Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Tissue proteomics repositories for data reanalysis

We are approaching the third decade since the establishment of the very first proteomics repositories back in the mid-'00s. New experimental approaches and technologies continuously enrich the field while producing vast amounts of mass spectrometry data. Together with initiatives to establish standard terminology and file formats, proteomics is rapidly transforming into a mature component of systems biology. Here we describe the ProteomeXchange consortium repositories. We specifically search, collect and evaluate public human tissue datasets (categorized as "complete" by the repository) submitted in 2015-2022, to both map the existing information and assess the data set reusability. Human tissue data are variably represented in the repositories reviewed, ranging between 10% and 25% of the total data submitted, with cancers being the most represented, followed by neuronal and cardiovascular diseases. About half of the retrieved data sets were found to lack annotations or metadata necessary to directly replicate the analysis. This poses a rough challenge to data reusability and highlights the need to increase awareness of the mage-tab file format for metadata in the community. Overall, proteomics repositories have evolved greatly over the past 7 years, as they have grown in size and become equipped with various powerful applications and tools that enable data searching and analytical tasks. However, to make the most of this potential, priority must be given to finding ways to secure detailed metadata for each submission, which is likely the next major milestone for proteomics repositories.

Plasmodium berghei TatD-like DNase hijacks host innate immunity by inhibiting the TLR9-NF-κB pathway

Neutrophils and macrophages confine pathogens by entrapping them in extracellular traps (ETs) through activating TLR9 function. However, plasmodial parasites secreted TatD-like DNases (TatD) to counteract ETs-mediated immune clearance. We found that TLR9 mutant mice increased susceptibility to rodent malaria, suggesting TLR9 is a key protein for host defense. We found that the proportion of neutrophils and macrophages in response to plasmodial parasite infection in the TLR9 mutant mice was significantly reduced compared to that of the WT mice. Importantly, PbTatD can directly bind to the surface TLR9 (sTLR9) on macrophages, which blocking the phosphorylation of mitogen-activated protein kinase and nuclear factor-κB, negatively regulated the signaling of ETs formation by both macrophages and neutrophils. Such, P. berghei TatD is a parasite virulence factor that can inhibit the proliferation of macrophages and neutrophils through directly binding to TLR9 receptors on the cell surface, thereby blocking the activation of the downstream MyD88-NF-kB pathways.

Proteomics analysis reveals the differential protein expression of female and male adult Toxocara canis using Orbitrap Astral analyzer

BACKGROUND

Toxocara canis, the most prevalent helminth in dogs and other canines, is one of the socioeconomically important zoonotic parasites, particularly affecting pediatric and adolescent populations in impoverished communities. However, limited information is available regarding the proteomes of female and male adult T. canis. To address this knowledge gap, we performed a comprehensive proteomic analysis to identify the proteins with differential abundance (PDAs) and gender-specifically expressed proteins between the two sexes adult T. canis.

METHODS

The comparative proteomic analysis was carried out by the Orbitrap mass spectrometry (MS) with asymmetric track lossless (Astral) analyzer. The difference analysis was conducted using t-test and the proteins verification was achieved through parallel reaction monitoring (PRM). The potential biological functions of identified adult T. canis proteins and PDAs were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The domain, transcription factor and subcellular localization of the identified proteins and PDAs were analyzed by InterPro, AnimalTFDB 4.0 and Cell-mPLOC 2.0 databases, respectively.

RESULTS

A total of 8565 somatic proteins of adult T. canis were identified. Compared to male adult, 682 up-regulated PDAs and 844 down-regulated PDAs were identified in female adult with P-values < 0.05 and |log2FC| > 1, including 139 proteins exclusively expressed in female and 272 proteins exclusively expressed in male. The GO annotation analysis using all PDAs revealed that the main biological processes, cellular components and molecular functions corresponded to aminoglycan metabolic process, extracellular region and protein tyrosine phosphatase activity, respectively. The KEGG analysis using all PDAs showed that the pathways were mainly associated with adipocytokine signaling pathway, proximal tubule bicarbonate reclamation and PPAR signaling pathway.

CONCLUSIONS

This study reveals the differential protein expression between female and male adult T. canis, providing valuable resource for developing the novel intervention strategies against T. canis infection in humans and animals, especially from the perspective of sexual development and reproduction.

Proapoptotic activity of JNK-sensitive BH3-only proteins underpins ovarian cancer response to replication checkpoint inhibitors

Recent studies indicate that replication checkpoint modulators (RCMs) such as inhibitors of CHK1, ATR, and WEE1 have promising monotherapy activity in solid tumors, including platinum-resistant high grade serous ovarian cancer (HGSOC). However, clinical response rates are generally below 30%. While RCM-induced DNA damage has been extensively examined in preclinical and clinical studies, the link between replication checkpoint interruption and tumor shrinkage remains incompletely understood. Here we utilized HGSOC cell lines and patient-derived xenografts (PDXs) to study events leading from RCM treatment to ovarian cancer cell death. These studies show that RCMs increase CDC25A levels and CDK2 signaling in vitro, leading to dysregulated cell cycle progression and increased replication stress in HGSOC cell lines independent of homologous recombination status. These events lead to sequential activation of JNK and multiple BH3-only proteins, including BCL2L11/BIM, BBC3/PUMA and the BMF, all of which are required to fully initiate RCM-induced apoptosis. Activation of the same signaling pathway occurs in HGSOC PDXs that are resistant to poly(ADP-ribose) polymerase inhibitors but respond to RCMs ex vivo with a decrease in cell number in 3-dimensional culture and in vivo with xenograft shrinkage or a significantly diminished growth rate. These findings identify key cell death-initiating events that link replication checkpoint inhibition to antitumor response in ovarian cancer.

Development and Validation of a Predictive Model for Resistance to Platinum-Based Chemotherapy in Patients with Ovarian Cancer through Proteomic Analysis

Platinum resistance in ovarian cancer poses a significant challenge, substantially impacting patient outcomes. Developing an accurate predictive model is crucial for improving clinical decision-making and guiding treatment strategies. Proteomic data from 217 high-grade serous ovarian cancer (HGSOC) biospecimens obtained from JHU, PNNL, and PTRC were used to construct a prediction model for identifying individuals who are resistant to platinum-based chemotherapy. A total of 6437 common proteins were detected across all data sets, with 26 proteins overlapping between the development cohorts JHU and PNNL. Using LASSO and logistic regression analysis, a six-protein model (P31323_PRKAR2B, Q13309_SKP2, Q14997_PSME4, Q6ZRP7_QSOX2, Q7LGA3_HS2ST1, and Q7Z2Z2_EFL1) was developed, which accurately predicted platinum resistance, with an AUC of 0.964 (95% CI, 0.929-0.999). Internal validation by resampling resulted in a C-index of 0.972 (95% CI 0.894-0.988). External validation performed on the PTRC cohort achieved an AUC of 0.855 (95% CI 0.748-0.963). Calibration curves showed good consistency, and DCA indicated superior clinical utility. The model also performed well in predicting PFS and OS at various time points. Based on these proteins, our predictive model can precisely predict platinum response and survival outcomes in HGSOC patients, which can assist clinicians in promptly identifying potentially platinum-resistant individuals.

Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients.

Autophagy unrelated transcriptional mechanisms of hydroxychloroquine resistance revealed by integrated multi-omics of evolved cancer cells

Hydroxychloroquine (HCQ) and chloroquine are repurposed drugs known to disrupt autophagy, a molecular recycling pathway essential for tumor cell survival, chemotherapeutic resistance, and stemness. We pursued a multi-omic strategy in OVCAR3 ovarian cancer and CCL218 colorectal cancer cells. Two genome-scale screens were performed. In the forward genetic screen, cell populations were passaged for 15 drug pulse-chases with HCQ or vehicle control. Evolved cells were collected and processed for bulk RNA-seq, exome-seq, and single-cell RNA-seq (scRNA-seq). In the reverse genetic screen, a pooled CRISPR-Cas9 library was used in cells over three pulse-chases of HCQ or vehicle control treatments. HCQ evolved cells displayed remarkably few mutational differences, but substantial transcriptional differences. Transcriptomes revealed multiple pathways associated with resistance to HCQ, including upregulation of glycolysis, exocytosis, and chromosome condensation/segregation, or downregulation of translation and apoptosis. The Cas9 screen identified only one autophagy gene. Chromosome condensation and segregation were confirmed to be disrupted by HCQ in live cells and organelle-free in vitro extracts. Transcriptional plasticity was the primary mechanism by which cells evolved resistance to HCQ. Neither autophagy nor the lysosome were substantive hits. Our analysis may serve as a model for how to better position repurposed drugs in oncology.

A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer

Background

Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients.

Methods

Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene modules along with their corresponding hub genes. A grand total of 113 predictive models were formed utilizing ten distinct machine learning algorithms along with the combination of the cancer genome atlas (TCGA)-OV dataset and the GSE140082 dataset. The most dependable clinical prognostic model was created utilizing the leave one out cross validation (LOOCV) framework. The validation process for the models was achieved by conducting survival curve analysis and receiver operating characteristic (ROC) analysis. The relationship between risk scores and immune cells was explored through the utilization of the Cibersort algorithm. Additionally, an analysis of drug sensitivity was carried out to anticipate chemotherapy responses across various risk groups. The genes implicated in the model were authenticated utilizing qRT-PCR, cell viability experiments, and EdU assay.

Results

This study developed a clinical prognostic model that includes ten risk genes. The results obtained from the training set of the study indicate that patients classified in the low-risk group experience a significant survival advantage compared to those in the high-risk group. The ROC analysis demonstrates that the model holds significant clinical utility. These results were verified using an independent dataset, strengthening the model's precision and dependability. The risk assessment provided by the model also serves as an independent prognostic factor for OV patients. The study also unveiled a noteworthy relationship between the risk scores calculated by the model and various immune cells, suggesting that the model may potentially serve as a valuable tool in forecasting responses to both immune therapy and chemotherapy in ovarian cancer patients. Notably, experimental evidence suggests that PFN1, one of the genes included in the model, is upregulated in human OV cell lines and has the capacity to promote cancer progression in in vitro models.

Conclusion

We have created an accurate and dependable clinical prognostic model for OV capable of predicting clinical outcomes and categorizing patients. This model effectively forecasts responses to both immune therapy and chemotherapy. By regulating the immune microenvironment and targeting the key gene PFN1, it may improve the prognosis for high-risk patients.

Mass Spectrometry-Based Proteomics for Classification and Treatment Optimisation of Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) presents a significant medical challenge due to its highly invasive nature, high rate of metastasis, and lack of drug-targetable receptors, which together lead to poor prognosis and limited treatment options. The traditional treatment guidelines for early TNBC are based on a multimodal approach integrating chemotherapy, surgery, and radiation and are associated with low overall survival and high relapse rates. Therefore, the approach to treating early TNBC has shifted towards neoadjuvant treatment (NAC), given to the patient before surgery and which aims to reduce tumour size, reduce the risk of recurrence, and improve the pathological complete response (pCR) rate. However, recent studies have shown that NAC is associated with only 30% of patients achieving pCR. Thus, novel predictive biomarkers are essential if treatment decisions are to be optimised and chemotherapy toxicities minimised. Given the heterogeneity of TNBC, mass spectrometry-based proteomics technologies offer valuable tools for the discovery of targetable biomarkers for prognosis and prediction of toxicity. These biomarkers can serve as critical targets for therapeutic intervention. This review aims to provide a comprehensive overview of TNBC diagnosis and treatment, highlighting the need for a new approach. Specifically, it highlights how mass spectrometry-based can address key unmet clinical needs by identifying novel protein biomarkers to distinguish and early prognostication between TNBC patient groups who are being treated with NAC. By integrating proteomic insights, we anticipate enhanced treatment personalisation, improved clinical outcomes, and ultimately, increased survival rates for TNBC patients.

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models

BACKGROUND

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome.

METHODS

Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm.

RESULTS

The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes.

CONCLUSION

Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Investigation of copper-induced intestinal damage and proteome alterations in Takifugu rubripes: Potential health risks and environmental toxicology detection

Copper is one of the predominant water pollutants. Excessive exposure to copper can cause harm to animal health, affecting the central nervous system and causing blood abnormalities. Cuproptosis is a novel form of cell death that differs from previous programmed cell death methods. However, the impact of copper on the intestines remains unclear. Therefore, we investigated the effects of different concentrations of copper exposure on the intestinal proteome of Takifugu rubripes (T. rubripes). Relevant biomarkers were used to detect cuproptosis. We revealed the crosstalk relationship between cuproptosis and self-rescue at different concentrations, and discussed the feasibility of using potential cuproptosis indicators as anti-infection factors. We observed intestinal damage in the three copper exposure groups, especially in T. rubripes treated with 100 and 500 μg/L copper, with shedding and breakage of intestinal villus and fuzzy and loose structure of intestinal mucosa. The presence of copper stress not only causes cuproptosis but also oxidative damage caused by reactive oxygen species (ROS). The results of quantitative proteomics by TMT showed that compared to the 50 and 100 μg/L copper exposure groups, the expression of glutaminase, pyruvate kinase, and skin mucus lectin in the 500 μg/L group was significantly increased. The positive mediators COX5A and CTNNB1, as well as the negative mediators CD4 and FDXR, were found to be differentially expressed. Using the protein expression trends of cuproptosis indicator factors FDX1 and DLAT to indicate the concentration of copper ions in the environment. In addition, we found a new effect of promoting ferroptosis: providing additional copper ions can activate the phenomenon of ferroptosis. Our results expand our understanding of the potential health risks of copper in T. rubripes. At the same time, it is of great significance for the process of copper poisoning and the development of new environmental toxicology detection reagents.

CMTM4 inhibits gastric tumorigenesis and metastasis

Background

CKLF-like MARVEL transmembrane domain-containing 4 (CMTM4) is involved in immune regulation and tumor progression; however, its role in gastric cancer (GC) remains unclear. This study explored the role and mechanism of CMTM4 in GC.

Methods

Immunohistochemistry was used to analyze CMTM4 expression in human gastric biopsied cells from patients with GC (N=23) or chronic superficial gastritis (N=23). To investigate the function of CMTM4 in GC cells, the gene CMTM4 was knocked down and overexpressed in human gastric adenocarcinoma cell line AGS. The gene CMTM4 was overexpressed in AGS cells and human gastric cell line SGC7901. Cell Counting Kit 8 (CCK-8) and cell clonogenic assays were used to analyze the proliferation of the GC cells. Flow cytometry was used to analyze the effects of CMTM4 on apoptosis and the cell cycle. Wound healing and transwell assays were used to analyze the migration and invasion of the gastric cells, respectively. The mechanism of CMTM4 in GC cells was explored using the tandem mass tags (TMTs) proteome and verified by western blot analysis.

Results

CMTM4 expression was more downregulated in the human GC tissues than the gastritis tissues. CMTM4 overexpression significantly inhibited the proliferation, migration, and invasion of the GC cells, whereas CMTM4 knockdown enhanced gastric cell proliferation (P>0.05), migration (P>0.05), and invasion (P>0.05). Flow cytometry showed that CMTM4 promoted apoptosis and resulted in G1/S arrest in the GC cells. In addition, the proteome and western blot results showed that STAT1 was significantly upregulated, and the STAT1 signaling pathways were enriched in the GC cells overexpressing CMTM4.

Conclusions

Our results suggest that CMTM4 plays a tumor-suppressive role in GC and may affect the growth, migration, and invasion of GC cells through the STAT1 signaling pathway. CMTM4 might have potential value as a prognosis marker and potential therapeutic target for GC therapy.

Combination of transcriptomic and proteomic approaches helps unravel the mechanisms of luteolin in inducing liver cancer cell death via targeting AKT1 and SRC

Introduction

Luteolin, a natural compound commonly used in traditional Chinese medicine, shows clinical potential as an anti-liver cancer agent. The mechanisms underlying the anti-liver cancer effect of luteolin are limited versus those reported for other cancers. Accordingly, this study was conducted to bridge the existing knowledge gap.

Methods

Transcriptomic and proteomic analyses of the response of the hepatocellular carcinoma cell line HuH-7 to luteolin were conducted, and a possible pathway was elucidated using confocal laser scanning microscopy (CLSM), flow cytometry, western blotting, qRT-PCR and bio-layer interferometry assay to systematically explore the possible mechanisms underlying the inhibition of the proliferation of liver cancer cells by luteolin.

Results and Discussion

Results showed that luteolin significantly inhibited HuH-7 cell proliferation. Transcriptomic and proteomic analyses collectively revealed that luteolin could promote cell cycle arrest and apoptosis in HuH-7 cells through transcription factors p53, nuclear factor kappa B (NF-κB), FOXO, ATF2, and TCF/LEF via AKT1, as well as the KEAP-NRF and SRC-STAT3 pathways. Furthermore, AKT1 and SRC were identified as the 2 targets of luteolin. Nuclear translocation of transcription factors p53 and NF-κB were affected by luteolin administration. Additionally, AKT1 activity affected normal metabolism in HuH-7 cells and resulted in the accumulation of reactive oxygen species, which activated MOMP and further promoted apoptosis. Our results systematically elucidate the mechanism of luteolin in inhibiting the proliferation of liver cancer cells, mainly through cell cycle arrest and apoptosis via targeting AKT1 and SRC.

Antitumor Effects and the Potential Mechanism of 10-HDA against SU-DHL-2 Cells

10-hydroxy-2-decenoic acid (10-HDA), which is a unique bioactive fatty acid of royal jelly synthesized by nurse bees for larvae and adult queen bees, is recognized for its dual utility in medicinal and nutritional applications. Previous research has indicated that 10-HDA exerts antitumor effects on numerous tumor cell lines, including colon cancer cells, A549 human lung cancer cells, and human hepatoma cells. The present study extends this inquiry to lymphoma, specifically evaluating the impact of 10-HDA on the SU-DHL-2 cell line. Our findings revealed dose-dependent suppression of SU-DHL-2 cell survival, with an IC50 of 496.8 μg/mL at a density of 3 × 106 cells/well after 24 h. For normal liver LO2 cells and human fibroblasts (HSFs), the IC50 values were approximately 1000 μg/mL and over 1000 μg/mL, respectively. The results of label-free proteomics revealed 147 upregulated and 347 downregulated differentially expressed proteins that were significantly enriched in the complement and coagulation cascades pathway (adjusted p-value = 0.012), including the differentially expressed proteins prothrombin, plasminogen, plasminogen, carboxypeptidase B2, fibrinogen beta chain, fibrinogen gamma chain, and coagulation factor V. The top three hub proteins, ribosomal protein L5, tumor protein p53, and ribosomal protein L24, were identified via protein-protein interaction (PPI) analysis. This result showed that the complement and coagulation cascade pathways might play a key role in the antitumor process of 10-HDA, suggesting a potential therapeutic avenue for lymphoma treatment. However, the specificity of the effect of 10-HDA on SU-DHL-2 cells warrants further investigation.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

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

Spatial multiomics reveals a subpopulation of fibroblasts associated with cancer stemness in human hepatocellular carcinoma

BACKGROUND

Cancer-associated fibroblasts (CAFs) are the prominent cell type in the tumor microenvironment (TME), and CAF subsets have been identified in various tumors. However, how CAFs spatially coordinate other cell populations within the liver TME to promote cancer progression remains unclear.

METHODS

We combined multi-region proteomics (6 patients, 24 samples), 10X Genomics Visium spatial transcriptomics (11 patients, 25 samples), and multiplexed imaging (92 patients, 264 samples) technologies to decipher the expression heterogeneity, functional diversity, spatial distribution, colocalization, and interaction of fibroblasts. The newly identified CAF subpopulation was validated by cells isolated from 5 liver cancer patients and in vitro functional assays.

RESULTS

We identified a liver CAF subpopulation, marked by the expression of COL1A2, COL4A1, COL4A2, CTGF, and FSTL1, and named F5-CAF. F5-CAF is preferentially located within and around tumor nests and colocalizes with cancer cells with higher stemness in hepatocellular carcinoma (HCC). Multiplexed staining of 92 patients and the bulk transcriptome of 371 patients demonstrated that the abundance of F5-CAFs in HCC was associated with a worse prognosis. Further in vitro experiments showed that F5-CAFs isolated from liver cancer patients can promote the proliferation and stemness of HCC cells.

CONCLUSIONS

We identified a CAF subpopulation F5-CAF in liver cancer, which is associated with cancer stemness and unfavorable prognosis. Our results provide potential mechanisms by which the CAF subset in the TME promotes the development of liver cancer by supporting the survival of cancer stem cells.

Integrated proteomics, transcriptomics, and metabolomics offer novel insights into Cd resistance and accumulation in Poa pratensis

Kentucky bluegrass (Poa pratensis L., KB) demonstrates superior performance in both cadmium (Cd) accumulation and tolerance; however, the regulatory mechanisms and detoxification pathways in this species remain unclear. Therefore, phenotype, root ultrastructure, cell wall components, proteomics, transcriptomics, and metabolomics were analyzed under the hydroponic system to investigate the Cd tolerance and accumulation mechanisms in the Cd-tolerant KB variety 'Midnight (M)' and the Cd-sensitive variety 'Rugby II (R)' under Cd stress. The M variety exhibited higher levels of hydroxyl and carboxyl groups as revealed by Fourier transform infrared spectroscopy spectral analysis. Additionally, a reduced abundance of polysaccharide degradation proteins was observed in the M variety. The higher abundance of glutathione S-transferase and content of L-cysteine-glutathione disulfide and oxidized glutathione in the M variety may contribute to better performance of the M variety under Cd stress. Additionally, the R variety had an enhanced content of carboxylic acids and derivatives, increasing the Cd translocation capacity. Collectively, the down-regulation of cell wall polysaccharide degradation genes coupled with the up-regulation of glutathione metabolism genes enhances the tolerance to Cd stress in KB. Additionally, lignification of the endodermis and the increase in carboxylic acids and derivatives play crucial roles in the redistribution of Cd in KB.

Proteomic landscape of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

Proteogenomic insights into the biology and treatment of pan-melanoma

Melanoma is one of the most prevalent skin cancers, with high metastatic rates and poor prognosis. Understanding its molecular pathogenesis is crucial for improving its diagnosis and treatment. Integrated analysis of multi-omics data from 207 treatment-naïve melanomas (primary-cutaneous-melanomas (CM, n = 28), primary-acral-melanomas (AM, n = 81), primary-mucosal-melanomas (MM, n = 28), metastatic-melanomas (n = 27), and nevi (n = 43)) provides insights into melanoma biology. Multivariate analysis reveals that PRKDC amplification is a prognostic molecule for melanomas. Further proteogenomic analysis combined with functional experiments reveals that the cis-effect of PRKDC amplification may lead to tumor proliferation through the activation of DNA repair and folate metabolism pathways. Proteome-based stratification of primary melanomas defines three prognosis-related subtypes, namely, the ECM subtype, angiogenesis subtype (with a high metastasis rate), and cell proliferation subtype, which provides an essential framework for the utilization of specific targeted therapies for particular melanoma subtypes. The immune classification identifies three immune subtypes. Further analysis combined with an independent anti-PD-1 treatment cohort reveals that upregulation of the MAPK7-NFKB signaling pathway may facilitate T-cell recruitment and increase the sensitivity of patients to immunotherapy. In contrast, PRKDC may reduce the sensitivity of melanoma patients to immunotherapy by promoting DNA repair in melanoma cells. These results emphasize the clinical value of multi-omics data and have the potential to improve the understanding of melanoma treatment.

Limitations of homologous recombination status testing and poly (ADP-ribose) polymerase inhibitor treatment in the current management of ovarian cancer

Homologous recombination (HR) is a highly conserved DNA repair system, in which aberrations can lead to the accumulation of DNA damage and genomic scars known as homologous recombination deficiency (HRD). The identification of mutations in key genes (i.e., BRCA1, and BRCA2 (BRCA)) and the quantification of large-scale structural variants (e.g., loss of heterozygosity) are indicators of the HRD phenotype. HRD is a stable biomarker and remains unchanged during recurrence, but fails to reveal the molecular profile of tumor progression. Moreover, interpretation of the current HRD score lacks comprehensiveness, especially for the HR-proficient group. Poly (ADP-ribose) polymerase (PARP) enzymes play an important role in the repair of DNA single-strand breaks, the blockage of which using PARP inhibitors (PARPi) can generate synthetic lethality in cancer cells with HRD. Although numerous studies have demonstrated that the benefit of PARPi is substantial in ovarian cancer (OC) patients, the efficacy is limited by the development of resistance, and seems to be irrespective of HR and/or BRCA mutation status. Moreover, in addition to improving progression-free survival, long-term benefit as overall survival brought by PARPi for advanced, recurrent and refractory OC patients remains unclear. Therefore, further investigations are needed to uncover the role of HR genes beyond BRCA and their interactions with other oncogenic pathways, to determine the value of HRD in the recurrent setting, and to identify alternative strategies for the precise management of advanced, refractory OC patients.

Proteomic and metabolomic revealed the effect of shading treatment on cigar tobacco

Shading or low light conditions are essential cultivation techniques for cigar wrapper tobacco leaves production, yet their impact on protein and metabolic regulatory networks is not well understood. In this study, we integrated proteomic and metabolomic analyses to uncover the potential molecular mechanisms affecting cigar tobacco leaves under shading treatment. Our findings include: (1) Identification of 780 significantly differentially expressed proteins (DEPs) in the cigar wrapper tobacco leaves, comprising 560 up-regulated and 220 down-regulated proteins, predominantly located in the chloroplast, cytoplasm, and nucleus, collectively accounting for 50.01%. (2) Discovery of 254 significantly differentially expressed metabolites (DEMs), including 148 up-regulated and 106 down-regulated metabolites. (3) KEGG pathway enrichment analysis revealed that the mevalonate (MVA) pathway within 'Terpenoid backbone biosynthesis' was inhibited, leading to a down-regulation of 'Sesquiterpenoid and triterpenoid biosynthesis'. Conversely, the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway was enhanced, resulting in an up-regulation of 'Monoterpenoid biosynthesis', 'Diterpenoid biosynthesis', and 'Carotenoid biosynthesis', thereby promoting the synthesis of terpenoids such as carotenoids and chlorophylls. Simultaneously, the Calvin cycle in 'Carbon fixation in photosynthetic organisms' was amplified, increasing photosynthetic efficiency. These results suggest that under low light conditions, cigar tobacco optimizes photosynthetic efficiency by reconfiguring its energy metabolism and terpenoid biosynthesis. This study contributes valuable insights into protein and metabolic analyses, paving the way for future functional studies on plant responses to low light.

Focal adhesion kinase signaling - tumor vulnerabilities and clinical opportunities

Focal adhesion kinase (FAK; encoded by PTK2) was discovered over 30 years ago as a cytoplasmic protein tyrosine kinase that is localized to cell adhesion sites, where it is activated by integrin receptor binding to extracellular matrix proteins. FAK is ubiquitously expressed and functions as a signaling scaffold for a variety of proteins at adhesions and in the cell cytoplasm, and with transcription factors in the nucleus. FAK expression and intrinsic activity are essential for mouse development, with molecular connections to cell motility, cell survival and gene expression. Notably, elevated FAK tyrosine phosphorylation is common in tumors, including pancreatic and ovarian cancers, where it is associated with decreased survival. Small molecule and orally available FAK inhibitors show on-target inhibition in tumor and stromal cells with effects on chemotherapy resistance, stromal fibrosis and tumor microenvironment immune function. Herein, we discuss recent insights regarding mechanisms of FAK activation and signaling, its roles as a cytoplasmic and nuclear scaffold, and the tumor-intrinsic and -extrinsic effects of FAK inhibitors. We also discuss results from ongoing and advanced clinical trials targeting FAK in low- and high-grade serous ovarian cancers, where FAK acts as a master regulator of drug resistance. Although FAK is not known to be mutationally activated, preventing FAK activity has revealed multiple tumor vulnerabilities that support expanding clinical combinatorial targeting possibilities.

The mouse multi-organ proteome from infancy to adulthood

The early-life organ development and maturation shape the fundamental blueprint for later-life phenotype. However, a multi-organ proteome atlas from infancy to adulthood is currently not available. Herein, we present a comprehensive proteomic analysis of ten mouse organs (brain, heart, lung, liver, kidney, spleen, stomach, intestine, muscle and skin) at three crucial developmental stages (1-, 4- and 8-weeks after birth) acquired using data-independent acquisition mass spectrometry. We detect and quantify 11,533 protein groups across the ten organs and obtain 115 age-related differentially expressed protein groups that are co-expressed in all organs from infancy to adulthood. We find that spliceosome proteins prevalently play crucial regulatory roles in the early-life development of multiple organs, and detect organ-specific expression patterns and sexual dimorphism. This multi-organ proteome atlas provides a fundamental resource for understanding the molecular mechanisms underlying early-life organ development and maturation.

Directional integration and pathway enrichment analysis for multi-omics data

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.

Multi-omic profiling of intraductal papillary neoplasms of the pancreas reveals distinct expression patterns and potential markers of progression

In order to advance our understanding of precancers in the pancreas, 69 pancreatic intraductal papillary neoplasms (IPNs), including 64 intraductal papillary mucinous neoplasms (IPMNs) and 5 intraductal oncocytic papillary neoplasms (IOPNs), 32 pancreatic cyst fluid samples, 104 invasive pancreatic ductal adenocarcinomas (PDACs), 43 normal adjacent tissues (NATs), and 76 macro-dissected normal pancreatic ducts (NDs) were analyzed by mass spectrometry. A total of 10,246 proteins and 22,284 glycopeptides were identified in all tissue samples, and 756 proteins with more than 1.5-fold increase in abundance in IPMNs relative to NDs were identified, 45% of which were also identified in cyst fluids. The over-expression of selected proteins was validated by immunolabeling. Proteins and glycoproteins overexpressed in IPMNs included those involved in glycan biosynthesis and the immune system. In addition, multiomics clustering identified two subtypes of IPMNs. This study provides a foundation for understanding tumor progression and targets for earlier detection and therapies.

Significance

This multilevel characterization of intraductal papillary neoplasms of the pancreas provides a foundation for understanding the changes in protein and glycoprotein expression during the progression from normal duct to intraductal papillary neoplasm, and to invasive pancreatic carcinoma, providing a foundation for informed approaches to earlier detection and treatment.

ATP6V0A1-dependent cholesterol absorption in colorectal cancer cells triggers immunosuppressive signaling to inactivate memory CD8+ T cells

Obesity shapes anti-tumor immunity through lipid metabolism; however, the mechanisms underlying how colorectal cancer (CRC) cells utilize lipids to suppress anti-tumor immunity remain unclear. Here, we show that tumor cell-intrinsic ATP6V0A1 drives exogenous cholesterol-induced immunosuppression in CRC. ATP6V0A1 facilitates cholesterol absorption in CRC cells through RAB guanine nucleotide exchange factor 1 (RABGEF1)-dependent endosome maturation, leading to cholesterol accumulation within the endoplasmic reticulum and elevated production of 24-hydroxycholesterol (24-OHC). ATP6V0A1-induced 24-OHC upregulates TGF-β1 by activating the liver X receptor (LXR) signaling. Subsequently, the release of TGF-β1 into the tumor microenvironment by CRC cells activates the SMAD3 pathway in memory CD8+ T cells, ultimately suppressing their anti-tumor activities. Moreover, we identify daclatasvir, a clinically used anti-hepatitis C virus (HCV) drug, as an ATP6V0A1 inhibitor that can effectively enhance the memory CD8+ T cell activity and suppress tumor growth in CRC. These findings shed light on the potential for ATP6V0A1-targeted immunotherapy in CRC.

The relationship between demographic factors and known risk factors with breast cancer in women aged 30-69

Background

Breast cancer is one of the most important causes of cancer deaths in women. The present study was conducted to determine the relationship between demographic factors and known risk factors with breast cancer in women aged 30-69.

Method

This case-control study was conducted with two matched and unmatched control groups. Three hundred fifty women aged 30-69 with breast cancer, 350 age-matched women without cancer, and 350 not age-matched women were included in the study. Controls were selected from the records of women whose breast cancer screening results were normal. Study subjects were evaluated regarding the risk factors for breast cancer. The data collection tool was a checklist including the risk factors investigated in the integrated health system. The collected data were analyzed utilizing SPSS22 software at a significance level of less than 0.05.

Results

The average age in the case group was 46.63±11.77 years and 49.61±8.39 in the unmatched control group. The average age of marriage in the case group was 21.54±4.31, and the average age of women at first pregnancy in the case group was 24.06±3.39 years. In the case group, 163 people (46.57%) lived in the city, 221 people (63.14%) were over 40 years old, and 337 people (96.28%) were married. In multivariate analysis, the variable 'age of marriage' 0.821 (0.691-0.976) and 'age of first pregnancy' 1.213 (1.020-1.443) showed a significant relationship with breast cancer which were observed as predictors of breast cancer in comparison to the unmatched control group (P-value <0.05).

Conclusion

The age of the first pregnancy and the type of delivery were observed as predictors of breast cancer. Therefore, by performing breast cancer screening in women who are exposed to these risk factors, early diagnosis of the disease and increasing the speed of their treatment can be significantly helped.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

Characterization of an expanded set of assays for immunomodulatory proteins using targeted mass spectrometry

Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer.

Integration of Proteomic and Metabolomic Data Reveals the Lipid Metabolism Disorder in the Liver of Rats Exposed to Simulated Microgravity

Long-term exposure to microgravity is considered to cause liver lipid accumulation, thereby increasing the risk of non-alcoholic fatty liver disease (NAFLD) among astronauts. However, the reasons for this persistence of symptoms remain insufficiently investigated. In this study, we used tandem mass tag (TMT)-based quantitative proteomics techniques, as well as non-targeted metabolomics techniques based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), to comprehensively analyse the relative expression levels of proteins and the abundance of metabolites associated with lipid accumulation in rat liver tissues under simulated microgravity conditions. The differential analysis revealed 63 proteins and 150 metabolites between the simulated microgravity group and the control group. By integrating differentially expressed proteins and metabolites and performing pathway enrichment analysis, we revealed the dysregulation of major metabolic pathways under simulated microgravity conditions, including the biosynthesis of unsaturated fatty acids, linoleic acid metabolism, steroid hormone biosynthesis and butanoate metabolism, indicating disrupted liver metabolism in rats due to weightlessness. Finally, we examined differentially expressed proteins associated with lipid metabolism in the liver of rats exposed to stimulated microgravity. These findings contribute to identifying the key molecules affected by microgravity and could guide the design of rational nutritional or pharmacological countermeasures for astronauts.

Recent advances in understanding the immune microenvironment in ovarian cancer

The occurrence of ovarian cancer (OC) is a major factor in women's mortality rates. Despite progress in medical treatments, like new drugs targeting homologous recombination deficiency, survival rates for OC patients are still not ideal. The tumor microenvironment (TME) includes cancer cells, fibroblasts linked to cancer (CAFs), immune-inflammatory cells, and the substances these cells secrete, along with non-cellular components in the extracellular matrix (ECM). First, the TME mainly plays a role in inhibiting tumor growth and protecting normal cell survival. As tumors progress, the TME gradually becomes a place to promote tumor cell progression. Immune cells in the TME have attracted much attention as targets for immunotherapy. Immune checkpoint inhibitor (ICI) therapy has the potential to regulate the TME, suppressing factors that facilitate tumor advancement, reactivating immune cells, managing tumor growth, and extending the survival of patients with advanced cancer. This review presents an outline of current studies on the distinct cellular elements within the OC TME, detailing their main functions and possible signaling pathways. Additionally, we examine immunotherapy rechallenge in OC, with a specific emphasis on the biological reasons behind resistance to ICIs.

Homologous recombination proficient subtypes of high-grade serous ovarian cancer: treatment options for a poor prognosis group

Approximately 50% of tubo-ovarian high-grade serous carcinomas (HGSCs) have functional homologous recombination-mediated (HR) DNA repair, so-called HR-proficient tumors, which are often associated with primary platinum resistance (relapse within six months after completion of first-line therapy), minimal benefit from poly(ADP-ribose) polymerase (PARP) inhibitors, and shorter survival. HR-proficient tumors comprise multiple molecular subtypes including cases with CCNE1 amplification, AKT2 amplification or CDK12 alteration, and are often characterized as "cold" tumors with fewer infiltrating lymphocytes and decreased expression of PD-1/PD-L1. Several new treatment approaches aim to manipulate these negative prognostic features and render HR-proficient tumors more susceptible to treatment. Alterations in multiple different molecules and pathways in the DNA damage response are driving new drug development to target HR-proficient cancer cells, such as inhibitors of the CDK or P13K/AKT pathways, as well as ATR inhibitors. Treatment combinations with chemotherapy or PARP inhibitors and agents targeting DNA replication stress have shown promising preclinical and clinical results. New approaches in immunotherapy are also being explored, including vaccines or antibody drug conjugates. Many approaches are still in the early stages of development and further clinical trials will determine their clinical relevance. There is a need to include HR-proficient tumors in ovarian cancer trials and to analyze them in a more targeted manner to provide further evidence for their specific therapy, as this will be crucial in improving the overall prognosis of HGSC and ovarian cancer in general.

DDN3.0: determining significant rewiring of biological network structure with differential dependency networks

MOTIVATION

Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent.

RESULTS

We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions.

AVAILABILITY AND IMPLEMENTATION

The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

Histopathology and proteomics are synergistic for High-Grade Serous Ovarian Cancer platinum response prediction

Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E) pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained Whole Slide Images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. This method outperforms the Homologous Recombination Deficiency (HRD) score in predicting platinum response and overall patient survival. The study sets new performance benchmarks and explores the intersection of histology and proteomics, highlighting phenotypes related to treatment response pathways, including homologous recombination, DNA damage response, nucleotide synthesis, apoptosis, and ER stress. This integrative approach has the potential to improve personalized treatment and provide insights into the therapeutic vulnerabilities of HGSOC.

Dormant origin firing promotes head-on transcription-replication conflicts at transcription termination sites in response to BRCA2 deficiency

BRCA2 is a tumor suppressor protein responsible for safeguarding the cellular genome from replication stress and genotoxicity, but the specific mechanism(s) by which this is achieved to prevent early oncogenesis remains unclear. Here, we provide evidence that BRCA2 acts as a critical suppressor of head-on transcription-replication conflicts (HO-TRCs). Using Okazaki-fragment sequencing (Ok-seq) and computational analysis, we identified origins (dormant origins) that are activated near the transcription termination sites (TTS) of highly expressed, long genes in response to replication stress. Dormant origins are a source for HO-TRCs, and drug treatments that inhibit dormant origin firing led to a reduction in HO-TRCs, R-loop formation, and DNA damage. Using super-resolution microscopy, we showed that HO-TRC events track with elongating RNA polymerase II, but not with transcription initiation. Importantly, RNase H2 is recruited to sites of HO-TRCs in a BRCA2-dependent manner to help alleviate toxic R-loops associated with HO-TRCs. Collectively, our results provide a mechanistic basis for how BRCA2 shields against genomic instability by preventing HO-TRCs through both direct and indirect means occurring at predetermined genomic sites based on the pre-cancer transcriptome.

CEA (CEACAM5) expression is common in muscle-invasive urothelial carcinoma of the bladder but unrelated to the disease course

Objectives

Carcinoembryonic antigen (CEA) is a cell surface glycoprotein that represents a promising therapeutic target. Serum measurement of shedded CEA can be utilized for monitoring of cancer patients.

Material and Methods

To evaluate the potential clinical significance of CEA expression in urothelial bladder neoplasms, CEA was analysed by immunohistochemistry in more than 2500 urothelial bladder carcinomas in a tissue microarray format.

Results

CEA staining was largely absent in normal urothelial cells but was observed in 30.4% of urothelial bladder carcinomas including 406 (16.7%) with weak, 140 (5.8%) with moderate, and 192 (7.9%) with strong staining. CEA positivity occurred in 10.9% of 411 pTaG2 low-grade, 32.0% of 178 pTaG2 high-grade, and 43.0% of 93 pTaG3 tumours (p < 0.0001). In 1335 pT2-4 carcinomas, CEA positivity (34.1%) was lower than in pTaG3 tumours. Within pT2-4 carcinomas, CEA staining was unrelated to pT, pN, grade, L-status, V-status, overall survival, recurrence free survival, and cancer specific survival (p > 0.25).

Conclusion

CEA increases markedly with grade progression in pTa tumours, and expression occurs in a significant fraction of pT2-4 urothelial bladder carcinomas. The high rate of CEA positivity in pT2-4 carcinomas offers the opportunity of using CEA serum measurement for monitoring the clinical course of these cancers. Moreover, CEA positive urothelial carcinomas are candidates for a treatment by targeted anti-CEA drugs.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

IGSF8 is an innate immune checkpoint and cancer immunotherapy target

Antigen presentation defects in tumors are prevalent mechanisms of adaptive immune evasion and resistance to cancer immunotherapy, whereas how tumors evade innate immunity is less clear. Using CRISPR screens, we discovered that IGSF8 expressed on tumors suppresses NK cell function by interacting with human KIR3DL2 and mouse Klra9 receptors on NK cells. IGSF8 is normally expressed in neuronal tissues and is not required for cell survival in vitro or in vivo. It is overexpressed and associated with low antigen presentation, low immune infiltration, and worse clinical outcomes in many tumors. An antibody that blocks IGSF8-NK receptor interaction enhances NK cell killing of malignant cells in vitro and upregulates antigen presentation, NK cell-mediated cytotoxicity, and T cell signaling in vivo. In syngeneic tumor models, anti-IGSF8 alone, or in combination with anti-PD1, inhibits tumor growth. Our results indicate that IGSF8 is an innate immune checkpoint that could be exploited as a therapeutic target.

Proteomic features of soft tissue tumours in adolescents and young adults

BACKGROUND

Adolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes.

METHODS

To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8).

RESULTS

Biological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients.

CONCLUSIONS

Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

Paradigm shift required for translational research on the brain

Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

Multivariate analysis for data mining to characterize poultry house environment in winter

The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1-3, 4-6, and 7-9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All 3 clustering methods were found to be suitable for grouping data, with FCM outperforming the other 2. Using data clustering, the broiler house spaces were divided into 3, 2, and 2 subspaces (clusters) for wk 1 to 3, 4 to 6, and 7 to 9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.

Proteogenomic Characterization of Bladder Cancer Reveals Sensitivity to Apoptosis Induced by Tumor Necrosis Factor-related Apoptosis-inducing Ligand in FGFR3-mutated Tumors

BACKGROUND

Molecular understanding of muscle-invasive (MIBC) and non-muscle-invasive (NMIBC) bladder cancer is currently based primarily on transcriptomic and genomic analyses.

OBJECTIVE

To conduct proteogenomic analyses to gain insights into bladder cancer (BC) heterogeneity and identify underlying processes specific to tumor subgroups and therapeutic outcomes.

DESIGN, SETTING, AND PARTICIPANTS

Proteomic data were obtained for 40 MIBC and 23 NMIBC cases for which transcriptomic and genomic data were already available. Four BC-derived cell lines harboring FGFR3 alterations were tested with interventions.

INTERVENTION

Recombinant tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), second mitochondrial-derived activator of caspases mimetic (birinapant), pan-FGFR inhibitor (erdafitinib), and FGFR3 knockdown.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Proteomic groups from unsupervised analyses (uPGs) were characterized using clinicopathological, proteomic, genomic, transcriptomic, and pathway enrichment analyses. Additional enrichment analyses were performed for FGFR3-mutated tumors. Treatment effects on cell viability for FGFR3-altered cell lines were evaluated. Synergistic treatment effects were evaluated using the zero interaction potency model.

RESULTS AND LIMITATIONS

Five uPGs, covering both NMIBC and MIBC, were identified and bore coarse-grained similarity to transcriptomic subtypes underlying common features of these different entities; uPG-E was associated with the Ta pathway and enriched in FGFR3 mutations. Our analyses also highlighted enrichment of proteins involved in apoptosis in FGFR3-mutated tumors, not captured through transcriptomics. Genetic and pharmacological inhibition demonstrated that FGFR3 activation regulates TRAIL receptor expression and sensitizes cells to TRAIL-mediated apoptosis, further increased by combination with birinapant.

CONCLUSIONS

This proteogenomic study provides a comprehensive resource for investigating NMIBC and MIBC heterogeneity and highlights the potential of TRAIL-induced apoptosis as a treatment option for FGFR3-mutated bladder tumors, warranting a clinical investigation.

PATIENT SUMMARY

We integrated proteomics, genomics, and transcriptomics to refine molecular classification of bladder cancer, which, combined with clinical and pathological classification, should lead to more appropriate management of patients. Moreover, we identified new biological processes altered in FGFR3-mutated tumors and showed that inducing apoptosis represents a new potential therapeutic option.

Multi-omics characterization of esophageal squamous cell carcinoma identifies molecular subtypes and therapeutic targets

Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal cancer and is characterized by an unfavorable prognosis. To elucidate the distinct molecular alterations in ESCC and investigate therapeutic targets, we performed a comprehensive analysis of transcriptomics, proteomics, and phosphoproteomics data derived from 60 paired treatment-naive ESCC and adjacent nontumor tissue samples. Additionally, we conducted a correlation analysis to describe the regulatory relationship between transcriptomic and proteomic processes, revealing alterations in key metabolic pathways. Unsupervised clustering analysis of the proteomics data stratified patients with ESCC into 3 subtypes with different molecular characteristics and clinical outcomes. Notably, subtype III exhibited the worst prognosis and enrichment in proteins associated with malignant processes, including glycolysis and DNA repair pathways. Furthermore, translocase of inner mitochondrial membrane domain containing 1 (TIMMDC1) was validated as a potential prognostic molecule for ESCC. Moreover, integrated kinase-substrate network analysis using the phosphoproteome nominated candidate kinases as potential targets. In vitro and in vivo experiments further confirmed casein kinase II subunit α (CSNK2A1) as a potential kinase target for ESCC. These underlying data represent a valuable resource for researchers that may provide better insights into the biology and treatment of ESCC.

Metabolomics and proteomics insights into subacute ruminal acidosis etiology and inhibition of proliferation of yak rumen epithelial cells in vitro

BACKGROUND

Untargeted metabolomics and proteomics were employed to investigate the intracellular response of yak rumen epithelial cells (YRECs) to conditions mimicking subacute rumen acidosis (SARA) etiology, including exposure to short-chain fatty acids (SCFA), low pH5.5 (Acid), and lipopolysaccharide (LPS) exposure for 24 h.

RESULTS

These treatments significantly altered the cellular morphology of YRECs. Metabolomic analysis identified significant perturbations with SCFA, Acid and LPS treatment affecting 259, 245 and 196 metabolites (VIP > 1, P < 0.05, and fold change (FC) ≥ 1.5 or FC ≤ 0.667). Proteomic analysis revealed that treatment with SCFA, Acid, and LPS resulted in differential expression of 1251, 1396, and 242 proteins, respectively (FC ≥ 1.2 or ≤ 0.83, P < 0.05, FDR < 1%). Treatment with SCFA induced elevated levels of metabolites involved in purine metabolism, glutathione metabolism, and arginine biosynthesis, and dysregulated proteins associated with actin cytoskeleton organization and ribosome pathways. Furthermore, SCFA reduced the number, morphology, and functionality of mitochondria, leading to oxidative damage and inhibition of cell survival. Gene expression analysis revealed a decrease the genes expression of the cytoskeleton and cell cycle, while the genes expression associated with inflammation and autophagy increased (P < 0.05). Acid exposure altered metabolites related to purine metabolism, and affected proteins associated with complement and coagulation cascades and RNA degradation. Acid also leads to mitochondrial dysfunction, alterations in mitochondrial integrity, and reduced ATP generation. It also causes actin filaments to change from filamentous to punctate, affecting cellular cytoskeletal function, and increases inflammation-related molecules, indicating the promotion of inflammatory responses and cellular damage (P < 0.05). LPS treatment induced differential expression of proteins involved in the TNF signaling pathway and cytokine-cytokine receptor interaction, accompanied by alterations in metabolites associated with arachidonic acid metabolism and MAPK signaling (P < 0.05). The inflammatory response and activation of signaling pathways induced by LPS treatment were also confirmed through protein interaction network analysis. The integrated analysis reveals co-enrichment of proteins and metabolites in cellular signaling and metabolic pathways.

CONCLUSIONS

In summary, this study contributes to a comprehensive understanding of the detrimental effects of SARA-associated factors on YRECs, elucidating their molecular mechanisms and providing potential therapeutic targets for mitigating SARA.