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.

Feed-forward stimulation of CAMK2 by the oncogenic pseudokinase PEAK1 generates a therapeutically 'actionable' signalling axis in triple negative breast cancer.

The PEAK family of pseudokinases, comprising PEAK1-3, are signalling scaffolds that play oncogenic roles in several poor prognosis human cancers, including triple negative breast cancer (TNBC). However, therapeutic targeting of pseudokinases is challenging due to their lack of catalytic activity. To address this, we screened for PEAK1 effectors by affinity purification and mass spectrometry, identifying calcium/calmodulin-dependent protein kinase 2 (CAMK2)D and CAMK2G. PEAK1 promoted CAMK2D/G activation in TNBC cells via a novel feed-forward mechanism involving PEAK1/PLCγ1/Ca 2+ signalling and direct binding via a consensus CAMK2 interaction motif in the PEAK1 N-terminus. In turn, CAMK2 phosphorylated PEAK1 to enhance association with PEAK2, which is critical for PEAK1 oncogenic signalling. To achieve pharmacologic targeting of PEAK1/CAMK2, we repurposed RA306, a second generation CAMK2 inhibitor under pre-clinical development for treatment of cardiovascular disease. RA306 demonstrated on-target activity against CAMK2 in TNBC cells and inhibited PEAK1-enhanced migration and invasion in vitro . Moreover, RA306 significantly attenuated TNBC xenograft growth and blocked metastasis in a manner mirrored by CRISPR-mediated PEAK1 ablation. Overall, these studies establish PEAK1 as a critical cell signalling nexus, identify a novel mechanism for regulation of Ca 2+ signalling and its integration with tyrosine kinase signals, and identify CAMK2 as a therapeutically 'actionable' target downstream of PEAK1.

Pediatric glioma histone H3.3 K27M/G34R mutations drive abnormalities in PML nuclear bodies.

BACKGROUND: Point mutations in histone variant H3.3 (H3.3K27M, H3.3G34R) and the H3.3-specific ATRX/DAXX chaperone complex are frequent events in pediatric gliomas. These H3.3 point mutations affect many chromatin modifications but the exact oncogenic mechanisms are currently unclear. Histone H3.3 is known to localize to nuclear compartments known as promyelocytic leukemia (PML) nuclear bodies, which are frequently mutated and confirmed as oncogenic drivers in acute promyelocytic leukemia. RESULTS: We find that the pediatric glioma-associated H3.3 point mutations disrupt the formation of PML nuclear bodies and this prevents differentiation down glial lineages. Similar to leukemias driven by PML mutations, H3.3-mutated glioma cells are sensitive to drugs that target PML bodies. We also find that point mutations in IDH1/2-which are common events in adult gliomas and myeloid leukemias-also disrupt the formation of PML bodies. CONCLUSIONS: We identify PML as a contributor to oncogenesis in a subset of gliomas and show that targeting PML bodies is effective in treating these H3.3-mutated pediatric gliomas.

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images.

BACKGROUND AND OBJECTIVE: High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs. METHODS: Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantifies the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification. RESULTS: Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction. CONCLUSIONS: Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.

Structural mapping of PEAK pseudokinase interactions identifies 14-3-3 as a molecular switch for PEAK3 signaling.

PEAK pseudokinases regulate cell migration, invasion and proliferation by recruiting key signaling proteins to the cytoskeleton. Despite lacking catalytic activity, alteration in their expression level is associated with several aggressive cancers. Here, we elucidate the molecular details of key PEAK signaling interactions with the adapter proteins CrkII and Grb2 and the scaffold protein 14-3-3. Our findings rationalize why the dimerization of PEAK proteins has a crucial function in signal transduction and provide biophysical and structural data to unravel binding specificity within the PEAK interactome. We identify a conserved high affinity 14-3-3 motif on PEAK3 and demonstrate its role as a molecular switch to regulate CrkII binding and signaling via Grb2. Together, our studies provide a detailed structural snapshot of PEAK interaction networks and further elucidate how PEAK proteins, especially PEAK3, act as dynamic scaffolds that exploit adapter proteins to control signal transduction in cell growth/motility and cancer.

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.

Cell-Type-Specific Signalling Networks Impacted by Prostate Epithelial-Stromal Intercellular Communication.

Prostate cancer is the second most common cause of cancer death in males. A greater understanding of cell signalling events that occur within the prostate cancer tumour microenvironment (TME), for example, between cancer-associated fibroblasts (CAFs) and prostate epithelial or cancer cells, may identify novel biomarkers and more effective therapeutic strategies for this disease. To address this, we used cell-type-specific labelling with amino acid precursors (CTAP) to define cell-type-specific (phospho)proteomic changes that occur when prostate epithelial cells are co-cultured with normal patient-derived prostate fibroblasts (NPFs) versus matched CAFs. We report significant differences in the response of BPH-1 benign prostate epithelial cells to CAF versus NPF co-culture. Pathway analysis of proteomic changes identified significant upregulation of focal adhesion and cytoskeleton networks, and downregulation of metabolism pathways, in BPH-1 cells cultured with CAFs. In addition, co-cultured CAFs exhibited alterations in stress, DNA damage, and cytoskeletal networks. Functional validation of one of the top differentially-regulated proteins in BPH-1 cells upon CAF co-culture, transglutaminase-2 (TGM2), demonstrated that knockdown of this protein significantly reduced the proliferation and migration of prostate epithelial cells. Overall, this study provides novel insights into intercellular communication in the prostate cancer TME that may be exploited to improve patient management.

Integrative characterisation of secreted factors involved in intercellular communication between prostate epithelial or cancer cells and fibroblasts.

Reciprocal interactions between prostate cancer cells and carcinoma-associated fibroblasts (CAFs) mediate cancer development and progression; however, our understanding of the signalling pathways mediating these cellular interactions remains incomplete. To address this, we defined secretome changes upon co-culture of prostate epithelial or cancer cells with fibroblasts that mimic bi-directional communication in tumours. Using antibody arrays, we profiled conditioned media from mono- and co-cultures of prostate fibroblasts, epithelial and cancer cells, identifying secreted proteins that are upregulated in co-culture compared to mono-culture. Six of these (CXCL10, CXCL16, CXCL6, FST, PDGFAA, IL-17B) were functionally screened by siRNA knockdown in prostate cancer cell/fibroblast co-cultures, revealing a key role for follistatin (FST), a secreted glycoprotein that binds and bioneutralises specific members of the TGF-ß superfamily, including activin A. Expression of FST by both cell types was required for the fibroblasts to enhance prostate cancer cell proliferation and migration, whereas FST knockdown in co-culture grafts decreased tumour growth in mouse xenografts. This study highlights the complexity of prostate cancer cell-fibroblast communication, demonstrates that co-culture secretomes cannot be predicted from individual cultures, and identifies FST as a tumour-microenvironment-derived secreted factor that represents a candidate therapeutic target.

The pseudokinase NRBP1 activates Rac1/Cdc42 via P-Rex1 to drive oncogenic signalling in triple-negative breast cancer.

We have determined that expression of the pseudokinase NRBP1 positively associates with poor prognosis in triple negative breast cancer (TNBC) and is required for efficient migration, invasion and proliferation of TNBC cells in culture as well as growth of TNBC orthotopic xenografts and experimental metastasis. Application of BioID/MS profiling identified P-Rex1, a known guanine nucleotide exchange factor for Rac1, as a NRBP1 binding partner. Importantly, NRBP1 overexpression enhanced levels of GTP-bound Rac1 and Cdc42 in a P-Rex1-dependent manner, while NRBP1 knockdown reduced their activation. In addition, NRBP1 associated with P-Rex1, Rac1 and Cdc42, suggesting a scaffolding function for this pseudokinase. NRBP1-mediated promotion of cell migration and invasion was P-Rex1-dependent, while constitutively-active Rac1 rescued the effect of NRBP1 knockdown on cell proliferation and invasion. Generation of reactive oxygen species via a NRBP1/P-Rex1 pathway was implicated in these oncogenic roles of NRBP1. Overall, these findings define a new function for NRBP1 and a novel oncogenic signalling pathway in TNBC that may be amenable to therapeutic intervention.

Identification of biological pathways and processes regulated by NEK5 in breast epithelial cells via an integrated proteomic approach.

Specific members of the Nima-Related Kinase (NEK) family have been linked to cancer development and progression, and a role for NEK5, one of the least studied members, in breast cancer has recently been proposed. However, while NEK5 is known to regulate centrosome separation and mitotic spindle assembly, NEK5 signalling mechanisms and function in this malignancy require further characterization. To this end, we established a model system featuring overexpression of NEK5 in the immortalized breast epithelial cell line MCF-10A. MCF-10A cells overexpressing NEK5 exhibited an increase in clonogenicity under monolayer conditions and enhanced acinar size and abnormal morphology in 3D Matrigel culture. Interestingly, they also exhibited a marked reduction in Src activation and downstream signalling. To interrogate NEK5 signalling and function in an unbiased manner, we applied a variety of MS-based proteomic approaches. Determination of the NEK5 interactome by Bio-ID identified a variety of protein classes including the kinesins KIF2C and KIF22, the mitochondrial proteins TFAM, TFB2M and MFN2, RhoH effectors and the negative regulator of Src, CSK. Characterization of proteins and phosphosites modulated upon NEK5 overexpression by global MS-based (phospho)proteomic profiling revealed impact on the cell cycle, DNA synthesis and repair, Rho GTPase signalling, the microtubule cytoskeleton and hemidesmosome assembly. Overall, the study indicates that NEK5 impacts diverse pathways and processes in breast epithelial cells, and likely plays a multifaceted role in breast cancer development and progression. Video Abstract.

Proteomic characterisation of prostate cancer intercellular communication reveals cell type-selective signalling and TMSB4X-dependent fibroblast reprogramming.

BACKGROUND: In prostate cancer, the tumour microenvironment (TME) represents an important regulator of disease progression and response to treatment. In the TME, cancer-associated fibroblasts (CAFs) play a key role in tumour progression, however the mechanisms underpinning fibroblast-cancer cell interactions are incompletely resolved. Here, we address this by applying cell type-specific labelling with amino acid precursors (CTAP) and mass spectrometry (MS)-based (phospho)proteomics to prostate cancer for the first time. METHODS: Reciprocal interactions between PC3 prostate cancer cells co-cultured with WPMY-1 prostatic fibroblasts were characterised using CTAP-MS. Signalling network changes were determined using Metascape and Enrichr and visualised using Cytoscape. Thymosin ß4 (TMSB4X) overexpression was achieved via retroviral transduction and assayed by ELISA. Cell motility was determined using Transwell and random cell migration assays and expression of CAF markers by indirect immunofluorescence. RESULTS: WPMY-1 cells co-cultured with PC3s demonstrated a CAF-like phenotype, characterised by enhanced PDGFRB expression and alterations in signalling pathways regulating epithelial-mesenchymal transition, cytoskeletal organisation and cell polarisation. In contrast, co-cultured PC3 cells exhibited more modest network changes, with alterations in mTORC1 signalling and regulation of the actin cytoskeleton. The expression of the actin binding protein TMSB4X was significantly decreased in co-cultured WPMY-1 fibroblasts, and overexpression of TMSB4X in fibroblasts decreased migration of co-cultured PC3 cells, reduced fibroblast motility, and protected the fibroblasts from being educated to a CAF-like phenotype by prostate cancer cells. CONCLUSIONS: This study highlights the potential of CTAP-MS to characterise intercellular communication within the prostate TME and identify regulators of cellular crosstalk such as TMSB4X.

Comprehensive methylome sequencing reveals prognostic epigenetic biomarkers for prostate cancer mortality.

BACKGROUND: Prostate cancer is a clinically heterogeneous disease with a subset of patients rapidly progressing to lethal-metastatic prostate cancer. Current clinicopathological measures are imperfect predictors of disease progression. Epigenetic changes are amongst the earliest molecular changes in tumourigenesis. To find new prognostic biomarkers to enable earlier intervention and improved outcomes, we performed methylome sequencing of DNA from patients with localised prostate cancer and long-term clinical follow-up. METHODS: We used whole-genome bisulphite sequencing (WGBS) to comprehensively map and compare DNA methylation of radical prostatectomy tissue between patients with lethal disease (n = 7) and non-lethal (n = 8) disease (median follow-up 19.5 years). Validation of differentially methylated regions (DMRs) was performed in an independent cohort (n = 185, median follow-up 15 years) using targeted multiplex bisulphite sequencing of candidate regions. Survival was assessed via univariable and multivariable analyses including clinicopathological measures (log-rank and Cox regression models). RESULTS: WGBS data analysis identified cancer-specific methylation patterns including CpG island hypermethylation, and hypomethylation of repetitive elements, with increasing disease risk. We identified 1420 DMRs associated with prostate cancer-specific mortality (PCSM), which showed enrichment for gene sets downregulated in prostate cancer and de novo methylated in cancer. Through comparison with public prostate cancer datasets, we refined the DMRs to develop an 18-gene prognostic panel. Applying this panel to an independent cohort, we found significant associations between PCSM and hypermethylation at EPHB3, PARP6, TBX1, MARCH6 and a regulatory element within CACNA2D4. Strikingly in a multivariable model, inclusion of CACNA2D4 methylation was a better predictor of PCSM versus grade alone (Harrell's C-index: 0.779 vs. 0.684). CONCLUSIONS: Our study provides detailed methylome maps of non-lethal and lethal prostate cancer and identifies novel genic regions that distinguish these patient groups. Inclusion of our DNA methylation biomarkers with existing clinicopathological measures improves prognostic models of prostate cancer mortality, and holds promise for clinical application.

Enhancing therapeutic anti-cancer responses by combining immune checkpoint and tyrosine kinase inhibition.

Over the past decade, immune checkpoint inhibitor (ICI) therapy has been established as the standard of care for many types of cancer, but the strategies employed have continued to evolve. Recently, much clinical focus has been on combining targeted therapies with ICI for the purpose of manipulating the immune setpoint. The latter concept describes the equilibrium between factors that promote and those that suppress anti-cancer immunity. Besides tumor mutational load and other cancer cell-intrinsic determinants, the immune setpoint is also governed by the cells of the tumor microenvironment and how they are coerced by cancer cells to support the survival and growth of the tumor. These regulatory mechanisms provide therapeutic opportunities to intervene and reduce immune suppression via application of small molecule inhibitors and antibody-based therapies against (receptor) tyrosine kinases and thereby improve the response to ICIs. This article reviews how tyrosine kinase signaling in the tumor microenvironment can promote immune suppression and highlights how therapeutic strategies directed against specific tyrosine kinases can be used to lower the immune setpoint and elicit more effective anti-tumor immunity.

CDK8 and CDK19 regulate intestinal differentiation and homeostasis via the chromatin remodeling complex SWI/SNF.

Initiation and maintenance of transcriptional states are critical for controlling normal tissue homeostasis and differentiation. The cyclin dependent kinases CDK8 and CDK19 (Mediator kinases) are regulatory components of Mediator, a highly conserved complex that orchestrates enhancer-mediated transcriptional output. While Mediator kinases have been implicated in the transcription of genes necessary for development and growth, its function in mammals has not been well defined. Using genetically defined models and pharmacological inhibitors, we showed that CDK8 and CDK19 function in a redundant manner to regulate intestinal lineage specification in humans and mice. The Mediator kinase module bound and phosphorylated key components of the chromatin remodeling complex switch/sucrose non-fermentable (SWI/SNF) in intestinal epithelial cells. Concomitantly, SWI/SNF and MED12-Mediator colocalized at distinct lineage-specifying enhancers in a CDK8/19-dependent manner. Thus, these studies reveal a transcriptional mechanism of intestinal cell specification, coordinated by the interaction between the chromatin remodeling complex SWI/SNF and Mediator kinase.

Preface: Novel Therapeutic Targets and Biomarkers in Prostate Cancer.

PREFACE: CRO special issue: Novel Therapeutic Targets and Biomarkers in Prostate Cancer.

Novel Therapeutic Targets and Biomarkers Associated with Prostate Cancer-Associated Fibroblasts (CAFs).

Despite advances in treatment, prostate cancer remains a significant cause of morbidity and mortality worldwide. While the vast majority of prostate cancer research has centered on malignant epithelial cells, the tumor mi croenvironment (TME) has recently become increasingly recognized as an important regulator of tumor progression and response to treatment. Among the diverse cell types within the TME, stromal fibroblasts, in particular cancer-associated fibroblasts (CAFs), play an important role in prostate cancer progression. This is highlighted by the prognostic value of CAF markers in prostate cancer, which can predict disease recurrence, metastasis, and patient survival. There are also an increasing number of studies that demonstrate the critical role of CAFs in mediating response to specific therapies and CAF signaling pathways as potential therapeutic targets. However, further investigation into the mechanisms that underpin the interactions between cancer cells and CAFs are required to develop novel therapeutic approaches and identify predictive and prognostic biomarkers in CAFs. In this review, we discuss the current knowledge of CAF-dependent regulatory pathways in prostate tumorigenesis and their prognostic and therapeutic potential. Furthermore, we explore the emerging models and technologies that are likely to progress this field of research in terms of discovery and translation to the clinic.

DEMoS: a deep learning-based ensemble approach for predicting the molecular subtypes of gastric adenocarcinomas from histopathological images.

MOTIVATION: The molecular subtyping of gastric cancer (adenocarcinoma) into four main subtypes based on integrated multiomics profiles, as proposed by The Cancer Genome Atlas (TCGA) initiative, represents an effective strategy for patient stratification. However, this approach requires the use of multiple technological platforms, and is quite expensive and time-consuming to perform. A computational approach that uses histopathological image data to infer molecular subtypes could be a practical, cost- and time-efficient complementary tool for prognostic and clinical management purposes. RESULTS: Here, we propose a deep learning ensemble approach (called DEMoS) capable of predicting the four recognized molecular subtypes of gastric cancer directly from histopathological images. DEMoS achieved tile-level area under the receiver-operating characteristic curve (AUROC) values of 0.785, 0.668, 0.762 and 0.811 for the prediction of these four subtypes of gastric cancer [i.e. (i) Epstein-Barr (EBV)-infected, (ii) microsatellite instability (MSI), (iii) genomically stable (GS) and (iv) chromosomally unstable tumors (CIN)] using an independent test dataset, respectively. At the patient-level, it achieved AUROC values of 0.897, 0.764, 0.890 and 0.898, respectively. Thus, these four subtypes are well-predicted by DEMoS. Benchmarking experiments further suggest that DEMoS is able to achieve an improved classification performance for image-based subtyping and prevent model overfitting. This study highlights the feasibility of using a deep learning ensemble-based method to rapidly and reliably subtype gastric cancer (adenocarcinoma) solely using features from histopathological images. AVAILABILITY AND IMPLEMENTATION: All whole slide images used in this study was collected from the TCGA database. This study builds upon our previously published HEAL framework, with related documentation and tutorials available at http://heal.erc.monash.edu.au. The source code and related models are freely accessible at https://github.com/Docurdt/DEMoS.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the "digital grade" cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.

Global ubiquitinome profiling identifies NEDD4 as a regulator of Profilin 1 and actin remodelling in neural crest cells.

The ubiquitin ligase NEDD4 promotes neural crest cell (NCC) survival and stem-cell like properties to regulate craniofacial and peripheral nervous system development. However, how ubiquitination and NEDD4 control NCC development remains unknown. Here we combine quantitative analysis of the proteome, transcriptome and ubiquitinome to identify key developmental signalling pathways that are regulated by NEDD4. We report 276 NEDD4 targets in NCCs and show that loss of NEDD4 leads to a pronounced global reduction in specific ubiquitin lysine linkages. We further show that NEDD4 contributes to the regulation of the NCC actin cytoskeleton by controlling ubiquitination and turnover of Profilin 1 to modulate filamentous actin polymerization. Taken together, our data provide insights into how NEDD4-mediated ubiquitination coordinates key regulatory processes during NCC development.