KinoViz: A User-Friendly Web Application for High-Throughput Kinome Profiling Analysis and Visualization in Cancer Research

Abstract

Kinases, at the signaling level, dynamically mediate uncontrolled cellular growth, survival and other cancer supporting processes. This, paired with the inherent druggability of kinases, points to the importance of measuring kinase activity, and that of inhibitors against them, directly, and to analyze this accurately. High-throughput kinome profiling technologies, such as the PamStation^®12, allow researchers to kinetically capture kinase activity, against a multitude of peptide targets simultaneously. Yet, the complex datasets produced often require advanced computational tools and bioinformatics expertise to properly analyze that are not intuitive or readily available. To address this gap, we developed KinoViz, a web-based application to simplify analysis and visualization of kinome array data. KinoViz offers a suite of interactive tools that enables users to upload raw peptide phosphorylation datasets and conduct in-depth analyses without the need for coding knowledge. Key features include modules for visualizing kinetic phosphorylation curves, identifying statistically significant peptide changes, exploring individual peptide profiles, and generating insightful visualizations such as heatmaps, network diagrams, and dimensionality reduction plots (PCA, UMAP). By making complex kinomic data more accessible and interpretable, KinoViz allows researchers to rapidly generate interactive visualizations and comparative analyses. We aim to expand KinoViz's analytical capabilities for more advanced use, including use in direct translational drug discovery.

Keywords: Ex Vivo Profling; Glioblastoma; Kinomics; Patient-derived xenografts.

Figure 1. Overview of the KinoViz web application interface and visualization modules.

(A) Data upload and preprocessing interface, featuring interactive AG Grid tables for data filtering and exploration. (B) Identification and ranking of peptides based on signal intensity, emphasizing peptides with the highest phosphorylation signals. (C) Visualization of kinetic phosphorylation patterns across multiple experimental cycles and exposure times. (D) Comparative phosphorylation intensity plots for selected peptides under different experimental conditions. (E) Heatmap illustrating hierarchical clustering patterns of peptide phosphorylation intensities across samples. (F) Dimensionality reduction analysis (principal component analysis, PCA) for exploration of global phosphorylation intensity patterns. (G) Peptide–peptide correlation network analysis demonstrating relationships and connectivity among peptide phosphorylation events. (H) Statistical summary module, including comparative bar charts highlighting the top phosphorylated peptides.