Pan-cancer proteogenomics characterization of tumor immunity

Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and blood-based molecular profiles that inform cancer heterogeneity, metastasis, and therapeutic resistance. Machine learning and AI-driven models further enhance these datasets by stratifying patient populations, predicting therapeutic responses, and expediting the discovery of actionable targets and companion biomarkers. This synergy between technology, automation, and high-dimensional data analytics enables individualized treatment strategies in melanoma, lung, and other cancer types. Aligned with international programs such as the Cancer Moonshot and the ICPC, the Lund Center's approach fosters open collaboration and data sharing on a global scale. This scalable, patient-centric biobanking paradigm provides an adaptable model for institutions aiming to unify clinical, molecular, and computational resources for transformative cancer research.

Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets

Cancer progression and therapeutic resistance are closely linked to a stemness phenotype. Here, we introduce a protein-expression-based stemness index (PROTsi) to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes. Utilizing datasets from the Clinical Proteomic Tumor Analysis Consortium across 11 tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Through integration of PROTsi with multi-omics, including protein post-translational modifications, we identify molecular features associated with stemness and proteins that act as active nodes within transcriptional networks, driving tumor aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets, both shared and tumor specific. These stemness-associated proteins demonstrate predictive value for clinical outcomes, as confirmed by immunohistochemistry in multiple samples. The findings emphasize PROTsi's efficacy as a valuable tool for selecting predictive protein targets, a crucial step in customizing anti-cancer therapy and advancing the clinical development of cures for cancer patients.

Inflammatory reprogramming of the solid tumor microenvironment by infiltrating clonal hematopoiesis is associated with adverse outcomes

Clonal hematopoiesis (CH)-the expansion of somatically mutated hematopoietic cells-is common in solid cancers. CH is associated with systemic inflammation, but its impact on tumor biology is underexplored. Here, we report the effects of CH on the tumor microenvironment (TME) using 1,550 treatment-naive patient samples from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) cohort. CH is present in 18.3% of patients, with one-third of CH mutations also detectable in tumor-derived DNA from the same individual (CH-Tum), reflecting CH-mutant leukocyte infiltration. Across cancers, the presence of CH-Tum is associated with worse survival outcomes. Molecular analyses reveal an association between CH-Tum and an immune-rich, inflammatory TME that is notably distinct from age-related gene expression changes. These effects are most prominent in glioblastoma, where CH correlates with pronounced macrophage infiltration, inflammation, and an aggressive, mesenchymal phenotype. Our findings demonstrate that CH shapes the TME, with potential applications as a biomarker in precision oncology.

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.

Finding a needle in a haystack: functional screening for novel targets in cancer immunology and immunotherapies

Genome-wide functional genetic screening has been widely used in the biomedicine field, which makes it possible to find a needle in a haystack at the genetic level. In cancer research, gene mutations are closely related to tumor development, metastasis, and recurrence, and the use of state-of-the-art powerful screening technologies, such as clustered regularly interspaced short palindromic repeat (CRISPR), to search for the most critical genes or coding products provides us with a new possibility to further refine the cancer mapping and provide new possibilities for the treatment of cancer patients. The use of CRISPR screening for the most critical genes or coding products has further refined the cancer atlas and provided new possibilities for the treatment of cancer patients. Immunotherapy, as a highly promising cancer treatment method, has been widely validated in the clinic, but it could only meet the needs of a small proportion of cancer patients. Finding new immunotherapy targets is the key to the future of tumor immunotherapy. Here, we revisit the application of functional screening in cancer immunology from different perspectives, from the selection of diverse in vitro and in vivo screening models to the screening of potential immune checkpoints and potentiating genes for CAR-T cells. The data will offer fresh therapeutic clues for cancer patients.

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.

Functional-proteomics-based investigation of the cellular response to farnesyltransferase inhibition in lung cancer

Farnesylation is a lipid post-translational modification of proteins crucial for protein membrane anchoring and cellular signaling. Farnesyltransferase inhibitors (FTIs), such as tipifarnib, are being tested in cancer therapy. However, the full impact of FTIs on farnesylation substrates remains poorly understood, thus limiting their use in precision medicine. In this study, we performed a global proteomics analysis to investigate farnesylation and the effects of tipifarnib in lung cancer cell lines. Using metabolic labeling and mass spectrometry, we identified farnesylated proteins and mapped their subcellular localization. We also analyzed tipifarnib-dependent protein relocalization and proteome-wide changes. Key findings include the potential therapeutic value of FTIs for NRAS-mutated melanoma and GNAQ/GNA11-mutated uveal melanoma by inhibiting INPP5A farnesylation. Additionally, we identified a synergistic drug combination involving tipifarnib and a ferroptosis inducer and discovered PTP4A1 as a regulator of interferon signaling. Our data, covering 15,080 proteins, offer valuable insights for future studies of farnesylation and FTIs.

Advances in the understanding and therapeutic manipulation of cancer immune responsiveness: a Society for Immunotherapy of Cancer (SITC) review

Cancer immunotherapy-including immune checkpoint inhibition (ICI) and adoptive cell therapy (ACT)-has become a standard, potentially curative treatment for a subset of advanced solid and liquid tumors. However, most patients with cancer do not benefit from the rapidly evolving improvements in the understanding of principal mechanisms determining cancer immune responsiveness (CIR); including patient-specific genetically determined and acquired factors, as well as intrinsic cancer cell biology. Though CIR is multifactorial, fundamental concepts are emerging that should be considered for the design of novel therapeutic strategies and related clinical studies. Recent advancements as well as novel approaches to address the limitations of current treatments are discussed here, with a specific focus on ICI and ACT.

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

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

FAP+ gastric cancer mesenchymal stromal cells via paracrining INHBA and remodeling ECM promote tumor progression

Gastric cancer (GC) mesenchymal stromal cells (GCMSCs) are the predominant components of the tumor microenvironment (TME) and play a role in the occurrence, development, and metastasis of tumors. However, GCMSCs exhibit phenotypic and functional heterogeneity. The key population of GCMSCs which are vital to tumor progression remains elusive. The expression of fibroblast activation protein (FAP) in gastric cancer was analyzed and verified using clinical pathology data and single-cell RNA sequencing database of gastric cancer patients. FAP positive GCMSCs (FAP+ GCMSCs) were isolated via flow cytometry and characterized through transcriptomic sequencing. The impact of conditioned medium from FAP+ GCMSCs on gastric cancer cell lines was assessed using Enzyme-linked immunosorbent assay (ELISA) and Western blot analyses. Additionally, immunohistochemistry (IHC) and Masson's trichrome staining were employed to explore the association between FAP+ GCMSCs and extracellular matrix (ECM) deposition in gastric cancer tissues. Our study demonstrates that FAP is predominantly expressed in the mesenchymal stromal cells within the gastric cancer milieu. FAP+ GCMSCs exhibited enhanced proliferation, migration, contraction, and tumor-promoting capabilities compared to their FAP- counterparts. These cells significantly increased proliferation and migration of gastric cancer cells through the paracrine secretion of Inhibin Subunit Beta A (INHBA) and activation of the SMAD2/3 signaling pathway. Moreover, FAP+ GCMSCs also induced collagen deposition in ECM and then up-regulated invasion and stemness of GC cells. Mechanistically, this process was mediated by the interaction of collagen with Integrin Subunit Beta 1 (ITGB1), triggering the phosphorylation of Focal Adhesion Kinase (FAK) and Yes Associated Transcriptional Regulator (YAP). Our findings reveal that FAP+ GCSMCs enhanced the GC progression via releasing cytokine INHBA and remodeling ECM providing a theoretical basis for further exploration of tumor stromal-targeting therapy of gastric cancer.

Mechanisms and technologies in cancer epigenetics

Cancer's epigenetic landscape, a labyrinthine tapestry of molecular modifications, has long captivated researchers with its profound influence on gene expression and cellular fate. This review discusses the intricate mechanisms underlying cancer epigenetics, unraveling the complex interplay between DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs. We navigate through the tumultuous seas of epigenetic dysregulation, exploring how these processes conspire to silence tumor suppressors and unleash oncogenic potential. The narrative pivots to cutting-edge technologies, revolutionizing our ability to decode the epigenome. From the granular insights of single-cell epigenomics to the holistic view offered by multi-omics approaches, we examine how these tools are reshaping our understanding of tumor heterogeneity and evolution. The review also highlights emerging techniques, such as spatial epigenomics and long-read sequencing, which promise to unveil the hidden dimensions of epigenetic regulation. Finally, we probed the transformative potential of CRISPR-based epigenome editing and computational analysis to transmute raw data into biological insights. This study seeks to synthesize a comprehensive yet nuanced understanding of the contemporary landscape and future directions of cancer epigenetic research.

A novel combined oxidative stress and extracellular matrix related predictive gene signature for keratoconus

Keratoconus (KC) is an ectatic cornea disease with high prevalence and asymptomatic at early stage, leading to decreased visual acuity and even cornea transplantation. However, the etiology mechanism of keratoconus is still poorly understood. Oxidative stress (OS) and extracellular matrix (ECM) remodeling play critical roles in keratoconus development. Here, based on keratoconus datasets from GEO database, we obtained 454 differentially expressed genes (DEGs), which were further intersected with oxidative stress (OS) and extracellular matrix (ECM) genesets from MSigDB database. A total of 17 OS- and ECM-related DEGs (OEDEGs) were identified. Feature genes were screened by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms, and a six-gene (COL1A1, CYP1B1, MMP3, HMOX1, FOS and GDF15) classification model was developed utilizing Logistic regression (LR), Support Vector Machine (SVM) and Naïve Bayes (NB) algorithms respectively, which was further verified in internal and external cohort. Subsequently, a predictive nomogram was constructed for KC patients. Six signature genes showed a strong correlation with the infiltration level of macrophages M1, neutrophils and eosinophils. Additionally, in vitro qRT-PCR validated the decreased expression of signature genes in either keratoconus clinical samples or human cornea epithelial (HCE) cells grown on soft hydrogel substrate. Finally, we revealed that CYP1B1 and GDF15 regulate cellular proliferation and response to oxidative stress. In conclusion, the developed combined OS and ECM gene signature showed excellent performance for keratoconus prediction, providing beneficial perspectives for keratoconus pathogenesis.

Sex differences in toxicities and survival outcomes among Japanese patients with Stage III colorectal cancer receiving adjuvant fluoropyrimidine monotherapy: A pooled analysis of 4 randomized controlled trials (JCOG2310A)

BACKGROUND

Fluoropyrimidine remains the key agent of adjuvant chemotherapy for stage III colorectal cancer (CRC). Western studies have shown that female sex is a favorable prognostic factor after surgery, but it is also a risk factor for adverse events (AEs) during adjuvant chemotherapy with fluoropyrimidine. However, little is known about whether sex differences in treatment outcomes exist in this setting in the Asian population.

METHODS

Patients with stage III CRC who received adjuvant fluoropyrimidine monotherapy in 4 randomized controlled trials were analyzed. Incidences of AEs and survival outcomes were compared between female and male patients.

RESULTS

A total of 3170 patients (female, 1516; male, 1654) were included in this analysis. Compared with males, females were less likely to have a relative dose intensity (≥90 %: female 59.1 % vs. male 67.6 %), with a higher proportion of requiring dose reduction (28.8 % vs. 20.4 %) and a lower proportion of completing adjuvant chemotherapy (77.0 % vs. 81.7 %). Multivariable analyses demonstrated that female sex was associated with a higher incidence of grade 3-4 AEs (odds ratio 1.80 [95 % CI 1.51-2.14]). Female sex was identified as a favorable prognostic factor for overall survival (hazard ratio [HR]: 0.80 [0.65-0.97]) and relapse-free survival (HR: 0.73 [0.63-0.85]) in multivariable analyses. Female patients had fewer time-to recurrence (TTR) events than male patients (5-year TTR: 17.7 % vs. 22.3 %).

CONCLUSION

Sex had implications for the development of AEs and survival outcomes of Japanese patients with stage III CRC who received adjuvant fluoropyrimidine monotherapy.

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

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

Improving design and normalization of multiplex proteomics study

Advances in multiplex mass spectrometry-based technologies have enabled high-throughput, quantitative proteome profiling of large cohort. However, certain experimental design configurations can amplify sample variability and introduce systematic biases. To address these challenges, we incorporated two novel features in a recent proteogenomic investigation: (1) the inclusion of two reference samples within each mass spectrometry run to serve as internal standards, and (2) the analysis of each specimen as technical replicates across two distinct mass spectrometry runs. Building on these enhancements, we present ProMix, a flexible analytical framework designed to fully leverage these supplementary experimental components. Using both simulated and real-world datasets, we demonstrate the improved performance of ProMix and highlight the advantages conferred by these refined experimental design strategies.

The 2024 Report on the Human Proteome from the HUPO Human Proteome Project

The Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify at least one isoform of every protein-coding gene and (2) to make proteomics an integral part of multiomics studies of human health and disease. The past year has seen major transitions for the HPP. neXtProt was retired as the official HPP knowledge base, UniProtKB became the reference proteome knowledge base, and Ensembl-GENCODE provides the reference protein target list. A function evidence FE1-5 scoring system has been developed for functional annotation of proteins, parallel to the PE1-5 UniProtKB/neXtProt scheme for evidence of protein expression. This report includes updates from neXtProt (version 2023-09) and UniProtKB release 2024_04, with protein expression detected (PE1) for 18138 of the 19411 GENCODE protein-coding genes (93%). The number of non-PE1 proteins ("missing proteins") is now 1273. The transition to GENCODE is a net reduction of 367 proteins (19,411 PE1-5 instead of 19,778 PE1-4 last year in neXtProt). We include reports from the Biology and Disease-driven HPP, the Human Protein Atlas, and the HPP Grand Challenge Project. We expect the new Functional Evidence FE1-5 scheme to energize the Grand Challenge Project for functional annotation of human proteins throughout the global proteomics community, including π-HuB in China.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Quantitative proteomic mass spectrometry of protein kinases to determine dynamic heterogeneity of the human kinome

The kinome is a dynamic system of kinases regulating signaling networks in cells and dysfunction of protein kinases contributes to many diseases. Regulation of the protein expression of kinases alters cellular responses to environmental changes and perturbations. We configured a library of 672 proteotypic peptides to quantify >300 kinases in a single LC-MS experiment using ten micrograms protein from human tissues including biopsies. This enables absolute quantitation of kinase protein abundance at attomole-femtomole expression levels, requiring no kinase enrichment and less than ten micrograms of starting protein from flash-frozen and formalin fixed paraffin embedded tissues. Breast cancer biopsies, organoids, and cell lines were analyzed using the SureQuant method, demonstrating the heterogeneity of kinase protein expression across and within breast cancer clinical subtypes. Kinome quantitation was coupled with nanoscale phosphoproteomics, providing a feasible method for novel clinical diagnosis and understanding of patient kinome responses to treatment.

Bioinformatic analysis of molecular characteristics and oncogenic features of CARD14 in human cancer

Aberrant caspase recruitment domain family member 14 (CARD14) signaling has been strongly associated with inflammatory skin conditions. CARD14 acts as a scaffold protein, ultimately activating the transcription factor NF-KB. Although primarily studied in the context of inflammation, recent research has suggested its potential implications in tumorigenesis. In this study, we gathered The Cancer Genome Atlas (TCGA) tumor data to gauge the involvement of CARD14 in cancer, including genetic alterations, expression patterns, survival correlations, immune cell infiltration and functional interactions across diverse cancer types. We found heightened CARD14 expression in most tumors and there was a significant correlation between CARD14 expression and the prognosis of patients for certain tumors. For instance, patients with higher CARD14 expression had a better prognosis in sarcoma, lung, cervix and head and neck cancers. Moreover, CARD14 expression positively correlated with neutrophil infiltration in most of the cancer types analyzed. Finally, enrichment analysis showed that epithelial development and differentiation pathways were involved in the functional mechanism of CARD14. Our results show that CARD14 may have the potential to become a prognostic biomarker in several cancers, hence, further prospective studies will be required for its validation.

LILRB2 promotes immune escape in breast cancer cells via enhanced HLA-A degradation

PURPOSE

Increased expression of leukocyte immunoglobulin-like receptor subfamily B member 2 (LILRB2) is associated with immune evasion in breast cancer (BC). The aim of this study to elucidate the role of LILRB2 in BC progression.

METHODS

LILRB2 expression in tumor tissues was detected by immunohistochemical staining. Human leukocyte antigen A (HLA-A) expression in BC cells was detected by Western blotting, and HLA-A ubiquitination was detected by immunoprecipitation and histidine pulldown assay. An in-situ tumor model was established in nude BALB/c mice to verify the role of LILRB2 in immune escape. Finally, the functions and potential mechanisms of LILRB2 in BC progression were explored using in silico data.

RESULTS

LILRB2 was upregulated in BC tissues and cells, and correlated positively with poor prognosis. LILRB2 promoted BC progression by downregulating HLA-A expression. Mechanistically, LILRB2 facilitates the ubiquitination and subsequent degradation of HLA-A by promoting the interaction between the ubiquitin ligase membrane-associated ring finger protein 9 (MARCH9) and HLA-A. In syngeneic graft mouse models, LILRB2-expressing BC cells evaded CD8 + T cells and inhibited the secretion of cytokines by the cytotoxic CD8 + T cells.

CONCLUSION

LILRB2 downregulates HLA-A to promote immune evasion in BC cells and is a promising new target for BC treatment.

Extrachromosomal circular DNAs in prostate adenocarcinoma: global characterizations and a novel prediction model

Background

The role of focal amplifications and extrachromosomal circular DNA (eccDNA) is still uncertain in prostate adenocarcinoma (PRAD). Here, we first mapped the global characterizations of eccDNA and then investigate the characterization of eccDNA-amplified key differentially expressed encoded genes (eKDEGs) in the progression, immune response and immunotherapy of PRAD.

Methods

Circular_seq was used in conjunction with the TCGA-PRAD transcriptome dataset to sequence, annotate, and filter for eccDNA-amplified differentially expressed coding genes (eDEGs) in PRAD and para-cancerous normal prostate tissues. Afterwards, risk models were created and eKDEGs linked to the PRAD prognosis were identified using Cox and Lasso regression analysis. The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration.

Results

In this research, there was no significant difference in the size, type, and chromosomal distribution of eccDNA in PRAD and para-cancerous normal prostate tissues. However, 4,290 differentially expressed eccDNAs were identified and 1,981 coding genes were amplified. Following that, 499 eDEGs were tested in conjunction with the transcriptome dataset from TCGA-PRAD. By using Cox and Lasso regression techniques, ZNF330 and PITPNM3 were identified as eKDEGs of PRAD, and a new PRAD risk model was conducted based on this. Survival analysis showed that the high-risk group of this model was associated with poor prognosis and validated in external data. Immune infiltration analysis showed that the model risks affected immune cell infiltration in PRAD, not only mediating changes in immune cell function, but also correlating with immunophenotyping. Furthermore, the high-risk group was negatively associated with anti-CTLA-4/anti-PD-1 response and mutational burden. In addition, Tumor Immune Dysfunction and Exclusion analyses showed that high-risk group was more prone to immune escape. Drug sensitivity analyses identified 10 drugs, which were instructive for PRAD treatment.

Conclusion

ZNF330 and PITPNM are the eKDEGs for PRAD, which can be used as potential new prognostic markers. The two-factor combined risk model can effectively assess the survival and prognosis of PRAD patients, but also can predict the different responses of immunotherapy to PRAD patients, which may provide new ideas for PRAD immunotherapy.

Proteomic and Phosphoproteomic analysis of thyroid papillary carcinoma: Identification of potential biomarkers for metastasis

Thyroid cancer has emerged as the most rapidly proliferating solid neoplasm. In this study, we included a cohort of patients who underwent sonographic assessment and surgical intervention at the Sir Run Run Shaw Hospital, associated with the School of Medicine at Zhejiang University, spanning from January 2019 to June 2020. Stratification of cases was based on a combination of preoperative ultrasonographic evaluations and postoperative histopathological diagnoses, resulting in three distinct groups: high-risk papillary thyroid carcinoma (PTC) labeled as C1, low-risk PTC designated as C2, and a control group (N) composed of benign thyroid tissue adjacent to the carcinoma. Proteomic and phosphoproteomic analyses were conducted on PTC specimens. The comparative assessment revealed that proteins up-regulated in the C1/N and C2/N groups were predominantly involved in functions such as amino acid binding, binding of phosphorylated compounds, and serine protease activity. Notably, proteins like NADH dehydrogenase, ATP synthase, oxidoreductases, and iron ion channels were significantly elevated in the C1 versus C2 comparative group. Through meticulous analysis of differential expression multiples, statistical significance, and involvement in metabolic pathways, this study identified eight potential biomarkers pertinent to PTC metastasis diagnostics, encompassing phosphorylated myosin 10, phosphorylated proline-directed protein kinase, leucine tRNA synthetase, 2-oxo-isovalerate dehydrogenase, succinic semialdehyde dehydrogenase, ADP/ATPtranslocase, pyruvate carboxylase, and fibrinogen. Therapeutic assays employing metformin, an AMP-activated protein kinase (AMPK) activator, alongside the phosphorylation-specific inhibitor ML-7 targeting Myosin10, demonstrated attenuated cellular proliferation, migration, and invasion capabilities in thyroid cancer cells, accompanied by a reduction in amino acid pools. Cellular colocalization and interaction studies elucidated that AMPK activation imposes an inhibitory influence on Myosin10 levels. The findings of this research corroborate the utility of proteomic and phosphoproteomic platforms in the identification of metastatic markers for PTC and suggest that modulation of AMPK activity, coupled with the inhibition of Myosin10 phosphorylation, may forge novel therapeutic avenues in the management of thyroid carcinoma. SIGNIFICANCE: The significance of our research lies in its potential to transform the current understanding and management of thyroid papillary carcinoma (PTC), particularly in its metastatic form. By integrating both proteomic and phosphoproteomic analyses, our study not only sheds light on the molecular alterations associated with PTC but also identifies eight novel biomarkers that could serve as indicators of metastatic potential.

Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics

The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

Cracking the Codes behind Cancer Cells' Immune Evasion

Immune evasion is a key phenomenon in understanding tumor recurrence, metastasis, and other critical steps in tumor progression. The tumor microenvironment (TME) is in constant flux due to the tumor's ability to release signals that affect it, while immune cells within it can impact cancer cell behavior. Cancer cells undergo several changes, which can change the enrichment of different immune cells and modulate the activity of existing immune cells in the tumor microenvironment. Cancer cells can evade immune surveillance by downregulating antigen presentation or expressing immune checkpoint molecules. High levels of tumor-infiltrating lymphocytes (TILs) correlate with better outcomes, and robust immune responses can control tumor growth. On the contrary, increased enrichment of Tregs, myeloid-derived suppressor cells, and M2-like anti-inflammatory macrophages can hinder effective immune surveillance and predict poor prognosis. Overall, understanding these immune evasion mechanisms guides therapeutic strategies. Researchers aim to modulate the TME to enhance immune surveillance and improve patient outcomes. In this review article, we strive to summarize the composition of the tumor immune microenvironment, factors affecting the tumor immune microenvironment (TIME), and different therapeutic modalities targeting the immune cells. This review is a first-hand reference to understand the basics of immune surveillance and immune evasion.

Pan-cancer analysis and single-cell analysis reveals FAM110B as a potential target for survival and immunotherapy

Background: FAM110B belongs to the family that has a 110 sequence similarity (FAM110) and is located in the centrosome and mitotic spindle. FAM110B has been linked to tumor cell growth in earlier research. Uncertainty exists regarding FAM110B's function within the tumor microenvironment is unclear as well as pan-cancer. Methods: In order to assess the variation in FAM110B expression within normal and pan-cancer tissues, we combined the TCGA and GTEx databases. The cBioPortal database and the GSCALite platform were used to examine the variation in genome and methylation alteration of FAM110B. Cox regression, Kaplan-Meier, and SangerBox were employed to examine the clinical features and prognosis of FAM110B and pan-cancer. The purpose of the correlational research was to investigate the associations within immunerelated genes, tumor mutation burden, microsatellite instability, immune-related genes, and immunological checkpoints and FAM110B expression. ESTIMATE, EPIC, QUANTISEQ, and MCPCOUNTER methods were used to calculate the interaction among FAM110B expression as well as the tumor immune microenvironment. The immunoinfiltration and function of FAM110B were analyzed by single-cell databases (TISCH and CancerSEA). Finally, we evaluated the sensitivity of FAM110B to small-molecule medications through GDSC and CTRP databases. Results: The transcription and protein expression of FAM110B varies significantly throughout cancer types, and this has predictive value for the prognosis of some tumors; including brain lower grade glioma (LGG), stomach adenocarcinoma (STAD), pancreatic adenocarcinoma (PAAD), etc. In the tumor microenvironment, the expression level of FAM110B was associated with immune cell infiltration, immune checkpoint immune regulatory genes, tumor mutational burden, and microsatellite fragility to a certain extent. Conclusion: This work investigates the possibility of utility of FAM110B as a marker to forecast pan-cancer immunotherapy response, providing a theoretical basis for cancer therapy.

Hallmarks of sex bias in immuno-oncology: mechanisms and therapeutic implications

Sex differences are present across multiple non-reproductive organ cancers, with male individuals generally experiencing higher incidence of cancer with poorer outcomes. Although some mechanisms underlying these differences are emerging, the immunological basis is not well understood. Observations from clinical trials also suggest a sex bias in conventional immunotherapies with male individuals experiencing a more favourable response and female individuals experiencing more severe adverse events to immune checkpoint blockade. In this Perspective article, we summarize the major biological hallmarks underlying sex bias in immuno-oncology. We focus on signalling from sex hormones and chromosome-encoded gene products, along with sex hormone-independent and chromosome-independent epigenetic mechanisms in tumour and immune cells such as myeloid cells and T cells. Finally, we highlight opportunities for future studies on sex differences that integrate sex hormones and chromosomes and other emerging cancer hallmarks such as ageing and the microbiome to provide a more comprehensive view of how sex differences underlie the response in cancer that can be leveraged for more effective immuno-oncology approaches.

The interplay between autophagy and cGAS-STING signaling and its implications for cancer

Autophagy is an intracellular process that targets various cargos for degradation, including members of the cGAS-STING signaling cascade. cGAS-STING senses cytosolic double-stranded DNA and triggers an innate immune response through type I interferons. Emerging evidence suggests that autophagy plays a crucial role in regulating and fine-tuning cGAS-STING signaling. Reciprocally, cGAS-STING pathway members can actively induce canonical as well as various non-canonical forms of autophagy, establishing a regulatory network of feedback mechanisms that alter both the cGAS-STING and the autophagic pathway. The crosstalk between autophagy and the cGAS-STING pathway impacts a wide variety of cellular processes such as protection against pathogenic infections as well as signaling in neurodegenerative disease, autoinflammatory disease and cancer. Here we provide a comprehensive overview of the mechanisms involved in autophagy and cGAS-STING signaling, with a specific focus on the interactions between the two pathways and their importance for cancer.