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Updated: May 6, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Reconstructing targetable pathways in lung cancer by integrating diverse omics data
O Alejandro Balbin1, John R Prensner, Anirban Sahu
11] Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 8109, USA [2] Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA [3] Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Abstract:
Global 'multi-omics' profiling of cancer cells harbours the potential for characterizing the signalling networks associated with specific oncogenes. Here we profile the transcriptome, proteome and phosphoproteome in a panel of non-small cell lung cancer (NSCLC) cell lines in order to reconstruct targetable networks associated with KRAS dependency. We develop a two-step bioinformatics strategy addressing the challenge of integrating these disparate data sets. We first define an 'abundance-score' combining transcript, protein and phospho-protein abundances to nominate differentially abundant proteins and then use the Prize Collecting Steiner Tree algorithm to identify functional sub-networks. We identify three modules centred on KRAS and MET, LCK and PAK1 and β-Catenin. We validate activation of these proteins in KRAS-dependent (KRAS-Dep) cells and perform functional studies defining LCK as a critical gene for cell proliferation in KRAS-Dep but not KRAS-independent NSCLCs. These results suggest that LCK is a potential druggable target protein in KRAS-Dep lung cancers.
Insights
This study profiles multi-omics data in non-small cell lung cancer (NSCLC) to map KRAS dependency networks. Researchers identified LCK as a potential druggable target crucial for KRAS-dependent NSCLC proliferation.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Global multi-omics profiling offers insights into cancer signaling networks.
- Understanding oncogene-associated networks is crucial for targeted therapies.
- Non-small cell lung cancer (NSCLC) often involves KRAS mutations, driving dependency.
Purpose of the Study:
- To reconstruct targetable signaling networks associated with KRAS dependency in NSCLC.
- To integrate multi-omics data (transcriptome, proteome, phosphoproteome) for network analysis.
- To identify novel therapeutic targets in KRAS-dependent NSCLC.
Main Methods:
- Developed a two-step bioinformatics strategy to integrate multi-omics data.
- Defined an 'abundance-score' combining transcript, protein, and phospho-protein levels.
- Utilized the Prize Collecting Steiner Tree algorithm to identify functional sub-networks.
Main Results:
- Identified three key modules centered on KRAS/MET, LCK/PAK1, and β-Catenin.
- Validated protein activation within these modules in KRAS-dependent cells.
- Demonstrated LCK's critical role in proliferation of KRAS-dependent NSCLC, but not KRAS-independent NSCLC.
Conclusions:
- LCK is a critical gene for proliferation in KRAS-dependent NSCLC.
- LCK represents a potential druggable target for KRAS-dependent lung cancers.
- Multi-omics data integration provides a powerful approach for identifying cancer-specific targets.
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