Phosphoproteomics of Primary Cells Reveals Druggable Kinase Signatures in Ovarian Cancer

Phosphoproteomics Reveals L1CAM-Associated Signaling Networks in High-Grade Serous Ovarian Carcinoma: Implications for Radioresistance and Tumorigenesis

Quantitative phosphoproteomics enables the comprehensive analysis of signaling pathways driven by overexpressed cancer receptors, revealing the molecular mechanisms that underpin tumor progression and therapy resistance. The glycoprotein L1 cell adhesion molecule (L1CAM) is overexpressed in high-grade serous ovarian carcinoma (HGSOC) and plays a crucial role in carcinogenesis by regulating cancer stem cell properties. Here, CRISPR-Cas9-mediated knockout of L1CAM in ovarian cancer OVCAR8 and OVCAR4 cells significantly impaired anchor-independent growth in soft agar assays and reduced clonogenic survival following external beam irradiation. In vivo, L1CAM knockout decreased cancer stem cell frequency and significantly decreased tumorigenicity. To uncover L1CAM-regulated signaling networks, we employed quantitative phosphoproteomics and proteomics. Bioinformatics analyses and validation studies revealed L1CAM-associated pathways that contribute to radioresistance through DNA repair processes and mammalian target or rapamycin complex 1 (mTORC1)-mediated signaling. In conclusion, our study established a link between L1CAM-dependent tumorigenesis and radioresistance, both hallmarks of cancer stemness, with phosphorylation of key proteins involved in DNA damage response. This study further emphasizes the value of quantitative phosphoproteomics in cancer research, showcasing its ability to enhance understanding of cancer progression and therapy resistance.

Multiplexed phosphoproteomics of low cell numbers using SPARCE

Understanding cellular diversity and disease mechanisms requires a global analysis of proteins and their modifications. While next-generation sequencing has advanced our understanding of cellular heterogeneity, it fails to capture downstream signalling networks. Ultrasensitive mass spectrometry-based proteomics enables unbiased protein-level analysis of low cell numbers, down to single cells. However, phosphoproteomics remains limited to high-input samples due to sample losses and poor reaction efficiencies associated with processing low cell numbers. Isobaric stable isotope labelling is a promising approach for reproducible and accurate quantification of low abundant phosphopeptides. Here, we introduce SPARCE (Streamlined Phosphoproteomic Analysis of Rare CElls) for multiplexed phosphoproteomic analysis of low cell numbers. SPARCE integrates cell isolation, water-based lysis, on-tip TMT labelling, and phosphopeptide enrichment. SPARCE outperforms traditional methods by enhancing labelling efficiency and phosphoproteome coverage. To demonstrate the utility of SPARCE, we analysed four patient-derived glioblastoma stem cell lines, reliably quantifying phosphosite changes from 1000 FACS-sorted cells. This workflow expands the possibilities for signalling analysis of rare cell populations.

Quantitative Proteomics and Phosphoproteomics Analysis of Patient-Derived Ovarian Cancer Stem Cells

High-grade serous ovarian carcinoma (HGSOC) is the deadliest gynecologic cancer. Key to the progression and ultimate lethality of this subtype is the intra-tumoral heterogeneity, which is defined as the coexistence of different cell types and populations within a single tumor. Among those, ovarian cancer stem cells (OCSCs) are a distinct subpopulation of tumor cells endowed with stem-like properties, which can survive current standard therapies, resulting in tumor recurrence. Here, we generated ex vivo primary OCSC-enriched three-dimensional (3D) spheres from 10 distinct treatment naive patient-derived adherent (2D) cultures. We used state-of-the-art quantitative mass spectrometry to characterize the molecular events associated with OCSCs by analyzing their proteome and phosphoproteome. Our data revealed a stemness-related protein signature, shared within a heterogeneous patient cohort, which correlates with chemo-refractoriness in a clinical proteomics dataset. Moreover, we identified targetable deregulated kinases and aberrant PDGF receptor activation in OCSCs. Pharmacological inhibition of PDGFR in adherent OC cells reduced the stemness potential, measured by sphere formation assay. Overall, we provide a valuable resource to identify new OCSC markers and putative targets for OCSC-directed therapies.

Early tolerance and late persistence as alternative drug responses in cancer

Bacteria withstand antibiotic treatment through three alternative mechanisms: resistance, persistence or tolerance. While resistance and persistence have been described, whether drug-induced tolerance exists in cancer cells remains largely unknown. Here, we show that human cancer cells elicit a tolerant response when exposed to commonly used chemotherapy regimens, propelled by the pervasive activation of autophagy, leading to the comprehensive activation of DNA damage repair pathways. After prolonged drug exposure, such tolerant responses morph into persistence, whereby the increased DNA damage repair is entirely reversed. The central regulator of mitophagy PINK1 drives this reduction in DNA repair via the cytoplasmic relocalization of the cell identity master HNF4A, thus hampering HNF4A transcriptional activation of DNA repair genes. We conclude that exposing cancer cells to relevant standard-of-care antitumour therapies induces a pervasive drug-induced tolerant response that might be broadly exploited to increase the impact of first-line, adjuvant treatments and debulking in advanced cancers.

Cyclin-dependent protein kinases and cell cycle regulation in biology and disease

Cyclin Dependent Kinases (CDKs) are closely connected to the regulation of cell cycle progression, having been first identified as the kinases able to drive cell division. In reality, the human genome contains 20 different CDKs, which can be divided in at least three different sub-family with different functions, mechanisms of regulation, expression patterns and subcellular localization. Most of these kinases play fundamental roles the normal physiology of eucaryotic cells; therefore, their deregulation is associated with the onset and/or progression of multiple human disease including but not limited to neoplastic and neurodegenerative conditions. Here, we describe the functions of CDKs, categorized into the three main functional groups in which they are classified, highlighting the most relevant pathways that drive their expression and functions. We then discuss the potential roles and deregulation of CDKs in human pathologies, with a particular focus on cancer, the human disease in which CDKs have been most extensively studied and explored as therapeutic targets. Finally, we discuss how CDKs inhibitors have become standard therapies in selected human cancers and propose novel ways of investigation to export their targeting from cancer to other relevant chronic diseases. We hope that the effort we made in collecting all available information on both the prominent and lesser-known CDK family members will help in identify and develop novel areas of research to improve the lives of patients affected by debilitating chronic diseases.

NMT2 alleviates depression-like behavior in a rat model of chronic unpredictable stress: An integrated proteomic and phosphoproteomic analysis

Proteomics has been widely used to investigate multiple diseases. Combining the analyses of proteomics with phosphoproteomics can be used to further explain the pathological mechanisms of depression. In this study, depression-like behavior was induced in a rat model of chronic unpredictable mild stress (CUMS). We subsequently conducted the sucrose preference test, open field experiment, and forced swimming test to assess depressive-like behavior. Proteomic and phosphoproteomic sequencing of the hippocampal tissues from depressive-like behavior and normal rats were analyzed to identify differentially expressed proteins (DEPs) and differentially phosphorylated proteins (DPPs). Differentially expressed phosphorylated proteins (DEPPs) were obtained by intersecting the DEPs and DPPs, and functional enrichment analysis, as well as ingenuity pathway analysis (IPA), were subsequently performed. The study also investigated correlations among the DEPPs and used qRT-PCR to quantify the expression levels of key genes. Five DEPPs were identified, Gys1, Nmt2, Lrp1, Bin1, and Atp1a1, which were found to activate the synaptogenesis signaling pathway, induce mitochondrial dysfunction, and activate the phosphoinositide biosynthesis and degradation pathways. The qRT-PCR results confirmed the proteomic findings for Gys1, Nmt2, Lrp1, and Atp1a1. Importantly, inhibiting Nmt2 was found to alleviate depression-like behavior and alleviate neuronal apoptosis in the hippocampus of CUMS rats. In conclusion, we identified five DEPPs associated with the synaptogenesis signaling pathway, mitochondrial dysfunction, and phosphoinositide biosynthesis and degradation in depression. Furthermore, NMT2 may be a potential target for the treatment or diagnosis of depression. Our findings provide novel insights into the molecular mechanisms of depression.

Proteomic landscape of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Tumor microenvironment-induced FOXM1 regulates ovarian cancer stemness

In ovarian tumors, the omental microenvironment profoundly influences the behavior of cancer cells and sustains the acquisition of stem-like traits, with major impacts on tumor aggressiveness and relapse. Here, we leverage a patient-derived platform of organotypic cultures to study the crosstalk between the tumor microenvironment and ovarian cancer stem cells. We discovered that the pro-tumorigenic transcription factor FOXM1 is specifically induced by the microenvironment in ovarian cancer stem cells, through activation of FAK/YAP signaling. The microenvironment-induced FOXM1 sustains stemness, and its inactivation reduces cancer stem cells survival in the omental niche and enhances their response to the PARP inhibitor Olaparib. By unveiling the novel role of FOXM1 in ovarian cancer stemness, our findings highlight patient-derived organotypic co-cultures as a powerful tool to capture clinically relevant mechanisms of the microenvironment/cancer stem cells crosstalk, contributing to the identification of tumor vulnerabilities.

Using Drosophila to uncover the role of organismal physiology and the tumor microenvironment in cancer

Cancer metastasis causes over 90% of cancer patient fatalities. Poor prognosis is determined by tumor type, the tumor microenvironment (TME), organ-specific biology, and animal physiology. While model organisms do not fully mimic the complexity of humans, many processes can be studied efficiently owing to the ease of genetic, developmental, and cell biology studies. For decades, Drosophila has been instrumental in identifying basic mechanisms controlling tumor growth and metastasis. The ability to generate clonal populations of distinct genotypes in otherwise wild-type animals makes Drosophila a powerful system to study tumor-host interactions at the local and global scales. This review discusses advancements in tumor biology, highlighting the strength of Drosophila for modeling TMEs and systemic responses in driving tumor progression and metastasis.

Proteomic profiling reveals that ESR1 mutations enhance cyclin-dependent kinase signaling

Three quarters of all breast cancers express the estrogen receptor (ER, ESR1 gene), which promotes tumor growth and constitutes a direct target for endocrine therapies. ESR1 mutations have been implicated in therapy resistance in metastatic breast cancer, in particular to aromatase inhibitors. ESR1 mutations promote constitutive ER activity and affect other signaling pathways, allowing cancer cells to proliferate by employing mechanisms within and without direct regulation by the ER. Although subjected to extensive genetic and transcriptomic analyses, understanding of protein alterations remains poorly investigated. Towards this, we employed an integrated mass spectrometry based proteomic approach to profile the protein and phosphoprotein differences in breast cancer cell lines expressing the frequent Y537N and Y537S ER mutations. Global proteome analysis revealed enrichment of mitotic and immune signaling pathways in ER mutant cells, while phosphoprotein analysis evidenced enriched activity of proliferation associated kinases, in particular CDKs and mTOR. Integration of protein expression and phosphorylation data revealed pathway-dependent discrepancies (motility vs proliferation) that were observed at varying degrees across mutant and wt ER cells. Additionally, protein expression and phosphorylation patterns, while under different regulation, still recapitulated the estrogen-independent phenotype of ER mutant cells. Our study is the first proteome-centric characterization of ESR1 mutant models, out of which we confirm estrogen independence of ER mutants and reveal the enrichment of immune signaling pathways at the proteomic level.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

DeepFLR facilitates false localization rate control in phosphoproteomics

Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.

Matrix Gla Protein drives stemness and tumor initiation in ovarian cancer

Ovarian cancer (OC) displays the highest mortality among gynecological tumors, mainly due to early peritoneal dissemination, the high frequency of tumor relapse following primary debulking, and the development of chemoresistance. All these events are thought to be initiated and sustained by a subpopulation of neoplastic cells, termed ovarian cancer stem cells (OCSC), that are endowed with self-renewing and tumor-initiating properties. This implies that interfering with OCSC function should offer novel therapeutic perspectives to defeat OC progression. To this aim, a better understanding of the molecular and functional makeup of OCSC in clinically relevant model systems is essential. We have profiled the transcriptome of OCSC vs. their bulk cell counterpart from a panel of patient-derived OC cell cultures. This revealed that Matrix Gla Protein (MGP), classically known as a calcification-preventing factor in cartilage and blood vessels, is markedly enriched in OCSC. Functional assays showed that MGP confers several stemness-associated traits to OC cells, including a transcriptional reprogramming. Patient-derived organotypic cultures pointed to the peritoneal microenvironment as a major inducer of MGP expression in OC cells. Furthermore, MGP was found to be necessary and sufficient for tumor initiation in OC mouse models, by shortening tumor latency and increasing dramatically the frequency of tumor-initiating cells. Mechanistically, MGP-driven OC stemness was mediated by the stimulation of Hedgehog signaling, in particular through the induction of the Hedgehog effector GLI1, thus highlighting a novel MGP/Hedgehog pathway axis in OCSC. Finally, MGP expression was found to correlate with poor prognosis in OC patients, and was increased in tumor tissue after chemotherapy, supporting the clinical relevance of our findings. Thus, MGP is a novel driver in OCSC pathophysiology, with a major role in stemness and in tumor initiation.

Integrated proteomic and phosphoproteomic analysis for characterization of colorectal cancer

Proteins and translationally modified proteins like phosphoproteins have essential regulatory roles in tumorigenesis. This study attempts to elucidate the dysregulated proteins driving colorectal cancer (CRC). To explore the differential proteins, we performed iTRAQ labeling proteomics and TMT labeling phosphoproteomics analysis of CRC tissues and adjacent non-cancerous tissues. The functions of quantified proteins were analyzed using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Subcellular localization analysis. Depending on the results, we identified 330 differential proteins and 82 phosphoproteins in CRC. GO and KEGG analyses demonstrated that protein changes were primarily associated with regulating biological and metabolic processes through binding to other molecules. Co-expression relationships between proteomic and phosphoproteomic analysis revealed that TMC5, SMC4, SLBP, VSIG2, and NDRG2 were significantly dysregulated differential proteins. Additionally, based on the predicted co-expression proteins, we identified that the stem-loop binding protein (SLBP) was up-regulated in CRC cells and promoted the proliferation and migration of CRC. This study reports an integrated proteomic and phosphoproteomic analysis of CRC to discern the functional impact of protein alterations and provides a candidate diagnostic biomarker or therapeutic target for CRC. SIGNIFICANCE: Combining one or more high-throughput omics technologies with bioinformatics to analyze biological samples and explore the links between biomolecules and their functions can provide more comprehensive and multi-level insights for disease mechanism research. Proteomics, phosphoproteomics, metabolomics and their combined analysis play an important role in the auxiliary diagnosis, the discovery of biomarkers and novel therapeutic targets for colorectal cancer. In this integrated proteomic and phosphoproteomic analysis, we identified proteins and phosphoproteins in colorectal cancer tissue and analyzed potential mechanisms contributing to progression in colorectal cancer. The results of this study provide a foundation to focus future experiments on the contribution of altered protein and phosphorylation patterns to prevention and treatment of colorectal cancer.

Resistance prediction in high-grade serous ovarian carcinoma with neoadjuvant chemotherapy using data-independent acquisition proteomics and an ovary-specific spectral library

High-grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5-year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced-stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high-quality ovary-specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan-human spectral library (DPHL), this spectral library provides 10% more ovary-specific and 3% more ovary-enriched proteins. This library was then applied to analyze data-independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six-protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log-rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary-specific spectral library for targeted proteome analysis, and propose a six-protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT-IDS treatment.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.

Patient-Derived In Vitro Models of Ovarian Cancer: Powerful Tools to Explore the Biology of the Disease and Develop Personalized Treatments

Epithelial ovarian cancer (OC) is the most lethal gynecological malignancy worldwide due to a late diagnosis caused by the lack of specific symptoms and rapid dissemination into the peritoneal cavity. The standard of care for OC treatment is surgical cytoreduction followed by platinum-based chemotherapy. While a response to this frontline treatment is common, most patients undergo relapse within 2 years and frequently develop a chemoresistant disease that has become unresponsive to standard treatments. Moreover, also due to the lack of actionable mutations, very few alternative therapeutic strategies have been designed as yet for the treatment of recurrent OC. This dismal clinical perspective raises the need for pre-clinical models that faithfully recapitulate the original disease and therefore offer suitable tools to design novel therapeutic approaches. In this regard, patient-derived models are endowed with high translational relevance, as they can better capture specific aspects of OC such as (i) the high inter- and intra-tumor heterogeneity, (ii) the role of cancer stem cells (a small subset of tumor cells endowed with tumor-initiating ability, which can sustain tumor spreading, recurrence and chemoresistance), and (iii) the involvement of the tumor microenvironment, which interacts with tumor cells and modulates their behavior. This review describes the different in vitro patient-derived models that have been developed in recent years in the field of OC research, focusing on their ability to recapitulate specific features of this disease. We also discuss the possibilities of leveraging such models as personalized platforms to design new therapeutic approaches and guide clinical decisions.

Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance

Acute myeloid leukemia (AML) is a heterogeneous disease with variable patient responses to therapy. Selinexor, an inhibitor of nuclear export, has shown promising clinical activity for AML. To identify the molecular context for monotherapy sensitivity as well as rational drug combinations, we profile selinexor signaling responses using phosphoproteomics in primary AML patient samples and cell lines. Functional phosphosite scoring reveals that p53 function is required for selinexor sensitivity consistent with enhanced efficacy of selinexor in combination with the MDM2 inhibitor nutlin-3a. Moreover, combining selinexor with the AKT inhibitor MK-2206 overcomes dysregulated AKT-FOXO3 signaling in resistant cells, resulting in synergistic anti-proliferative effects. Using high-throughput spatial proteomics to profile subcellular compartments, we measure global proteome and phospho-proteome dynamics, providing direct evidence of nuclear translocation of FOXO3 upon combination treatment. Our data demonstrate the potential of phosphoproteomics and functional phosphorylation site scoring to successfully pinpoint key targetable signaling hubs for rational drug combinations.

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Phosphoproteomics with functional scoring uncovers context for selinexor sensitivity

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Functional p53 correlates with selinexor sensitivity, which is enhanced by nutlin-3a

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Dysregulated AKT-FOXO3 drives selinexor resistance, which is overcome with MK-2206

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Spatial proteomics reveals selinexor-induced nucleocytoplasmic protein shuttling

Targeting transcription in heart failure via CDK7/12/13 inhibition

Heart failure with reduced ejection fraction (HFrEF) is associated with high mortality, highlighting an urgent need for new therapeutic strategies. As stress-activated cardiac signaling cascades converge on the nucleus to drive maladaptive gene programs, interdicting pathological transcription is a conceptually attractive approach for HFrEF therapy. Here, we demonstrate that CDK7/12/13 are critical regulators of transcription activation in the heart that can be pharmacologically inhibited to improve HFrEF. CDK7/12/13 inhibition using the first-in-class inhibitor THZ1 or RNAi blocks stress-induced transcription and pathologic hypertrophy in cultured rodent cardiomyocytes. THZ1 potently attenuates adverse cardiac remodeling and HFrEF pathogenesis in mice and blocks cardinal features of disease in human iPSC-derived cardiomyocytes. THZ1 suppresses Pol II enrichment at stress-transactivated cardiac genes and inhibits a specific pathologic gene program in the failing mouse heart. These data identify CDK7/12/13 as druggable regulators of cardiac gene transactivation during disease-related stress, suggesting that HFrEF features a critical dependency on transcription that can be therapeutically exploited.

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing

In large-scale quantitative mass spectrometry (MS)-based phosphoproteomics, isobaric labeling with tandem mass tags (TMTs) coupled with offline high-pH reversed-phase peptide chromatographic fractionation maximizes depth of coverage. To investigate to what extent limited sample amounts affect sensitivity and dynamic range of the analysis due to sample losses, we benchmarked TMT-based fractionation strategies against single-shot label-free quantification with spectral library-free data independent acquisition (LFQ-DIA), for different peptide input per sample. To systematically examine how peptide input amounts influence TMT-fractionation approaches in a phosphoproteomics workflow, we compared two different high-pH reversed-phase fractionation strategies, microflow (MF) and stage-tip fractionation (STF), while scaling the peptide input amount down from 12.5 to 1 μg per sample. Our results indicate that, for input amounts higher than 5 μg per sample, TMT labeling, followed by microflow fractionation (MF) and phospho-enrichment, achieves the deepest phosphoproteome coverage, even compared to single shot direct-DIA analysis. Conversely, STF of enriched phosphopeptides (STF) is optimal for lower amounts, below 5 μg/peptide per sample. As a result, we provide a decision tree to help phosphoproteomics users to choose the best workflow as a function of sample amount.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

BRCA1 mutations in high-grade serous ovarian cancer are associated with proteomic changes in DNA repair, splicing, transcription regulation and signaling

Despite recent advances in the management of BRCA1 mutated high-grade serous ovarian cancer (HGSC), the physiology of these tumors remains poorly understood. Here we provide a comprehensive molecular understanding of the signaling processes that drive HGSC pathogenesis with the addition of valuable ubiquitination profiling, and their dependency on BRCA1 mutation-state directly in patient-derived tissues. Using a multilayered proteomic approach, we show the tight coordination between the ubiquitination and phosphorylation regulatory layers and their role in key cellular processes related to BRCA1-dependent HGSC pathogenesis. In addition, we identify key bridging proteins, kinase activity, and post-translational modifications responsible for molding distinct cancer phenotypes, thus providing new opportunities for therapeutic intervention, and ultimately advance towards a more personalized patient care.

Single cell-derived spheroids capture the self-renewing subpopulations of metastatic ovarian cancer

High Grade Serous Ovarian cancer (HGSOC) is a major unmet need in oncology, due to its precocious dissemination and the lack of meaningful human models for the investigation of disease pathogenesis in a patient-specific manner. To overcome this roadblock, we present a new method to isolate and grow single cells directly from patients' metastatic ascites, establishing the conditions for propagating them as 3D cultures that we refer to as single cell-derived metastatic ovarian cancer spheroids (sMOCS). By single cell RNA sequencing (scRNAseq) we define the cellular composition of metastatic ascites and trace its propagation in 2D and 3D culture paradigms, finding that sMOCS retain and amplify key subpopulations from the original patients' samples and recapitulate features of the original metastasis that do not emerge from classical 2D culture, including retention of individual patients' specificities. By enabling the enrichment of uniquely informative cell subpopulations from HGSOC metastasis and the clonal interrogation of their diversity at the functional and molecular level, this method provides a powerful instrument for precision oncology in ovarian cancer.

Integrated molecular profiling of patient-derived ovarian cancer models identifies clinically relevant signatures and tumor vulnerabilities

High‐grade serous ovarian carcinoma (HGSOC) is a highly aggressive and intractable neoplasm, mainly because of its rapid dissemination into the abdominal cavity, a process that is favored by tumor‐associated peritoneal ascites. The precise molecular alterations involved in HGSOC onset and progression remain largely unknown due to the high biological and genetic heterogeneity of this tumor. We established a set of different tumor samples (termed the As11‐set) derived from a single HGSOC patient, consisting of peritoneal ascites, primary tumor cells, ovarian cancer stem cells (OCSC) and serially propagated tumor xenografts. The As11‐set was subjected to an integrated RNA‐seq and DNA‐seq analysis which unveiled molecular alterations that marked the different types of samples. Our profiling strategy yielded a panel of signatures relevant in HGSOC and in OCSC biology. When such signatures were used to interrogate the TCGA dataset from HGSOC patients, they exhibited prognostic and predictive power. The molecular alterations also identified potential vulnerabilities associated with OCSC, which were then tested functionally in stemness‐related assays. As a proof of concept, we defined PI3K signaling as a novel druggable target in OCSC.

Inward Outward Signaling in Ovarian Cancer: Morpho-Phospho-Proteomic Profiling Upon Application of Hypoxia and Shear Stress Characterizes the Adaptive Plasticity of OVCAR-3 and SKOV-3 Cells

With the onset of resistance, ovarian cancer cells display almost unpredictable adaptive potential. This may derive from the tumor genetic ancestry and can be additionally tailored by post translational protein modifications (PTMs). In this study, we took advantage of high-end (phospho)-proteome analysis combined with multiparametric morphometric profiling in high-grade serous (OVCAR-3) and non-serous (SKOV-3) ovarian carcinoma cells. For functional experiments, we applied two different protocols, representing typical conditions of the abdominal cavity and of the growing tumor tissue: on the one side hypoxia (oxygen 1%) which develops within the tumor mass or is experienced during migration/extravasation in non-vascularized areas. On the other hand, fluid shear stress (250 rpm, 2.8 dyn/cm²) which affects tumor surface in the peritoneum or metastases in the bloodstream. After 3 hours incubation, treatment groups were clearly distinguishable by PCA analysis. Whereas basal proteome profiles of OVCAR-3 and SKOV-3 cells appeared almost unchanged, phosphoproteome analysis revealed multiple regulatory events. These affected primarily cellular structure and proliferative potential and consolidated in the proteome signature after 24h treatment. Upon oxygen reduction, metabolism switched toward glycolysis (e.g. upregulation hexokinase-2; HK2) and cell size increased, in concerted regulation of pathways related to Rho-GTPases and/or cytoskeletal elements, resembling a vasculogenic mimicry response. Shear stress regulated proteins governing cell cycle and structure, as well as the lipid metabolism machinery including the delta(14)-sterol reductase, kinesin-like proteins (KIF-22/20A) and the actin-related protein 2/3 complex. Independent microscopy-based validation experiments confirmed cell-type specific morphometric responses. In conclusion, we established a robust workflow enabling the description of the adaptive potential of ovarian cancer cells to physical and chemical stressors typical for the abdominal cavity and supporting the identification of novel molecular mechanisms sustaining tumor plasticity and pharmacologic resistance.

Proteomic Profiling of the Tumor Microenvironment

The tumor microenvironment forms a complex pro-tumorigenic milieu constituted by extracellular matrix, surrounding stroma, infiltrating cell populations, and signaling molecules. Proteomic studies have the potential to reveal how individual cell populations within the tumor tissue modulate the microenvironment through protein secretion and consequently alter their protein expression and localization to adapt to this milieu. As a result, proteomic approaches have uncovered how these dynamic components communicate and promote tumor development and progression. The characterization of these mechanisms is relevant for the identification of clinically targetable pathways and for the development of diagnostic tools. Here we describe a method based on the isolation of individual cell compartments and the chromatographic fractionation of intact proteins, followed by enzymatic digestion of individual fractions, and mass-spectrometry analysis, for the profiling of tumor microenvironment cell populations.

Interaction between Ras and Src clones causes interdependent tumor malignancy via Notch signaling in Drosophila

Cancer tissue often comprises multiple tumor clones with distinct oncogenic alterations such as Ras or Src activation, yet the mechanism by which tumor heterogeneity drives cancer progression remains elusive. Here, we show in Drosophila imaginal epithelium that clones of Ras- or Src-activated benign tumors interact with each other to mutually promote tumor malignancy. Mechanistically, Ras-activated cells upregulate the cell-surface ligand Delta while Src-activated cells upregulate its receptor Notch, leading to Notch activation in Src cells. Elevated Notch signaling induces the transcriptional repressor Zfh1/ZEB1, which downregulates E-cadherin and cell death gene hid, leading to Src-activated invasive tumors. Simultaneously, Notch activation in Src cells upregulates the cytokine Unpaired/IL-6, which activates JAK-STAT signaling in neighboring Ras cells. Elevated JAK-STAT signaling upregulates the BTB-zinc-finger protein Chinmo, which downregulates E-cadherin and thus generates Ras-activated invasive tumors. Our findings provide a mechanistic explanation for how tumor heterogeneity triggers tumor progression via cell-cell interactions.

Phosphomatics: interactive interrogation of substrate-kinase networks in global phosphoproteomics datasets

MOTIVATION

Mass spectrometry-based phosphoproteomics can routinely identify and quantify thousands of phosphorylated peptides from a single experiment. However interrogating possible upstream kinases and identifying key literature for phosphorylation sites is laborious and time-consuming.

RESULTS

Here, we present Phosphomatics-a publicly available web resource for interrogating phosphoproteomics data. Phosphomatics allows researchers to upload phosphoproteomics data and interrogate possible relationships from a substrate-, kinase- or pathway-centric viewpoint.

AVAILABILITY AND IMPLEMENTATION

Phosphomatics is freely available via the internet at: https://phosphomatics.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers

Despite recent technological advancements allowing the characterization of cancers at a molecular level along with biomarkers for cancer diagnosis, the management of ovarian cancers (OC) remains challenging. Proteins assume functions encoded by the genome and the complete set of proteins, termed the proteome, reflects the health state. Comprehending the circulatory proteomic profiles for OC subtypes, therefore, has the potential to reveal biomarkers with clinical utility concerning early diagnosis or to predict response to specific therapies. Furthermore, characterization of the proteomic landscape of tumor-derived tissue, cell lines, and PDX models has led to the molecular stratification of patient groups, with implications for personalized therapy and management of drug resistance. Here, we review single and multiple marker panels that have been identified through proteomic investigations of patient sera, effusions, and other biospecimens. We discuss their clinical utility and implementation into clinical practice.

Evolution of proteomics technologies for understanding respiratory syncytial virus pathogenesis

Introduction: Respiratory syncytial virus (RSV) is a major human pathogen associated with long term morbidity. RSV replication occurs primarily in the epithelium, producing a complex cellular response associated with acute inflammation and long-lived changes in pulmonary function and allergic disease. Proteomics approaches provide important insights into post-transcriptional regulatory processes including alterations in cellular complexes regulating the coordinated innate response and epigenome.Areas covered: Peer-reviewed proteomics studies of host responses to RSV infections and proteomics techniques were analyzed. Methodologies identified include 1)." bottom-up" discovery proteomics, 2). Organellar proteomics by LC-gel fractionation; 3). Dynamic changes in protein interaction networks by LC-MS; and 4). selective reaction monitoring MS. We introduce recent developments in single-cell proteomics, top-down mass spectrometry, and photo-cleavable surfactant chemistries that will have impact on understanding how RSV induces extracellular matrix (ECM) composition and airway remodeling.Expert opinion: RSV replication induces global changes in the cellular proteome, dynamic shifts in nuclear proteins, and remodeling of epigenetic regulatory complexes linked to the innate response. Pathways discovered by proteomics technologies have led to deeper mechanistic understanding of the roles of heat shock proteins, redox response, transcriptional elongation complex remodeling and ECM secretion remodeling in host responses to RSV infections and pathological sequelae.

Systematic Characterization of Recurrent Genomic Alterations in Cyclin-Dependent Kinases Reveals Potential Therapeutic Strategies for Cancer Treatment

Recurrent copy-number alterations, mutations, and transcript fusions of the genes encoding CDKs/cyclins are characterized in >10,000 tumors. Genomic alterations of CDKs/cyclins are dominantly driven by copy number aberrations. In contrast to cell-cycle-related CDKs/cyclins, which are globally amplified, transcriptional CDKs/cyclins recurrently lose copy numbers across cancers. Although mutations and transcript fusions are relatively rare events, CDK12 exhibits recurrent mutations in multiple cancers. Among the transcriptional CDKs, CDK7 and CDK12 show the most significant copy number loss and mutation, respectively. Their genomic alterations are correlated with increased sensitivities to DNA-damaging drugs. Inhibition of CDK7 preferentially represses the expression of genes in the DNA-damage-repair pathways and impairs the activity of homologous recombination. Low-dose CDK7 inhibitor treatment sensitizes cancer cells to PARP inhibitor-induced DNA damage and cell death. Our analysis provides genomic information for identification and prioritization of drug targets for CDKs and reveals rationales for treatment strategies.

Proteomic Studies on the Management of High-Grade Serous Ovarian Cancer Patients: A Mini-Review

High-grade serous ovarian cancer (HGSC) remains the most common and deadly subtype of ovarian cancer. It is characterized by its late diagnosis and frequent relapse despite standardized treatment with cytoreductive surgery and platinum-based chemotherapy. The past decade has seen significant advances in the clinical management and molecular understanding of HGSC following the publication of the Cancer Genome Atlas (TCGA) researchers and the introduction of targeted therapies with anti-angiogenic drugs and poly(ADP-ribose) polymerase inhibitors in specific subgroups of patients. We provide a comprehensive review of HGSC, focusing on the most important molecular advances aimed at providing a better understanding of the disease and its response to treatment. We emphasize the role that proteomic technologies are now playing in these two aspects of the disease, through the identification of proteins and their post-translational modifications in ovarian cancer tumors. Finally, we highlight how the integration of proteomics with genomics, exemplified by the work performed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), can guide the development of new biomarkers and therapeutic targets.

Signaling Pathways in Cancer: Therapeutic Targets, Combinatorial Treatments, and New Developments

Molecular alterations in cancer genes and associated signaling pathways are used to inform new treatments for precision medicine in cancer. Small molecule inhibitors and monoclonal antibodies directed at relevant cancer-related proteins have been instrumental in delivering successful treatments of some blood malignancies (e.g., imatinib with chronic myelogenous leukemia (CML)) and solid tumors (e.g., tamoxifen with ER positive breast cancer and trastuzumab for HER2-positive breast cancer). However, inherent limitations such as drug toxicity, as well as acquisition of de novo or acquired mechanisms of resistance, still cause treatment failure. Here we provide an up-to-date review of the successes and limitations of current targeted therapies for cancer treatment and highlight how recent technological advances have provided a new level of understanding of the molecular complexity underpinning resistance to cancer therapies. We also raise three basic questions concerning cancer drug discovery based on molecular markers and alterations of selected signaling pathways, and further discuss how combination therapies may become the preferable approach over monotherapy for cancer treatments. Finally, we consider novel therapeutic developments that may complement drug delivery and significantly improve clinical response and outcomes of cancer patients.

Exosomes in ovarian cancer ascites promote epithelial-mesenchymal transition of ovarian cancer cells by delivery of miR-6780b-5p

The poor prognosis of ovarian cancer is mainly due to metastasis, and the specific mechanism underlying ovarian cancer metastasis is not clear. Ascites-derived exosomes (ADEs) play an important role in the progression of ovarian cancer, but the mechanism is unknown. Here, we found that ADEs promoted ovarian cancer metastasis not only in vitro but also in vivo. This promotive function was based on epithelial-mesenchymal transition (EMT) of ovarian cancer cells. Bioinformatics analysis of RNA sequencing microarray data indicated that miR-6780b-5p may be the key microRNA (miRNA) in ADEs that facilitates cancer metastasis. Moreover, the expression of exosomal miR-6780b-5p correlated with tumor metastasis in ovarian cancer patients. miR-6780b-5p overexpression promoted and miR-6780b-5p downregulation suppressed EMT of ovarian cancer cells. These results suggest that ADEs transfer miR-6780b-5p to ovarian cancer cells, promoting EMT and finally facilitating ovarian cancer metastasis.

Mass spectrometry-based characterization of histones in clinical samples: applications, progresses, and challenges

In the last 15 years, increasing evidence linking epigenetics to various aspects of cancer biology has prompted the investigation of histone post-translational modifications (PTMs) and histone variants in the context of clinical samples. The studies performed so far demonstrated the potential of this type of investigations for the discovery of both potential epigenetic biomarkers for patient stratification and novel epigenetic mechanisms potentially targetable for cancer therapy. Although traditionally the analysis of histones in clinical samples was performed through antibody-based methods, mass spectrometry (MS) has emerged as a more powerful tool for the unbiased, comprehensive, and quantitative investigation of histone PTMs and variants. MS has been extensively used for the analysis of epigenetic marks in cell lines and animal tissue and, thanks to recent technological advances, is now ready to be applied also to clinical samples. In this review, we will provide an overview on the quantitative MS-based analysis of histones, their PTMs and their variants in cancer clinical samples, highlighting current achievements and future perspectives for this novel field of research. Among the different MS-based approaches currently available for histone PTM profiling, we will focus on the 'bottom-up' strategy, namely the analysis of short proteolytic peptides, as it has been already successfully employed for the analysis of clinical samples.

Alteration of protein expression and spliceosome pathway activity during Barrett's carcinogenesis

BACKGROUND

Barrett's esophagus (BE) is a known precursor lesion and the strongest risk factor for esophageal adenocarcinoma (EAC), a common and lethal type of cancer. Prediction of risk, the basis for efficient intervention, is commonly solely based on histologic examination. This approach is challenged by problems such as inter-observer variability in the face of the high heterogeneity of dysplastic tissue. Molecular markers might offer an additional way to understand the carcinogenesis and improve the diagnosis-and eventually treatment. In this study, we probed significant proteomic changes during dysplastic progression from BE into EAC.

METHODS

During endoscopic mucosa resection, epithelial and stromal tissue samples were collected by laser capture microdissection from 10 patients with normal BE and 13 patients with high-grade dysplastic/EAC. Samples were analyzed by mass spectrometry-based proteomic analysis. Expressed proteins were determined by label-free quantitation, and gene set enrichment was used to find differentially expressed pathways. The results were validated by immunohistochemistry for two selected key proteins (MSH6 and XPO5).

RESULTS

Comparing dysplastic/EAC to non-dysplastic BE, we found in equal volumes of epithelial tissue an overall up-regulation in terms of protein abundance and diversity, and determined a set of 226 differentially expressed proteins. Significantly higher expressions of MSH6 and XPO5 were validated orthogonally and confirmed by immunohistochemistry.

CONCLUSIONS

Our results demonstrate that disease-related proteomic alterations can be determined by analyzing minute amounts of cell-type-specific collected tissue. Further analysis indicated that alterations of certain pathways associated with carcinogenesis, such as micro-RNA trafficking, DNA damage repair, and spliceosome activity, exist in dysplastic/EAC.

Phosphoproteomic strategies in cancer research: a minireview

Understanding the cellular processes is central to comprehend disease conditions and is also true for cancer research. Proteomic studies provide significant insight into cancer mechanisms and aid in the diagnosis and prognosis of the disease. Phosphoproteome is one of the most studied complements of the whole proteome given its importance in the understanding of cellular processes such as signaling and regulations. Over the last decade, several new methods have been developed for phosphoproteome analysis. A significant amount of these efforts pertains to cancer research. The current use of powerful analytical instruments in phosphoproteomic approaches has paved the way for deeper and sensitive investigations. However, these methods and techniques need further improvements to deal with challenges posed by the complexity of samples and scarcity of phosphoproteins in the whole proteome, throughput and reproducibility. This review aims to provide a comprehensive summary of the variety of steps used in phosphoproteomic methods applied in cancer research including the enrichment and fractionation strategies. This will allow researchers to evaluate and choose a better combination of steps for their phosphoproteome studies.

A cell-of-origin epigenetic tracer reveals clinically distinct subtypes of high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is a major unmet need in oncology. The remaining uncertainty on its originating tissue has hampered the discovery of molecular oncogenic pathways and the development of effective therapies.

METHODS

We used an approach based on the retention in tumors of a DNA methylation trace (OriPrint) that distinguishes the two putative tissues of origin of HGSOC, the fimbrial (FI) and ovarian surface epithelia (OSE), to stratify HGSOC by several clustering methods, both linear and non-linear. The identified tumor subtypes (FI-like and OSE-like HGSOC) were investigated at the RNAseq level to stratify an in-house cohort of macrodissected HGSOC FFPE samples to derive overall and disease-free survival and identify specific transcriptional alterations of the two tumor subtypes, both by classical differential expression and weighted correlation network analysis. We translated our strategy to published datasets and verified the co-occurrence of previously described molecular classification of HGSOC. We performed cytokine analysis coupled to immune phenotyping to verify alterations in the immune compartment associated with HGSOC. We identified genes that are both differentially expressed and methylated in the two tumor subtypes, concentrating on PAX8 as a bona fide marker of FI-like HGSOC.

RESULTS

We show that: - OriPrint is a robust DNA methylation tracer that exposes the tissue of origin of HGSOC. - The tissue of origin of HGSOC is the main determinant of DNA methylation variance in HGSOC. - The tissue of origin is a prognostic factor for HGSOC patients. - FI-like and OSE-like HGSOC are endowed with specific transcriptional alterations that impact patients' prognosis. - OSE-like tumors present a more invasive and immunomodulatory phenotype, compatible with its worse prognostic impact. - Among genes that are differentially expressed and regulated in FI-like and OSE-like HGSOC, PAX8 is a bona fide marker of FI-like tumors.

CONCLUSIONS

Through an integrated approach, our work demonstrates that both FI and OSE are possible origins for human HGSOC, whose derived subtypes are both molecularly and clinically distinct. These results will help define a new roadmap towards rational, subtype-specific therapeutic inroads and improved patients' care.

Phosphoproteomic analysis identifies CLK1 as a novel therapeutic target in gastric cancer

BACKGROUND

Phosphorylation is an important regulatory mechanism of protein activity in cells. Studies in various cancers have reported perturbations in kinases resulting in aberrant phosphorylation of oncoproteins and tumor suppressor proteins.

METHODS

In this study, we carried out quantitative phosphoproteomic analysis of gastric cancer tissues and corresponding xenograft samples. Using these data, we employed bioinformatics analysis to identify aberrant signaling pathways. We further performed molecular inhibition and silencing of the upstream regulatory kinase in gastric cancer cell lines and validated its effect on cellular phenotype. Through an ex vivo technology utilizing patient tumor and blood sample, we sought to understand the therapeutic potential of the kinase by recreating the tumor microenvironment.

RESULTS

Using mass spectrometry-based high-throughput analysis, we identified 1,344 phosphosites and 848 phosphoproteins, including differential phosphorylation of 177 proteins (fold change cut-off ≥ 1.5). Our data showed that a subset of differentially phosphorylated proteins belonged to splicing machinery. Pathway analysis highlighted Cdc2-like kinase (CLK1) as upstream kinase. Inhibition of CLK1 using TG003 and CLK1 siRNA resulted in a decreased cell viability, proliferation, invasion and migration as well as modulation in the phosphorylation of SRSF2. Ex vivo experiments which utilizes patient's own tumor and blood to recreate the tumor microenvironment validated the use of CLK1 as a potential target for gastric cancer treatment.

CONCLUSIONS

Our data indicates that CLK1 plays a crucial role in the regulation of splicing process in gastric cancer and that CLK1 can act as a novel therapeutic target in gastric cancer.

A New Workflow for the Analysis of Phosphosite Occupancy in Paired Samples by Integration of Proteomics and Phosphoproteomics Data Sets

Knowledge of phosphosite occupancy is important to explain biological functions for phosphoproteomics studies. Determination of occupancy using three ratios, i.e., protein ratio, phosphopeptide ratio, and its unmodified peptide counterpart ratio between a pair of samples, is straightforward but suffers from large variances. Here, an optimized protocol of offline fractionation and LC-MS analysis combined with an integrated data processing approach was developed to improve the reliability of the phosphosite occupancy determination. An outlier score S was introduced to evaluate the deviation between the ratio of absolute occupancy and relative occupancy and was further used to define the bounds of a credible interval of absolute occupancy. For a preset product-moment correlation coefficient, the credible interval can be resolved through the S value. Using this strategy, more than 176k unique peptide sequences covering 11k protein groups and 32k phosphosites were identified from one paired hepatocellular carcinoma (HCC) sample and about 3000 reliable phosphosite occupancies were finally determined. By bioinformatics analysis, we characterized the biological properties associated with phosphorylation sites with different quantified occupancies from the paired HCC sample. Data are available via ProteomeXchange with identifier PXD019045.

Phosphoproteomics reveals the apoptotic regulation of aspirin in the placenta of preeclampsia-like mice

Preeclampsia (PE) is a severe gestational complication, and dysfunctional placenta plays an essential role in PE pathogenesis. Although low-dose aspirin is currently the most promising prophylactic drug for PE prevention, the exact mechanism of aspirin remains unclear. A previous study reported that treatment with low-dose aspirin could ameliorate PE-like symptoms in lipopolysaccharide (LPS)-induced PE-like mouse model. This study aimed to uncover the potential mechanism of aspirin action in PE through quantitative phosphoproteomics comparison. We established the following four groups: a control (CTRL) group, an LPS-treated (L) group, an LPS + aspirin co-treatment (LA) group, and an aspirin-treated (A) group. A total of 4350 phosphosites and 4170 phosphopeptides from 1866 phosphoproteins were identified in the placenta on embryonic day 13.5. Among the significantly altered phosphoproteins identified, apoptosis-related pathways were significantly regulated in both the L group vs. CTRL group and the LA group vs. L group comparisons. We demonstrated that apoptosis was increased in the placenta of PE-like mice and was inhibited in the LA group by quantify the apoptosis-positive cells and the protein levels of cleaved caspase 3, 8, and 9. Moreover, the phosphorylation of HSP90β (S254) and GSK3β (Y216) may be a crucial factor in the aspirin-mediated regulation of apoptosis according to protein-protein interaction analysis. This study revealed that apoptosis regulation is a mechanism of aspirin action in PE, particularly in women with over-activated inflammation. The phosphorylation of HSP90β (S254) and GSK3β (Y216) could be the key intervention targets.

Phosphoproteomics of short-term hedgehog signaling in human medulloblastoma cells

BACKGROUND

Aberrant hedgehog (HH) signaling is implicated in the development of various cancer entities such as medulloblastoma. Activation of GLI transcription factors was revealed as the driving force upon pathway activation. Increased phosphorylation of essential effectors such as Smoothened (SMO) and GLI proteins by kinases including Protein Kinase A, Casein Kinase 1, and Glycogen Synthase Kinase 3 β controls effector activity, stability and processing. However, a deeper and more comprehensive understanding of phosphorylation in the signal transduction remains unclear, particularly during early response processes involved in SMO activation and preceding GLI target gene regulation.

METHODS

We applied temporal quantitative phosphoproteomics to reveal phosphorylation dynamics underlying the short-term chemical activation and inhibition of early hedgehog signaling in HH responsive human medulloblastoma cells. Medulloblastoma cells were treated for 5.0 and 15 min with Smoothened Agonist (SAG) to induce and with vismodegib to inhibit the HH pathway.

RESULTS

Our phosphoproteomic profiling resulted in the quantification of 7700 and 10,000 phosphosites after 5.0 and 15 min treatment, respectively. The data suggest a central role of phosphorylation in the regulation of ciliary assembly, trafficking, and signal transduction already after 5.0 min treatment. ERK/MAPK signaling, besides Protein Kinase A signaling and mTOR signaling, were differentially regulated after short-term treatment. Activation of Polo-like Kinase 1 and inhibition of Casein Kinase 2A1 were characteristic for vismodegib treatment, while SAG treatment induced Aurora Kinase A activity. Distinctive phosphorylation of central players of HH signaling such as SMO, SUFU, GLI2 and GLI3 was observed only after 15 min treatment.

CONCLUSIONS

This study provides evidence that phosphorylation triggered in response to SMO modulation dictates the localization of hedgehog pathway components within the primary cilium and affects the regulation of the SMO-SUFU-GLI axis. The data are relevant for the development of targeted therapies of HH-associated cancers including sonic HH-type medulloblastoma. A deeper understanding of the mechanisms of action of SMO inhibitors such as vismodegib may lead to the development of compounds causing fewer adverse effects and lower frequencies of drug resistance. Video Abstract.

Long Non-Coding RNA HAND2-AS1 Acts as a Tumor Suppressor in High-Grade Serous Ovarian Carcinoma

Long non-coding RNAs (lncRNAs) are increasingly being identified as crucial regulators in pathologies like cancer. High-grade serous ovarian carcinoma (HGSC) is the most common subtype of ovarian cancer (OC), one of the most lethal gynecological malignancies. LncRNAs, especially in cancers such as HGSC, could play a valuable role in diagnosis and even therapy. From RNA-sequencing analysis performed between an OC cell line, SKOV3, and a Fallopian Tube (FT) cell line, FT194, an important long non-coding RNA, HAND2 Anti sense RNA 1 (HAND2-AS1), was observed to be significantly downregulated in OCs when compared to FT. Its downregulation in HGSC was validated in different datasets and in a panel of HGSC cell lines. Furthermore, this study shows that the downregulation of HAND2-AS1 is caused by promoter hypermethylation in HGSC and behaves as a tumor suppressor in HGSC cell lines. Since therapeutic relevance is of key importance in HGSC research, for the first time, HAND2-AS1 upregulation was demonstrated to be one of the mechanisms through which HDAC inhibitor Panobinostat could be used in a strategy to increase HGSC cells’ sensitivity to chemotherapeutic agents currently used in clinical trials. To unravel the mechanism by which HAND2-AS1 exerts its role, an in silico mRNA network was constructed using mRNAs whose expressions were positively and negatively correlated with this lncRNA in HGSC. Finally, a putative ceRNA network with possible miRNA targets of HAND2-AS1 and their mRNA targets was constructed, and the enriched Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified.

Phosphoproteomics Reveals Key Regulatory Kinases and Modulated Pathways Associated with Ovarian Cancer Tumors

Background

Ovarian cancer (OC) is the seventh most common cancer worldwide for women. However, there are no sufficient diagnostic methods and few treatment options available due to poor understanding of its pathogenic mechanisms.

Methods

To comprehensively analyze the phosphoproteomic characterization for OC, we took advantage of a quantitative global phosphoproteomics method, titanium(IV) immobilized metal affinity chromatography (Ti⁴⁺-IMAC) coupled to nanoscale liquid chromatography and quadrupole time-of-flight tandem mass spectrometry (nanoLC/Q-TOF-MS/MS) on ovarian tissue samples obtained from five OC patients and five matched controls.

Results

A total of 722 phosphorylated sites corresponding to 534 proteins were significantly different (fold change ≥ 2, p < 0.01) between OC patients and the controls. Among them, 83 transcription factors mainly consisted of transcription cofactors, zf-C2H2, and chromatin remodeling factors and 29 kinases were included. Further functional analysis suggested significantly biological processes were highly enriched and involved in the pathogenesis of OC, especially fructose and mannose metabolism. Moreover, the regulatory roles of modulated pathways, including MAPK, ErbB, and GnRH signaling pathways were also identified as critical processes involved in OC. The results here highlighted key phosphorylated proteins, particularly kinases, and the corresponding cancer-related metabolic and signal pathways that played important roles in the development of OC. Additionally, the expression levels of two kinases, phosphorylated CDK (T14) and phosphorylated PRKCQ (S695), were validated by Western blot analysis in the other group of ovarian tissue samples.

Conclusion

Altogether, our data not only provided novel insights into the potential biomarkers and therapy options for OC but also extended our knowledge on its pathophysiological mechanism.

Tandem Mass Tag Approach Utilizing Pervanadate BOOST Channels Delivers Deeper Quantitative Characterization of the Tyrosine Phosphoproteome

Dynamic tyrosine phosphorylation is fundamental to a myriad of cellular processes. However, the inherently low abundance of tyrosine phosphorylation in the proteome and the inefficient enrichment of phosphotyrosine(pTyr)-containing peptides has led to poor pTyr peptide identification and quantitation, critically hindering researchers' ability to elucidate signaling pathways regulated by tyrosine phosphorylation in systems where cellular material is limited. The most popular approaches to wide-scale characterization of the tyrosine phosphoproteome use pTyr enrichment with pan-specific, anti-pTyr antibodies from a large amount of starting material. Methods that decrease the amount of starting material and increase the characterization depth of the tyrosine phosphoproteome while maintaining quantitative accuracy and precision would enable the discovery of tyrosine phosphorylation networks in rarer cell populations. To achieve these goals, the BOOST (Broad-spectrum Optimization Of Selective Triggering) method leveraging the multiplexing capability of tandem mass tags (TMT) and the use of pervanadate (PV) boost channels (cells treated with the broad-spectrum tyrosine phosphatase inhibitor PV) selectively increased the relative abundance of pTyr-containing peptides. After PV boost channels facilitated selective fragmentation of pTyr-containing peptides, TMT reporter ions delivered accurate quantitation of each peptide for the experimental samples while the quantitation from PV boost channels was ignored. This method yielded up to 6.3-fold boost in pTyr quantification depth of statistically significant data derived from contrived ratios, compared with TMT without PV boost channels or intensity-based label-free (LF) quantitation while maintaining quantitative accuracy and precision, allowing quantitation of over 2300 unique pTyr peptides from only 1 mg of T cell receptor-stimulated Jurkat T cells. The BOOST strategy can potentially be applied in analyses of other post-translational modifications where treatments that broadly elevate the levels of those modifications across the proteome are available.

Visualize omics data on networks with Omics Visualizer, a Cytoscape App

Cytoscape is an open-source software used to analyze and visualize biological networks. In addition to being able to import networks from a variety of sources, Cytoscape allows users to import tabular node data and visualize it onto networks. Unfortunately, such data tables can only contain one row of data per node, whereas omics data often have multiple rows for the same gene or protein, representing different post-translational modification sites, peptides, splice isoforms, or conditions. Here, we present a new app, Omics Visualizer, that allows users to import data tables with several rows referring to the same node, connect them to one or more networks, and visualize the connected data onto networks. Omics Visualizer uses the Cytoscape enhancedGraphics app to show the data either in the nodes (pie visualization) or around the nodes (donut visualization), where the colors of the slices represent the imported values. If the user does not provide a network, the app can retrieve one from the STRING database using the Cytoscape stringApp. The Omics Visualizer app is freely available at https://apps.cytoscape.org/apps/omicsvisualizer.

Visualize omics data on networks with Omics Visualizer, a Cytoscape App

Cytoscape is an open-source software used to analyze and visualize biological networks. In addition to being able to import networks from a variety of sources, Cytoscape allows users to import tabular node data and visualize it onto networks. Unfortunately, such data tables can only contain one row of data per node, whereas omics data often have multiple rows for the same gene or protein, representing different post-translational modification sites, peptides, splice isoforms, or conditions. Here, we present a new app, Omics Visualizer, that allows users to import data tables with several rows referring to the same node, connect them to one or more networks, and visualize the connected data onto networks. Omics Visualizer uses the Cytoscape enhancedGraphics app to show the data either in the nodes (pie visualization) or around the nodes (donut visualization), where the colors of the slices represent the imported values. If the user does not provide a network, the app can retrieve one from the STRING database using the Cytoscape stringApp. The Omics Visualizer app is freely available at https://apps.cytoscape.org/apps/omicsvisualizer.