Biology subjects

Robinson, G. W.

Publications and source records attributed to Robinson, G. W..

4 recordsLinked to original sources

bioRxiv2026

Developmental determinants of male bias in medulloblastoma

Boys experience an overall increased incidence of several childhood cancers, including medulloblastoma, a clinically heterogeneous cerebellar tumor. In subtypes of Group 3 and Group 4 medulloblastoma, males are three times more prevalent than females. As medulloblastoma is suspected to initiate during fetal development, we hypothesized that this sex bias reflects a combination of prenatal, sex-specific developmental processes and somatic alterations. To test these hypotheses, we compiled a large multi-omics dataset from children with medulloblastoma, which revealed sex-specific alterations, including frequent loss of the inactive X chromosome in females with Group 4. Generation of a sex-matched single-cell transcriptome atlas of the developing murine cerebellum enabled investigation of putative developmental factors underlying sex bias. Progenitors giving rise to Group 3/4 subgroups were more abundant, more proliferative, and harbored more open chromatin for recruitment of LMX1A and OTX2, master transcription factors defining Group 3/4 identity. Advanced genetically engineered mouse models and human cerebellar organoids were leveraged to determine whether sexual dimorphism arises from intrinsic or extrinsic factors. These models showed that the XY genotype contributed to the phenotype, but the predominant effect was driven by presence of the male gonadal hormone testosterone. Our findings provide a sex-specific genetic and neurodevelopmental explanation for male bias in an aggressive pediatric brain tumor. Outcomes from this study may inform novel treatment strategies delivered according to sex and are likely to be broadly applicable to other sex-biased malignancies arising in early life. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/714163v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@566e41org.highwire.dtl.DTLVardef@607ae9org.highwire.dtl.DTLVardef@25679aorg.highwire.dtl.DTLVardef@1b4beec_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

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↗

bioRxiv2025

High-resolution digital dissociation of brain tumors with deep multimodal autoencoder

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvi-ronment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MODE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MODE was evaluated through rigorous simulation experiments and real multiomic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.

bioinformatics↗

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↗