CPPA: A Web Tool for Exploring Proteomic and Phosphoproteomic Data in Cancer

Ontolomics-P: Advancing Proteomics Data Interpretation through GPT-4o Reannotated Topic Ontology and Data-Driven Analysis

The interpretation of proteomics data often relies on functional enrichment analysis, such as Gene Ontology (GO) enrichment, to uncover the biological functions of proteins, as well as the examination of protein expression patterns across data sets like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. However, conventional approaches to functional enrichment frequently produce extensive and redundant term lists, complicating interpretation and synthesis. Moreover, the absence of specialized tools tailored to proteomics researchers limits the efficient exploration of protein expression within specific biological contexts. To address these challenges, we developed Ontolomics-P, a user-friendly web-based tool designed to advance proteomics data interpretation. Ontolomics-P integrates topic modeling using latent Dirichlet allocation (LDA) with GO semantic similarity analysis, enabling the consolidation of redundant terms into coherent topics. These topics are further refined and reannotated using the GPT-4o language model, creating a novel topics database that provides precise and interpretable insights into shared biological functions. Additionally, Ontolomics-P incorporates quantitative proteomic data from 10 diverse cancer types archived in the CPTAC database, allowing for a comprehensive exploration of protein expression profiles from a data-driven perspective. Through detailed case studies, we demonstrate the tool's capacity to streamline workflows, simplify interpretation, and provide actionable biological insights. Ontolomics-P represents a significant advancement in proteomics data analysis, offering innovative solutions for functional annotation, quantitative exploration, and visualization, ultimately empowering researchers to accelerate discoveries in systems biology and beyond.

Discovery of 4-(2-(methylamino)thiazol-5-yl)pyrimidin-2-amine derivatives as novel cyclin-dependent kinase 12 (CDK12) inhibitors for the treatment of esophageal squamous cell carcinoma

The transcriptional cyclin-dependent protein kinase 12 (CDK12), a potential target in various cancers, was recently discovered with a dramatic amplification in esophageal cancer (EC). In this study, we conducted an online database analysis that revealed CDK12 to be overexpressed in esophageal squamous cell carcinoma (ESCC) tissue samples from patients. Furthermore, survival analysis indicated that CDK12 can serve as a prognostic indicator for ESCC patients. In addition, CDK12 knockdown had been shown to reduce the proliferation of ESCC cells. The present study also details the design, synthesis, and biological evaluation of new CDK12 inhibitors which bear the scaffold of 4-(2-(methylamino)thiazol-5-yl)pyrimidin-2-amine. Among the synthesized compounds, H63 has been identified as a potent inhibitor of CDK12 with excellent anti-ESCC activity. Mechanistically, H63 blocked transcription elongation, downregulated the G1-phase core genes to induce cell cycle arrest, and altered the CDK12-ATM/ATR-CHEK1/CHEK2 signaling axis to cause DNA damage. In addition, H63 exhibited favorable pharmacokinetic properties, good safety, and prominent anti-ESCC activity in vivo. The present study suggests that CDK12 is a promising target for ESCC treatment, and H63 is a promising candidate for further clinical development as an anti-ESCC drug.

PhosCancer: A comprehensive database for investigating protein phosphorylation in human cancer

Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data.

iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.