A proteogenomics data-driven knowledge base of human cancer

Multi-omics analysis reveals CMTR1 upregulation in cancer and roles in ribosomal protein gene expression and tumor growth

BACKGROUND

CMTR1 (cap methyltransferase 1), a key nuclear mRNA cap methyltransferase, catalyzes 2'-O-methylation of the first transcribed nucleotide, a critical step in mRNA cap formation. Previous studies have implicated CMTR1 in embryonic stem cell differentiation and immune responses during viral infection; however, its role in cancer biology remains largely unexplored. This study aims to elucidate CMTR1's function in cancer progression and evaluate its potential as a novel therapeutic target in certain cancer types.

METHODS

We conducted a comprehensive multi-omics analysis of CMTR1 across various human cancers using TCGA and CPTAC datasets. Functional studies were performed using CRISPR-mediated knockout and siRNA knockdown in human and mouse basal-like breast cancer models. Transcriptomic and pathway enrichment analyses were carried out in CMTR1 knockout/knockdown models to identify CMTR1-regulated genes. In silico screening and biochemical assays were employed to identify novel CMTR1 inhibitors.

RESULTS

Multi-omics analysis revealed that CMTR1 is significantly upregulated at the mRNA, protein, and phosphoprotein levels across multiple cancer types in the TCGA and CPTAC datasets. Functional studies demonstrated that CMTR1 depletion significantly inhibits tumor growth both in vitro and in vivo. Transcriptomic analysis of CMTR1 knockout cells revealed that CMTR1 primarily regulates ribosomal protein genes and other transcripts containing 5' Terminal Oligopyrimidine (TOP) motifs. Additionally, CMTR1 affects the expression of snoRNA host genes and snoRNAs, suggesting a broader role in RNA metabolism. Mechanistic studies indicated that CMTR1's target specificity is partly determined by mRNA structure, particularly the presence of 5'TOP motifs. Finally, through in silico screening and biochemical assays, we identified several novel CMTR1 inhibitors, including N97911, which demonstrated in vitro growth inhibition activity in breast cancer cells.

CONCLUSIONS

Our findings establish CMTR1 as an important player in cancer biology, regulating critical aspects of RNA metabolism and ribosome biogenesis. The study highlights CMTR1's potential as a therapeutic target in certain cancer types and provides a foundation for developing novel cancer treatments targeting mRNA cap methylation.

THBS2-producing matrix CAFs promote colorectal cancer progression and link to poor prognosis via the CD47-MAPK axis

Cancer-associated fibroblasts (CAFs) display significant functional and molecular heterogeneity within the tumor microenvironment, playing diverse roles in cancer progression. Employing single-cell RNA sequencing data of colorectal cancer (CRC), we identified a subset of matrix CAFs (mCAFs) as a critical subtype that secretes THBS2, a molecule linked to advanced cancer stages and poor prognosis. Spatial transcriptomics and multiplex immunohistochemistry revealed clear spatial colocalization between THBS2-producing mCAFs and tumor cells. Mechanically, CAF-secreted THBS2 binds to CD47 on tumor cells, triggering the MAPK/ERK5 signaling pathway, which enhances tumor progression. The tumor-promoting role of THBS2 was further validated using fibroblast-specific THBS2 knockout mice, patient-derived organoids, and xenografts. Moreover, the transcription factor CREB3L1 was identified as a regulator of the transformation of normal fibroblasts into THBS2-producing mCAFs. These findings underscore the pivotal role of THBS2 in CRC progression and highlight the therapeutic potential of targeting the THBS2-CD47 axis and CREB3L1 in CRC.

Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, yet our limited knowledge about the regulation and function of most phosphosites hampers the extraction of meaningful biological insights. To address this challenge, we integrate machine learning with phosphoproteomic data from 1195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network that maps the co-regulation of 26,280 phosphosites. By incorporating network features from CoPheeMap into a second machine learning model, namely CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA uncovers 24,015 associations between 9399 phosphosites and 104 serine/threonine kinases, shedding light on many unannotated phosphosites and understudied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. Through the application of CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and those associated with cancer, we demonstrate their effectiveness in systematically elucidating phosphosites of interest. These analyses unveil dysregulated signaling processes in human cancer and identify understudied kinases as potential therapeutic targets.

Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets

Phosphorylation in eukaryotic cells plays a key role in regulating cell signaling and disease progression. Despite the ability to detect thousands of phosphosites in a single experiment using high-throughput technologies, the kinases responsible for regulating these sites are largely unidentified. To solve this, we collected the quantitative data at the transcriptional, protein, and phosphorylation levels of 10 159 samples from 23 tumor datasets and 15 adjacent normal tissue datasets. Our analysis aimed to uncover the potential impact and linkage of kinase-phosphosite (KPS) pairs through experimental evidence in publications and prediction tools commonly used. We discovered that both experimentally validated and tool-predicted KPS pairs were enriched in groups where there is a significant correlation between kinase expression/phosphorylation level and the phosphorylation level of phosphosite. This suggested that a quantitative correlation could infer the KPS interconnections. Furthermore, the Spearman's correlation coefficient for these pairs were notably higher in tumor samples, indicating that these regulatory interactions are particularly pronounced in tumors. Consequently, building on the KPS correlations of different datasets as predictive features, we have developed an innovative approach that employed an oversampling method combined with and XGBoost algorithm (SMOTE-XGBoost) to predict potential kinase-specific phosphorylation sites in proteins. Moreover, the computed correlations and predictions of kinase-phosphosite interconnections were integrated into the eKPI database (https://ekpi.omicsbio.info/). In summary, our study could provide helpful information and facilitate further research on the regulatory relationship between kinases and phosphosites.

Integrated Analysis of Proteome and Transcriptome Profiling Reveals Pan-Cancer-Associated Pathways and Molecular Biomarkers

Understanding dysregulated genes and pathways in cancer is critical for precision oncology. Integrating mass spectrometry-based proteomic data with transcriptomic data presents unique opportunities for systematic analyses of dysregulated genes and pathways in pan-cancer. Here, we compiled a comprehensive set of datasets, encompassing proteomic data from 2404 samples and transcriptomic data from 7752 samples across 13 cancer types. Comparisons between normal or adjacent normal tissues and tumor tissues identified several dysregulated pathways including mRNA splicing, interferon pathway, fatty acid metabolism, and complement coagulation cascade in pan-cancer. Additionally, pan-cancer upregulated and downregulated genes (PCUGs and PCDGs) were also identified. Notably, RRM2 and ADH1B, two genes which belong to PCUGs and PCDGs, respectively, were identified as robust pan-cancer diagnostic biomarkers. TNM stage-based comparisons revealed dysregulated genes and biological pathways involved in cancer progression, among which the dysregulation of complement coagulation cascade and epithelial-mesenchymal transition are frequent in multiple types of cancers. A group of pan-cancer continuously upregulated and downregulated proteins in different tumor stages (PCCUPs and PCCDPs) were identified. We further constructed prognostic risk stratification models for corresponding cancer types based on dysregulated genes, which effectively predict the prognosis for patients with these cancers. Drug prediction based on PCUGs and PCDGs as well as PCCUPs and PCCDPs revealed that small molecule inhibitors targeting CDK, HDAC, MEK, JAK, PI3K, and others might be effective treatments for pan-cancer, thereby supporting drug repurposing. We also developed web tools for cancer diagnosis, pathologic stage assessment, and risk evaluation. Overall, this study highlights the power of combining proteomic and transcriptomic data to identify valuable diagnostic and prognostic markers as well as drug targets and treatments for cancer.

The pan-cancer analysis of LRG1 and its potential role in kidney renal clear cell carcinoma

Leucine-rich α-2 glycoprotein 1 (LRG1) is a secreted glycoprotein implicated in various diseases, yet its role across multiple cancers remains insufficiently explored. Consequently, we conducted a comprehensive bioinformatics analysis, exploring LRG1 expression patterns, prognostic implications, and potential therapeutic associations in a pan-cancer context. Additionally, we collected gene expression and clinical data from patients with kidney renal clear cell carcinoma (KIRC) from TCGA, conducting gene set enrichment analysis (GSEA) and Cox proportional hazards regression analysis to explore the potential regulatory role of LRG1 in KIRC. Our study revealed that LRG1 expression is upregulated in 18 cancer types, with elevated levels correlating with poor prognostic outcomes in several cancers, notably KIRC. Epigenetic analysis showed hypomethylation in the LRG1 promoter region, potentially contributing to the overexpression of LRG1. Moreover, LRG1 expression was linked to immunotherapeutic responses and altered drug sensitivities, particularly influencing the efficacy of tyrosine kinase inhibitors. In KIRC, high LRG1 expression was associated with the activation of key pathways, including angiogenesis, epithelial-mesenchymal transition (EMT), and hypoxia signalling. We identified key gene pairs interacting with LRG1 in KIRC, including CARD14 and CYP8B1, with CARD14 overexpression correlating with poorer clinical outcomes and CYP8B1 indicating a favourable prognosis. In conclusion, LRG1 emerges as a potential biomarker for prognosis and immunotherapy responsiveness in both pan-cancer and KIRC contexts. This study provides a theoretical foundation for further research on the therapeutic potential of target regulating LRG1 in cancer treatment.

Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base

Large-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers. Tumor proteins having significant pan-cancer associations with muscle were referenced against secretome proteins secreted to blood from the Human Protein Atlas, then verified as increased in paired tumor vs. normal tissues in pan-cancer manner. This workflow revealed seven secreted proteins from cancers afflicting kidneys, head and neck, lungs and pancreas that classified as protein-binding activity modulator, extracellular matrix protein or intercellular signaling molecule. Concordance of these biomarkers with validated molecular signatures of cachexia and senescence supported relevance to muscle and cachexia disease biology, and high tumor expression of the biomarker set associated with lower overall survival. In this article, we discuss avenues by which skeletal muscle and cachexia may be regulated by these candidate pan-cancer proteins secreted to blood, and conceptualize a strategy that considers them collectively as a biomarker signature with potential for refinement by data analytics and radiogenomics for predictive testing of future risk in a non-invasive, blood-based panel amenable to broad uptake and early management.

Defining ortholog-specific UHRF1 inhibition by STELLA for cancer therapy

UHRF1 maintains DNA methylation by recruiting DNA methyltransferases to chromatin. In mouse, these dynamics are potently antagonized by a natural UHRF1 inhibitory protein STELLA, while the comparable effects of its human ortholog are insufficiently characterized, especially in cancer cells. Herein, we demonstrate that human STELLA (hSTELLA) is inadequate, while mouse STELLA (mSTELLA) is fully proficient in inhibiting the abnormal DNA methylation and oncogenic functions of UHRF1 in human cancer cells. Structural studies reveal a region of low sequence homology between these STELLA orthologs that allows mSTELLA but not hSTELLA to bind tightly and cooperatively to the essential histone-binding, linked tandem Tudor domain and plant homeodomain (TTD-PHD) of UHRF1, thus mediating ortholog-specific UHRF1 inhibition. For translating these findings to cancer therapy, we use a lipid nanoparticle (LNP)-mediated mRNA delivery approach in which the short mSTELLA, but not hSTELLA regions are required to reverse cancer-specific DNA hypermethylation and impair colorectal cancer tumorigenicity.

Investigating proteogenomic divergence in patient-derived xenograft models of ovarian cancer

Within ovarian cancer research, patient-derived xenograft (PDX) models recapitulate histologic features and genomic aberrations found in original tumors. However, conflicting data from published studies have demonstrated significant transcriptional differences between PDXs and original tumors, challenging the fidelity of these models. We employed a quantitative mass spectrometry-based proteomic approach coupled with generation of patient-specific databases using RNA-seq data to investigate the proteogenomic landscape of serially-passaged PDX models established from two patients with distinct subtypes of ovarian cancer. We demonstrate that the utilization of patient-specific databases guided by transcriptional profiles increases the depth of human protein identification in PDX models. Our data show that human proteomes of serially passaged PDXs differ significantly from their patient-derived tumor of origin. Analysis of differentially abundant proteins revealed enrichment of distinct biological pathways with major downregulated processes including extracellular matrix organization and the immune system. Finally, we investigated the relative abundances of ovarian cancer-related proteins identified from the Cancer Gene Census across serially passaged PDXs, and found their protein levels to be unstable across PDX models. Our findings highlight features of distinct and dynamic proteomes of serially-passaged PDX models of ovarian cancer.

PPM1G-mediated TBL1X mRNA splicing promotes cell migration in hepatocellular carcinoma

The progression of hepatocellular carcinoma (HCC) is coincident with aberrant splicing of numerous tumor-related genes. Identification of the tumor-specific splice variants that facilitate HCC metastasis may provide a more comprehensive insight into the mechanisms of HCC metastasis. Through RNA sequencing and bioinformatic analyses, PPM1G was identified as a biomarker associated with HCC metastasis. Our data mapped a transcriptome-wide landscape of alternative splicing events modulated by PPM1G in HCC. Notably, we characterized the exon six-skipping transcript of TBL1X as an onco-splice variant regulated by PPM1G. Experimental validation revealed the enrichment of TBL1X-S in response to PPM1G overexpression. Moreover, mRNA stability analyses revealed that PPM1G prolonged the half-life of the TBL1X-S transcript. Both PPM1G and TBL1X-S exhibited metastasis-promoting phenotypes, with PPM1G-driven metastasis in HCC being partially dependent on TBL1X-S. Mechanistically, different TBL1X splice variants showed varying affinities for ZEB1, with TBL1X-S significantly enhancing ZEB1 activation and repressing CDH1 transcription, potentially accelerating the epithelial-mesenchymal transition (EMT) process. In conclusion, our study highlights the biological role of PPM1G and TBL1X-S in tumor metastasis. The PPM1G/TBL1X-S signaling axis presents a new view for investigating liver cancer metastasis mechanisms.

THBS2 + cancer-associated fibroblasts promote EMT leading to oxaliplatin resistance via COL8A1-mediated PI3K/AKT activation in colorectal cancer

Cancer-associated fibroblasts (CAFs) exert multiple tumor-promoting functions and are key contributors to drug resistance. The mechanisms by which specific subsets of CAFs facilitate oxaliplatin resistance in colorectal cancer (CRC) have not been fully explored. This study found that THBS2 is positively associated with CAF activation, epithelial-mesenchymal transition (EMT), and chemoresistance at the pan-cancer level. Together with single-cell RNA sequencing and spatial transcriptomics analyses, we identified THBS2 specifically derived from subsets of CAFs, termed THBS2 + CAFs, which could promote oxaliplatin resistance by interacting with malignant cells via the collagen pathway in CRC. Mechanistically, COL8A1 specifically secreted from THBS2 + CAFs directly interacts with the ITGB1 receptor on resistant malignant cells, activating the PI3K-AKT signaling pathway and promoting EMT, ultimately leading to oxaliplatin resistance in CRC. Moreover, elevated COL8A1 promotes EMT and contributes to CRC oxaliplatin resistance, which can be mitigated by ITGB1 knockdown or AKT inhibitor. Collectively, these results highlight the crucial role of THBS2 + CAFs in promoting oxaliplatin resistance of CRC by activating EMT and provide a rationale for a novel strategy to overcome oxaliplatin resistance in CRC.

Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm

We developed a Bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer. We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides. Notably, approximately 64% of liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments. This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.

Multi-omics analysis reveals CMTR1 upregulation in cancer and roles in ribosomal protein gene expression and tumor growth

The mRNA cap methyltransferase CMTR1 plays a crucial role in RNA metabolism and gene expression regulation, yet its significance in cancer remains largely unexplored. Here, we present a comprehensive multi-omics analysis of CMTR1 across various human cancers, revealing its widespread upregulation and potential as a therapeutic target. Integrating transcriptomic and proteomic data from a large set of cancer samples, we demonstrate that CMTR1 is upregulated at the mRNA, protein, and phosphoprotein levels across multiple cancer types. Functional studies using CRISPR-mediated knockout and siRNA knockdown in breast cancer models show that CMTR1 depletion significantly inhibits tumor growth both in vitro and in vivo . Transcriptomic analysis reveals that CMTR1 primarily regulates ribosomal protein genes and other transcripts containing 5' Terminal Oligopyrimidine (TOP) motifs. Additionally, CMTR1 affects the expression of snoRNA host genes and snoRNAs, suggesting a broader role in RNA metabolism. Mechanistically, we propose that CMTR1's target specificity is partly determined by mRNA structure, particularly the presence of 5'TOP motifs. Furthermore, we identify a novel CMTR1 inhibitor, N97911, through in silico screening and biochemical assays, which demonstrates significant anti-tumor activity in vitro . Our findings establish CMTR1 as a key player in cancer biology, regulating critical aspects of RNA metabolism and ribosome biogenesis, and highlight its potential as a therapeutic target across multiple cancer types.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

Pan-cancer proteogenomics expands the landscape of therapeutic targets

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

RBPs: an RNA editor's choice

RNA-binding proteins (RBPs) play a key role in gene expression and post-transcriptional RNA regulation. As integral components of ribonucleoprotein complexes, RBPs are susceptible to genomic and RNA Editing derived amino acid substitutions, impacting functional interactions. This article explores the prevalent RNA Editing of RBPs, unravelling the complex interplay between RBPs and RNA Editing events. Emphasis is placed on their influence on single amino acid variants (SAAVs) and implications for disease development. The role of Proteogenomics in identifying SAAVs is briefly discussed, offering insights into the RBP landscape. RNA Editing within RBPs emerges as a promising target for precision medicine, reshaping our understanding of genetic and epigenetic variations in health and disease.

BioLadder: A bioinformatic platform primarily focused on proteomic data analysis

BioLadder (https://www.bioladder.cn/) is an online data analysis platform designed for proteomics research, which includes three classes of experimental data analysis modules and four classes of common data analysis modules. It allows for a variety of proteomics analyses to be conducted easily and efficiently. Additionally, most modules can also be utilized for the analysis of other omics data. To facilitate user experience, we have carefully designed four different kinds of functions for customers to quickly and accurately utilize the relevant analysis modules.

WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics

Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org.

Cancer-associated mutations in protein kinase C theta are loss-of-function

The Ca2+-independent, but diacylglycerol-regulated, novel protein kinase C (PKC) theta (θ) is highly expressed in hematopoietic cells where it participates in immune signaling and platelet function. Mounting evidence suggests that PKCθ may be involved in cancer, particularly blood cancers, breast cancer, and gastrointestinal stromal tumors, yet how to target this kinase (as an oncogene or as a tumor suppressor) has not been established. Here, we examine the effect of four cancer-associated mutations, R145H/C in the autoinhibitory pseudosubstrate, E161K in the regulatory C1A domain, and R635W in the regulatory C-terminal tail, on the cellular activity and stability of PKCθ. Live-cell imaging studies using the genetically-encoded fluorescence resonance energy transfer-based reporter for PKC activity, C kinase activity reporter 2 (CKAR2), revealed that the pseudosubstrate and C1A domain mutations impaired autoinhibition to increase basal signaling. This impaired autoinhibition resulted in decreased stability of the protein, consistent with the well-characterized behavior of Ca2+-regulated PKC isozymes wherein mutations that impair autoinhibition are paradoxically loss-of-function because the mutant protein is degraded. In marked contrast, the C-terminal tail mutation resulted in enhanced autoinhibition and enhanced stability. Thus, the examined mutations were loss-of-function by different mechanisms: mutations that impaired autoinhibition promoted the degradation of PKC, and those that enhanced autoinhibition stabilized an inactive PKC. Supporting a general loss-of-function of PKCθ in cancer, bioinformatics analysis revealed that protein levels of PKCθ are reduced in diverse cancers, including lung, renal, head and neck, and pancreatic. Our results reveal that PKCθ function is lost in cancer.

Frequent loss of FAM126A expression in colorectal cancer results in selective FAM126B dependency

Most advanced colorectal cancer (CRC) patients cannot benefit from targeted therapy due to lack of actionable targets. By mining data from the DepMap, we identified FAM126B as a specific vulnerability in CRC cell lines exhibiting low FAM126A expression. Employing a combination of genetic perturbation and inducible protein degradation techniques, we demonstrate that FAM126A and FAM126B function in a redundant manner to facilitate the recruitment of PI4KIIIα to the plasma membrane for PI4P synthesis. Examination of data from TCGA and GTEx revealed that over 7% of CRC tumor samples exhibited loss of FAM126A expression, contrasting with uniform FAM126A expression in normal tissues. In both CRC cell lines and tumor samples, promoter hypermethylation correlated with the loss of FAM126A expression, which could be reversed by DNA methylation inhibitors. In conclusion, our study reveals that loss of FAM126A expression results in FAM126B dependency, thus proposing FAM126B as a therapeutic target for CRC treatment.

IL-1α facilitates GSH synthesis to counteract oxidative stress in oral squamous cell carcinoma under glucose-deprivation

Understanding the intrinsic mechanisms underpinning cancer metabolism and therapeutic resistance is of central importance for effective nutrition-starvation therapies. Here, we report that Interleukin 1A (IL1A) mRNA and IL-1α protein facilitate glutathione (GSH) synthesis to counteract oxidative stress and resistance against nutrition-starvation therapy in oral squamous cell carcinoma (OSCC). The expression of IL1A mRNA was elevated in the case of OSCC associated with unfavorable clinical outcomes. Both IL1A mRNA and IL-1α protein expression were increased under glucose-deprivation in vitro and in vivo. The transcription of IL1A mRNA was regulated in an NRF2-dependent manner in OSCC cell lines under glucose-deprivation. Moreover, the IL-1α conferred resistance to oxidative stress via GSH synthesis in OSCC cell lines. The intratumoral administration of siRNAs against IL1A mRNA markedly reversed GSH production and sensitized OSCC cells to Anlotinib in HN6 xenograft models. Overall, the current study demonstrates novel evidence that the autocrine IL-1α favors endogenous anti-oxidative process and confers therapeutic resistance to nutrition-starvation in OSCCs.

Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, but limited knowledge about the regulation and function of most phosphosites restricts our ability to extract meaningful biological insights from phosphoproteomics data. To address this, we combine machine learning and phosphoproteomic data from 1,195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network mapping the co-regulation of 26,280 phosphosites. Integrating network features from CoPheeMap into a machine learning model, CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA reveals 24,015 associations between 9,399 phosphosites and 104 serine/threonine kinases, including many unannotated phosphosites and under-studied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. By applying CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and cancer-associated phosphosites, we demonstrate the effectiveness of these tools in systematically illuminating phosphosites of interest, revealing dysregulated signaling processes in human cancer, and identifying under-studied kinases as putative therapeutic targets.

IDPpub: Illuminating the Dark Phosphoproteome Through PubMed Mining

Global phosphoproteomics experiments quantify tens of thousands of phosphorylation sites. However, data interpretation is hampered by our limited knowledge on functions, biological contexts, or precipitating enzymes of the phosphosites. This study establishes a repository of phosphosites with associated evidence in biomedical abstracts, using deep learning-based natural language processing techniques. Our model for illuminating the dark phosphoproteome through PubMed mining (IDPpub) was generated by fine-tuning BioBERT, a deep learning tool for biomedical text mining. Trained using sentences containing protein substrates and phosphorylation site positions from 3000 abstracts, the IDPpub model was then used to extract phosphorylation sites from all MEDLINE abstracts. The extracted proteins were normalized to gene symbols using the National Center for Biotechnology Information gene query, and sites were mapped to human UniProt sequences using ProtMapper and mouse UniProt sequences by direct match. Precision and recall were calculated using 150 curated abstracts, and utility was assessed by analyzing the CPTAC (Clinical Proteomics Tumor Analysis Consortium) pan-cancer phosphoproteomics datasets and the PhosphoSitePlus database. Using 10-fold cross validation, pairs of correct substrates and phosphosite positions were extracted with an average precision of 0.93 and recall of 0.94. After entity normalization and site mapping to human reference sequences, an independent validation achieved a precision of 0.91 and recall of 0.77. The IDPpub repository contains 18,458 unique human phosphorylation sites with evidence sentences from 58,227 abstracts and 5918 mouse sites in 14,610 abstracts. This included evidence sentences for 1803 sites identified in CPTAC studies that are not covered by manually curated functional information in PhosphoSitePlus. Evaluation results demonstrate the potential of IDPpub as an effective biomedical text mining tool for collecting phosphosites. Moreover, the repository (http://idppub.ptmax.org), which can be automatically updated, can serve as a powerful complement to existing resources.