{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T18:09:51Z","timestamp":1778609391152,"version":"3.51.4"},"reference-count":60,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Mongkut\u00e2\u20ac\u2122s University of Technology Thonburi (KMUTT), Thailand Science Research and Innovation (TSRI), and National Science, Research and Innovation Fund","award":["FRB680074\/0164"],"award-info":[{"award-number":["FRB680074\/0164"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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. First, we used a Random Walk with Restart (RWR) algorithm to propagate the effects of drug combinations on a molecular interaction network tailored to A549 NSCLC. This approach transformed sparse drug-target data into comprehensive molecular profiles for 607 drug combinations. These profiles were then analyzed using Graph-regularized Non-negative Matrix Factorization (GNMF) to classify drug combinations based on metagenes, representing common patterns of impact on key biological pathways. Our analysis successfully identified clusters highly enriched with synergistic drug pairs. Notably, a single feature, Metagene 2, consistently drove synergy in seven of these clusters. Pathway enrichment analysis indicated that Metagene 2 is primarily associated with the interconnected RAS, MAPK, and PI3K\/AKT signaling pathways. This observation led to specific mechanistic hypotheses: for instance, synergy with dasatinib appears to result from co-targeting SRC compensatory pathways, while the enhanced effects of paclitaxel combinations arise from partner drugs disrupting the PI3K\/AKT pathway, which in turn modulates Tau protein activity. In conclusion, the metagene-based classification provides an interpretable and rational approach for uncovering the systemic biological mechanisms responsible for drug synergy. This framework offers a valuable tool for designing effective, multi-target therapeutic strategies to overcome drug resistance in NSCLC.<\/jats:p>","DOI":"10.1371\/journal.pone.0343902","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:31:13Z","timestamp":1778607073000},"page":"e0343902","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncovering the mechanisms of synergistic drug combinations in non-small cell lung cancer through metagene-based classification"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8954-2044","authenticated-orcid":true,"given":"Comkrit","family":"Lomloy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9564-6749","authenticated-orcid":true,"given":"Piyanut 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