Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients.

Integrative proteogenomic profiling of high-risk prostate cancer samples from Chinese patients indicates metabolic vulnerabilities and diagnostic biomarkers

Prostate cancer (PCa) exhibits significant geoethnic disparities as reflected by distinct variations in the cancer genome and disease progression. Here, we perform a comprehensive proteogenomic characterization of localized high-risk PCa utilizing paired tumors and nearby tissues from 125 Chinese male patients, with the primary objectives of identifying potential biomarkers, unraveling critical oncogenic events and delineating molecular subtypes with poor prognosis. Our integrated analysis highlights the utility of GOLM1 as a noninvasive serum biomarker. Phosphoproteomics analysis reveals the crucial role of Ser331 phosphorylation on FOXA1 in regulating FOXA1-AR-dependent cistrome. Notably, our proteomic profiling identifies three distinct subtypes, with metabolic immune-desert tumors (S-III) emerging as a particularly aggressive subtype linked to poor prognosis and BCAT2 catabolism-driven PCa progression. In summary, our study provides a comprehensive resource detailing the unique proteomic and phosphoproteomic characteristics of PCa molecular pathogenesis and offering valuable insights for the development of diagnostic and therapeutic strategies.

Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction

Prostate cancer (PCa) is the most common malignant tumor in men. Currently, there are few prognosis indicators for predicting PCa outcomes and guiding treatments. Here, we perform comprehensive proteomic profiling of 918 tissue specimens from 306 Chinese patients with PCa using data-independent acquisition mass spectrometry (DIA-MS). We identify over 10,000 proteins and define three molecular subtypes of PCa with significant clinical and proteomic differences. We develop a 16-protein panel that effectively predicts biochemical recurrence (BCR) for patients with PCa, which is validated in six published datasets and one additional 99-biopsy-sample cohort by targeted proteomics. Interestingly, this 16-protein panel effectively predicts BCR across different International Society of Urological Pathology (ISUP) grades and pathological stages and outperforms the D'Amico risk classification system in BCR prediction. Furthermore, double knockout of NUDT5 and SEPTIN8, two components from the 16-protein panel, significantly suppresses the PCa cells to proliferate, invade, and migrate, suggesting the combination of NUDT5 and SEPTIN8 may provide new approaches for PCa treatment.

Dual inhibition of ATR and DNA-PKcs radiosensitizes ATM-mutant prostate cancer

In advanced castration resistant prostate cancer (CRPC), mutations in the DNA damage response (DDR) gene ataxia telangiectasia mutated ( ATM ) are common. While poly(ADP-ribose) polymerase inhibitors are approved in this context, their clinical efficacy remains limited. Thus, there is a compelling need to identify alternative therapeutic avenues for ATM mutant prostate cancer patients. Here, we generated matched ATM-proficient and ATM-deficient CRPC lines to elucidate the impact of ATM loss on DDR in response to DNA damage via irradiation. Through unbiased phosphoproteomic screening, we unveiled that ATM-deficient CRPC lines maintain dependence on downstream ATM targets through activation of ATR and DNA-PKcs kinases. Dual inhibition of ATR and DNA-PKcs effectively inhibited downstream γH2AX foci formation in response to irradiation and radiosensitized ATM-deficient lines to a greater extent than either ATM-proficient controls or single drug treatment. Further, dual inhibition abrogated residual downstream ATM pathway signaling and impaired replication fork dynamics. To circumvent potential toxicity, we leveraged the RUVBL1/2 ATPase inhibitor Compound B, which leads to the degradation of both ATR and DNA-PKcs kinases. Compound B effectively radiosensitized ATM-deficient CRPC in vitro and in vivo , and impacted replication fork dynamics. Overall, dual targeting of both ATR and DNA-PKcs is necessary to block DDR in ATM-deficient CRPC, and Compound B could be utilized as a novel therapy in combination with irradiation in these patients.

Insights into the role of FoxL2 in tebuconazole-induced male- biased sex differentiation of zebrafish

Tebuconazole (TEB) is a commonly used fungicide that inhibits the aromatase Cyp19A and downregulates the transcription factor forkhead box L2 (FoxL2), leading to male-biased sex differentiation in zebrafish larvae. However, the specific mechanism by which FoxL2 functions following TEB exposure remains unclear. In this study, the phosphorylation sites and kinase-specific residues in zebrafish FoxL2 protein (zFoxL2) were predicted. Subsequently, recombinant zFoxL2 was prepared via prokaryotic expression, and a polyclonal rabbit-anti-zFoxL2 antibody was generated. Zebrafish fibroblast (ZF4) cells were exposed to 100-μM TEB alone for 8 h, after which changes in the expression of genes involved in the foxl2 regulatory pathway (akt1, pi3k, cyp19a1b, c/ebpb and sox9a) were detected. When co-exposed to 1-μM estradiol and 100-μM TEB, the expression of these key genes tended to be restored. Interestingly, TEB did not affect the expression of the foxl2 gene or protein but it significantly suppressed the phosphorylation of FoxL2 (pFoxL2) at serine 238 (decreased by 43.64 %, p = 0.009). Co-immunoprecipitation assays showed that, following exposure to 100-μM TEB, the total precipitated proteins in ZF4 cells decreased by 17.02 % (p = 0.029) and 31.39 % (p = 0.027) in the anti-zFoxL2 antibody group and anti-pFoxL2 (ser238) antibody group, respectively, indicating that TEB suppressed the capacity of the FoxL2 protein to bind to other proteins via repression of its own phosphorylation. The pull-down assay confirmed this conclusion. This study preliminarily elucidated that the foxl2 gene functions via post-translational regulation through hypophosphorylation of its encoded protein during TEB-induced male-biased sex differentiation.

PhosNetVis: a web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/.

Sequencing of Phosphopeptides Using a Sequential Charge Inversion Ion/Ion Reaction and Electron Capture Dissociation Workflow

Protein phosphorylation, a common post-translational modification (PTM), is fundamental in a plethora of biological processes, most importantly in modulating cell signaling pathways. Matrix-assisted laser desorption/ionization (MALDI) coupled to tandem mass spectrometry (MS/MS) is an attractive method for phosphopeptide characterization due to its high speed, low limit of detection, and surface sampling capabilities. However, MALDI analysis of phosphopeptides is constrained by relatively low abundances in biological samples and poor relative ionization efficiencies in positive ion mode. Additionally, MALDI tends to produce singly charged ions, generally limiting the accessible MS/MS techniques that can be used for peptide sequencing. For example, collision induced dissociation (CID) is readily amendable to the analysis of singly charged ions, but results in facile loss of phosphoric acid, precluding the localization of the PTM. Electron-based dissociation methods (e.g., electron capture dissociation, ECD) are well suited for PTM localization, but require multiply charged peptide cations to avoid neutralization during ECD. Conversely, phosphopeptides are readily ionized using MALDI in negative ion mode. If the precursor ions are first formed in negative ion mode, a gas-phase charge inversion ion/ion reaction could then be used to transform the phosphopeptide anions produced via MALDI into multiply charged cations that are well-suited for ECD. Herein we demonstrate a multistep workflow combining a charge inversion ion/ion reaction that first transforms MALDI-generated phosphopeptide monoanions into multiply charged cations, and then subjects these multiply charged phosphopeptide cations to ECD for sequence determination and phosphate bond localization.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen-deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform into emergent aggressive variant prostate cancer (AVPC), which has neuroendocrine (NE)-like features. In this work, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflect and retain key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. We compared 15 NE versus 33 AdCa samples, which included six different PDX tumors for each group in biological replicates, and identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of concordance from PDX tumor-matched protein and mRNA revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

IMPLICATIONS

Overall, our study highlights the importance of protein-based identification when compared with RNA and provides a rich resource of new and feasible targets for clinical assay development and in understanding the underlying biology of these tumors.

Multi-omics characterization of esophageal squamous cell carcinoma identifies molecular subtypes and therapeutic targets

Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal cancer and is characterized by an unfavorable prognosis. To elucidate the distinct molecular alterations in ESCC and investigate therapeutic targets, we performed a comprehensive analysis of transcriptomics, proteomics, and phosphoproteomics data derived from 60 paired treatment-naive ESCC and adjacent nontumor tissue samples. Additionally, we conducted a correlation analysis to describe the regulatory relationship between transcriptomic and proteomic processes, revealing alterations in key metabolic pathways. Unsupervised clustering analysis of the proteomics data stratified patients with ESCC into 3 subtypes with different molecular characteristics and clinical outcomes. Notably, subtype III exhibited the worst prognosis and enrichment in proteins associated with malignant processes, including glycolysis and DNA repair pathways. Furthermore, translocase of inner mitochondrial membrane domain containing 1 (TIMMDC1) was validated as a potential prognostic molecule for ESCC. Moreover, integrated kinase-substrate network analysis using the phosphoproteome nominated candidate kinases as potential targets. In vitro and in vivo experiments further confirmed casein kinase II subunit α (CSNK2A1) as a potential kinase target for ESCC. These underlying data represent a valuable resource for researchers that may provide better insights into the biology and treatment of ESCC.

Unravelling the molecular basis of PARP inhibitor resistance in prostate cancer with homologous recombination repair deficiency

Prostate cancer is a disease with heterogeneous characteristics, making its treatability and curability dependent on the cancer's stage. While prostate cancer is often indolent, some cases can be aggressive and evolve into metastatic castration-resistant prostate cancer (mCRPC), which is lethal. A significant subset of individuals with mCRPC exhibit germline and somatic variants in components of the DNA damage repair (DDR) pathway. Recently, PARP inhibitors (PARPi) have shown promise in treating mCRPC patients who carry deleterious alterations in BRCA2 and 13 other DDR genes that are important for the homologous recombination repair (HRR) pathway. These inhibitors function by trapping PARP, resulting in impaired PARP activity and increased DNA damage, ultimately leading to cell death through synthetic lethality. However, the response to these inhibitors only lasts for 3-4 months, after which the cancer becomes PARPi resistant. Cancer cells can develop resistance to PARPi through numerous mechanisms, such as secondary reversion mutations in DNA repair pathway genes, heightened drug efflux, loss of PARP expression, HRR reactivation, replication fork stability, and upregulation of Wnt/Catenin and ABCB1 pathways. Overcoming PARPi resistance is a critical and complex process, and there are two possible ways to sensitize the resistance. The first approach is to potentiate the PARPi agents through chemo/radiotherapy and combination therapy, while the second approach entails targeting different signaling pathways. This review article highlights the latest evidence on the resistance mechanism of PARPi in lethal prostate cancer and discusses additional therapeutic opportunities available for prostate cancer patients with DDR gene alterations who do not respond to PARPi.

Adipose Triglyceride Lipase Is a Therapeutic Target in Advanced Prostate Cancer That Promotes Metabolic Plasticity

Lipid metabolism plays a central role in prostate cancer. To date, the major focus has centered on de novo lipogenesis and lipid uptake in prostate cancer, but inhibitors of these processes have not benefited patients. A better understanding of how cancer cells access lipids once they are created or taken up and stored could uncover more effective strategies to perturb lipid metabolism and treat patients. Here, we identified that expression of adipose triglyceride lipase (ATGL), an enzyme that controls lipid droplet homeostasis and a previously suspected tumor suppressor, correlates with worse overall survival in men with advanced, castration-resistant prostate cancer (CRPC). Molecular, genetic, or pharmacologic inhibition of ATGL impaired human and murine prostate cancer growth in vivo and in cell culture or organoids under conditions mimicking the tumor microenvironment. Mass spectrometry imaging demonstrated that ATGL profoundly regulates lipid metabolism in vivo, remodeling membrane composition. ATGL inhibition induced metabolic plasticity, causing a glycolytic shift that could be exploited therapeutically by cotargeting both metabolic pathways. Patient-derived phosphoproteomics identified ATGL serine 404 as a target of CAMKK2-AMPK signaling in CRPC cells. Mutation of serine 404 did not alter the lipolytic activity of ATGL but did decrease CRPC growth, migration, and invasion, indicating that noncanonical ATGL activity also contributes to disease progression. Unbiased immunoprecipitation/mass spectrometry suggested that mutation of serine 404 not only disrupts existing ATGL protein interactions but also leads to new protein-protein interactions. Together, these data nominate ATGL as a therapeutic target for CRPC and provide insights for future drug development and combination therapies.

SIGNIFICANCE

ATGL promotes prostate cancer metabolic plasticity and progression through both lipase-dependent and lipase-independent activity, informing strategies to target ATGL and lipid metabolism for cancer treatment.

Citron Kinase Is a Druggable Target in Treatment-Resistant Prostate Cancer

Prolonged treatment with androgen deprivation therapy (ADT) inevitably leads to castration-resistant prostate cancer (CRPC). Development of novel androgen-targeting agents and chemo/radiotherapies has resulted in improved survival. However, metastatic CRPC remains incurable. New therapeutics are greatly needed, and exploration of novel pathways such as the mechanisms underlying prostate cancer cell proliferation could potentially augment the natural course of CRPC. In the latest issue of Cancer Research, Rawat and colleagues delved deeply into the mechanistic role of citron kinase (CIT) in orchestrating prostate cancer proliferation and revealed its catalytic activity as a druggable target for treatment-resistant prostate cancer. The researchers utilized in vitro and in vivo methodologies to elucidate the function of CIT in mediating uncontrolled interphase progression and prostate cancer growth. Furthermore, the authors employed both androgen receptor-dependent and independent models to validate the significance of CIT kinase activity as a crucial factor in driving treatment-resistant prostate cancer growth. At a mechanistic level they determined that the E2F2-Skp2-p27 axis regulates CIT expression. Finally, they defined the landscape of CIT substrates in prostate cancer that encompasses a spectrum of cellular functions that spans key proliferation regulators to alternative splicing events. This comprehensive work provides insights into CIT as a potential biomarker for prostate cancer treatment resistance and disease progression and establishes the CIT kinase domain as a druggable target in CRPC. See related article by Rawat et al., p. 4142.

Metastatic cells exploit their stoichiometric niche in the network of cancer ecosystems

Metastasis is a nonrandom process with varying degrees of organotropism-specific source-acceptor seeding. Understanding how patterns between source and acceptor tumors emerge remains a challenge in oncology. We hypothesize that organotropism results from the macronutrient niche of cells in source and acceptor organs. To test this, we constructed and analyzed a metastatic network based on 9303 records across 28 tissue types. We found that the topology of the network is nested and modular with scale-free degree distributions, reflecting organotropism along a specificity/generality continuum. The variation in topology is significantly explained by the matching of metastatic cells to their stoichiometric niche. Specifically, successful metastases are associated with higher phosphorus content in the acceptor compared to the source organ, due to metabolic constraints in proliferation crucial to the invasion of new tissues. We conclude that metastases are codetermined by processes at source and acceptor organs, where phosphorus content is a limiting factor orchestrating tumor ecology.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

The synthesis and functionalization of metal organic frameworks and their applications for the selective separation of proteins/peptides

Recently, proteins separation has drawn great interest for the full investigation of a proteome because the proteins separation is the precondition when conducting clinical research or proteomics research. Metal organic frameworks (MOFs) are fabricated via covalent connection between organic ligands and metal ions/clusters units. MOFs have attracted much attention due to the ultra-high specific surface area, tunable structure, more metal site or unsaturated site, and chemical stability. Over the past decade, different functionalization types of MOFs have been reported in combination with amino acids, nucleic acids, proteins, polymers, and nanoparticles for various applications. In this review, the synthesis and functionalization of MOFs have been thoroughly discussed, and we introduced the existing problems and development trends in these fields. Furthermore, MOFs as advanced adsorbents for selective separation of proteins/peptides are summarized. Additionally, we present a comprehensive prospects and challenges in the preparation of robust functional MOFs-based adsorbents and make a final outlook on their future development prospects in selective separation of proteins/peptides.

Complementary analysis of proteome-wide proteomics reveals changes in RNA binding protein-profiles during prostate cancer progression

BACKGROUND

Accumulating evidence indicates importance of RNA regulation in cancer. This includes events such as splicing, translation, and regulation of noncoding RNAs, functions which are governed by RNA binding proteins (RBPs).

AIMS

To find which RBPs could be relevant for prostate cancer, we performed systematic screening of RBP expression in clinical prostate cancer.

METHODS AND RESULTS

We interrogated four proteome-wide proteomics datasets including tumor samples of primary, castration resistant, and metastatic prostate cancer. We found that, while the majority of RBPs are expressed but not significantly altered during prostate cancer development and progression, expression of several RBPs increases in advanced disease. Interestingly, most of the differentially expressed RBPs are not targets of differential posttranscriptional phosphorylation during disease progression. The RBPs undergoing expression changes have functions in, especially, poly(A)-RNA binding, nucleocytoplasmic transport, and cellular stress responses, suggesting that these may play a role in formation of castration resistance. Pathway analyzes indicate that increased ribosome production and chromatin-related functions of RBPs are also linked to castration resistant and metastatic prostate cancers. We selected a group of differentially expressed RBPs and studied their role in cultured prostate cancer cells. With siRNA screens, several of these were indicated in survival (DDX6, EIF4A3, PABPN1), growth (e.g., EIF5A, HNRNPH2, LRRC47, and NVL), and migration (e.g., NOL3 and SLTM) of prostate cancer cells. Our analyzes further show that RRP9, a U3 small nucleolar protein essential for ribosome formation, undergoes changes at protein level during metastasis in prostate cancer.

CONCLUSION

In this work, we recognized significant molecular alterations in RBP profiles during development and evolution of prostate cancer. Our study further indicates several functionally significant RBPs warranting further investigation for their functions and possible targetability in prostate cancer.

PLEKHS1 drives PI3Ks and remodels pathway homeostasis in PTEN-null prostate

The PIP3/PI3K network is a central regulator of metabolism and is frequently activated in cancer, commonly by loss of the PIP3/PI(3,4)P2 phosphatase, PTEN. Despite huge research investment, the drivers of the PI3K network in normal tissues and how they adapt to overactivation are unclear. We find that in healthy mouse prostate PI3K activity is driven by RTK/IRS signaling and constrained by pathway feedback. In the absence of PTEN, the network is dramatically remodeled. A poorly understood YXXM- and PIP3/PI(3,4)P2-binding PH domain-containing adaptor, PLEKHS1, became the dominant activator and was required to sustain PIP3, AKT phosphorylation, and growth in PTEN-null prostate. This was because PLEKHS1 evaded pathway-feedback and experienced enhanced PI3K- and Src-family kinase-dependent phosphorylation of Y258XXM, eliciting PI3K activation. hPLEKHS1 mRNA and activating Y419 phosphorylation of hSrc correlated with PI3K pathway activity in human prostate cancers. We propose that in PTEN-null cells receptor-independent, Src-dependent tyrosine phosphorylation of PLEKHS1 creates positive feedback that escapes homeostasis, drives PIP3 signaling, and supports tumor progression.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform to emergent aggressive variant prostate cancer (AVPC) which has neuroendocrine (NE)-like features. To this end, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflects and retains key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. When we compared 15 NE versus 33 AdCa PDX samples, we identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of protein and RNA concordance from these tumors revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

Construction and experimental validation of a macrophage cell senescence-related gene signature to evaluate the prognosis, immunotherapeutic sensitivity, and chemotherapy response in bladder cancer

Tumor-associated macrophages (TAMs) are pivotal components of tumor microenvironment (TME), and senescent TAMs contribute to the alternation of the profiles of TME. However, the potential biological mechanisms and the prognosis value of senescent macrophages are largely unknown, especially in bladder cancer (BLCA). Based on the single-cell RNA sequencing of a primary BLCA sample, 23 macrophage-related genes were identified. Genomic difference analysis, LASSO, and Cox regression were used to develop the risk model. TCGA-BLCA cohort (n = 406) was utilized as the training cohort, and then, three independent cohorts (n = 90, n = 221, n = 165) from Gene Expression Omnibus, clinical samples from the local hospital (n = 27), and in vitro cell experiments were used for external validation. Aldo-keto reductase family 1 member B (AKR1B1), inhibitor of DNA binding 1 (ID1), and transforming growth factor beta 1 (TGFB1I1) were determined and included in the predictive model. The model serves as a promising tool to evaluate the prognosis in BLCA (pooled hazard ratio = 2.51, 95% confidence interval = [1.43; 4.39]). The model was also effective for the prediction of immunotherapeutic sensitivity and chemotherapy treatment outcomes, which were further confirmed by IMvigor210 cohort (P < 0.01) and GDSC dataset, respectively. Twenty-seven BLCA samples from the local hospital proved that the risk model was associated with the malignant degree (P < 0.05). At last, the human macrophage THP-1 and U937 cells were treated with H2O2 to mimic the senescent process in macrophage, and the expressions of these molecules in the model were detected (all P < 0.05).Overall, a macrophage cell senescence-related gene signature was constructed to predict the prognosis, immunotherapeutic response, and chemotherapy sensitivity in BLCA, which provides novel insights to uncover the underlying mechanisms of macrophage senescence.

MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.

Saracatinib synergizes with enzalutamide to downregulate AR activity in CRPC

Prostate cancer (PCa) remains the most diagnosed non-skin cancer amongst the American male population. Treatment for localized prostate cancer consists of androgen deprivation therapies (ADTs), which typically inhibit androgen production and the androgen receptor (AR). Though initially effective, a subset of patients will develop resistance to ADTs and the tumors will transition to castration-resistant prostate cancer (CRPC). Second generation hormonal therapies such as abiraterone acetate and enzalutamide are typically given to men with CRPC. However, these treatments are not curative and typically prolong survival only by a few months. Several resistance mechanisms contribute to this lack of efficacy such as the emergence of AR mutations, AR amplification, lineage plasticity, AR splice variants (AR-Vs) and increased kinase signaling. Having identified SRC kinase as a key tyrosine kinase enriched in CRPC patient tumors from our previous work, we evaluated whether inhibition of SRC kinase synergizes with enzalutamide or chemotherapy in several prostate cancer cell lines expressing variable AR isoforms. We observed robust synergy between the SRC kinase inhibitor, saracatinib, and enzalutamide, in the AR-FL+/AR-V+ CRPC cell lines, LNCaP95 and 22Rv1. We also observed that saracatinib significantly decreases AR Y534 phosphorylation, a key SRC kinase substrate residue, on AR-FL and AR-Vs, along with the AR regulome, supporting key mechanisms of synergy with enzalutamide. Lastly, we also found that the saracatinib-enzalutamide combination reduced DNA replication compared to the saracatinib-docetaxel combination, resulting in marked increased apoptosis. By elucidating this combination strategy, we provide pre-clinical data that suggests combining SRC kinase inhibitors with enzalutamide in select patients that express both AR-FL and AR-Vs.

Saracatinib synergizes with enzalutamide to downregulate androgen receptor activity in castration resistant prostate cancer

Prostate cancer (PCa) remains the most diagnosed non-skin cancer amongst the American male population. Treatment for localized prostate cancer consists of androgen deprivation therapies (ADTs), which typically inhibit androgen production and the androgen receptor (AR). Though initially effective, a subset of patients will develop resistance to ADTs and the tumors will transition to castration-resistant prostate cancer (CRPC). Second generation hormonal therapies such as abiraterone acetate and enzalutamide are typically given to men with CRPC. However, these treatments are not curative and typically prolong survival only by a few months. Several resistance mechanisms contribute to this lack of efficacy such as the emergence of AR mutations, AR amplification, lineage plasticity, AR splice variants (AR-Vs) and increased kinase signaling. Having identified SRC kinase as a key tyrosine kinase enriched in CRPC patient tumors from our previous work, we evaluated whether inhibition of SRC kinase synergizes with enzalutamide or chemotherapy in several prostate cancer cell lines expressing variable AR isoforms. We observed robust synergy between the SRC kinase inhibitor, saracatinib, and enzalutamide, in the AR-FL+/AR-V+ CRPC cell lines, LNCaP95 and 22Rv1. We also observed that saracatinib significantly decreases AR Y 534 phosphorylation, a key SRC kinase substrate residue, on AR-FL and AR-Vs, along with the AR regulome, supporting key mechanisms of synergy with enzalutamide. Lastly, we also found that the saracatinib-enzalutamide combination reduced DNA replication compared to the saracatinib-docetaxel combination, resulting in marked increased apoptosis. By elucidating this combination strategy, we provide pre-clinical data that suggests combining SRC kinase inhibitors with enzalutamide in select patients that express both AR-FL and AR-Vs.

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.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Analysis of 1276 Haplotype-Resolved Genomes Allows Characterization of Cis- and Trans-Abundant Genes

Many methods for haplotyping have materialized, but their application on a significant scale has been rare to date. Here we summarize analyses that were carried out in 1092 genomes from the 1000 Genomes Consortium and validated in an unprecedented number of 184 PGP genomes that have been experimentally haplotype-resolved by application of the Long-Fragment Read (LFR) technology. These analyses provided first insights into the diplotypic nature of human genomes and its potential functional implications. Thus, protein-changing variants were not randomly distributed between the two homologues of 18,121 autosomal protein-coding genes but occurred significantly more frequently in cis than in trans configurations in virtually each of the 1276 phased genomes. This resulted in global cis/trans ratios of ~60:40, establishing "cis abundance" as a universal characteristic of diploid human genomes. This phenomenon was based on two different classes of genes, a larger one exhibiting cis configurations of protein-changing variants in excess, so-called "cis-abundant" genes, and a smaller one of "trans-abundant" genes. These two gene classes, which together constitute a common diplotypic exome, were further functionally distinguished by means of gene ontology (GO) and pathway enrichment analysis. Moreover, they were distinguishable in terms of their effects on the human interactome, where they constitute distinct cis and trans modules, as shown with network propagation on a large integrated protein-protein interaction network. These analyses, recently performed with updated database and analysis tools, further consolidated the characterization of cis- and trans-abundant genes while expanding previous results. In this chapter, we present the key results along with the materials and methods to motivate readers to investigate these findings independently and gain further insights into the diplotypic nature of genes and genomes.

Three-Dimensional MAX-Ti3 AlC2 Nanomaterials for Dual-Selective and Highly Efficient Enrichment of Phosphorylated and Glycosylated Peptides

Dual-selective enrichment of phosphopeptides and glycopeptides of post-translational modifications (PTMs) in the complex biological samples are challenging. In this work, considering the versatile properties including surface abundant metal sites and electrostatic attraction between Ti₃ C₂ -layers and Al-layers, layered ternary carbides Ti₃ AlC₂ nanomaterials was successfully applied for the first time as an affinity adsorbent for the dual-selective capture of phosphopeptides and glycopeptides. Especially, the Ti₃ AlC₂ nanomaterials had an excellent detection sensitivity for phosphopeptides (1×10^-11  M) and a good selectivity for glycopeptides with a low molar ratio of 1 : 500 of HRP (horseradish peroxidase) to BSA (bovine serum albumin). Furthermore, Ti₃ AlC₂ nanomaterials was also applied for dual-selective enrichment of phosphopeptides and glycopeptides from mouse brain neocortex lysate and human serum lysate respectively before mass spectrometry (MS) analysis, yielding twenty-two unique phosphopeptides from thirteen phosphoproteins and fifty-three unique glycopeptides from thirty-seven glycoproteins, respectively. This work will open a new avenue and will greatly promote sample preparation for mass spectrometric analysis in phosphoproteomics and glycoproteomics research.

Proteomics reveals MRPL4 as a high-risk factor and a potential diagnostic biomarker for prostate cancer

Through digital rectal examinations (DRE) and routine prostate-specific antigen (PSA) screening, early prostate cancer (PC) treatment has become possible. However, PC is a complex and heterogeneous disease. In vivo, cancer cells can invade adjacent tissues and metastasize to other tissues resulting in hard cures. Therefore, the key to improving PC patients' survival time is preventing cancer cells' metastasis. We used mass spectrometry to profile primary PC in patients with versus without metastatic PC. We named these two groups of PC patients as high-risk primary PC (n = 11) and low-risk primary PC (n = 7), respectively. At the same time, patients with benign prostatic hyperplasia (BPH, n = 6) were used as controls to explore the possible factors driving PC metastasis. Based on comprehensive mass spectrometry analysis and biological validation, we found significant upregulation of MRPL4 expression in high-risk primary PC relative to low-risk primary PC and BPH. Further, through research of the extensive clinical cohort data in the database, we discovered that MRPL4 could be a high-risk factor for PC and serve as a potential diagnostic biomarker. The MRPL4 might be used as an auxiliary indicator for clinical status/stage of primary PC to predict patient survival time.

Phosphoproteomics of extracellular vesicles integrated with multiomics analysis reveals novel kinase networks for lung cancer

Phosphorylation regulates the functions of proteins and aberrant phosphorylation often leads to a variety of diseases, including cancers. Extracellular vesicles (EVs) are important messengers in the microenvironment and their proteome contributes to cancer genesis and metastasis, while the kinases that driving EVs proteins' phosphorylation are less known. Clinical tissue samples from 13 patients with non-small-cell lung cancer (NSCLC) were utilized to isolate cancer EVs and adjacent normal EVs. Through quantitative phosphoproteomics analysis, 2473 phosphorylation sites on 1567 proteins were successfully identified and quantified. Accordingly, 152 kinases were identified, and 25 of them were differentially expressed. Based on Tied Diffusion through Interacting Events (TieDIE) algorithm, we integrated genomic and transcriptomic data sets of NSCLC from TCGA with our phosphoproteome data set to construct signaling networks. Through database integration and multiomics enrichment analysis, a compact network of 234 nodes with 1599 edges was constructed, which consisted of 34 transcription factors, 33 kinases, 63 aberrant genes, and 172 linking proteins. Rarely studied phosphorylation sites were specifically enriched. Key phosphoproteins of network nodes were validated in patients' EVs, including MAPK6^S189 , IKBKE^S172 , SRC^Y530 , CDK7^S164 , and CDK1^T14 . These networks depict intrinsic signal-regulation derived from EVs' phosphoproteins, providing a comprehensive and pathway-based strategy for in-depth lung cancer research.

Protein kinase CK2 – diverse roles in cancer cell biology and therapeutic promise

The association of protein kinase CK2 (formerly casein kinase II or 2) with cell growth and proliferation in cells was apparent at early stages of its investigation. A cancer-specific role for CK2 remained unclear until it was determined that CK2 was also a potent suppressor of cell death (apoptosis); the latter characteristic differentiated its function in normal versus malignant cells because dysregulation of both cell growth and cell death is a universal feature of cancer cells. Over time, it became evident that CK2 exerts its influence on a diverse range of cell functions in normal as well as in transformed cells. As such, CK2 and its substrates are localized in various compartments of the cell. The dysregulation of CK2 is documented in a wide range of malignancies; notably, by increased CK2 protein and activity levels with relatively moderate change in its RNA abundance. High levels of CK2 are associated with poor prognosis in multiple cancer types, and CK2 is a target for active research and testing for cancer therapy. Aspects of CK2 cellular roles and targeting in cancer are discussed in the present review, with focus on nuclear and mitochondrial functions and prostate, breast and head and neck malignancies.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

MEK inhibitor sensitivity in BRAF fusion-driven prostate cancer

PURPOSE

The identification of subpopulations harboring druggable targets has become a major step forward in the subclassification of solid tumors into small groups suitable for specific therapies. BRAF fusions represent a paradigm of uncommon and targetable oncogenic events and have been widely correlated to the development of specific malignancies. However, they are only present in a limited frequency across most common tumor types. At this regard, we performed a genomic screening aimed to identifying rare variants associated to advanced prostate cancer development.

METHODS

Tumoral tissue genomic screening of 41 patients developing advanced prostate cancer was performed at our center as part of the GETHI XX study. The project, sponsored by the Spanish Collaborative Group in Rare Cancers (GETHI), aims to analyze the molecular background of rare tumors and to discover unfrequent molecular variants in common tumors.

RESULTS

Here we present the clinical outcome and an in-deep molecular analysis performed in a case harboring a SND1-BRAF fusion gene. The identification of such rearrangement in a patient refractory to standard therapies led to the administration of trametinib (MEK inhibitor). Despite unsensitive to standard therapies, the patient achieved a dramatic response to trametinib. A comprehensive study of the tumor demonstrated this event to be a trunk alteration with higher expression of MEK in areas of tumor invasion.

CONCLUSIONS

Our study describes the patient-driven discovery of the first BRAF fusion-driven prostate cancer effectively treated with trametinib. Consequently, MAPK pathway activation could define a new subtype of prostate cancer susceptible to a tailored management.

Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks

Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single-cell proteomics or large-scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers.

Emerging Proteins in CRPC: Functional Roles and Clinical Implications

Prostate cancer (PCa) is the most common cancer in men in the western world, but the lack of specific and sensitive markers often leads to overtreatment of prostate cancer which eventually develops into castration-resistant prostate cancer (CRPC). Novel protein markers for diagnosis and management of CRPC will be promising. In this review, we systematically summarize and discuss the expression pattern of emerging proteins in tissue, cell lines, and serum when castration-sensitive prostate cancer (CSPC) progresses to CRPC; focus on the proteins involved in CRPC growth, invasion, metastasis, metabolism, and immune microenvironment; summarize the current understanding of the regulatory mechanisms of emerging proteins in CSPC progressed to CRPC at the molecular level; and finally summarize the clinical applications of emerging proteins as diagnostic marker, prognostic marker, predictive marker, and therapeutic marker.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

eEF2K Activity Determines Synergy to Cotreatment of Cancer Cells With PI3K and MEK Inhibitors

PI3K-mammalian target of rapamycin and MAPK/ERK kinase (MEK)/mitogen-activated protein kinase (MAPK) are the most frequently dysregulated signaling pathways in cancer. A problem that limits the success of therapies that target individual PI3K-MAPK members is that these pathways converge to regulate downstream functions and often compensate each other, leading to drug resistance and transient responses to therapy. In order to overcome resistance, therapies based on cotreatments with PI3K/AKT and MEK/MAPK inhibitors are now being investigated in clinical trials, but the mechanisms of sensitivity to cotreatment are not fully understood. Using LC-MS/MS-based phosphoproteomics, we found that eukaryotic elongation factor 2 kinase (eEF2K), a key convergence point downstream of MAPK and PI3K pathways, mediates synergism to cotreatment with trametinib plus pictilisib (which target MEK1/2 and PI3Kα/δ, respectively). Inhibition of eEF2K by siRNA or with a small molecule inhibitor reversed the antiproliferative effects of the cotreatment with PI3K plus MEK inhibitors in a cell model-specific manner. Systematic analysis in 12 acute myeloid leukemia cell lines revealed that eEF2K activity was increased in cells for which PI3K plus MEKi cotreatment is synergistic, while PKC potentially mediated resistance to such cotreatment. Together, our study uncovers eEF2K activity as a key mediator of responses to PI3Ki plus MEKi and as a potential biomarker to predict synergy to cotreatment in cancer cells.

Mapping the O-GlcNAc Modified Proteome: Applications for Health and Disease

O-GlcNAc is a pleotropic, enigmatic post-translational modification (PTM). This PTM modifies thousands of proteins differentially across tissue types and regulates diverse cellular signaling processes. O-GlcNAc is implicated in numerous diseases, and the advent of O-GlcNAc perturbation as a novel class of therapeutic underscores the importance of identifying and quantifying the O-GlcNAc modified proteome. Here, we review recent advances in mass spectrometry-based proteomics that will be critical in elucidating the role of this unique glycosylation system in health and disease.

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.