Biology subjects

Bowie, W.

Publications and source records attributed to Bowie, W..

3 recordsLinked to original sources

bioRxiv2025

CancerSTFormer enables multi-scale analysis of spot-resolution spatial transcriptomes and dissects the gene and immune regulatory responses of targeted therapies

The growing number of spot-resolution sequencing based spatial transcriptomic (ST) datasets provides an unprecedented opportunity to study multicellular spatial niches driving cancer transitions. However studying niche-level behavior of tumors remains challenging as it requires a multi-scale approach to modeling the spatial niches and the ability to predict possible effects of genetic perturbations on spatial niches. We propose CancerSTFormer, consisting of a pair of spatially aware transcriptomic foundation models to accommodate niche modeling at different length scales. These models, at the 50{micro}m-Local and 250{micro}m-Extended scales, possess unique capabilities to recover ligand-target gene relationships, niche-specific differentially expressed genes, and organ-specific metastasis associated genes in diverse cancer applications. CancerSTFormer can also reveal the regulatory effects of immune-checkpoint blockade therapies, and other targeted therapies, on patients tumors through perturbation analysis, and accurately recapitulated perturbation responses from a spatial Perturb-map experiment. By reusing existing spot-resolution ST studies at scale, this tool transforms the vast spot-resolution ST data into a resource for understanding how gene perturbation impact spatial niches in cancer, while also providing ST-driven, gene-based refinement of treatment-resistance and sensitivity signatures derived from existing bulk transcriptomic studies, enhancing signature interpretation.

bioinformatics↗

bioRxiv2025

Anti-CSF-1R therapy with combined immuno- chemotherapy coordinate an adaptive immune response to eliminate macrophage enriched Triple Negative Breast Cancers

Patients diagnosed with metastatic triple negative breast cancer (mTNBC) have limited treatment options, are more prone to develop resistance and are associated with high mortality. A cold tumor immune microenvironment (TIME) characterized by low T cells and high tumor associated macrophages (TAMs) in mTNBC is associated with the failure of standard-of-care chemotherapy and immune checkpoint blockade (ICB) treatment. We demonstrate that the combination of immunomodulatory metronomic Cyclophosphamide (CTX) coupled with anti-CSF-1R antibody targeted therapy (SNDX-ms6352) and anti-PD-1 (ICB), was highly effective against aggressive metastatic syngeneic Trp53 null TNBC genetically engineered mouse models (GEMMs) that present with high macrophage infiltration. Mechanistically, CSF-1R inhibition along with CTX disrupted the M-CSF/CSF-1R axis which upregulated IL-17, IL-15 and type II interferon resulting in elevated B- and T cell infiltration. Addition of an anti-PD-1 maintenance dose helped overcome de novo PD-L1 intra-tumoral heterogeneity (ITH) associated recurrence in lung and liver mTNBC.

cancer biology↗

bioRxiv2021

KERA: Analysis Tool for Multi-Process, Multi-State Single-Molecule Data

Molecular machines within cells dynamically assemble, disassemble, and reorganize. Molecular interactions between their components can be observed at the single-molecule level and quantified using colocalization single-molecule spectroscopy (CoSMoS), in which individual labeled molecules are seen transiently associating with a surface-tethered partner, or other total internal reflection fluorescence microscopy (TIRFM) approaches in which the interactions elicit changes in fluorescence in the labeled surface-tethered partner. When multiple interacting partners can form ternary, quaternary and higher order complexes, the types of spatial and temporal organization of these complexes can be deduced from the order of appearance and reorganization of the components. Time evolution of complex architectures can be followed by changes in the fluorescence behavior in multiple channels. Here, we describe the kinetic event resolving algorithm (KERA), a software tool for organizing and sorting the discretized fluorescent trajectories from a range of single-molecule experiments. KERA organizes the data in groups by transition patterns, and displays exhaustive dwell-time data for each interaction sequence. Enumerating and quantifying sequences of molecular interactions provides important information regarding the underlying mechanism of the assembly, dynamics and architecture of the macromolecular complexes. We demonstrate KERAs utility by analyzing conformational dynamics of two DNA binding proteins: RPA and XPD helicase.

biophysics↗