Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current tumor-node-metastasis or Veterans Administration Lung Study Group staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent cohort, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggested potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better progression-free survival and overall survival after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics.

BACKGROUND: Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application. METHODS: Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system. RESULTS: The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis. CONCLUSIONS: PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics.

BACKGROUND: Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. RESULTS: Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. CONCLUSION: Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy.

Although diabetic nephropathy (DN) is the leading cause of the end-stage renal disease, the exact regulation mechanisms remain unknown. In this study, we integrated the transcriptomics and proteomics profiles of glomeruli isolated from 50 biopsy-proven DN patients and 25 controls to investigate the latest findings about DN pathogenesis. First, 1152 genes exhibited differential expression at the mRNA or protein level, and 364 showed significant association. These strong correlated genes were divided into four different functional modules. Moreover, a regulatory network of the transcription factors (TFs)-target genes (TGs) was constructed, with 30 TFs upregulated at the protein levels and 265 downstream TGs differentially expressed at the mRNA levels. These TFs are the integration centers of several signal transduction pathways and have tremendous therapeutic potential for regulating the aberrant production of TGs and the pathological process of DN. Furthermore, 29 new DN-specific splice-junction peptides were discovered with high confidence; these peptides may play novel functions in the pathological course of DN. So, our in-depth integrative transcriptomics-proteomics analysis provided deeper insights into the pathogenesis of DN and opened the potential avenue for finding new therapeutic interventions. MS raw files were deposited into the proteomeXchange with the dataset identifier PXD040617.

Expanding individualized therapeutic options via genoproteomics.

Clinical next-generation sequencing (NGS) tests have enabled treatment recommendations for cancer patients with driver gene mutations. Targeted therapy options for patients without driver gene mutations are currently unavailable. Herein, we performed NGS and proteomics tests on 169 formalin-fixed paraffin-embedded (FFPE) samples of non-small cell lung cancers (NSCLC, 65), colorectal cancers (CRC, 61), thyroid carcinomas (THCA, 14), gastric cancers (GC, 2), gastrointestinal stromal tumors (GIST, 11), and malignant melanomas (MM, 6). Of the 169 samples, NGS detected 14 actionable mutated genes in 73 samples, providing treatment options for 43% of the patients. Proteomics identified 61 actionable clinical drug targets approved by the FDA or undergoing clinical trials in 122 samples, providing treatment options for 72% of the patients. In vivo experiments demonstrated that the Mitogen-Activated Protein Kinase (MEK) inhibitor could block lung tumor growth in mice with overexpression of Map2k1 protein. Therefore, protein overexpression is a potentially feasible indicator for guiding targeted therapies. Collectively, our analysis suggests that combining NGS and proteomics (genoproteomics) could expand the targeted treatment options to 85% of cancer patients.

Transcriptomics-proteomics Integration reveals alternative polyadenylation driving inflammation-related protein translation in patients with diabetic nephropathy.

BACKGROUND: Diabetic nephropathy (DN) is a complex disease involving the upregulation of many inflammation-related proteins. Alternative polyadenylation (APA), a crucial post-transcriptional regulatory mechanism, has been proven to play vital roles in many inflammatory diseases. However, it is largely unknown whether and how APA exerts function in DN. METHODS: We performed transcriptomics and proteomics analysis of glomeruli samples isolated from 50 biopsy-proven DN patients and 25 control subjects. DaPars and QAPA algorithms were adopted to identify APA events from RNA-seq data. The qRT-PCR analysis was conducted to verify 3'UTR length alteration. Short and long 3'UTRs isoforms were also overexpressed in podocytes under hyperglycemia condition for examining protein expression. RESULTS: We detected transcriptome-wide 3'UTR APA events in DN, and found that APA-mediated 3'UTR lengthening of genes (APA genes) increased their expression at protein but not mRNA level. Increased protein level of 3'UTR lengthening gene was validated in podocytes under hyperglycemia condition. Pathway enrichment analysis showed that APA genes were enriched in inflammation-related biological processes including endoplasmic reticulum stress pathways, NF-κB signaling and autophagy. Further bioinformatics analysis demonstrated that 3'UTR APA of genes probably altered the binding sites for RNA-binding proteins, thus enhancing protein translation. CONCLUSION: This study revealed for the first time that 3'UTR lengthening of APA genes contributed to the progression of DN by elevating the translation of corresponding proteins, providing new insight and a rich resource for investigating DN mechanisms.

Global profiling of AMG510 modified proteins identified tumor suppressor KEAP1 as an off-target.

KRAS inhibitor AMG510 covalently modifies the G12C residue and inactivates the KRAS/G12C function. Because there are many reactive cysteines in the proteome, it is important to characterize AMG510 on-target modification and off-targets. Here, we presented a streamlined workflow to measure abundant AMG510 modified peptides including that of KRAS/G12C by direct profiling, and a pan-AMG510 antibody peptide IP workflow to profile less abundant AMG510 off-targets. We identified over 300 off-target sites with three distinct kinetic patterns, expanding the AMG510 modified proteome involved in the nucleocytoplasmic transport, response to oxidative stress, adaptive immune system, and glycolysis. We found that AMG510 covalently modified cys339 of ALDOA and inhibited its enzyme activity. Moreover, AMG510 modified KEAP1 cys288 and induced NRF2 accumulation in the nuclear of NSCLC cells independent of KRAS/G12C mutation. Our study provides a comprehensive resource of protein off-targets of AMG510 and elucidates potential toxicological sideeffects for this covalent KRASG12C inhibitor.

The Association of R-Loop Binding Proteins Subtypes with CIN Implicates Therapeutic Strategies in Colorectal Cancer.

Chromosomal instability (CIN) covers approximately 65 to 70% of colorectal cancer patients and plays an essential role in cancer progression. However, the molecular features and therapeutic strategies related to those patients are still controversial. R-loop binding proteins (RLBPs) exert significant roles in transcription and replication. Here, integrative colorectal cancer proteogenomic analysis identified two RLBPs subtypes correlated with distinct prognoses. Cluster I (CI), represented by high expression of RLBPs, was associated with the CIN phenotype. While Cluster II (CII) with the worst prognosis and low expression of RLBPs was composed of a high percentage of patients with mucinous adenocarcinoma or right-sided colon cancer. The molecular feature analysis revealed that the active RNA processing, ribosome synthesis, and aberrant DNA damage repair were shown in CI, a high inflammatory signaling pathway, and lymphocyte infiltration was enriched in CII. In addition, we revealed 42 tumor-associated RLBPs proteins. The CI with high expression of tumor-associated proteins was sensitive to drugs targeting genome integrity and EGFR in both cell and organoid models. Thus, our study unveils a significant molecular association of the CIN phenotype with RLBPs, and also provides a powerful resource for further functional exploration of RLBPs in cancer progression and therapeutic application.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.

Proteomics provides individualized options of precision medicine for patients with gastric cancer.

While precision medicine driven by genome sequencing has revolutionized cancer care, such as lung cancer, its impact on gastric cancer (GC) has been minimal. GC patients are routinely treated with chemotherapy, but only a fraction of them receive the clinical benefit. There is an urgent need to develop biomarkers or algorithms to select chemo-sensitive patients or apply targeted therapy. Here, we carried out retrospective analyses of 1,020 formalin-fixed, paraffin-embedded GC surgical resection samples from 5 hospitals and developed a mass spectrometry-based workflow for proteomic subtyping of GC. We identified two proteomic subtypes: the chemo-sensitive group (CSG) and the chemo-insensitive group (CIG) in the discovery set. The 5-year overall survival of CSG was significantly improved in patients who had received adjuvant chemotherapy after surgery compared with those who received surgery only (64.2% vs. 49.6%; Cox P-value=0.002), whereas no such improvement was observed in CIG (50.0% vs. 58.6%; Cox P-value=0.495). We validated these results in an independent validation set. Further, differential proteome analysis uncovered 9 FDA-approved drugs that may be applicable for targeted therapy of GC. A prospective study is warranted to test these findings for future GC patient care.

NQO1 is a determinant for cellular sensitivity to anti-tumor agent Napabucasin.

Napabucasin (NAPA) is thought to be a potent cancer stemness inhibitor in different types of cancer cell lines. While it has shown promising activity in early phase clinical trials, two recent phase III NAPA clinical trials failed to meet the primary endpoint of overall survival. The reason for the failure is not clear, but a possible way to revive the clinical trial is to stratify patients with biomarkers that could predict NAPA response. Here, we report the identification of NAD(P)H dehydrogenase 1 (NQO1) as a major determinant of NAPA efficacy. A proteomic profiling of cancer cell lines revealed that NQO1 abundance is negatively correlated with IC50; in vitro assays showed that NAPA is a substrate for NQO1, which mediates the generation of ROS that leads to cell death. Furthermore, activation of an NQO1 transcription factor NRF2 by chemicals, including an FDA approved drug, can increase the NAPA cytotoxicity. Our findings suggest a potential use of NQO1 expression as a companion diagnostic test to identify patients in future NAPA trials and a combination strategy to expand the application of NAPA-based regimens for cancer therapy.

A time-resolved multi-omic atlas of the developing mouse liver.

Liver organogenesis and development are composed of a series of complex, well-orchestrated events. Identifying key factors and pathways governing liver development will help elucidate the physiological and pathological processes including those of cancer. We conducted multidimensional omics measurements including protein, mRNA, and transcription factor (TF) DNA-binding activity for mouse liver tissues collected from embryonic day 12.5 (E12.5) to postnatal week 8 (W8), encompassing major developmental stages. These data sets reveal dynamic changes of core liver functions and canonical signaling pathways governing development at both mRNA and protein levels. The TF DNA-binding activity data set highlights the importance of TF activity in early embryonic development. A comparison between mouse liver development and human hepatocellular carcinoma (HCC) proteomic profiles reveal that more aggressive tumors are characterized with the activation of early embryonic development pathways, whereas less aggressive ones maintain liver function-related pathways that are elevated in the mature liver. This work offers a panoramic view of mouse liver development and provides a rich resource to explore in-depth functional characterization.

Comparative Proteomic Analysis of Histone Modifications upon Acridone Derivative 8a-Induced CCRF-CEM Cells by Data Independent Acquisition.

The lead compound acridone derivative 8a showed potent antiproliferative activity by inducing DNA damage through direct stacking with DNA bases and triggering ROS in CCRF-CEM cells. To define the chromatin alterations during DNA damage sensing and repair, a detailed quantitative map of single and coexisting histone post-translational modifications (PTMs) in CCRF-CEM cells affected by 8a was performed by the Data Independent Acquisition (DIA) method on QE-plus. A total of 79 distinct and 164 coexisting histone PTMs were quantified, of which 16 distinct histone PTMs were significantly altered when comparing 8a-treated cells with vehicle control cells. The changes in histone PTMs were confirmed by Western blotting analysis for three H3 and one H4 histone markers. The up-regulated dimethylation on H3K9, H3K36, and H4K20 implied that CCRF-CEM cells might accelerate DNA damage repair to counteract the DNA lesion induced by 8a, which was verified by an increment in the 53BP1 foci localization at the damaged DNA. Most of the significantly altered PTMs were involved in transcriptional regulation, including down-regulated acetylation on H3K18, H3K27, and H3K122, and up-regulated di- and trimethylation on H3K9 and H3K27. This transcription-silencing phenomenon was associated with G2/M cell cycle arrest after 8a treatment by flow cytometry. This study shows that the DIA proteomics strategy provides a sensitive and accurate way to characterize the coexisting histone PTMs changes and their cross-talk in CCRF-CEM cells after 8a treatment. Specifically, histone PTMs rearrange transcription-silencing, and cell cycle arrest DNA damage repair may contribute to the mechanism of epigenetic response affected by 8a.

Phosphoproteomics Enables Molecular Subtyping and Nomination of Kinase Candidates for Individual Patients of Diffuse-Type Gastric Cancer.

The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments.

A Cross-Linking-Aided Immunoprecipitation/Mass Spectrometry Workflow Reveals Extensive Intracellular Trafficking in Time-Resolved, Signal-Dependent Epidermal Growth Factor Receptor Proteome.

Ligand binding to the cell surface receptors initiates signaling cascades that are commonly transduced through a protein-protein interaction (PPI) network to activate a plethora of response pathways. However, tools to capture the membrane PPI network are lacking. Here, we describe a cross-linking-aided mass spectrometry workflow for isolation and identification of signal-dependent epidermal growth factor receptor (EGFR) proteome. We performed protein cross-linking in cell culture at various time points following EGF treatment, followed by immunoprecipitation of endogenous EGFR and analysis of the associated proteins by quantitative mass spectrometry. We identified 140 proteins with high confidence during a 2 h time course by data-dependent acquisition and further validated the results by parallel reaction monitoring. A large proportion of proteins in the EGFR proteome function in endocytosis and intracellular protein transport. The EGFR proteome was highly dynamic with distinct temporal behavior; 10 proteins that appeared in all time points constitute the core proteome. Functional characterization showed that loss of the FYVE domain-containing proteins altered the EGFR intracellular distribution but had a minor effect on EGFR proteome or signaling. Thus, our results suggest that the EGFR proteome include functional regulators that influence EGFR signaling and bystanders that are captured as the components of endocytic vesicles. The high-resolution spatiotemporal information of these molecules facilitates the delineation of many pathways that could determine the strength and duration of the signaling, as well as the location and destination of the receptor.

Molecular subtyping of cancer and nomination of kinase candidates for inhibition with phosphoproteomics: Reanalysis of CPTAC ovarian cancer.

BACKGROUND: Molecular subtyping of cancer aimed to predict patient overall survival (OS) and nominate drug targets for patient treatments is central to precision oncology. Owing to the rapid development of phosphoproteomics, we can now measure thousands of phosphoproteins in human cancer tissues. However, limited studies report how to analyse the complex phosphoproteomic data for cancer subtyping and to nominate druggable kinase candidates. FINDINGS: In this work, we reanalysed the phosphoproteomic data of high-grade serous ovarian cancer (HGSOC) from the Clinical Proteomic Tumour Analysis Consortium (CPTAC). Our analysis classified HGSOC into 5 major subtypes that were associated with different OS and appeared to be more accurate than that achieved with protein profiling. We provided a workflow to identify 29 kinases whose increased activities in tumours are associated with poor survival. The altered kinase signalling landscape of HGSOC included the PI3K/AKT/mTOR, cell cycle and MAP kinase signalling pathways. We also developed a "patient-specific" hierarchy of clinically actionable kinases and selected kinase inhibitors by considering kinase activation and kinase inhibitor selectivity. INTERPRETATION: Our study offered a global phosphoproteomics data analysis workflow to aid in cancer molecular subtyping, determining phosphorylation-based cancer hallmarks and facilitating nomination of kinase inhibition in cancer.

A time-resolved multi-omic atlas of the developing mouse stomach.

The mammalian stomach is structurally highly diverse and its organ functionality critically depends on a normal embryonic development. Although there have been several studies on the morphological changes during stomach development, a system-wide analysis of the underlying molecular changes is lacking. Here, we present a comprehensive, temporal proteome and transcriptome atlas of the mouse stomach at multiple developmental stages. Quantitative analysis of 12,108 gene products allows identifying three distinct phases based on changes in proteins and RNAs and the gain of stomach functions on a longitudinal time scale. The transcriptome indicates functionally important isoforms relevant to development and identifies several functionally unannotated novel splicing junction transcripts that we validate at the peptide level. Importantly, many proteins differentially expressed in stomach development are also significantly overexpressed in diffuse-type gastric cancer. Overall, our study provides a resource to understand stomach development and its connection to gastric cancer tumorigenesis.