewal ability both in vitro and in vivo. Access to recurrent MB (rMB) post-therapy allowed us to investigate the changes in the surfaceome of MB cells using proteomics profiling to identify promising rMB-specific targets for rational development of novel immunotherapies.

To model the problem of radiation resistance in prostate cancer, cell lines mimicking a clinical course of conventionally fractionated or hypofractionated radiotherapy have been generated. Proteomic analysis of radiation resistant and radiosensitive DU145 prostate cancer cells detected 4410 proteins. Over 400 proteins were differentially expressed across both radiation resistant cell lines and pathway analysis revealed enrichment in epithelial to mesenchymal transition, glycolysis and hypoxia. From the radiation resistant protein candidates, the cell surface protein CD44 was identified in the glycolysis and epithelial to mesenchymal transition pathways and may serve as a potential therapeutic target.

The development of functionally selective or biased ligands is a promising approach towards drugs with less side effects. Biased ligands for G protein-coupled receptors can selectively induce G protein activation or β-arrestin recruitment. The consequences of this selective action on cellular functions, however, are not fully understood. Here, we investigated the impact of five biased and balanced dopamine D2 receptor agonists and antagonists on the global protein expression in HEK293T cells by untargeted nanoscale liquid chromatography–tandem mass spectrometry. The proteome analysis detected 5290 protein groups. Hierarchical clustering and principal component analysis based on the expression levels of 1462 differential proteins led to a separation of antagonists and balanced agonist from the control treatment, while the biased ligands demonstrated larger similarities to the control. Functional analysis of affected proteins revealed that the antagonists haloperidol and sulpiride regulated exocytosis and peroxisome function. The balanced agonist quinpirole, but not the functionally selective agonists induced a downregulation of proteins involved in synaptic signaling. The β-arrestin-preferring agonist BM138, however, regulated several proteins related to neuron function and the dopamine receptor-mediated signaling pathway itself. The G protein-selective partial agonist MS308 influenced rather broad functional terms such as DNA processing and mitochondrial translation.

Bidirectional communication between cells and their microenvironment is crucial for both normal tissue homeostasis and tumor growth. During the development of oral tongue squamous cell carcinoma (OTSCC), cancer-associated fibroblasts (CAFs) create a supporting niche by maintaining a bidirectional crosstalk with cancer cells, mediated by classically secreted factors and various nanometer-sized vesicles, termed as extracellular vesicles (EVs). To better understand the role of CAFs within the tumor stroma and elucidate the mechanism by which secreted proteins contribute to OTSCC progression, we isolated and characterized patient-derived CAFs from resected tumors with matched adjacent tissue fibroblasts (AFs). Our strategy employed shotgun proteomics to comprehensively characterize the proteomes of these matched fibroblast populations. Our goals were to identify CAF-secreted factors (EVs and soluble) that can functionally modulate OTSCC cells in vitro and to identify novel CAF-associated biomarkers. Comprehensive proteomic analysis identified 4247 proteins, the most detailed description of a pro-tumorigenic stroma to date. We demonstrated functional effects of CAF secretomes (EVs and conditioned media) on OTSCC cell growth and migration. Comparative proteomics identified novel proteins associated with a CAF-like state. Specifically, MFAP5, a protein component of extracellular microfibrils, was enriched in CAF secretomes. Using in vitro assays, we demonstrated that MFAP5 activated OTSCC cell growth and migration via activation of MAPK and AKT pathways. Using a tissue microarray of richly annotated primary human OTSCCs, we demonstrated an association of MFAP5 expression with patient survival. In summary, our proteomics data of patient-derived stromal fibroblasts provide a useful resource for future mechanistic and biomarker studies.

Resistance to genotoxic therapies and tumor recurrence are hallmarks of glioblastoma (GBM), an aggressive brain tumor. Here, we explore the functional drivers of post-treatment recurrent GBM. By conducting genome-wide CRISPR-Cas9 screens in patient-derived GBM models, we uncover distinct genetic dependencies in recurrent tumor cells that were absent in their patient-matched primary predecessors, accompanied by increased mutational burden and differential transcript and protein expression. These analyses support parallel tumor-intrinsic mechanisms of treatment resistance which rely on acquisition of immunosuppressive capacity, including a defective mismatch repair pathway, ablation of PTEN activity, and a novel combination of de novo mutations in SWI/SNF components. We map a multilayered genetic and functional response to resist chemoradiotherapy and drive tumor recurrence, identifying protein tyrosine phosphatase 4A2 (PTP4A2) as a novel driver of self-renewal, proliferation and tumorigenicity at GBM recurrence. Mechanistically, genetic perturbation and a small molecule inhibitor of PTP4A2 repress axon guidance activity through a dephosphorylation axis with roundabout guidance receptor 1 (ROBO1) and exploit a genetic dependency on ROBO signaling. Importantly, engineered anti-ROBO1 single-domain antibodies also mimic the effects of PTP4A2 inhibition. We conclude that functional reprogramming drives tumorigenicity and present a dependence on a PTP4A2-ROBO1 signaling axis at GBM recurrence.

Availability of lung cancer models that closely mimic human tumors remains a significant gap in cancer research, as tumor cell lines and mouse models may not recapitulate the spectrum of lung cancer heterogeneity seen in patients. We aimed to establish a patient-derived tumor xenograft (PDX) resource from surgically resected non-small cell lung cancer (NSCLC). Fresh tumor tissue from surgical resection was implanted and grown in the subcutaneous pocket of non-obese severe combined immune deficient (NOD SCID) gamma mice. Subsequent passages were in NOD SCID mice. A subset of matched patient and PDX tumors and non-neoplastic lung tissues were profiled by whole exome sequencing, single nucleotide polymorphism (SNP) and methylation arrays, and phosphotyrosine (pY)-proteome by mass spectrometry. The data were compared to published NSCLC datasets of NSCLC primary and cell lines. 127 stable PDXs were established from 441 lung carcinomas representing all major histological subtypes: 52 adenocarcinomas, 62 squamous cell carcinomas, one adeno-squamous carcinoma, five sarcomatoid carcinomas, five large cell neuroendocrine carcinomas, and two small cell lung cancers. Somatic mutations, gene copy number and expression profiles, and pY-proteome landscape of 36 PDXs showed greater similarity with patient tumors than with established cell lines. Novel somatic mutations on cancer associated genes were identified but only in PDXs, likely due to selective clonal growth in the PDXs that allows detection of these low allelic frequency mutations. The results provide the strongest evidence yet that PDXs established from lung cancers closely mimic the characteristics of patient primary tumors.

Cancer metabolism adapts the metabolic network of its tissue-of-origin. However, breast cancer is not a disease of a singular origin. Multiple epithelial populations serve as the culprit cell-of-origin for specific breast cancer subtypes, yet knowledge surrounding the metabolic network of normal mammary epithelial cells is limited. Here, we show that mammary populations have cell type-specific metabolic programs. Primary human breast cell proteomes of basal, luminal progenitor, and mature luminal populations revealed their unique enrichment of metabolic proteins. Luminal progenitors had higher abundance of electron transport chain subunits and capacity for oxidative phosphorylation, whereas basal cells were more glycolytic. Targeting oxidative phosphorylation and glycolysis with inhibitors exposed distinct metabolic vulnerabilities of the mammary lineages. Computational analysis indicated that breast cancer subtypes retain metabolic features of their putative cell-of-origin. Lineage-restricted metabolic identities of normal mammary cells partly explain breast cancer metabolic heterogeneity and rationalize targeting subtype-specific metabolic vulnerabilities to advance breast cancer therapy.

Cancer metabolism adapts the metabolic network of its tissue of origin. However, breast cancer is not a disease of a single origin. Multiple epithelial populations serve as the culprit cell of origin for specific breast cancer subtypes, yet our knowledge of the metabolic network of normal mammary epithelial cells is limited. Using a multi-omic approach, here we identify the diverse metabolic programmes operating in normal mammary populations. The proteomes of basal, luminal progenitor and mature luminal cell populations revealed enrichment of glycolysis in basal cells and of oxidative phosphorylation in luminal progenitors. Single-cell transcriptomes corroborated lineage-specific metabolic identities and additional intra-lineage heterogeneity. Mitochondrial form and function differed across lineages, with clonogenicity correlating with mitochondrial activity. Targeting oxidative phosphorylation and glycolysis with inhibitors exposed lineage-rooted metabolic vulnerabilities of mammary progenitors. Bioinformatics indicated breast cancer subtypes retain metabolic features of their putative cell of origin. Thus, lineage-rooted metabolic identities of normal mammary cells may underlie breast cancer metabolic heterogeneity and targeting these vulnerabilities could advance breast cancer therapy.

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.

Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to-phenotype continuum DNA→RNA→Protein→Disease. An Omics Array integrating DNA gene copy number, mRNA transcriptome, and quantified proteome was assembled into a genetic map representing non-small cell lung carcinoma (NSCLC). Data were collected from patient-matched normal lung, primary tumors, and patient tumor-derived xenograft (PDX) tumors. Dysregulated proteins not previously implicated as cancer drivers were found encoded throughout the genome including but not limited to regions of recurrent DNA amplification/deletion in NSCLC. Unsupervised clustering revealed signatures comprising metabolism proteins particularly highly recapitulated between matched primary and PDX tumors, and which distinguished between the major NSCLC histological subtypes adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Interrogation of The Cancer Genome Atlas (TCGA) revealed sizeable cohorts of NSCLC patients with DNA alterations in genes encoding the metabolism proteome signatures, and accompanied by differences in survival. Similar to the proteome signatures from which they were extrapolated, the gene mutation signatures with prognostic impact discriminated between the lung ADC and SCC subtypes. Serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in serine/glycine and folate-dependent one-carbon metabolism, is upregulated in the proteomes of NSCLC primary and PDX tumours, and is implicated as a driver of recurrent chromosome 12q14.1 amplification in NSCLC. SHMT2, along with other enzymes implicated as anti-folate targets, is also part of a metabolism signature associated with poor outcome in lung ADC. The interrogation of cancer genomes and proteomes for alterations that are related products of selective pressures driving the cancer phenotype may be a general approach to uncover and group together cryptic, polygenic cancer drivers, which might represent new anti-cancer therapeutic targets. Note: This abstract was not presented at the conference. Citation Format: Michael F. Moran, Thomas Kislinger, Lei Li, Vladimir Ignatchenko, Yuhong Wei, Christine To, Paul Taylor, Jiefei Tong, Nhu An Pham, Melania Pintilie, Lakshmi Muthuswamy, Frances A. Shepherd, Ming Sound Tsao. Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-49.

As the most common primary brain tumor in adults causing death, Glioblastoma (GBM) remains a therapeutic challenge. Unchanged for almost two decades, standard therapy is ineffective at preventing disease relapse with a median patient survival of < 15 months. Stem cell-like subpopulations of tumor cells, known as brain tumor initiating cells (BTICs), evade standard therapy and lead to relapse. Whereas previous studies largely focus on pre-treatment primary GBM (pGBM), we conducted a panel of genome-wide CRISPR-Cas9 gene knockout screens to determine modulators of treatment resistance and de novo genetic vulnerabilities arising at disease recurrence. Using our in vitro model of conventional therapy, we identified genes modulating sensitivity and resistance to Temozolomide and/or radiation therapy in patient-derived pGBM BTICs. Genes modulating sensitivity belong to Fanconi anaemia nuclear complex, interstrand cross link repair, and regulation of stem cell maintenance and differentiation. Following in vitro validation of gene knockouts conferring treatment sensitization in multiple pGBM BTIC lines, we continued to conduct the first genome-wide CRISPR-Cas9 screens in patient-derived rGBM BTICs. Focusing on genetic vulnerabilities arising de novo at disease relapse, we introduce the context-specific role of protein tyrosine phosphatase 4A2 (PTP4A2) in rGBM. Genetic knockout or small molecule targeting of PTP4A2 leads to a context-specific vulnerability of rGBM self renewal capacity and in vivo tumorigenecity. To continue our analysis of treatment-refractory GBM and overcome intertumoral heterogeneity, we conducted genome-wide CRISPR-Cas9 gene knockout screens and whole cell proteomics on patient-matched pGBM and rGBM BTICs. With >1000 differentially essential genes, combined functional genetic and proteomic analyses implicates genes involved in mRNA splicing, nucleotide metabolism, and activation of gene expression by sterol regulatory element-binding protein. Together, our functional genetic approach elucidates novel genes regulating treatment resistance and disease recurrence in GBM.

Secreted proteins are of tremendous biological interest since they can act as ligands for receptors to activate downstream signalling cascades or be used as biomarkers if altered abundance is correlated with a specific pathological state. Proteins can be secreted either as soluble molecules or as part of extracellular vesicles (i.e., exosomes or microvesicles). The complete proteomic profiling of secretomes requires analysis of secreted proteins and extracellular vesicles. Hence, the method described here enriches for microvesicles, exosomes, and secreted proteins from conditioned media using differential centrifugation. The three fractions are then analyzed by mass spectrometry-based proteomics for in-depth characterization and comparison of the protein secretome of cell lines.

Secreted proteins are of tremendous biological interest since they can act as ligands for receptors to activate downstream signalling cascades or be used as biomarkers if altered abundance is correlated with a specific pathological state. Proteins can be secreted either as soluble molecules or as part of extracellular vesicles (i.e., exosomes or microvesicles). The complete proteomic profiling of secretomes requires analysis of secreted proteins and extracellular vesicles. Hence, the method described here enriches for microvesicles, exosomes, and secreted proteins from conditioned media using differential centrifugation. The three fractions are then analyzed by mass spectrometry-based proteomics for in-depth characterization and comparison of the protein secretome of cell lines.

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor in humans, bearing an overwhelmingly poor prognosis and characterized by a diverse cellular phenotype and genetic heterogeneity. Despite multimodal therapy, patients on average experience relapse at 9 months and median survival rarely extends beyond 15 months. Treatment of human GBM with standard-of-care (SOC) chemo-radiotherapy invariably leads to resistance. Poor patient survival correlates with marker expression of brain tumor-initiating cells (BTICs), which are implicated in therapeutic resistance and may explain tumor relapse. Targeting the cells that drive GBM formation as well as its inevitable and rapid recurrence post-therapy has remained a major challenge, likely due to intra-tumoral heterogeneity. Proteomic profiling constitutes a promising tool to identify novel cancer targets. In this study, we have adopted a non-biased mass spectrometry approach to identify cell surface peptides isolated from patient-derived GBM BTIC lines through a glycocapture approach. We have identified proteins that are differentially expressed in recurrent GBM BTICs compared to primary GBM BTICs. Moreover, we perform comparative proteomics from recurrent GBM BTICs generated from our in vivo mouse-adapted therapy model, which has the distinct advantage of generating recurrent, treatment-refractory GBM. Our surface proteomic profiling thus provides a comprehensive network of signaling molecules selectively enriched in recurrent GBM cells. Identification of cell surface proteins that may drive treatment resistance will contribute greatly to identify novel immunotherapeutic targets for GBM.

Venn diagrams are graphical representations of the relationships among multiple sets of objects and are often used to illustrate similarities and differences among genomic and proteomic datasets. All currently existing tools for producing Venn diagrams evince one of two traits; they require expertise in specific statistical software packages (such as R), or lack the flexibility required to produce publication-quality figures. We describe a simple tool that addresses both shortcomings, Venn Diagram Interactive Software (VennDIS), a JavaFX-based solution for producing highly customizable, publication-quality Venn, and Euler diagrams of up to five sets. The strengths of VennDIS are its simple graphical user interface and its large array of customization options, including the ability to modify attributes such as font, style and position of the labels, background color, size of the circle/ellipse, and outline color. It is platform independent and provides real-time visualization of figure modifications. The created figures can be saved as XML files for future modification or exported as high-resolution images for direct use in publications.

The histamine receptors (HRs) represent a subclass of G protein-coupled receptors (GPCRs) and comprise four subtypes. Due to their numerous physiological and pathological effects, HRs are popular drug targets for the treatment of allergic reactions or the regulation of gastric acid secretion. Hence, an understanding of the functional selectivity of HR ligands has gained importance. These ligands can bind to specific GPCRs and selectively activate defined pathways. Supporting the activation of a therapeutically necessary pathway without the activation of other signaling cascades can result in drugs with more specific activity and fewer side effects. To evaluate the cellular consequences resulting from receptor binding, comprehensive analyses of cellular protein alterations upon incubation with ligands are required. For this purpose, endothelial cells were treated with histamine, as the endogenous ligand of HRs, to obtain a global overview of its cellular effects. Quantitative proteomics and pathway analyses of histamine-treated and untreated cells revealed enrichment of the nuclear factor-κB and tumor necrosis factor signaling pathways, cytokine‒cytokine receptor interactions, complement and coagulation cascades and acute inflammatory processes upon histamine treatment. This strategy offers the opportunity to monitor HR-mediated signaling in a multidimensional manner. This article is protected by copyright. All rights reserved

Background Onco-proteogenomics aims to understand how changes in a cancer’s genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database. Methods We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines. Results We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes. Conclusions We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0454-9) contains supplementary material, which is available to authorized users.

Molecular communication between cancer cells and its stromal microenvironment is a key factor for cancer progression. Alongside classic secretory pathways, it has recently been proposed that small membranous vesicles are alternative mediators of intercellular communication. Exosomes carry an effector-rich proteome with the ability to modulate various functional properties of the recipient cell. In this study, exosomes isolated from four epithelial ovarian cancer cell lines (OVCAR3, OVCAR433, OVCAR5 and SKOV3) were characterized using mass spectrometry-based proteomics. Using an optimized workflow consisting of efficient exosome solubilization and the latest generation of proteomic instrumentation, we demonstrate improved detection depth. Systematic comparison of our cancer cell line exosome proteome against public data (Exocarta) and the recently published NCI 60 proteome revealed enrichment of functional categories related to signaling biology and biomarker discovery.

Current protocols for the screening of prostate cancer cannot accurately discriminate clinically indolent tumors from more aggressive ones. One reliable indicator of outcome has been the determination of organ-confined versus nonorgan-confined disease but even this determination is often only made following prostatectomy. This underscores the need to explore alternate avenues to enhance outcome prediction of prostate cancer patients. Fluids that are proximal to the prostate, such as expressed prostatic secretions (EPS), are attractive sources of potential prostate cancer biomarkers as these fluids likely bathe the tumor. Direct-EPS samples from 16 individuals with extracapsular (n = 8) or organ-confined (n = 8) prostate cancer were used as a discovery cohort, and were analyzed in duplicate by a nine-step MudPIT on a LTQ-Orbitrap XL mass spectrometer. A total of 624 unique proteins were identified by at least two unique peptides with a 0.2% false discovery rate. A semiquantitative spectral counting algorithm identified 133 significantly differentially expressed proteins in the discovery cohort. Integrative data mining prioritized 14 candidates, including two known prostate cancer biomarkers: prostate-specific antigen and prostatic acid phosphatase, which were significantly elevated in the direct-EPS from the organ-confined cancer group. These and five other candidates (SFN, MME, PARK7, TIMP1, and TGM4) were verified by Western blotting in an independent set of direct-EPS from patients with biochemically recurrent disease (n = 5) versus patients with no evidence of recurrence upon follow-up (n = 10). Lastly, we performed proof-of-concept SRM-MS-based relative quantification of the five candidates using unpurified heavy isotope-labeled synthetic peptides spiked into pools of EPS-urines from men with extracapsular and organ-confined prostate tumors. This study represents the first efforts to define the direct-EPS proteome from two major subclasses of prostate cancer using shotgun proteomics and verification in EPS-urine by SRM-MS.

Umbilical cord blood (CB) is a convenient and broadly used source of hematopoietic stem cells (HSCs) for allogeneic stem cell transplantation. However, limiting numbers of HSCs remain a major constraint for its clinical application. Although one feasible option would be to expand HSCs to improve therapeutic outcome, available protocols and the molecular mechanisms governing the selfrenewal of HSCs are unclear. Here, we show that ectopic expression of a single microRNA (miRNA), miR-125a, in purified murine and human multipotent progenitors (MPPs) resulted in increased self-renewal and robust long-term multi-lineage repopulation in transplanted recipient mice. Using quantitative proteomics and western blot analysis, we identified a restricted set of miR-125a targets involved in conferring long-term repopulating capacity to MPPs in humans and mice. Our findings offer the innovative potential to use MPPs with enhanced self-renewal activity to augment limited sources of HSCs to improve clinical protocols.

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers., Proteomic technologies are capable of identifying thousands of proteins in biological samples, but biomarker applications are lagging. Here the authors use Multiple Reaction Monitoring Mass Spectrometry to delineate peptide signatures that accurately distinguish between defined prostate cancer patient risk groups.

Summary The advent of reprogramming and its impact on stem cell biology has renewed interest in lineage restriction in mammalian embryos, the source of embryonic (ES), epiblast (EpiSC), trophoblast (TS), and extraembryonic endoderm (XEN) stem cell lineages. Isolation of specific cell types during stem cell differentiation and reprogramming, and also directly from embryos, is a major technical challenge because few cell-surface proteins are known that can distinguish each cell type. We provide a large-scale proteomic resource of cell-surface proteins for the four embryo-derived stem cell lines. We validated 27 antibodies against lineage-specific cell-surface markers, which enabled investigation of specific cell populations during ES-EpiSC reprogramming and ES-to-XEN differentiation. Identified markers also allowed prospective isolation and characterization of viable lineage progenitors from blastocysts by flow cytometry. These results provide a comprehensive stem cell proteomic resource and enable new approaches to interrogate the mechanisms that regulate cell fate specification., Highlights ► Cell-surface proteome for four embryo-derived stem cell lineages ► Comprehensive resource of signaling, adhesion and migration proteins ► Enables isolation of specific cell types during differentiation and reprogramming ► Allows prospective isolation of lineage progenitors directly from blastocysts, Rugg-Gunn et al. provide a large-scale proteomic resource of cell-surface proteins for four embryo-derived stem cell lines: ES, EpiSC, TS, and XEN. Antibodies against lineage-specific cell-surface markers allowed investigation of specific cell populations during differentiation and reprogramming and also prospective isolation of viable lineage progenitors from blastocysts by flow cytometry.

Urinary expressed prostatic secretion or "EPS-urine" is proximal tissue fluid that is collected after a digital rectal exam (DRE). EPS-urine is a rich source of prostate-derived proteins that can be used for biomarker discovery for prostate cancer (PCa) and other prostatic diseases. We previously conducted a comprehensive proteome analysis of direct expressed prostatic secretions (EPS). In the current study, we defined the proteome of EPS-urine employing Multidimensional Protein Identification Technology (MudPIT) and providing a comprehensive catalogue of this body fluid for future biomarker studies. We identified 1022 unique proteins in a heterogeneous cohort of 11 EPS-urines derived from biopsy negative noncancer diagnoses with some benign prostatic diseases (BPH) and low-grade PCa, representative of secreted prostate and immune system-derived proteins in a urine background. We further applied MudPIT-based proteomics to generate and compare the differential proteome from a subset of pooled urines (pre-DRE) and EPS-urines (post-DRE) from noncancer and PCa patients. The direct proteomic comparison of these highly controlled patient sample pools enabled us to define a list of prostate-enriched proteins detectable in EPS-urine and distinguishable from a complex urine protein background. A combinatorial analysis of both proteomics data sets and systematic integration with publicly available proteomics data of related body fluids, human tissue transcriptomic data, and immunohistochemistry images from the Human Protein Atlas database allowed us to demarcate a robust panel of 49 prostate-derived proteins in EPS-urine. Finally, we validated the expression of seven of these proteins using Western blotting, supporting the likelihood that they originate from the prostate. The definition of these prostatic proteins in EPS-urine samples provides a reference for future investigations for prostatic-disease biomarker studies.

Epithelial ovarian cancer (EOC) is the most common gynecological cancer and the ninth most common cancer overall. Major problems associated with EOC include poorly characterized disease progression, disease heterogeneity, lack of early detection markers and the development of chemoresistance. Early detection and treatment of EOC would significantly benefit from routine screening tests on available biofluids. We built on our experience in analyzing ovarian cancer ascites and present an analysis pipeline that combines discovery-based proteomics, bioinformatics prioritization and targeted proteomics quantification using Selected Reaction Monitoring Mass Spectrometry (SRM-MS). Ascitic fluids from patients with serous-type epithelial ovarian cancer were analyzed using comprehensive shotgun proteomics and compared to noncancerous ascitic fluids from patients with benign ovarian tumors. Integration of our data with published mRNA transcriptomic and proteomic data sets led to a panel of 51 candidate proteins. Systematic SRM-MS assay development was performed for a subset of these proteins using both synthetic peptides (13 proteins) and stable isotope labeled standards (4 proteins). Subsequently, precise relative quantification by stable isotope dilution-SRM (SID-SRM) in independent ascites and serum samples was performed as a proof-of-concept validation. The analysis strategy outlined here lays the foundation for future experiments using both larger numbers of patient samples and additional candidate proteins, and provides a template for the proteomics-based discovery of cancer biomarkers.

Nonsmall cell lung carcinoma (NSCLC) accounts for 80% of lung cancers. The most prevalent subtypes of NSCLC are adenocarcinoma (ADC) and squamous cell carcinoma (SCC), which combined account for approximately 90%. Ten resected NSCLC patient tumors (5 ADC and 5 SCC) were directly introduced into severely immune deficient (NOD-SCID) mice, and the resulting xenograft tumors were analyzed by standard histology and immunohistochemistry (IHC) and by proteomics profiling. Mass spectrometry (MS) methods involving 1- and 2-dimensional LC-MS/MS, and multiplexed selective reaction monitoring (SRM, or MRM), were applied to identify and quantify the xenograft proteomes. Hierarchical clustering of protein profiles distinguished between the ADC and SCC subtypes. The differential expression of 178 proteins, including a comprehensive panel of intermediate filament keratin proteins, was found to constitute a distinctive proteomic signature associated with the NSCLC subtypes. Epidermal growth factor receptor (EGFR) was expressed in ADC and SCC xenografts, and EGFR network activation was assessed by phosphotyrosine profiling by Western blot analysis and SRM measurement of EGFR levels, and mutation analysis. A multiplexed SRM/MRM method provided relative quantification of several keratin proteins, EGFR and plakophilin-1 in single LC-MS/MS runs. The protein quantifications by SRM and MS/MS spectral counting were associated with superior dynamic range and reproducibility but were otherwise consistent with orthogonal methods including IHC and Western immuno blotting. These findings illustrate the potential to develop a comprehensive MS-based platform in oncologic pathology for better classification and potentially treatment of NSCLC patients.

It is expected that clinically obtainable fluids that are proximal to organs contain a repertoire of secreted proteins and shed cells reflective of the physiological state of that tissue and thus represent potential sources for biomarker discovery, investigation of tissue-specific biology, and assay development. The prostate gland secretes many proteins into a prostatic fluid that combines with seminal vesicle fluids to promote sperm activation and function. Proximal fluids of the prostate that can be collected clinically are seminal plasma and expressed prostatic secretion (EPS) fluids. In the current study, MudPIT-based proteomics was applied to EPS obtained from nine men with prostate cancer and resulted in the confident identification of 916 unique proteins. Systematic bioinformatics analyses using publicly available microarray data of 21 human tissues (Human Gene Atlas), the Human Protein Atlas database, and other published proteomics data of shed/secreted proteins were performed to systematically analyze this comprehensive proteome. Therefore, we believe this data will be a valuable resource for the research community to study prostate biology and potentially assist in the identification of novel prostate cancer biomarkers. To further streamline this process, the entire data set was deposited to the Tranche repository for use by other researchers.

High resolution peptide separation is pivotal for successful shot-gun proteomics. The need for capable techniques propels invention and improvement of ever more sophisticated approaches. Recently, Agilent Technologies has introduced the OFFGEL fractionator, which conducts peptide separation by isoelectric focusing in an off-gel setup. This platform has been shown to accomplish high resolution of peptides for diverse sample types, yielding valuable advantages over comparable separation techniques. In this study, we deliver the first comparison of the newly emerging OFFGEL approach to the well-established on-line MudPIT platform. Samples from a membrane-enriched fraction isolated from murine C2C12 cells were subjected to replicate analysis by OFFGEL (12 fractions, pH 3 – 10) followed by RP-LC-MS/MS or 12-step on-line MudPIT. OFFGEL analyses yielded 1398 proteins (identified by 10,269 peptides) while 1428 proteins (11,078 peptides) were detected with the MudPIT approach. Thus, our data shows that both platforms produce highly comparable results in terms of protein/peptide identifications and reproducibility for the sample type analyzed. We achieve more accurate peptide focusing after OFFGEL fractionation with 88 % of all peptides binned to a single fraction, as compared to 61 % of peptides detected in only one step in MudPIT analyses. Our study suggests that both platforms are equally capable of high quality peptide separation of a sample with medium complexity, rendering them comparably valuable for comprehensive proteomic analyses.