Campbell Riddick Coleman

All Publications

• Precision Immunotherapeutics for Glioblastoma: Current Approaches and Emerging Strategies in 2026. Cells Poe, J., Kim, C., Coleman, C., Nguyen, H., Velazhahan, V., Bergsneider, B., Sanker, V., Kim, S., Chen, Y., Abikenari, M., Choi, J., Lim, M. 2026; 15 (6)

  Abstract

  Glioblastoma (GBM) persists as one of the greatest challenges in the treatment of human cancer, despite extensive efforts to leverage the therapeutic potential of immunotherapy. While checkpoint blockade and other forms of immunotherapy have revolutionized the treatment of various cancers, their therapeutic efficacy in GBM has been hindered by the profound immunosuppressive environment, spatial heterogeneity, and dynamic immune metabolic challenges associated with the tumor microenvironment. In this review, we will synthesize recent advances and insights to develop a next-generation framework for GBM immunotherapy based on systems biology approaches to understanding the complex interplay between GBM and the immune system, as opposed to single-axis approaches to immune activation and modulation. We will discuss how the functional competence of the interferon system, myeloid antigen presentation status, T-cell clone status, spatial organization of the immune microenvironment, and resource competition between GBM and the immune system dictate therapeutic responsiveness. Furthermore, the current paper elucidates how recent advances in spatial transcriptomics, single-cell analysis, and high-parameter imaging enable us to understand how immune phenotype status varies across GBM regions and treatment status, and how this information can be used to develop predictive and pharmacodynamic biomarkers of therapeutic efficacy and failure. We will then discuss how these advances form the basis for rational combination approaches to GBM immunotherapy, which involve the integration of checkpoint blockade with metabolic reprogramming, myeloid modulation, and interferon system reactivation, and how artificial intelligence-based analytics and adaptive clinical trial design can guide the development of biomarker-based therapeutic selection approaches.

  View details for DOI 10.3390/cells15060561

  View details for PubMedID 41892350

  View details for PubMedCentralID PMC13025625

• Structural determinants of signal speed: Estimated axonal latency and its multimodal validation during face processing in autism. bioRxiv : the preprint server for biology Coleman, C. R., Nance, M. G., Jacokes, Z., Druzgal, T. J., Arutiunian, V., Kresse, A., Sullivan, C. A., Santhosh, M., Neuhaus, E., Borland, H., Bernier, R. A., Bookheimer, S. Y., Dapretto, M., Jack, A., Kleinhans, N. M., Jeste, S., McPartland, J. C., Naples, A., Geschwind, D., Gupta, A. R., Webb, S. J., Pelphrey, K. A., Van Horn, J. D., Newman, B. T., Puglia, M. H. 2025

  Abstract

  It has not previously been possible to investigate the fundamental relationship between axonal structure - which dictates action potential transmission - and human neuronal function in vivo. Here, we introduce a novel metric of axonal signal speed, estimated axonal latency (EAL), derived from the relationship between axonal diameter, myelination, and length measured via MRI. We validate EAL along two pathways of the face processing network by relating it to N170 latency, an electrophysiological marker of face processing speed measured via EEG. Our results show that EAL along these pathways predicts N170 latency specifically during face processing. Moreover, we demonstrate that individuals with and without autism rely upon different pathways, potentially providing a structural account for autism-related face processing differences. By establishing this relationship between EEG-based electrical function and MRI-based axonal microstructure, we provide a non-invasive, spatially detailed estimate of neuronal processing speed that can inform our understanding of brain function, development, and disorder.

  View details for DOI 10.1101/2025.03.19.644214

  View details for PubMedID 40166310

  View details for PubMedCentralID PMC11957106

• Towards Comprehensive Connectivity Modeling. Neuroinformatics Coleman, C., Van Horn, J. D. 2024; 22 (3): 225-227

  View details for DOI 10.1007/s12021-024-09676-4

  View details for PubMedID 38926268

  View details for PubMedCentralID 1794324