Virtual cells learn how states respond to interventions, which requires perturbation data. For GBM the library contains in vivo Perturb-seq with radiotherapy in mouse models, drug-signature integration, organoid drug screens and one organoid temozolomide preprint; large-scale perturbation atlases are mostly non-GBM.
VC0042 Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution. — Zhang J et al., Cell, 2026
VC0019 Predicting cellular responses to perturbation across diverse contexts with State. — Adduri AK et al., Cell, 2026
VC0130 UniPert-G2CP bridges genetic and chemical screens from molecular representation to phenotype modeling — Li et al., Cell, 2026
VC0097 Automated and personalized glioblastoma tumor organoid drug screening platform exposes sensitivity to proteasome and HDAC inhibitors. — Jungwirth G et al., NPJ precision oncology, 2026
VC0132 Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations — Suter et al., Nature Communications, 2026
VC0095 BRD2 bromodomain-mediated regulation of cell state plasticity modulates therapy response in glioblastoma. — Vadla R et al., Neuro-oncology, 2025
VC0129 · preprint DELPHAI, AI Agent for Predicting Drug Response and Resistance — Peng et al., preprint, 2025
VC0048 Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. — Ahlmann-Eltze C, Huber W, Anders S., Nature methods, 2025
VC0041 · preprint Tahoe-100M : A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling — Zhang J et al., bioRxiv, 2025
VC0070 In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma. — Liu SJ et al., Genome biology, 2024
VC0040 Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. — Replogle JM et al., Cell, 2022