Biology subjects

Gajjar, A.

Publications and source records attributed to Gajjar, A..

2 recordsLinked to original sources

bioRxiv2025

Hidden-driver inference reveals synergistic brain-penetrant therapies for medulloblastoma

Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood-brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis), a systems biology framework that computationally prioritizes synergistic, BBB-permeable drug combinations by identifying hidden drivers sustaining oncogenic programs. Integrating MB-specific networks, transcriptomic data, and drug-gene interactions, SINBA nominated 32 candidates, of which 19 were experimentally validated as synergistic. Through iterative prioritization and experimental refinement, the MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in xenograft and immunocompetent models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of developmental origins and immune reprogramming. These findings establish SINBA as a computationally assisted discovery framework for clinically actionable combinations in high-risk MB.

cancer biology↗

bioRxiv2020

Patient-Derived Orthotopic Xenografts and Cell Lines from Pediatric High-Grade Glioma Recapitulate the Heterogeneity of Histopathology, Molecular Signatures, and Drug Response

Pediatric high-grade glioma (pHGG) is a major contributor to cancer-related death in children. In vitro and in vivo disease models reflecting the intimate connection between developmental context and pathogenesis of pHGG are essential to advance understanding and identify therapeutic vulnerabilities. We established 21 patient-derived pHGG orthotopic xenograft (PDOX) models and eight matched cell lines from diverse groups of pHGG. These models recapitulated histopathology, DNA methylation signatures, mutations and gene expression patterns of the patient tumors from which they were derived, and included rare subgroups not well-represented by existing models. We deployed 16 new and existing cell lines for high-throughput screening (HTS). In vitro HTS results predicted variable in vivo response to inhibitors of PI3K/mTOR and MEK signaling pathways. These unique new models and an online interactive data portal to enable exploration of associated detailed molecular characterization and HTS chemical sensitivity data provide a rich resource for pediatric brain tumor research.

cancer biology↗