Phosphoproteomics Enables Molecular Subtyping and Nomination of Kinase Candidates for Individual Patients of Diffuse-Type Gastric Cancer

Current development of molecular classifications of gastric cancer based on omics (Review)

Gastric cancer (GC) is a complex and heterogeneous disease with significant phenotypic and genetic variation. Traditional classification systems rely mainly on the evaluation of clinical pathological features and conventional biomarkers and might not capture the diverse clinical processes of individual GCs. The latest discoveries in omics technologies such as next‑generation sequencing, proteomics and metabolomics have provided crucial insights into potential genetic alterations and biological events in GC. Clustering strategies for identifying subtypes of GC might offer new tools for improving GC treatment and clinical trial outcomes by enabling the development of therapies tailored to specific subtypes. However, the feasibility and therapeutic significance of implementing molecular classifications of GC in clinical practice need to addressed. The present review examines the current molecular classifications, delineates the prevailing landscape of clinically relevant molecular features, analyzes their correlations with traditional GC classifications, and discusses potential clinical applications.

Comprehensive landscape of gastric cancer-targeted therapy and identification of CSNK2A1 as a potential target

Objective

To conduct a comprehensive analysis of the landscape of gastric cancer (GC)-targeted therapy clinical trials and identify potential therapeutic targets.

Methods

A systematic search and analysis of the Cochrane Central Register of Controlled Trials (CENTRAL) was performed to retrieve all GC clinical trials published up to June 30, 2022. Approved therapeutic targets for 11 common cancers were compiled and analyzed. The role of CSNK2A1 in GC was investigated using bioinformatics tools such as GEPIA, KMPLOT, SangerBox, STRING, ACLBI, and TIMER. Four gastric cancer cell lines (AGS, HGC, MGC, BGC) and one normal gastric mucosa cell line (GES-1) were utilized to assess the sensitivity to the CSNK2A1 inhibitor CX-4945. Quantitative real-time polymerase chain reaction (qPCR) was employed to quantify the cellular expression of CSNK2A1. Cellular apoptosis was evaluated using flow cytometry and Western blot analysis.

Results

The failure rate of GC randomized controlled clinical trials (RCTs) was strikingly high, accounting for 74.29 % (26/35) of the trials. Among the 35 approved targets in 11 different cancers, 13 targets were rigorously evaluated and identified as potential therapeutic targets for GC. Bioinformatics analysis revealed that CSNK2A1 is closely associated with multiple biological characteristics in GC, and its increased expression correlated significantly with enhanced sensitivity to CX-4945 treatment. Flow cytometry and Western blot analysis consistently demonstrated concentration-dependent apoptosis induced by CX-4945 in GC cell lines.

Conclusions

The high failure rate of GC clinical trials highlights the need for a more scientific and precise approach in target identification and clinical trial design. CSNK2A1 emerges as a promising therapeutic target for GC, and its expression level could potentially serve as a biomarker for predicting sensitivity to CX-4945 treatment. Further research is warranted to elucidate the underlying molecular mechanisms and validate the clinical significance of CSNK2A1 in GC therapy.

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.

Proteome-based molecular subtyping and therapeutic target prediction in gastric cancer

Different molecular classifications for gastric cancer (GC) have been proposed based on multi-omics platforms with the long-term goal of improved precision treatment. However, the GC (phospho)proteome remains incompletely characterized, particularly at the level of tyrosine phosphorylation. In addition, previous multiomics-based stratification of patient cohorts has lacked identification of corresponding cell line models and comprehensive validation of broad or subgroup-selective therapeutic targets. To address these knowledge gaps, we applied a reverse approach, undertaking the most comprehensive (phospho)proteomic analysis of GC cell lines to date and cross-validating this using publicly available data. Mass spectrometry (MS)-based (phospho)proteomic and tyrosine phosphorylation datasets were subjected to individual or integrated clustering to identify subgroups that were subsequently characterized in terms of enriched molecular processes and pathways. Significant congruence was detected between cell line proteomic and specific patient-derived transcriptomic subclassifications. Many protein kinases exhibiting 'outlier' expression or phosphorylation in the cell line dataset exhibited genomic aberrations in patient samples and association with poor prognosis, with casein kinase I isoform delta/epsilon (CSNK1D/E) being experimentally validated as potential therapeutic targets. Src family kinases were predicted to be commonly hyperactivated in GC cell lines, consistent with broad sensitivity to the next-generation Src inhibitor eCF506. In addition, phosphoproteomic and integrative clustering segregated the cell lines into two subtypes, with epithelial-mesenchyme transition (EMT) and proliferation-associated processes enriched in one, designated the EMT subtype, and metabolic pathways, cell-cell junctions, and the immune response dominating the features of the other, designated the metabolism subtype. Application of kinase activity prediction algorithms and interrogation of gene dependency and drug sensitivity databases predicted that the mechanistic target of rapamycin kinase (mTOR) and dual specificity mitogen-activated protein kinase kinase 2 (MAP2K2) represented potential therapeutic targets for the EMT and metabolism subtypes, respectively, and this was confirmed using selective inhibitors. Overall, our study provides novel, in-depth insights into GC proteomics, kinomics, and molecular taxonomy and reveals potential therapeutic targets that could provide the basis for precision treatments.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics

BACKGROUND

Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application.

METHODS

Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system.

RESULTS

The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis.

CONCLUSIONS

PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

E-cadherin loss drives diffuse-type gastric tumorigenesis via EZH2-mediated reprogramming

Diffuse-type gastric adenocarcinoma (DGAC) is a deadly cancer often diagnosed late and resistant to treatment. While hereditary DGAC is linked to CDH1 mutations, the role of CDH1/E-cadherin inactivation in sporadic DGAC tumorigenesis remains elusive. We discovered CDH1 inactivation in a subset of DGAC patient tumors. Analyzing single-cell transcriptomes in malignant ascites, we identified two DGAC subtypes: DGAC1 (CDH1 loss) and DGAC2 (lacking immune response). DGAC1 displayed distinct molecular signatures, activated DGAC-related pathways, and an abundance of exhausted T cells in ascites. Genetically engineered murine gastric organoids showed that Cdh1 knock-out (KO), KrasG12D, Trp53 KO (EKP) accelerates tumorigenesis with immune evasion compared with KrasG12D, Trp53 KO (KP). We also identified EZH2 as a key mediator promoting CDH1 loss-associated DGAC tumorigenesis. These findings highlight DGAC's molecular diversity and potential for personalized treatment in CDH1-inactivated patients.

Profiling Kinase Activities for Precision Oncology in Diffuse Gastric Cancer

Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B (BAZ1B), WNK lysine-deficient protein kinase 1 (WNK1), and myosin light-chain kinase (MLCK) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 (AAK1), to be hypophosphorylated. LMNA hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor (HDGF), heat shock protein 90 (HSP90), and FTH1 as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.

CDH1 loss promotes diffuse-type gastric cancer tumorigenesis via epigenetic reprogramming and immune evasion

Diffuse-type gastric adenocarcinoma (DGAC) is a deadly cancer often diagnosed late and resistant to treatment. While hereditary DGAC is linked to CDH1 gene mutations, causing E-Cadherin loss, its role in sporadic DGAC is unclear. We discovered CDH1 inactivation in a subset of DGAC patient tumors. Analyzing single-cell transcriptomes in malignant ascites, we identified two DGAC subtypes: DGAC1 (CDH1 loss) and DGAC2 (lacking immune response). DGAC1 displayed distinct molecular signatures, activated DGAC-related pathways, and an abundance of exhausted T cells in ascites. Genetically engineered murine gastric organoids showed that Cdh1 knock-out (KO), KrasG12D, Trp53 KO (EKP) accelerates tumorigenesis with immune evasion compared to KrasG12D, Trp53 KO (KP). We also identified EZH2 as a key mediator promoting CDH1 loss-associated DGAC tumorigenesis. These findings highlight DGAC's molecular diversity and potential for personalized treatment in CDH1-inactivated patients.

Integrated proteome and phosphoproteome analysis of gastric adenocarcinoma reveals molecular signatures capable of stratifying patient outcome

Metastasis is one of the main causes of low survival rate of gastric cancer patients. Exploring key proteins in the progression of gastric adenocarcinoma (GAC) may provide new candidates for prognostic biomarker development and therapeutic intervention. We applied quantitative mass spectrometry to compare the proteome and phosphoproteome of primary tumor tissues between GAC patients with and without lymph node metastasis (LNM). We then performed an integrated analysis of the proteomic and transcriptomic data to reveal the molecular features. We quantified a total of 5536 proteins, and we found 218 upregulated and 49 downregulated proteins in tumor samples from patients with LNM compared to those without LNM. Clustering analysis identified a number of hub proteins that have been previously shown to play important roles in gastric cancer progression. We also found that two extracellular proteins, TNXB and SPON1, are overexpressed in patients with LNM, which correlates with poor survival of GAC patients. Overexpression of TNXB and SPON1 was validated by western blotting and immunohistochemistry. Furthermore, treating gastric cancer cells with anti-TNXB antibody significantly reduced cell migration. Finally, quantitative phosphoproteomic analysis combined with activity-based kinase capture revealed a number of activated kinases in primary tumor tissues from patients with LNM, among which GSK3 might be a new target that warrants further study. Our study provides a snapshot of the proteome and phosphoproteome of GAC tumor tissues that have metastatic potential, and identifies potential biomarkers for GAC progression.

Diffuse gastric cancer: Emerging mechanisms of tumor initiation and progression

Gastric cancer is globally the fourth leading cause of cancer-related deaths. Patients with diffuse-type gastric cancer (DGC) particularly have a poor prognosis that only marginally improved over the last decades, as conventional chemotherapies are frequently ineffective and specific therapies are unavailable. Early-stage DGC is characterized by intramucosal lesions of discohesive cells, which can be present for many years before the emergence of advanced DGC consisting of highly proliferative and invasive cells. The mechanisms underlying the key steps of DGC development and transition to aggressive tumors are starting to emerge. Novel mouse- and organoid models for DGC, together with multi-omic analyses of DGC tumors, revealed contributions of both tumor cell-intrinsic alterations and gradual changes in the tumor microenvironment to DGC progression. In this review, we will discuss how these recent findings are leading towards an understanding of the cellular and molecular mechanisms responsible for DGC initiation and malignancy, which may provide opportunities for targeted therapies.

Proteome-centric cross-omics characterization and integrated network analyses of triple-negative breast cancer

We report a comprehensive proteomic study of a 90-case cohort of paired samples of triple-negative breast cancer (TNBC) in quantification, phosphorylation, and DNA-binding capacity. Four integrative subtypes (iP-1-4) are stratified on the basis of global proteome and phosphoproteome, each of which exhibits distinct molecular and pathway features. Scaffold and co-expression network analyses of three proteomic datasets, integrated with those from genome and transcriptome of the same cohort, reveal key pathways and master regulators that, characteristic of TNBC subtypes, play important regulatory roles within and between scaffold sub-structures and co-expression communities. We find that NAE1 is a potential drug target for subtype iP-1, and a series of key molecules in fatty acid metabolism, such as AKT1/FASN, are plausible targets for subtype iP-2. Libraries of proteins, pathways and networks of TNBC provide a valuable molecular infrastructure for further clinical exploration and in-depth studies of the molecular mechanisms of the disease.

Protein phosphatase 2A catalytic subunit β suppresses PMA/ionomycin-induced T-cell activation by negatively regulating PI3K/Akt signaling

The precise regulation of the T-cell activation process is critical for overall immune homeostasis. Although protein phosphatase 2A (PP2A) is required for T-cell development and function, the role of PPP2CB, which is the catalytic subunit β isoform of PP2A, remains unknown. In the present study, using a T cell-specific knockout mouse of PPP2CB (PPP2CB^fl/fl Lck-Cre⁺ ), we demonstrated that PPP2CB was dispensable for T-cell development in the thymus and peripheral lymphoid organs. Furthermore, PPP2CB deletion did not affect T-cell receptor (TCR)-induced T-cell activation or cytokine-induced T-cell responses; however, it specifically enhanced phorbol myristate acetate (PMA) plus ionomycin-induced T-cell activation with increased cellular proliferation, elevated CD69 and CD25 expression, and enhanced cytokine production (inteferon-γ, interleukin-2 and tumor necrosis factor). Mechanistic analyses suggested that the PPP2CB deletion enhanced activation of the phosphoinositide 3-kinase/Akt signaling pathway and Ca²⁺ flux following stimulation with PMA plus ionomycin. Moreover, the specific PI3K inhibitor rescued the augmented cell activation in PPP2CB-deficient T cells. Using mass spectrometry-based phospho-peptide analysis, we identified potential substrates of PPP2CB during PMA plus ionomycin-induced T-cell activation. Collectively, our study provides evidence of the specific role of PPP2CB in controlling PMA plus ionomycin-induced T-cell activation.

Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine

The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells.

MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY

A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.

Bcl-2-associated transcription factor 1 Ser290 phosphorylation mediates DNA damage response and regulates radiosensitivity in gastric cancer

BACKGROUND

DNA damage response plays critical roles in tumor pathogenesis and radiotherapy resistance. Protein phosphorylation is a critical mechanism in regulation of DNA damage response; however, the key mediators for radiosensitivity in gastric cancer still needs further exploration.

METHODS

A quick label-free phosphoproteomics using high-resolution mass spectrometry and an open search approach was applied to paired tumor and adjacent tissues from five patients with gastric cancer. The dysregulated phosphoproteins were identified and their associated-pathways analyzed using Gene Set Enrichment Analysis (GSEA). The mostly regulated phosphoproteins and their potential functions were validated by the specific antibodies against the phosphorylation sites. Specific protein phosphorylation was further analyzed by functional and clinical approaches.

RESULTS

832 gastric cancer-associated unique phosphorylated sites were identified, among which 25 were up- and 52 down-regulated. Markedly, the dysregulated phosphoproteins were primarily enriched in DNA-damage-response-associated pathways. Particularly, the phosphorylation of Bcl-2-associated transcription factor 1 (BCLAF1) at Ser290 was significantly upregulated in tumor. The upregulation of BCLAF1 Ser290 phosphorylation (pBCLAF1 (Ser290)) in tumor was confirmed by tissue microarray studies and further indicated in association with poor prognosis of gastric cancer patients. Eliminating the phosphorylation of BCLAF1 at Ser290 suppressed gastric cancer (GC) cell proliferation. Upregulation of pBCLAF1 (Ser290) was found in association with irradiation-induced γ-H2AX expression in the nucleus, leading to an increased DNA damage repair response, and a marked inhibition of irradiation-induced cancer cell apoptosis.

CONCLUSIONS

The phosphorylation of BCLAF1 at Ser290 is involved in the regulation of DNA damage response, indicating an important target for the resistance of radiotherapy.

Integrating site-specific peptide reporters and targeted mass spectrometry enables rapid substrate-specific kinase assay at the nanogram cell level

Dysregulation of phosphorylation-mediated signaling drives the initiation and progression of many diseases. A substrate-specific kinase assay capable of quantifying the altered site-specific phosphorylation of its phenotype-dependent substrates provides better specificity to monitor a disease state. We report a sensitive and rapid substrate-specific kinase assay by integrating site-specific peptide reporter and multiple reaction monitoring (MRM)-MS platform for relative and absolute quantification of substrate-specific kinase activity at the sensitivity of nanomolar kinase and nanogram cell lysate. Using non-small cell lung cancer as a proof-of-concept, three substrate peptides selected from constitutive phosphorylation in tumors (HDGF-S165, RALY-S135, and NRD1-S94) were designed to demonstrate the feasibility. The assay showed good accuracy (<15% nominal deviation) and reproducibility (<15% CV). In PC9 cells, the measured activity for HDGF-S165 was 3.2 ± 0.2 fmol μg^-1 min^-1, while RALY-S135 and NRD1-S94 showed 4- and 20-fold higher activity at the sensitivity of 25 ng and 5 ng lysate, respectively, suggesting different endogenous kinases for each substrate peptide. Without the conventional shotgun phosphoproteomics workflow, the overall pipeline from cell lysate to MS data acquisition only takes 3 h. The multiplexed analysis revealed differences in the phenotype-dependent substrate phosphorylation profiles across six NSCLC cell lines and suggested a potential association of HDGF-S165 and NRD1-S94 with TKI resistance. With the ease of design, sensitivity, accuracy, and reproducibility, this approach may offer rapid and sensitive assays for targeted quantification of the multiplexed substrate-specific kinase activity of small amounts of sample.

From proteomic landscape to single-cell oncoproteomics

Introduction: Proteomic profiling plays an important role in the exploration of cancer from molecular mechanisms to clinical diagnosis and treatment. In recent years, the advent of new technologies has promoted oncoproteomics from the initial global style to a refined single-cell level.Areas Covered: Among them, the development of microfluidic devices, the improvement of liquid mass spectrometry in accuracy and trace sample handling processes, and the emergence of protein sequencing have contributed to the oncoproteomic analysis at the single-cell level.Expert Opinion: The proteomic analysis at the global level and the single-cell level gives different perspectives while combining them can reveal more comprehensive oncoproteomics and help cancer research and treatment strategies.

Recent Advances of Functional Proteomics in Gastrointestinal Cancers- a Path towards the Identification of Candidate Diagnostic, Prognostic, and Therapeutic Molecular Biomarkers

Gastrointestinal (GI) cancer remains one of the common causes of morbidity and mortality. A high number of cases are diagnosed at an advanced stage, leading to a poor survival rate. This is primarily attributed to the lack of reliable diagnostic biomarkers and limited treatment options. Therefore, more sensitive, specific biomarkers and curative treatments are desirable. Functional proteomics as a research area in the proteomic field aims to elucidate the biological function of unknown proteins and unravel the cellular mechanisms at the molecular level. Phosphoproteomic and glycoproteomic studies have emerged as two efficient functional proteomics approaches used to identify diagnostic biomarkers, therapeutic targets, the molecular basis of disease and mechanisms underlying drug resistance in GI cancers. In this review, we present an overview on how functional proteomics may contribute to the understanding of GI cancers, namely colorectal, gastric, hepatocellular carcinoma and pancreatic cancers. Moreover, we have summarized recent methodological developments in phosphoproteomics and glycoproteomics for GI cancer studies.

Predicting Peritoneal Dissemination of Gastric Cancer in the Era of Precision Medicine: Molecular Characterization and Biomarkers

Gastric cancer (GC) is a leading cause of worldwide cancer-related death. Being a highly heterogeneous disease, the current treatment of GC has been suboptimal due to the lack of subtype-dependent therapies. Peritoneal dissemination (PD) is a common pattern of GC metastasis associated with poor prognosis. Therefore, it is urgently necessary to identify patients at high risk of PD. PD is found to be associated with Lauren diffuse type GC. Molecular profiling of GC, especially diffuse type GC, has been utilized to identify molecular alterations and has given rise to various molecular classifications, shedding light on the underlying mechanism of PD and enabling identification of patients at higher PD risk. In addition, a series of diagnositc and prognostic biomarkers of PD from serum, peritoneal lavages and primary GCs have been reported. This comprehensive review summarizes findings on the multi-omic characteristics of diffuse type GC, the clinical significance of updating molecular classifications of GC in association with PD risk and research advances in PD-associated biomarkers.