Abstract 2498: PharmaRonNet: integrating multi-omics data to reveal key biological pathways
Kaiqiang Hu, Wanyu Tao, Yunyang Zhang, Pengwei Pan, Fang He · Cancer Research · 2025
Abstract In this study, we present PharmaRonNet, a new bioinformatics tool based on the ResponseNet algorithm. PharmaRonNet constructs weighted networks from either single-omics or multi-omics data, revealing key pathways and mediators involved in specific biological or pathological processes. To validate PharmaRonNet’s functionality, a case study was conducted using a public transcriptomics dataset of the liver cancer cell line HepG2 treated with insulin. PharmaRonNet successfully identified several insulin-response-related genes as key factors, outperforming standard methods such as differentially expressed genes (DEGs) analysis and other gene network-building tools. Subsequently, PharmaRonNet was used to investigate the mechanisms leading to resistance to the selective CDK4/6 inhibitor, Palbociclib, in hormone receptor (HR)-positive breast cancer. With WES, transcriptome, and proteome data from the Palbociclib-resistant MCF7 breast cancer cell line, PharmaRonNet revealed the involvement of tyrosine phosphorylation and cyclic nucleotide-mediated signaling along with cell growth regulation. Within these pathways, certain "core" genes were identified with the most linkages within the PharmaRonNet. Synergy effect tests were then performed to validate the roles of these key factors in mediating Palbociclib resistance. Overall, PharmaRonNet helps link different layers of omics data, enabling the discovery of hidden mechanisms often overlooked by canonical bioinformatics analysis, and enhancing our understanding of complex biological phenomena. These insights pave the way for developing novel strategies to combat drug resistance in cancers. Citation Format: Kaiqiang Hu, Wanyu Tao, Yunyang Zhang, Pengwei Pan, Fang He. PharmaRonNet: integrating multi-omics data to reveal key biological pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2498.