Pan-cancer proteogenomics connects oncogenic drivers to functional states

Precision proteogenomics reveals pan-cancer impact of germline variants

We investigate the impact of germline variants on cancer patients' proteomes, encompassing 1,064 individuals across 10 cancer types. We introduced an approach, "precision peptidomics," mapping 337,469 coding germline variants onto peptides from patients' mass spectrometry data, revealing their potential impact on post-translational modifications, protein stability, allele-specific expression, and protein structure by leveraging the relevant protein databases. We identified rare pathogenic and common germline variants in cancer genes potentially affecting proteomic features, including variants altering protein abundance and structure and variants in kinases (ERBB2 and MAP2K2) impacting phosphorylation. Precision peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively. Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins. Polygenic risk scores correlated with distal effects from risk variants. Our findings emphasize the contribution of germline genetics to cancer heterogeneity and high-throughput precision peptidomics.

Pan-cancer analysis shapes the understanding of cancer biology and medicine

Advances in multi-omics datasets and analytical methods have revolutionized cancer research, offering a comprehensive, pan-cancer perspective. Pan-cancer studies identify shared mechanisms and unique traits across different cancer types, which are reshaping diagnostic and treatment strategies. However, continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine. This review summarized key findings from pan-cancer research and  explored their potential to drive future advancements in oncology.

Extracellular vesicles in cancer´s communication: messages we can read and how to answer

Extracellular vesicles (EVs) are emerging as critical mediators of intercellular communication in the tumor microenvironment (TME), profoundly influencing cancer progression. These nano-sized vesicles, released by both tumor and stromal cells, carry a diverse cargo of proteins, nucleic acids, and lipids, reflecting the dynamic cellular landscape and mediating intricate interactions between cells. This review provides a comprehensive overview of the biogenesis, composition, and functional roles of EVs in cancer, highlighting their significance in both basic research and clinical applications. We discuss how cancer cells manipulate EV biogenesis pathways to produce vesicles enriched with pro-tumorigenic molecules, explore the specific contributions of EVs to key hallmarks of cancer, such as angiogenesis, metastasis, and immune evasion, emphasizing their role in shaping TME and driving therapeutic resistance. Concurrently, we submit recent knowledge on how the cargo of EVs can serve as a valuable source of biomarkers for minimally invasive liquid biopsies, and its therapeutic potential, particularly as targeted drug delivery vehicles and immunomodulatory agents, showcasing their promise for enhancing the efficacy and safety of cancer treatments. By deciphering the intricate messages carried by EVs, we can gain a deeper understanding of cancer biology and develop more effective strategies for early detection, targeted therapy, and immunotherapy, paving the way for a new era of personalized and precise cancer medicine with the potential to significantly improve patient outcomes.

Multi-omics and machine learning-driven CD8+ T cell heterogeneity score for head and neck squamous cell carcinoma

The heterogeneity of head and neck squamous cell carcinoma (HNSCC) poses a significant challenge to treatment, underscoring the urgent need for more precise and personalized therapeutic approaches. CD8+ T cells, integral components of the tumor immune microenvironment, have emerged as key targets for immunotherapy. Our research has established a correlation between a decrease in CD8+ T cell score and a poor clinical prognosis, highlighting the prognostic value of this biomarker. By analyzing the gene expression related to CD8+ T cells, we have differentiated HNSCC into cold and hot tumor subtypes, uncovering disparities in clinical prognosis and responses to immunotherapy. Utilizing eight machine learning methods, we identified the key gene OLR1. Single-cell analysis of HNSCC tissues and peripheral blood, along with spatial transcriptome analysis, revealed that OLR1 predominantly functions in macrophages, modulating the immune microenvironment of HNSCC. The expression level of OLR1 may serve as a predictive marker for immunotherapy responses. Moreover, drug sensitivity analysis and molecular docking studies have indicated that simvastatin and pazopanib are potential inhibitors of OLR1. These findings suggest that simvastatin and pazopanib could open up innovative potential therapeutic avenues for individuals with HNSCC.

Deciphering functional tumor-immune crosstalk through highly multiplexed imaging and deep visual proteomics

Deciphering the intricate tumor-immune interactions within the microenvironment is crucial for advancing cancer immunotherapy. Here, we introduce mipDVP, an advanced approach integrating highly multiplexed imaging, single-cell laser microdissection, and sensitive mass spectrometry to spatially profile the proteomes of distinct cell populations in a human colorectal and tonsil cancer with high sensitivity. In a colorectal tumor-a representative cold tumor-we uncovered spatial compartmentalization of an immunosuppressive macrophage barrier that potentially impedes T cell infiltration. Spatial proteomic analysis revealed distinct functional states of T cells in different tumor compartments. In a tonsil cancer sample-a hot tumor-we identified significant proteomic heterogeneity among cells influenced by proximity to cytotoxic T cell subtypes. T cells in the tumor parenchyma exhibit metabolic adaptations to hypoxic regions. Our spatially resolved, highly multiplexed strategy deciphers the complex cellular interplay within the tumor microenvironment, offering valuable insights for identifying immunotherapy targets and predictive signatures.

Integrative In Silico Analysis to Identify Functional and Structural Impacts of nsSNPs on Programmed Cell Death Protein 1 (PD-1) Protein and UTRs: Potential Biomarkers for Cancer Susceptibility

Background: Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is critical in immune checkpoint regulation and cancer immune evasion. Variants in PDCD1 may alter its function, impacting cancer susceptibility and disease progression. Objectives: This study evaluates the structural, functional, and regulatory impacts of non-synonymous single-nucleotide polymorphisms (nsSNPs) in the PDCD1 gene, focusing on their pathogenic and oncogenic roles. Methods: Computational tools, including PredictSNP1.0, I-Mutant2.0, MUpro, HOPE, MutPred2, Cscape, Cscape-Somatic, GEPIA2, cBioPortal, and STRING, were used to analyze 695 nsSNPs in the PD1 protein. The analysis covered structural impacts, stability changes, regulatory effects, and oncogenic potential, focusing on conserved domains and protein-ligand interactions. Results: The analysis identified 84 deleterious variants, with 45 mapped to conserved regions like the Ig V-set domain essential for ligand-binding interactions. Stability analyses identified 78 destabilizing variants with significant protein instability (ΔΔG values). Ten nsSNPs were identified as potential cancer drivers. Expression profiling showed differential PDCD1 expression in tumor versus normal tissues, correlating with improved survival in skin melanoma but limited value in ovarian cancer. Regulatory SNPs disrupted miRNA-binding sites and transcriptional regulation, affecting PDCD1 expression. STRING analysis revealed key PD-1 protein partners within immune pathways, including PD-L1 and PD-L2. Conclusions: This study highlights the significance of PDCD1 nsSNPs as potential biomarkers for cancer susceptibility, advancing the understanding of PD-1 regulation. Experimental validation and multi-omics integration are crucial to refine these findings and enhance theraputic strategies.

PKN2 Is a Dependency of the Mesenchymal-like Cancer Cell State

Cancer cells exploit a mesenchymal-like transcriptional state (MLS) to survive drug treatments. Although the MLS is well characterized, few therapeutic vulnerabilities targeting this program have been identified. In this study, we systematically identify the dependency network of mesenchymal-like cancers through an analysis of gene essentiality scores in ∼800 cancer cell lines, nominating a poorly studied kinase, PKN2, as a top therapeutic target of the MLS. Coessentiality relationships, biochemical experiments, and genomic analyses of patient tumors revealed that PKN2 promotes mesenchymal-like cancer growth through a PKN2-SAV1-TAZ signaling mechanism. Notably, pairing genetic PKN2 inhibition with clinically relevant targeted therapies against EGFR, KRAS, and BRAF suppresses drug resistance by depleting mesenchymal-like drug-tolerant persister cells. These findings provide evidence that PKN2 is a core regulator of the Hippo tumor suppressor pathway and highlight the potential of PKN2 inhibition as a generalizable therapeutic strategy to overcome drug resistance driven by the MLS across cancer contexts. Significance: This work identifies PKN2 as a core member of the Hippo signaling pathway, and its inhibition blocks YAP/TAZ-driven tumorigenesis. Furthermore, this study discovers PKN2-TAZ as arguably the most selective dependency of mesenchymal-like cancers and supports specific inhibition of PKN2 as a provocative strategy to overcome drug resistance in diverse cancer contexts. See related commentary by Shen and Tan, p. 458.

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.

Decoding the functional impact of the cancer genome through protein-protein interactions

Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.

Tumor Metastasis: Mechanistic Insights and Therapeutic Intervention

Metastasis remains a leading cause of cancer‐related deaths, defined by a complex, multi‐step process in which tumor cells spread and form secondary growths in distant tissues. Despite substantial progress in understanding metastasis, the molecular mechanisms driving this process and the development of effective therapies remain incompletely understood. Elucidating the molecular pathways governing metastasis is essential for the discovery of innovative therapeutic targets. The rapid advancements in sequencing technologies and the expansion of biological databases have significantly deepened our understanding of the molecular drivers of metastasis and associated drug resistance. This review focuses on the molecular drivers of metastasis, particularly the roles of genetic mutations, epigenetic changes, and post‐translational modifications in metastasis progression. We also examine how the tumor microenvironment influences metastatic behavior and explore emerging therapeutic strategies, including targeted therapies and immunotherapies. Finally, we discuss future research directions, stressing the importance of novel treatment approaches and personalized strategies to overcome metastasis and improve patient outcomes. By integrating contemporary insights into the molecular basis of metastasis and therapeutic innovation, this review provides a comprehensive framework to guide future research and clinical advancements in metastatic cancer.

Advancements in proteogenomics for preclinical targeted cancer therapy research

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

The spatial landscape of cancer hallmarks reveals patterns of tumor ecological dynamics and drug sensitivity

Tumors are complex ecosystems of interacting cell types. The concept of cancer hallmarks distills this complexity into underlying principles that govern tumor growth. Here, we explore the spatial distribution of cancer hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that hallmark activity is spatially organized, with the cancer compartment contributing to the activity of seven out of 13 hallmarks, while the tumor microenvironment (TME) contributes to the activity of the rest. Additionally, we discover that genomic distance between tumor subclones correlates with differences in hallmark activity, even leading to clone-hallmark specialization. Finally, we demonstrate interdependent relationships between hallmarks at the junctions of TME and cancer compartments and how they relate to sensitivity to different neoadjuvant treatments in 33 bladder cancer patients from the DUTRENEO trial. In conclusion, our findings may improve our understanding of tumor ecology and help identify new drug biomarkers.

PIK3CA mutation fortifies molecular determinants for immune signaling in vascular cancers

Angiosarcomas are a group of vascular cancers that form malignant blood vessels. These malignancies are seemingly inflamed primarily due to their pathognomonic nature, which consists of irregular endothelium and tortuous blood channels. PIK3CA mutations are oncogenic and disrupt the PI3K pathway. In this study, we aimed to define the molecular and functional consequences of oncogenic PIK3CA mutations in angiosarcoma. We first generated two isogenic hemangiosarcoma cell lines harboring the H1047R hotspot mutations in PIK3CA gene using CRISPR/Cas9. We found PIK3CA-mutant cells established distinct molecular signatures in global gene expression and chromatin accessibility, which were associated with enrichment of immune cytokine signaling, including IL-6, IL-8, and MCP-1. These molecular processes were disrupted by the PI3K-α specific inhibitor, alpelisib. We also observed that the molecular distinctions in PIK3CA-mutant cells were linked to metabolic reprogramming in glycolytic activity and mitochondrial respiration. Our multi-omics analysis revealed that activating PIK3CA mutations regulate molecular machinery that contributes to phenotypic alterations and resistance to alpelisib. Furthermore, we identified potential therapeutic vulnerabilities of PIK3CA mutations in response to PI3K-α inhibition mediated by MAPK signaling. In summary, we demonstrate that PIK3CA mutations perpetuate PI3K activation and reinforce immune enrichment to promote drug resistance in vascular cancers.

Patient-derived xenograft model in cancer: establishment and applications

The patient-derived xenograft (PDX) model is a crucial in vivo model extensively employed in cancer research that has been shown to maintain the genomic characteristics and pathological structure of patients across various subtypes, metastatic, and diverse treatment histories. Various treatment strategies utilized in PDX models can offer valuable insights into the mechanisms of tumor progression, drug resistance, and the development of novel therapies. This review provides a comprehensive overview of the establishment and applications of PDX models. We present an overview of the history and current status of PDX models, elucidate the diverse construction methodologies employed for different tumors, and conduct a comparative analysis to highlight the distinct advantages and limitations of this model in relation to other in vivo models. The applications are elucidated in the domain of comprehending the mechanisms underlying tumor development and cancer therapy, which highlights broad applications in the fields of chemotherapy, targeted therapy, delivery systems, combination therapy, antibody-drug conjugates and radiotherapy. Furthermore, the combination of the PDX model with multiomics and single-cell analyses for cancer research has also been emphasized. The application of the PDX model in clinical treatment and personalized medicine is additionally emphasized.

A novel ubiquitination-related gene signature for overall survival prediction in patients with liver hepatocellular carcinoma

Liver hepatocellular carcinoma (LIHC) is a highly heterogeneous disease, necessitating the discovery of novel biomarkers to enhance individualized treatment approaches. Recent research has shown the significant involvement of ubiquitin-related genes (UbRGs) in the progression of LIHC. However, the prognostic value of UbRGs in LIHC has not been investigated. In this study, the mRNA expression profiles and clinical data were obtained from public databases of LIHC patients. The least absolute shrinkage and selection operator Cox regression model was employed to construct a multigene signature in the TCGA cohort. Our results showed that a twelve UbRGs signature was developed to categorize patients into two risk groups, with significant differences in expression between LIHC and normal tissues. Patients in the high-risk group exhibited significantly reduced overall survival (OS) and progression-free survival compared to those in the low-risk group. The risk score was identified as an independent predictor for OS in multivariate Cox regression analyses. Receiver operating characteristic curve analysis confirmed the predictive capacity of the signature. Functional analysis revealed enrichment of immune-related pathways and differences in immune status between the two risk groups. The risk score was correlated with 35 transcription factors and 26 eRNA enhancers, and positively associated with tumor mutation burden. Patients in the high-risk group demonstrated decreased sensitivity to targeted and chemotherapeutic drugs than those in the low-risk group. In conclusion, our study identified a twelve UbRGs signature that may serve as a prognostic predictor for LIHC patients and and provide valuable insights for cancer treatment.

Revolutionizing ESCC prognosis: the efficiency of tumor-infiltrating immune cells (TIIC) signature score

BACKGROUND

Patients suffer from esophageal squamous cell carcinoma (ESCC), which is the ninth highly aggressive malignancy. Tumor-infiltrating immune cells (TIIC) exert as major component of the tumor microenvironment (TME), showing possible prognostic value in ESCC.

METHODS

Transcriptome data and scRNA-seq data of ESCC samples were extracted from the GEO and TCGA databases. Tissue Specific Index (TSI) was defined to identify potential TIIC-RNAs from the TME. Twenty machine learning algorithms were further applied to evaluate the prognostic efficacy of TIIC signature score. Gene colocalization analysis was performed. Differences in CNV on chromosomes and SNP sites of prognostic model genes were calculated.

RESULTS

The most reliable model of TIIC signature score was developed based on three prognostic TIIC-RNAs. It showed a higher C-index than any other reported prognostic models. ESCC patients with high TIIC signature score showed poorer survival outcomes than low TIIC signature score. The activity of most immune cells decreased with the increase of TIIC score. TIIC signature score showed difference in the expression levels and methylation levels of DEGs. There was also significant different correlation with the degree of CNV amplification and CNV deletion of the immune checkpoint genes. Gene colocalization analysis showed two prognostic model genes (ATP6V0E1 and BIRC2). MR analysis found that rs148710154 and rs75146099 SNP sites of TIIC-RNA gene had a significant correlation between them gastro-oesophageal reflux and ESCC.

CONCLUSION

TIIC signature score was the first time developed which provided a novel strategy and guidance for the prognosis and immunotherapy of ESCC. It also gave the evidence in the important role of immune cells from the TME in the treatment of cancers.

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.

Proteomics: An In-Depth Review on Recent Technical Advances and Their Applications in Biomedicine

Proteins hold pivotal importance since many diseases manifest changes in protein activity. Proteomics techniques provide a comprehensive exploration of protein structure, abundance, and function in biological samples, enabling the holistic characterization of overall changes in organisms. Nowadays, the breadth of emerging methodologies in proteomics is unprecedentedly vast, with constant optimization of technologies in sample processing, data collection, data analysis, and its scope of application is steadily transitioning from the bench to the clinic. Here, we offer an insightful review of the technical developments in proteomics and its applications in biomedicine over the past 5 years. We focus on its profound contributions in profiling disease spectra, discovering new biomarkers, identifying promising drug targets, deciphering alterations in protein conformation, and unearthing protein-protein interactions. Moreover, we summarize the cutting-edge technologies and potential breakthroughs in the proteomics pipeline and provide the principal challenges in proteomics. Based on these, we aspire to broaden the applicability of proteomics and inspire researchers to enhance our understanding of complex biological systems by utilizing such techniques.

Co-expression-wide association studies link genetically regulated interactions with complex traits

Transcriptome- and proteome-wide association studies (TWAS/PWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. To address this limitation, we introduce the co-expression-wide association study (COWAS) method, which can identify pairs of genes or proteins whose genetically regulated co-expression is associated with complex traits. COWAS first trains models to predict expression and co-expression conditional on genetic variation, and then tests for association between imputed co-expression and the trait of interest while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own. We also show how COWAS can help disentangle direct and interaction effects, providing a richer picture of the molecular networks that mediate genetic effects on disease outcomes.

Comprehensive review on leucine-rich pentatricopeptide repeat-containing protein (LRPPRC, PPR protein): A burgeoning target for cancer therapy

Leucine-rich pentatricopeptide repeat-containing (LRPPRC), known as the gene mutations that cause Leigh Syndrome French Canadian, encodes a high molecular weight PPR protein (157,905 Da), LRPPRC. LRPPRC binds to DNA, RNA, and proteins to regulate transcription and translation, leading to changes in cell fate. Increasing evidence indicates that LRPPRC plays a pivotal role in various human diseases, particularly cancer in recent years. Here, we review the structure, function, molecular mechanism, as well as inhibitors of LRPPRC. LRPPRC expression elevates in most cancer types and high expression of LRPPRC predicts the poor prognosis of cancer patients. Targeting LRPPRC suppresses tumor progression by affecting several cancer hallmarks, including signal transduction, cancer metabolism, and immune regulation. LRPPRC is a promising target in cancer research, serving as both a biomarker and therapeutic target. Further studies are required to extend the understanding of LRPPRC function and molecular mechanism, as well as to refine novel therapeutic strategies targeting LRPPRC in cancer therapy.

Deep Learning of radiology-genomics integration for computational oncology: A mini review

In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.

IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.

Regulation of HNRNP family by post-translational modifications in cancer

Heterogeneous nuclear ribonucleoproteins (HNRNPs) represent a large family of RNA-binding proteins consisting of more than 20 members and have attracted great attention with their distinctive roles in cancer progression by regulating RNA splicing, transcription, and translation. Nevertheless, the cancer-specific modulation of HNRNPs has not been fully elucidated. The research of LC-MS/MS technology has documented that HNRNPs were widely and significantly targeted by different post-translational modifications (PTMs), which have emerged as core regulators in shaping protein functions and are involved in multiple physiological processes. Accumulating studies have highlighted that several PTMs are involved in the mechanisms of HNRNPs regulation in cancer and may be suitable therapeutic targets. In this review, we summarize the existing evidence describing how PTMs modulate HNRNPs functions on gene regulation and the involvement of their dysregulation in cancer, which will help shed insights on their clinical impacts as well as possible therapeutic tools targeting PTMs on HNRNPs.

Tumour evolution and microenvironment interactions in 2D and 3D space

To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.

Epithelial‑derived head and neck squamous tumourigenesis (Review)

Head and neck squamous cell carcinomas (HNSCCs), a heterogeneous group of cancers that arise from the mucosal epithelia cells in the head and neck areas, present great challenges in diagnosis, treatment and prognosis due to their complex aetiology and various clinical manifestations. Several factors, including smoking, alcohol consumption, oncogenic genes, growth factors, Epstein‑Barr virus and human papillomavirus infections can contribute to HNSCC development. The unpredictable tumour microenvironment adds to the complexity of managing HNSCC. Despite significant advances in therapies, the prediction of outcome after treatment for patients with HNSCC remains poor, and the 5‑year overall survival rate is low due to late diagnosis. Early detection greatly increases the chances of successful treatment. The present review aimed to bring together the latest findings related to the molecular mechanisms of HNSCC carcinogenesis and progression. Comprehensive genomic, transcriptomic, metabolomic, microbiome and proteomic analyses allow researchers to identify important biological markers such as genetic alterations, gene expression signatures and protein markers that drive HNSCC tumours. These biomarkers associated with the stages of initiation, progression and metastasis of cancer are useful in the management of patients with cancer in order to improve their life expectancy and quality of life.

Clinical significance of circulating biomarkers of immune-checkpoint molecules with atezolizumab plus bevacizumab therapy in unresectable hepatocellular carcinoma

BACKGROUND

The aims of this study were to identify clinically significant biomarkers of a response to atezolizumab plus bevacizumab (ATZ + BV) therapy and to develop target strategies against unresectable hepatocellular carcinoma (u-HCC).

METHOD

We first investigated the potential of circulating tumor DNA (ctDNA) to serve as a biomarker for predicting the therapeutic outcome in 24 u-HCC patients treated with ATZ + BV therapy. Next, we analyzed levels of immune-related cytokines in blood samples from 134 u-HCC patients who received ATZ + BV. For this, serum immune-related molecules or cancer-immune cycle-related molecules that have been reported in HCC patient sera, namely CD274, LAG-3, CCL2, 4, 5, CXCL1, 9, 10, 12, 13, CX3CL1, CCR5, IFNγ and IL-6, 8 were measured using enzyme-linked immunosorbent assay.

RESULTS

More than 1% of variant read frequency (VRF) mutations were found in TP53, APC, PIK3CA and VHL, although with no correlation with treatment response. Among the 15 cytokines evaluated, CXCL9 and LAG-3 levels were significantly different between patients with objective response (OR), stable disease (SD), and progressive disease (PD) following ATZ + BV treatment. Receiver-operating characteristic curve analyses of CXCL9 (cut-off value: 419.1 pg/ml) and LAG-3 (cut-off value: 3736.3 pg/ml) indicated areas of 0.779 and 0.697, respectively, for differentiating PD from non-PD and OR from non-OR. In multivariate analysis of progression-free survival (PFS) and overall survival (OS), high serum CXCL9 (hazard ratio (HR) and 95% confidence interval (CI): 0.412 (0.251-0.677) (p = 0.0005) for PFS and 0.252 (0.125-0.508) (p = 0.0001) for OS), and low serum LAG-3 (HR and 95% CI 0.419 (0.249-0.705) (p = 0.0011) for PFS and 0.294 (0.140-0.617) (p = 0.0012) for OS) were independent positive predictive factors.

CONCLUSION

Although, as far as we examined, no ctDNA mutations in blood were found to be related to ATZ + BV treatment efficacy, serum CXCL9 and LAG-3 levels, which are related to the cancer-immune cycle, were associated with treatment efficacy and could be predictive markers of the efficacy of ATZ + BV treatment in HCC patients.

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

Sideroflexin-1 promotes progression and sensitivity to lapatinib in triple-negative breast cancer by inhibiting TOLLIP-mediated autophagic degradation of CIP2A

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and it lacks specific therapeutic targets and effective treatment protocols. By analyzing a proteomic TNBC dataset, we found significant upregulation of sideroflexin 1 (SFXN1) in tumor tissues. However, the precise function of SFXN1 in TNBC remains unclear. Immunoblotting was performed to determine SFXN1 expression levels. Label-free quantitative proteomics and liquid chromatography-tandem mass spectrometry were used to identify the downstream targets of SFXN1. Mechanistic studies of SFXN1 and cellular inhibitor of PP2A (CIP2A) were performed using immunoblotting, immunofluorescence staining, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Functional experiments were used to investigate the role of SFXN1 in TNBC cells. SFXN1 was significantly overexpressed in TNBC tumor tissues and was associated with unfavorable outcomes in patients with TNBC. Functional experiments demonstrated that SFXN1 promoted TNBC growth and metastasis in vitro and in vivo. Mechanistic studies revealed that SFXN1 promoted TNBC progression by inhibiting the autophagy receptor TOLLIP (toll interacting protein)-mediated autophagic degradation of CIP2A. The pro-tumorigenic effect of SFXN1 overexpression was partially prevented by lapatinib-mediated inhibition of the CIP2A/PP2A/p-AKT pathway. These findings may provide a new targeted therapy for patients with TNBC.

Coordinated Targeting of S6K1/2 and AXL Disrupts Pyrimidine Biosynthesis in PTEN-Deficient Glioblastoma

Intrinsic resistance to targeted therapeutics in PTEN-deficient glioblastoma (GBM) is mediated by redundant signaling networks that sustain critical metabolic functions. Here, we demonstrate that coordinated inhibition of the ribosomal protein S6 kinase 1 (S6K1) and the receptor tyrosine kinase AXL using LY-2584702 and BMS-777607 can overcome network redundancy to reduce GBM tumor growth. This combination of S6K1 and AXL inhibition suppressed glucose flux to pyrimidine biosynthesis. Genetic inactivation studies to map the signaling network indicated that both S6K1 and S6K2 transmit growth signals in PTEN-deficient GBM. Kinome-wide ATP binding analysis in inhibitor-treated cells revealed that LY-2584702 directly inhibited S6K1, and substrate phosphorylation studies showed that BMS-777607 inactivation of upstream AXL collaborated to reduce S6K2-mediated signal transduction. Thus, combination targeting of S6K1 and AXL provides a kinase-directed therapeutic approach that circumvents signal transduction redundancy to interrupt metabolic function and reduce growth of PTEN-deficient GBM.

SIGNIFICANCE

Therapy for glioblastoma would be advanced by incorporating molecularly targeted kinase-directed agents, similar to standard of care strategies in other tumor types. Here, we identify a kinase targeting approach to inhibit the metabolism and growth of glioblastoma.

Multi-scale signaling and tumor evolution in high-grade gliomas

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

PTPN23[Thr] variant reduces susceptibility and tumorigenesis in esophageal squamous cell carcinoma through dephosphorylation of EGFR

Post-translational modifications (PTMs) have emerged as pivotal regulators of the development of cancers, including esophageal squamous cell carcinoma (ESCC). Here, we conducted a comprehensive analysis of PTM-related genetic variants associated with ESCC risk using large-scale genome-wide and exome-wide association datasets. We observed significant enrichment of PTM-related variants in the ESCC risk loci and identified five variants that were significantly associated with ESCC risk. Among them, rs6780013 in PTPN23 exhibited the highest level of significance in ESCC susceptibility in 9,728 ESCC cases and 10,977 controls (odds ratio [OR] = 0.85, 95 % confidence interval [CI] = 0.81- 0.89, P = 9.77 × 10-14). Further functional investigations revealed that PTPN23[Thr] variant binds to EGFR and modulates its phosphorylation at Thr699. PTPN23[Thr] variant substantially inhibited ESCC cell proliferation both in vitro and in vivo. Our findings underscore the critical role of PTPN23[Thr]-EGFR interaction in ESCC development, providing more insights into the pathogenesis of this cancer.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

Time-Resolved Ultraviolet Photodissociation Mass Spectrometry Probes the Mutation-Induced Alterations in Protein Stability and Unfolding Dynamics

How mutations impact protein stability and structure dynamics is crucial for understanding the pathological process and rational drug design. Herein, we establish a time-resolved native mass spectrometry (TR-nMS) platform via a rapid-mixing capillary apparatus for monitoring the acid-initiated protein unfolding process. The molecular details in protein structure unfolding are further profiled by a 193 nm ultraviolet photodissociation (UVPD) analysis of the structure-informative photofragments. Compared with the wild-type dihydrofolate reductase (WT-DHFR), the M42T/H114R mutant (MT-DHFR) exhibits a significant stability decrease in TR-nMS characterization. UVPD comparisons of the unfolding intermediates and original DHFR forms indicate the special stabilization effect of cofactor NADPH on DHFR structure, and the M42T/H114R mutations lead to a significant decrease in NADPH-DHFR interactions, thus promoting the structure unfolding. Our study paves the way for probing the mutation-induced subtle changes in the stability and structure dynamics of drug targets.

IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures

MOTIVATION

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis.

RESULTS

To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vicruiser/3Dmapper.

Neutrophil profiling illuminates anti-tumor antigen-presenting potency

Neutrophils, the most abundant and efficient defenders against pathogens, exert opposing functions across cancer types. However, given their short half-life, it remains challenging to explore how neutrophils adopt specific fates in cancer. Here, we generated and integrated single-cell neutrophil transcriptomes from 17 cancer types (225 samples from 143 patients). Neutrophils exhibited extraordinary complexity, with 10 distinct states including inflammation, angiogenesis, and antigen presentation. Notably, the antigen-presenting program was associated with favorable survival in most cancers and could be evoked by leucine metabolism and subsequent histone H3K27ac modification. These neutrophils could further invoke both (neo)antigen-specific and antigen-independent T cell responses. Neutrophil delivery or a leucine diet fine-tuned the immune balance to enhance anti-PD-1 therapy in various murine cancer models. In summary, these data not only indicate the neutrophil divergence across cancers but also suggest therapeutic opportunities such as antigen-presenting neutrophil delivery.

Uncovering dark matter in cancer by identifying epigenetic drivers

The complex relationship between chromatin accessibility, transcriptional regulation, and cancer transitions presents a daunting puzzle. Terekhanova et al. created a pan-cancer epigenetic and transcriptomic atlas at single-cell resolution, yielding important insights into the underlying chromatin architecture of cancer transitions and novel discoveries with the potential to advance precision medicine.

Illuminating function of the understudied druggable kinome

The human kinome, with more than 500 proteins, is crucial for cell signaling and disease. Yet, about one-third of kinases lack in-depth study. The Data and Resource Generating Center for Understudied Kinases has developed multiple resources to address this challenge including creation of a heavy amino acid peptide library for parallel reaction monitoring and quantitation of protein kinase expression, use of understudied kinases tagged with a miniTurbo-biotin ligase to determine interaction networks by proximity-dependent protein biotinylation, NanoBRET probe development for screening chemical tool target specificity in live cells, characterization of small molecule chemical tools inhibiting understudied kinases, and computational tools for defining kinome architecture. These resources are available through the Dark Kinase Knowledgebase, supporting further research into these understudied protein kinases.

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

Proteogenomic data and resources for pan-cancer analysis

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.