Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

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.

Small cell lung cancer: emerging subtypes, signaling pathways, and therapeutic vulnerabilities

Small cell lung cancer (SCLC) is a recalcitrant cancer characterized by early metastasis, rapid tumor growth and poor prognosis. In recent decades, the epidemiology, initiation and mutation characteristics of SCLC, as well as abnormal signaling pathways contributing to its progression, have been widely studied. Despite extensive investigation, fewer drugs have been approved for SCLC. Recent advancements in multi-omics studies have revealed diverse classifications of SCLC that are featured by distinct characteristics and therapeutic vulnerabilities. With the accumulation of SCLC samples, different subtypes of SCLC and specific treatments for these subtypes were further explored. The identification of different molecular subtypes has opened up novel avenues for the treatment of SCLC; however, the inconsistent and uncertain classification of SCLC has hindered the translation from basic research to clinical applications. Therefore, a comprehensives review is essential to conclude these emerging subtypes and related drugs targeting specific therapeutic vulnerabilities within abnormal signaling pathways. In this current review, we summarized the epidemiology, risk factors, mutation characteristics of and classification, related molecular pathways and treatments for SCLC. We hope that this review will facilitate the translation of molecular subtyping of SCLC from theory to clinical application.

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

YTHDF3 phase separation regulates HSPA13-dependent clear cell renal cell carcinoma development and immune evasion

Insufficient understanding about the immune evasion mechanism leads to the inability in predicting current immunotherapy effects in clear cell renal cell carcinoma (ccRCC) and sensitizing ccRCC to immunotherapy. RNA binding proteins (RBPs) can promote tumor progression and immune evasion. However, research on RBPs, particularly m6A reader YTHDF3, in ccRCC development and immune evasion is limited. In this study, we found that YTHDF3 level was downregulated in ccRCC and was an independent prognostic biomarker for ccRCC. Decreased YTHDF3 expression was correlated with the malignancy, immune evasion, and poor response to anti-programmed death ligand 1 (PD-L1)/CTLA-4 in ccRCC. YTHDF3 overexpression restrained ccRCC cell malignancy, PD-L1 expression, CD8+ T cell infiltration and activities in vivo, indicating its inhibitory role in ccRCC development and immune evasion. Mechanistically, YTHDF3 WT was found to have phase separation characteristics and suppress ccRCC malignancy and immune evasion. Whereas YTHDF3 mutant, which disrupted phase separation, abolished its function. YTHDF3 enhanced the degradation of its target mRNA HSPA13 by phase separation and recruiting DDX6, resulting in the downregulation of the downstream immune checkpoint PD-L1. HSPA13 overexpression restored ccRCC malignancy and immune evasion suppressed by YTHDF3 overexpression. In all, our results identify a new model of YTHDF3 in regulating ccRCC progression and immune evasion through phase separation.

Dysfunction of CD8+ T cells around tumor cells leads to occult lymph node metastasis in NSCLC patients

Occult lymph node metastasis (OLNM) is one of the main causes of regional recurrence in inoperable N0 non-small cell lung cancer (NSCLC) patients following stereotactic ablation body radiotherapy (SABR) treatment. The integration of immunotherapy and SABR (I-SABR) has shown preliminary efficiency in mitigating this recurrence. Therefore, it is necessary to explore the functional dynamics of critical immune effectors, particularly CD8+ T cells in the development of OLNM. In this study, tissue microarrays (TMAs) and multiplex immunofluorescence (mIF) were used to identify CD8+ T cells and functional subsets (cytotoxic CD8+ T cells/predysfunctional CD8+ T cells (CD8+ Tpredys)/dysfunctional CD8+ T cells (CD8+ Tdys)/other CD8+ T cells) among the no lymph node metastasis, OLNM, and clinically evident lymph node metastasis (CLNM) groups. As the degree of lymph node metastasis escalated, the density of total CD8+ T cells and CD8+ Tdys cells, as well as their proximity to tumor cells, increased progressively and remarkably in the invasive margin (IM). In the tumor center (TC), both the density and proximity of CD8+ Tpredys cells to tumor cells notably decreased in the OLNM group compared with the group without metastasis. Furthermore, positive correlations were found between the dysfunction of CD8+ T cells and HIF-1α+CD8 and cancer microvessels (CMVs). In conclusion, the deterioration in CD8+ T cell function and interactive dynamics between CD8+ T cells and tumor cells play a vital role in the development of OLNM in NSCLC. Strategies aimed at improving hypoxia or targeting CMVs could potentially enhance the efficacy of I-SABR.

Molecular mechanism of ferroptosis and its application in the treatment of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urinary tract, the incidence of which is continuously increasing and affects human health worldwide. Despite advances in existing treatments, treatment outcomes still need to be improved due to higher rates of postoperative recurrence, chemotherapy resistance, etc.; thus, there is an urgent need for innovative therapeutic approaches. Ferroptosis is a recently found type of regulated cell death that is characterized primarily by the buildup of lipid peroxidation products and fatal reactive oxygen species created by iron metabolism, which plays a crucial role in tumor progression and therapy.With the molecular mechanisms associated with ferroptosis being increasingly studied and refined, triggering ferroptosis by regulators that target ferroptosis and ccRCC may be the key to developing potential therapeutic strategies for ccRCC. Therefore, ferroptosis is expected to be a new breakthrough in treating ccRCC. This paper examines the mechanism of ferroptosis, the regulatory mechanism of ferroptosis in ccRCC, and the potential application of ferroptosis in combination with other therapies for the treatment of ccRCC. The goal is to offer novel perspectives for the research and clinical application of ferroptosis in the treatment of ccRCC.

Kinase activities in pancreatic ductal adenocarcinoma with prognostic and therapeutic avenues

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read-out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)-based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho-subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho-subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype-associated activity profiles can guide therapeutic combination approaches in tumor and stroma-enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.

An ultraconserved snoRNA-like element in long noncoding RNA CRNDE promotes ribosome biogenesis and cell proliferation

Cancer cells frequently upregulate ribosome production to support tumorigenesis. While small nucleolar RNAs (snoRNAs) are critical for ribosome biogenesis, the roles of other classes of noncoding RNAs in this process remain largely unknown. Here we performed CRISPRi screens to identify essential long noncoding RNAs (lncRNAs) in renal cell carcinoma (RCC) cells. This revealed that an alternatively-spliced isoform of lncRNA Colorectal Neoplasia Differentially Expressed containing an ultraconserved element (UCE), referred to as CRNDE UCE, is required for RCC cell proliferation. CRNDE UCE localizes to the nucleolus and promotes 60S ribosomal subunit biogenesis. The UCE of CRNDE functions as an unprocessed C/D box snoRNA that directly interacts with ribosomal RNA precursors. This facilitates delivery of eIF6, a key 60S biogenesis factor, which binds to CRNDE UCE through a sequence element adjacent to the UCE. These findings highlight the functional versatility of snoRNA sequences and expand the known mechanisms through which noncoding RNAs orchestrate ribosome biogenesis.

STAC3 as a poor prognostic biomarker in renal clear cell carcinoma: relationship with immune infiltration

Calcium ions (Ca2+) are crucial in tumorigenesis and progression, with their elevated levels indicating a negative prognosis in Kidney Renal Clear Cell Carcinoma (KIRC). The influence of genes regulating calcium ions on the survival outcomes of KIRC patients and their interaction with the tumor's immune microenvironment is yet to be fully understood. This study analyzed gene expression data from KIRC tumor and adjacent non-tumor tissues using the TCGA-KIRC dataset to pinpoint genes that are differentially expressed in KIRC. Intersection of these genes with those regulating calcium ions highlighted specific calcium ion-regulating genes that exhibit differential expression in KIRC. Subsequently, prognostic risk models were developed using univariate Cox and LASSO-Cox regression analyses to verify their diagnostic precision. Additionally, the study investigated the correlation between tumor immunity and KIRC patient outcomes, assessing the contribution of STAC3 genes to tumor immunity. Further exploration entailed SSGASE, single-cell analysis, pseudotime analysis and both in vivo and in vitro experiments to evaluate STAC3's role in tumor immunity and progression. Notably, STAC3 was significantly overexpressed in tumor specimens and positively correlated with the degree of malignancy of KIRC, affecting patients' prognosis. Elevated STAC3 expression correlated with enhanced immune infiltration in KIRC tumors. Furthermore, silencing STAC3 curtailed KIRC cell proliferation, migration, invasion, and stemness properties. Experimental models in mice confirmed that STAC3 knockdown led to a reduction in tumor growth. Elevated STAC3 expression is intricately linked with immune infiltration in KIRC tumors, as well as with the aggressive biological behaviors of tumor cells, including their proliferation, migration, and invasion. Targeting STAC3 presents a promising strategy to augment the efficacy of current therapeutic approaches and to better the survival outcomes of patients with KIRC.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

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.

MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses

Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.

HIF-2α expression and metabolic signaling require ACSS2 in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an aggressive cancer driven by VHL loss and aberrant HIF-2α signaling. Identifying means to regulate HIF-2α thus has potential therapeutic benefit. Acetyl-CoA synthetase 2 (ACSS2) converts acetate to acetyl-CoA and is associated with poor patient prognosis in ccRCC. Here we tested the effects of ACSS2 on HIF-2α and cancer cell metabolism and growth in ccRCC models and clinical samples. ACSS2 inhibition reduced HIF-2α levels and suppressed ccRCC cell line growth in vitro, in vivo, and in cultures of primary ccRCC patient tumors. This treatment reduced glycolytic signaling, cholesterol metabolism, and mitochondrial integrity, all of which are consistent with loss of HIF-2α. Mechanistically, ACSS2 inhibition decreased chromatin accessibility and HIF-2α expression and stability. While HIF-2α protein levels are widely regulated through pVHL-dependent proteolytic degradation, we identify a potential pVHL-independent pathway of degradation via the E3 ligase MUL1. We show that MUL1 can directly interact with HIF-2α and that overexpression of MUL1 decreased HIF-2α levels in a manner partially dependent on ACSS2. These findings identify multiple mechanisms to regulate HIF-2α stability and ACSS2 inhibition as a strategy to complement HIF-2α-targeted therapies and deplete pathogenically stabilized HIF-2α.

Dynamic changes of SCGN expression imply different phases of clear cell renal cell carcinoma progression

The secretagogin (SCGN) was originally identified as a secreted calcium-binding protein present in the cytoplasm. Recent studies have found that SCGN has a close relationship with cancer. However, its role in the occurrence, progression, and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. In this study, we utilized a mutual authentication method based on public databases and clinical samples to determine the role of SCGN in the progression and prognosis of ccRCC. Firstly, we comprehensively analyzed the expression characteristics of SCGN in ccRCC in several public databases. Subsequently, we systematically evaluated SCGN expression on 252 microarrays of ccRCC tissues from different grades. It was found that SCGN was absent in all the normal kidney tissues and significantly overexpressed in ccRCC tumor tissues. In addition, the expression level of SCGN gradually decreased with an increase in tumor grade, and the percentage of SCGN staining positivity over 50% was 86.7% (13/15) and 73.4% (58/79) in Grade1 and Grade2, respectively, while it was only 8.3% (12/144) in Grade3, and the expression of SCGN was completely absent in Grade4 (0/14) and distant metastasis group (0/4). Additionally, the expression of SCGN was strongly correlated with the patient's prognosis, with the higher the expression levels of SCGN being associated with longer overall survival and disease-free survival of patients. In conclusion, our results suggest that reduced expression of SCGN in cancer cells is correlated with the progression and prognosis of ccRCC.

PML restrains p53 activity and cellular senescence in clear cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC), the major subtype of RCC, is frequently diagnosed at late/metastatic stage with 13% 5-year disease-free survival. Functional inactivation of the wild-type p53 protein is implicated in ccRCC therapy resistance, but the detailed mechanisms of p53 malfunction are still poorly characterized. Thus, a better understanding of the mechanisms of disease progression and therapy resistance is required. Here, we report a novel ccRCC dependence on the promyelocytic leukemia (PML) protein. We show that PML is overexpressed in ccRCC and that PML depletion inhibits cell proliferation and relieves pathologic features of anaplastic disease in vivo. Mechanistically, PML loss unleashed p53-dependent cellular senescence thus depicting a novel regulatory axis to limit p53 activity and senescence in ccRCC. Treatment with the FDA-approved PML inhibitor arsenic trioxide induced PML degradation and p53 accumulation and inhibited ccRCC expansion in vitro and in vivo. Therefore, by defining non-oncogene addiction to the PML gene, our work uncovers a novel ccRCC vulnerability and lays the foundation for repurposing an available pharmacological intervention to restore p53 function and chemosensitivity.

Integrated proteogenomic analysis for inherited bone marrow failure syndrome

Recent advances in in-depth data-independent acquisition proteomic analysis have enabled comprehensive quantitative analysis of >10,000 proteins. Herein, an integrated proteogenomic analysis for inherited bone marrow failure syndrome (IBMFS) was performed to reveal their biological features and to develop a proteomic-based diagnostic assay in the discovery cohort; dyskeratosis congenita (n = 12), Fanconi anemia (n = 11), Diamond-Blackfan anemia (DBA, n = 9), Shwachman-Diamond syndrome (SDS, n = 6), ADH5/ALDH2 deficiency (n = 4), and other IBMFS (n = 18). Unsupervised proteomic clustering identified eight independent clusters (C1-C8), with the ribosomal pathway specifically downregulated in C1 and C2, enriched for DBA and SDS, respectively. Six patients with SDS had significantly decreased SBDS protein expression, with two of these not diagnosed by DNA sequencing alone. Four patients with ADH5/ALDH2 deficiency showed significantly reduced ADH5 protein expression. To perform a large-scale rapid IBMFS screening, targeted proteomic analysis was performed on 417 samples from patients with IBMFS-related hematological disorders (n = 390) and healthy controls (n = 27). SBDS and ADH5 protein expressions were significantly reduced in SDS and ADH5/ALDH2 deficiency, respectively. The clinical application of this first integrated proteogenomic analysis would be useful for the diagnosis and screening of IBMFS, where appropriate clinical screening tests are lacking.

Deciphering anoikis resistance and identifying prognostic biomarkers in clear cell renal cell carcinoma epithelial cells

This study tackles the persistent prognostic and management challenges of clear cell renal cell carcinoma (ccRCC), despite advancements in multimodal therapies. Focusing on anoikis, a critical form of programmed cell death in tumor progression and metastasis, we investigated its resistance in cancer evolution. Using single-cell RNA sequencing from seven ccRCC patients, we assessed the impact of anoikis-related genes (ARGs) and identified differentially expressed genes (DEGs) in Anoikis-related epithelial subclusters (ARESs). Additionally, six ccRCC RNA microarray datasets from the GEO database were analyzed for robust DEGs. A novel risk prognostic model was developed through LASSO and multivariate Cox regression, validated using BEST, ULCAN, and RT-PCR. The study included functional enrichment, immune infiltration analysis in the tumor microenvironment (TME), and drug sensitivity assessments, leading to a predictive nomogram integrating clinical parameters. Results highlighted dynamic ARG expression patterns and enhanced intercellular interactions in ARESs, with significant KEGG pathway enrichment in MYC + Epithelial subclusters indicating enhanced anoikis resistance. Additionally, all ARESs were identified in the spatial context, and their locational relationships were explored. Three key prognostic genes-TIMP1, PECAM1, and CDKN1A-were identified, with the high-risk group showing greater immune infiltration and anoikis resistance, linked to poorer prognosis. This study offers a novel ccRCC risk signature, providing innovative approaches for patient management, prognosis, and personalized treatment.

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Bile Acid Metabolism Mediates Cholesterol Homeostasis and Promotes Tumorigenesis in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) incidence has risen steadily over the last decade. Elevated lipid uptake and storage is required for ccRCC cell viability. As stored cholesterol is the most abundant component in ccRCC intracellular lipid droplets, it may also play an important role in ccRCC cellular homeostasis. In support of this hypothesis, ccRCC cells acquire exogenous cholesterol through the high-density lipoprotein receptor SCARB1, inhibition or suppression of which induces apoptosis. Here, we showed that elevated expression of 3 beta-hydroxy steroid dehydrogenase type 7 (HSD3B7), which metabolizes cholesterol-derived oxysterols in the bile acid biosynthetic pathway, is also essential for ccRCC cell survival. Development of an HSD3B7 enzymatic assay and screening for small-molecule inhibitors uncovered the compound celastrol as a potent HSD3B7 inhibitor with low micromolar activity. Repressing HSD3B7 expression genetically or treating ccRCC cells with celastrol resulted in toxic oxysterol accumulation, impaired proliferation, and increased apoptosis in vitro and in vivo. These data demonstrate that bile acid synthesis regulates cholesterol homeostasis in ccRCC and identifies HSD3B7 as a plausible therapeutic target.

SIGNIFICANCE

The bile acid biosynthetic enzyme HSD3B7 is essential for ccRCC cell survival and can be targeted to induce accumulation of cholesterol-derived oxysterols and apoptotic cell death.

Papillary Thyroid Cancer Remodels the Genetic Information Processing Pathways

The genetic causes of the differentiated, highly treatable, and mostly non-fatal papillary thyroid cancer (PTC) are not yet fully understood. The mostly accepted PTC etiology blames the altered sequence or/and expression level of certain biomarker genes. However, tumor heterogeneity and the patient's unique set of favoring factors question the fit-for-all gene biomarkers. Publicly accessible gene expression profiles of the cancer nodule and the surrounding normal tissue from a surgically removed PTC tumor were re-analyzed to determine the cancer-induced alterations of the genomic fabrics responsible for major functional pathways. Tumor data were compared with those of standard papillary and anaplastic thyroid cancer cell lines. We found that PTC regulated numerous genes associated with DNA replication, repair, and transcription. Results further indicated that changes of the gene networking in functional pathways and the homeostatic control of transcript abundances also had major contributions to the PTC phenotype occurrence. The purpose to proliferate and invade the entire gland may explain the substantial transcriptomic differences we detected between the cells of the cancer nodule and those spread in homo-cellular cultures (where they need only to survive). In conclusion, the PTC etiology should include the complex molecular mechanisms involved in the remodeling of the genetic information processing pathways.

Proteogenomics in Nephrology: A New Frontier in Nephrological Research

Proteogenomics represents a transformative intersection in nephrology, uniting genomics, transcriptomics, and proteomics to unravel the molecular intricacies of kidney diseases. This review encapsulates the methodological essence of proteogenomics and its profound implications in chronic kidney disease (CKD) research. We explore the proteogenomic pipeline, highlighting the integrated analysis of genomic, transcriptomic, and proteomic data and its pivotal role in enhancing our understanding of kidney pathologies. Through case studies, we showcase the application of proteogenomics in clear cell renal cell carcinoma (ccRCC) and Autosomal Recessive Polycystic Kidney Disease (ARPKD), emphasizing its potential in personalized treatment strategies and biomarker discovery. The review also addresses the challenges in proteogenomic analysis, including data integration complexities and bioinformatics limitations, and proposes solutions for advancing the field. Ultimately, this review underscores the prospective future of proteogenomics in nephrology, particularly in advancing personalized medicine and providing novel therapeutic insights.

Molecular mechanisms and clinical relevance of endothelial cell cross-talk in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer in adults and stands out as one of the most vascularized and immune-infiltrated solid tumors. Overproduction of vascular endothelial growth factor A promotes uncontrolled growth of abnormal vessels and immunosuppression, and the tumor microenvironment (TME) has a prominent role in disease progression, drug targeting and drug response, and for patient outcome.

Methods

Studies of experimental models, large-scale omics approaches, and patient prognosis and therapy prediction, using gene expression signatures and tissue biomarker analysis, have been reviewed for enhanced understanding of the endothelium in ccRCC and the interplay with the surrounding TME.

Results

Preclinical and clinical studies have discovered molecular mechanisms of endothelial cross-talk of relevance for disease progression, patient prognosis, and therapy prediction. There is, however, a lack of representative ccRCC experimental models. Omics approaches have identified clinically relevant subsets of angiogenic and immune-infiltrated tumors with distinct molecular signatures and distinct endothelial cell and immune cell populations in patients.

Conclusions

Recent genetically engineered ccRCC mouse models together with emerging evidence from single cell RNA sequencing data open up for future validation studies, including multiplex imaging of ccRCC patient cohorts. These studies are of importance for therapy benefit and personalized treatment of ccRCC patients.

E-cadherin interacts with EGFR resulting in hyper-activation of ERK in multiple models of breast cancer

The loss of intercellular adhesion molecule E-cadherin is a hallmark of the epithelial-mesenchymal transition (EMT), during which tumor cells transition into an invasive phenotype. Accordingly, E-cadherin has long been considered a tumor suppressor gene; however, E-cadherin expression is paradoxically correlated with breast cancer survival rates. Using novel multi-compartment organoids and multiple in vivo models, we show that E-cadherin promotes a hyper-proliferative phenotype in breast cancer cells via interaction with the transmembrane receptor EGFR. The E-cad and EGFR interaction results in activation of the MEK/ERK signaling pathway, leading to a significant increase in proliferation via activation of transcription factors, including c-Fos. Pharmacological inhibition of MEK activity in E-cadherin positive breast cancer significantly decreases both tumor growth and macro-metastasis in vivo. This work provides evidence for a novel role of E-cadherin in breast tumor progression and identifies a new target to treat hyper-proliferative E-cadherin-positive breast tumors, thus providing the foundation to utilize E-cadherin as a biomarker for specific therapeutic success.

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma

PURPOSE

Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761).

RESULTS

After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score.

CONCLUSION

We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.

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.

HIF1α-dependent uncoupling of glycolysis suppresses tumor cell proliferation

Hypoxia-inducible factor-1α (HIF1α) attenuates mitochondrial activity while promoting glycolysis. However, lower glycolysis is compromised in human clear cell renal cell carcinomas, in which HIF1α acts as a tumor suppressor by inhibiting cell-autonomous proliferation. Here, we find that, unexpectedly, HIF1α suppresses lower glycolysis after the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) step, leading to reduced lactate secretion in different tumor cell types when cells encounter a limited pyruvate supply such as that typically found in the tumor microenvironment in vivo. This is because HIF1α-dependent attenuation of mitochondrial oxygen consumption increases the NADH/NAD+ ratio that suppresses the activity of the NADH-sensitive GAPDH glycolytic enzyme. This is manifested when pyruvate supply is limited, since pyruvate acts as an electron acceptor that prevents the increment of the NADH/NAD+ ratio. Furthermore, this anti-glycolytic function provides a molecular basis to explain how HIF1α can suppress tumor cell proliferation by increasing the NADH/NAD+ ratio.

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.

Well-Paired-Seq2: High-Throughput and High-Sensitivity Strategy for Characterizing Low RNA-Content Cell/Nucleus Transcriptomes

Single-cell RNA sequencing (scRNA-seq) is a transformative technology that unravels the intricate cellular state heterogeneity. However, the Poisson-dependent cell capture and low sensitivity in scRNA-seq methods pose challenges for throughput and samples with a low RNA-content. Herein, to address these challenges, we present Well-Paired-Seq2 (WPS2), harnessing size-exclusion and quasi-static hydrodynamics for efficient cell capture. WPS2 exploits molecular crowding effect, tailing activity enhancement in reverse transcription, and homogeneous enzymatic reaction in the initial bead-based amplification to achieve 3116 genes and 8447 transcripts with an average of ∼20000 reads per cell. WPS2 detected 1420 more genes and 4864 more transcripts than our previous Well-Paired-Seq. It sensitively characterizes transcriptomes of low RNA-content single cells and nuclei, overcoming the Poisson limit for cell and barcoded bead capture. WPS2 also profiles transcriptomes from frozen clinical samples, revealing heterogeneous tumor copy number variations and intercellular crosstalk in clear cell renal cell carcinomas. Additionally, we provide the first single-cell-level characterization of rare metanephric adenoma (MA) and uncover potential specific markers. With the advantages of high sensitivity and high throughput, WPS2 holds promise for diverse basic and clinical research.

Proteomics applications in next generation induced pluripotent stem cell models

INTRODUCTION

Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations.

AREAS COVERED

We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems.

EXPERT OPINIONS

We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.

Metabolic alterations in hereditary and sporadic renal cell carcinoma

Kidney cancer is the seventh leading cause of cancer in the world, and its incidence is on the rise. Renal cell carcinoma (RCC) is the most common form and is a heterogeneous disease comprising three major subtypes that vary in their histology, clinical course and driver mutations. These subtypes include clear cell RCC, papillary RCC and chromophobe RCC. Molecular analyses of hereditary and sporadic forms of RCC have revealed that this complex and deadly disease is characterized by metabolic pathway alterations in cancer cells that lead to deregulated oxygen and nutrient sensing, as well as impaired tricarboxylic acid cycle activity. These metabolic changes facilitate tumour growth and survival. Specifically, studies of the metabolic features of RCC have led to the discovery of oncometabolites - fumarate and succinate - that can promote tumorigenesis, moonlighting functions of enzymes, and substrate auxotrophy owing to the disruption of pathways that enable the production of arginine and cholesterol. These metabolic alterations within RCC can be exploited to identify new therapeutic targets and interventions, in combination with novel approaches that minimize the systemic toxicity of metabolic inhibitors and reduce the risk of drug resistance owing to metabolic plasticity.

Integrated stress response plasticity governs normal cell adaptation to chronic stress via the PP2A-TFE3-ATF4 pathway

The integrated stress response (ISR) regulates cell fate during conditions of stress by leveraging the cell's capacity to endure sustainable and efficient adaptive stress responses. Protein phosphatase 2A (PP2A) activity modulation has been shown to be successful in achieving both therapeutic efficacy and safety across various cancer models; however, the molecular mechanisms driving its selective antitumor effects remain unclear. Here, we show for the first time that ISR plasticity relies on PP2A activation to regulate drug response and dictate cellular fate under conditions of chronic stress. We demonstrate that genetic and chemical modulation of the PP2A leads to chronic proteolytic stress and triggers an ISR to dictate cell fate. More specifically, we uncovered that the PP2A-TFE3-ATF4 pathway governs ISR cell plasticity during endoplasmic reticular and cellular stress independent of the unfolded protein response. We further show that normal cells reprogram their genetic signatures to undergo ISR-mediated adaptation and homeostatic recovery thereby successfully avoiding toxicity following PP2A-mediated stress. Conversely, oncogenic specific cytotoxicity induced by chemical modulation of PP2A is achieved by activating chronic and irreversible ISR in cancer cells. Our findings propose that a differential response to chemical modulation of PP2A is determined by intrinsic ISR plasticity, providing a novel biological vulnerability to selectively induce cancer cell death and improve targeted therapeutic efficacy.

Systems Approaches to Cell Culture-Derived Extracellular Vesicles for Acute Kidney Injury Therapy: Prospects and Challenges

Acute kidney injury (AKI) is a heterogeneous syndrome, comprising diverse etiologies of kidney insults that result in high mortality and morbidity if not well managed. Although great efforts have been made to investigate underlying pathogenic mechanisms of AKI, there are limited therapeutic strategies available. Extracellular vesicles (EV) are membrane-bound vesicles secreted by various cell types, which can serve as cell-free therapy through transfer of bioactive molecules. In this review, we first overview the AKI syndrome and EV biology, with a particular focus on the technical aspects and therapeutic application of cell culture-derived EVs. Second, we illustrate how multi-omic approaches to EV miRNA, protein, and genomic cargo analysis can yield new insights into their mechanisms of action and address unresolved questions in the field. We then summarize major experimental evidence regarding the therapeutic potential of EVs in AKI, which we subdivide into stem cell and non-stem cell-derived EVs. Finally, we highlight the challenges and opportunities related to the clinical translation of animal studies into human patients.

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

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

Widespread alteration of protein autoinhibition in human cancers

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins. Reduced autoinhibition underlies the tumorigenic effect of some known cancer drivers, but whether autoinhibition is altered generally in cancer remains elusive. Here, we demonstrate that cancer-associated missense mutations, in-frame insertions/deletions, and fusion breakpoints are enriched within inhibitory allosteric switches (IASs) across all cancer types. Selection for IASs that are recurrently mutated in cancers identifies established and unknown cancer drivers. Recurrent missense mutations in IASs of these drivers are associated with distinct, cancer-specific changes in molecular signaling. For the specific case of PPP3CA, the catalytic subunit of calcineurin, we provide insights into the molecular mechanisms of altered autoinhibition by cancer mutations using biomolecular simulations, and demonstrate that such mutations are associated with transcriptome changes consistent with increased calcineurin signaling. Our integrative study shows that autoinhibition-modulating genetic alterations are positively selected for by cancer cells.

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

GALNT2 targeted by miR-139-5p promotes proliferation of clear cell renal cell carcinoma via inhibition of LATS2 activation

Polypeptide N-Acetylgalactosaminyltransferase (GALNTs) are critical enzymes that initiate mucin type-O glycosylation, and are closely associated with the occurrence and development of multiple cancers. However, the significance of GALNT2 in clear cell renal cell carcinoma (ccRCC) progression remains largely undetermined. Based on public multi-omics analysis, GALNT2 was strongly elevated in ccRCC versus adjoining nontumor tissues, and it displayed a relationship with poor overall survival (OS) of ccRCC patients. In addition, GALNT2 over-expression accelerated proliferation of renal cancer cell (RCC) lines. In contrast, GALNT2 knockdown using shRNAs suppressed cell proliferation, and this was rescued by LATS2 knockdown. Similarly, GALNT2 deficiency enhanced p-LATS2/LATS2 expression. LATS2 is activated by phosphorylation (p-LATS2) and, in turn, phosphorylate the downstream substrate protein YAP. Phosphorylated YAP (p-YAP) stimulated its degradation and cytoplasmic retention, as it was unable to translocate to the nucleus. This resulted in reduced cell proliferation. Subsequently, we explored the upstream miRNAs of GALNT2. Using dual luciferase reporter assay, we revealed that miR-139-5p interacted with the 3' UTR of GALNT2. Low miR-139-5p expression was associated with worse ccRCC patient outcome. Based on our experiments, miR-139-5p overexpression inhibited RCC proliferation, and this phenotype was rescued by GALNT2 overexpression. Given these evidences, the miR-139-5p-GALNT2-LATS2 axis is critical for RCC proliferation, and it is an excellent candidate for a new therapeutic target in ccRCC.

Integrated proteogenomic characterization of glioblastoma evolution

The evolutionary trajectory of glioblastoma (GBM) is a multifaceted biological process that extends beyond genetic alterations alone. Here, we perform an integrative proteogenomic analysis of 123 longitudinal glioblastoma pairs and identify a highly proliferative cellular state at diagnosis and replacement by activation of neuronal transition and synaptogenic pathways in recurrent tumors. Proteomic and phosphoproteomic analyses reveal that the molecular transition to neuronal state at recurrence is marked by post-translational activation of the wingless-related integration site (WNT)/ planar cell polarity (PCP) signaling pathway and BRAF protein kinase. Consistently, multi-omic analysis of patient-derived xenograft (PDX) models mirror similar patterns of evolutionary trajectory. Inhibition of B-raf proto-oncogene (BRAF) kinase impairs both neuronal transition and migration capability of recurrent tumor cells, phenotypic hallmarks of post-therapy progression. Combinatorial treatment of temozolomide (TMZ) with BRAF inhibitor, vemurafenib, significantly extends the survival of PDX models. This study provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance, highlighting promising therapeutic strategies for clinical intervention.

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Novel inhibitors targeting the PGK1 metabolic enzyme in glycolysis exhibit effective antitumor activity against kidney renal clear cell carcinoma in vitro and in vivo

Our previous research has revealed phosphoglycerate kinase 1 (PGK1) enhances tumorigenesis and sorafenib resistance of kidney renal clear cell carcinoma (KIRC) by regulating glycolysis, so that PGK1 is a promising drug target. Herein we performed structure-based virtual screening and series of anticancer pharmaceutical experiments in vitro and in vivo to identify novel small-molecule PGK1-targeted compounds. As results, the compounds CHR-6494 and Z57346765 were screened and confirmed to specifically bind to PGK1 and significantly reduced the metabolic enzyme activity of PGK1 in glycolysis, which inhibited KIRC cell proliferation in a dose-dependent manner. While CHR-6494 showed greater anti-KIRC efficacy and fewer side effects than Z57346765 on nude mouse xenograft model. Mechanistically, CHR-9464 impeded glycolysis by decreasing the metabolic enzyme activity of PGK1 and suppressed histone H3T3 phosphorylation to inhibit KIRC cell proliferation. Z57346765 induced expression changes of genes related to cell metabolism, DNA replication and cell cycle. Overall, we screened two novel PGK1 inhibitors, CHR-6494 and Z57346765, for the first time and discovered their potent anti-KIRC effects by suppressing PGK1 metabolic enzyme activity in glycolysis.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC.

Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma

Adjuvant chemotherapy benefits patients with resected pancreatic ductal adenocarcinoma (PDAC), but the compromised physical state of post-operative patients can hinder compliance. Biomarkers that identify candidates for prompt adjuvant therapy are needed. In this prospective observational study, 1,171 patients with PDAC who underwent pancreatectomy were enrolled and extensively followed-up. Proteomic profiling of 191 patient samples unveiled clinically relevant functional protein modules. A proteomics-level prognostic risk model was established for PDAC, with its utility further validated using a publicly available external cohort. More importantly, through an interaction effect regression analysis leveraging both clinical and proteomic datasets, we discovered two biomarkers (NDUFB8 and CEMIP2), indicative of the overall sensitivity of patients with PDAC to adjuvant chemotherapy. The biomarkers were validated through immunohistochemistry on an internal cohort of 386 patients. Rigorous validation extended to two external multicentic cohorts-a French multicentric cohort (230 patients) and a cohort from two grade-A tertiary hospitals in China (466 patients)-enhancing the robustness and generalizability of our findings. Moreover, experimental validation through functional assays was conducted on PDAC cell lines and patient-derived organoids. In summary, our cohort-scale integration of clinical and proteomic data demonstrates the potential of proteomics-guided prognosis and biomarker-aided adjuvant chemotherapy for PDAC.

Pan-cancer proteogenomics characterization of tumor immunity

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

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.

Advanced Molecular Solutions for Cancer Therapy-The Good, the Bad, and the Ugly of the Biomarker Paradigm

Identifying the most effective actionable molecules whose "smart" manipulation might selectively kill/slow down/stop the proliferation of cancer cells, with few side effects on the normal cells of the tissue, was for decades the single major objective of countless investigators [...].