Biology subjects

Rozowsky, J. S.

Publications and source records attributed to Rozowsky, J. S..

4 recordsLinked to original sources

bioRxiv2026

Single-cell multi-omic analyses resolve the cellular diversity of ALK/ROS1/MET/NTRK-fused gliomas in infants and older children

Pediatric cancers are thought to arise from dysregulation of developmental programs, otherwise tightly regulated in time and space. Infant-type hemispheric gliomas (IHGs) arise in early childhood, driven by characteristic ALK/ROS1/MET/NTRK receptor tyrosine kinase (RTK) gene fusions. We dissected the cellular hierarchies of 24 fusion-positive gliomas, spanning infants through adolescents, using single-cell and single-nucleus RNA/ATAC-seq, and spatial transcriptomics. We identified five cancer cell states, with radial glia-like cells at the apex of a neoplastic hierarchy resembling neuronal- and glial-like trajectories. Neuronal-like cells were enriched in most IHGs but diminished in ROS1-fused IHGs and older patients. Integration of chromatin profiling revealed FOS/JUN-driven oncogenic programs and high inferred plasticity across all cancer cell populations. Myeloid cells, the most abundant non-neoplastic population, comprised distinct subgroups, suggesting context-dependent functions. Despite lacking high-order structure, spatial transcriptomics revealed discrete cellular niches within IHGs. Collectively, our findings elucidate the cellular states and developmental programs underlying IHGs and RTK-fused gliomas in older patients, opening new avenues for research and therapy innovation.

cancer biology↗

bioRxiv2026

A context dependent hierarchy of APOBEC3A and APOBEC3B mutators in lung adenocarcinoma

APOBEC3 cytosine deaminases are major sources of cancer mutations. In lung adenocarcinoma (LUAD), APOBEC3 activity drives tumor evolution and therapy resistance, nominating these enzymes as therapeutic targets. Although APOBEC3A drives the major portion of APOBEC3-associated mutational signature burdens across cancers, the relative contributions of individual APOBEC3 paralogs in LUAD remain unclear, limiting effective targeting. Here, we define the roles of endogenously misregulated APOBEC3 deaminases in LUAD using CRISPR-Cas9 knockouts and whole-genome sequencing of 197 long-term-propagated single-cell clones. We show that APOBEC3A and APOBEC3B both generate mutations but display marked heterogeneity, ranging from single-enzyme to dual activity or minimal mutagenesis, which is not predicted by commonly used mRNA and protein levels, or deaminase assays. Using these genetically defined systems, we provide the first causal evidence that endogenous APOBEC3A and APOBEC3B generate the indel signature InD9a in tumors, linking it to uracil excision following cytosine deamination. Together, these findings reveal heterogeneous APOBEC3 contributions to LUAD mutagenesis and highlight the need for biomarkers that resolve individual APOBEC3 activities for their effective targeting.

cancer biology↗

bioRxiv2024

Single-cell multidimensional profiling of tumor cell heterogeneity in supratentorial ependymomas

Supratentorial ependymomas are aggressive childhood brain cancers that retain features of neurodevelopmental cell types and segregate into molecularly and clinically distinct subgroups, suggesting different developmental roots. The developmental signatures as well as microenvironmental factors underlying aberrant cellular transformation and behavior across each supratentorial ependymoma subgroup are unknown. Here we integrated single cell- and spatial transcriptomics, as well as in vitro and in vivo live-cell imaging to define supratentorial ependymoma cell states, spatial organization, and dynamic behavior within the neural microenvironment. We find that individual tumor subgroups harbor two distinct progenitor-like cell states reminiscent of early human brain development and diverge in the extent of neuronal or ependymal differentiation. We further uncover several modes of spatial organization of these tumors, including a high order architecture influenced by mesenchymal and hypoxia signatures. Finally, we identify an unappreciated role for brain-resident cells in shifting supratentorial ependymoma cellular heterogeneity towards neuronal-like cells that co-opt immature neuronal morphology and invasion mechanisms. Collectively, these findings provide a multidimensional framework to integrate transcriptional and phenotypic characterization of tumor heterogeneity in supratentorial ependymoma and its potential clinical implications.

cancer biology↗

bioRxiv2018

A cross-organism framework for supervised enhancer prediction with epigenetic pattern recognition and targeted validation

Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. Only a few mammalian enhancers have been validated, making it difficult to train statistical models for their identification properly. Instead, postulated patterns of genomic features have been used heuristically for identification. The development of massively parallel assays allows for the characterization of large numbers of enhancers for the first time. Here, we developed a framework that uses Drosophila STARR-seq data to create shape-matching filters based on enhancer-associated meta-profiles of epigenetic features. We combined these features with supervised machine learning algorithms (e.g., support vector machines) to predict enhancers. We demonstrated that our model could be applied to predict enhancers in mammalian species (i.e., mouse and human). We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines. Overall, the validations involved 153 enhancers in 6 mouse tissues and 4 human cell lines. The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription-factor binding patterns at predicted enhancers and promoters in human cell lines. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.

bioinformatics↗