A Multi-Omics Resource for Neuroblastoma Research: Molecular Profiles and Drug Response Data for Classical NB Cell Lines

Neuroblastoma remains one of the most challenging pediatric cancers to treat. Despite decades of research, high-risk disease still carries a poor prognosis, and finding effective therapies requires a deep understanding of the molecular landscape driving each tumor. To support that mission, we are excited to announce the release of a comprehensive multi-omics dataset covering the most widely used classical neuroblastoma cell lines — an openly accessible resource designed to accelerate discovery across the field.

What Is in the Dataset?

This resource brings together multiple layers of molecular and pharmacological data for a panel of classical neuroblastoma cell lines. In one place, researchers can now access:

Transcriptomics (mRNA expression) Genome-wide gene expression profiles capturing the transcriptional state of each cell line. These data allow researchers to explore pathway activity, subtype classification, and gene regulatory networks.

DNA copy number variation Genome-scale DNA copy number profiles, including the characteristic large-scale chromosomal gains and losses that define neuroblastoma biology — such as 1p deletion, 11q loss, and 17q gain — as well as focal amplifications like MYCN.

Somatic mutations Mutational data across the cell line panel, including known neuroblastoma driver alterations in genes such as ALK, ATRX, and PHOX2B, enabling genotype–phenotype association studies.

Drug response profiling (200 compounds) Dose–response data for 200 drugs spanning a broad range of mechanisms of action — including targeted therapies, chemotherapeutics, epigenetic modulators, and investigational compounds. Response metrics include IC50, AUC, and curve parameters, enabling robust pharmacological comparisons.

Why This Resource Matters

Neuroblastoma cell lines are indispensable tools in cancer research. Yet, until now, molecular and pharmacological data for these lines have been scattered across publications and databases — generated under different conditions, with inconsistent methods, making direct comparisons difficult.

By profiling the same cell lines across all data modalities under standardized conditions, this dataset makes it possible to:

  • Link molecular features to drug sensitivity. Which genomic alterations or expression signatures predict response to a given compound? This dataset is powered to answer that question at scale.
  • Identify subtype-specific vulnerabilities. Neuroblastoma is a biologically heterogeneous disease. By integrating multi-omics data with drug response, researchers can identify therapeutic opportunities tied to distinct molecular subtypes.
  • Benchmark computational methods. The breadth of data modalities makes this a valuable benchmark resource for multi-omics data integration algorithms, machine learning models, and network-based analyses.
  • Prioritize pre-clinical experiments. Instead of testing compounds based on intuition alone, researchers can mine the pharmacological data to rationally select candidates with the strongest molecular rationale.

The Cell Lines

The panel covers the classical neuroblastoma cell lines that have been the workhorses of the field for decades — including lines representing different MYCN status, ALK mutational background, differentiation state, and treatment history. This breadth ensures that findings from the dataset are broadly informative across the biological spectrum of neuroblastoma.



How to Access the Data

The full dataset is freely available at:

https://r2platform.com/pmc_nb_drugs/


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