Uncovering the Mechanisms of Synergistic Drug Combinations in Non-small Cell Lung Cancer Through Metagene-based Classification
Comkrit Lomloy, Piyanut Ratphibun Yamashita, Teerasit Termsaithong, Teeraphan Laomettachit
Abstract:
Drug resistance remains a significant challenge in treating non-small cell lung cancer (NSCLC). Identifying synergistic drug combinations that simultaneously target multiple signaling pathways is crucial to overcoming drug resistance, yet challenging due to the extensive search space. To address this issue, we developed a computational framework that combines network analysis and clustering based on matrix factorization to gain mechanistic insights into highly synergistic drug combinations in the A549 NSCLC cell line.
Introduction:
Lung cancer remains a major global health issue. In 2022, an estimated 2.5 million new cases were diagnosed, accounting for 12.4% of all new cancer cases worldwide. It was also the leading cause of cancer-related deaths, with approximately 1.8 million fatalities. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of these cases. Among NSCLC, adenocarcinoma is the most common type, comprising around 40% of cases.
Materials and Methods:
Key information includes the names of the drugs in the drug pairs and their corresponding synergistic scores. Next, protein targets for each drug in the combinations were obtained from DrugBank. Subsequently, a cancer-specific sub-network was extracted from the Parsimonious Composite Network (PCNet). For each drug combination, drug targets for each drug in the combination were mapped onto the extracted cancer network.
Discussion:
The challenge of drug resistance in non-small cell lung cancer (NSCLC) necessitates a shift from single-target therapeutics to rational, multi-target combination strategies. The underlying rationale for this shift stems from observations that combining interventions on functionally proximal genes leads to greater efficacy than using single agents.
Acknowledgments:
T.L. acknowledges the use of Grammarly to correct grammar and improve language clarity. The authors appreciate the editor and reviewers’ valuable suggestions, which have greatly improved the manuscript.
Citation: Lomloy C, Yamashita PR, Termsaithong T, Laomettachit T (2026) Uncovering the mechanisms of synergistic drug combinations in non-small cell lung cancer through metagene-based classification. PLoS One 21(5): e0343902. https://doi.org/10.1371/journal.pone.0343902
Editor: Erfan Ghadirzadeh, Mazandaran University of Medical Sciences, Islamic Republic of Iran
Received: November 3, 2025; Accepted: February 12, 2026; Published: May 12, 2026.
Copyright: © 2026 Lomloy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files. The programming scripts and related data are available from our GitHub repository: https://github.com/systemsbiomedicine/A549-drug-synergy-with-network-propagation.
Funding: This work was supported by King Mongkut’s University of Technology Thonburi (KMUTT), Thailand Science Research and Innovation (TSRI), and National Science, Research and Innovation Fund (NSRF) (Fiscal year 2025, Grant number FRB680074/0164 to TL and TT). CL was supported by the Petchra Pra Jom Klao Master’s Degree Research Scholarship from King Mongkut’s University of Technology Thonburi. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
