Transcriptomic Correlation Identifies Cell Model Representatives for MYCN-Amplified Pediatric Neuroblastoma, Downstream Impact of Model Choice on Functional Interpretation, and Potential Drug Repositioning Candidates
Issued Date
2026-03-01
Resource Type
eISSN
15578100
Scopus ID
2-s2.0-105033862864
Pubmed ID
41642108
Journal Title
Omics A Journal of Integrative Biology
Volume
30
Issue
3
Start Page
134
End Page
145
Rights Holder(s)
SCOPUS
Bibliographic Citation
Omics A Journal of Integrative Biology Vol.30 No.3 (2026) , 134-145
Suggested Citation
Venkatraman S., Pongchaikul P., Balasubramanian B., Anurathapan U., Meller J., Tohtong R., Hongeng S., Chutipongtanate S. Transcriptomic Correlation Identifies Cell Model Representatives for MYCN-Amplified Pediatric Neuroblastoma, Downstream Impact of Model Choice on Functional Interpretation, and Potential Drug Repositioning Candidates. Omics A Journal of Integrative Biology Vol.30 No.3 (2026) , 134-145. 145. doi:10.1177/15578100261419486 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116026
Title
Transcriptomic Correlation Identifies Cell Model Representatives for MYCN-Amplified Pediatric Neuroblastoma, Downstream Impact of Model Choice on Functional Interpretation, and Potential Drug Repositioning Candidates
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Abstract
Neuroblastoma (NB) is the most common extracranial solid malignancy of children, and MYCN amplification defines a high-risk subtype with poor outcomes. Although widely used in preclinical drug discovery, NB cell lines are often selected based on availability rather than the molecular characteristics of patient-derived tumors, leading to a critical translational gap between experimental outcomes and clinical relevance. To address this, we developed a rank-based transcriptomic correlation framework to assess the concordance between patient-derived tumors (n = 642; combined from the SEQC/MAQC-III and TARGET cohorts) and publicly available NB cell lines (n = 39). This system-level analysis enabled the identification of cell model representatives (CMRs) that closely recapitulate the gene expression landscapes of clinical tumors. COG-N-557, SMS-KAN, and NB-SD emerged as the top CMRs for MYCN-amplified tumors, whereas COG-N-549, FELIX, and SK-N-SH were identified for MYCN-nonamplified tumors. Pathway enrichment analyses indicated that MYCN-amplified CMRs retain key transcriptional programs involved in neuronal development and tumor proliferation, supporting their biological relevance. Leveraging these models, we integrated pharmacogenomic connectivity mapping and drug-gene network analyses to uncover kinase inhibitors and epigenetic modulators as promising therapeutic candidates capable of targeting MYCN-driven transcriptional programs, despite MYCN being an undruggable oncogene. In conclusion, this study addresses a fundamental systems biology and translational research gap by establishing a data-driven framework for selecting NB cell lines that accurately reflect patient-derived tumor biology with direct implications for prioritizing therapeutically relevant drug candidates. Future studies should prioritize the top CMRs as in vitro models to enhance translational relevance and accelerate precision drug discovery in high-risk pediatric NB.