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Mayo Clinic PS-OP

Image-Based Models of Tumor-Immune Dynamics in Glioblastoma (GBM)

The use of immunotherapy to treat cancer continues to generate hope and excitement among those involved in cancer care and research as well as patients. However, our inability to explain why some patients do not respond to immunotherapy, combined with our inability to identify early response or predict the responders, poses serious challenges in this field.

Currently, biopsies serve as the most informative way to assess the immunological activity within a cancerous area. However, clinicians are spatially and temporally limited in the number of biopsies that they can obtain from patients, especially in cases of brain cancer. Clear evidence of tumor-immune environment heterogeneity across patients suggests that clinicians will need to use an individualized approach in order to accurately assess patient tumor’s specific immune environment and the evolution of these complex systems.

This PS-OP uses computational modeling and artificial intelligence to bridge the spatial scales of the cellular content comprising each Magnetic Resonance Imaging (MRI) scan at the voxel level, but also to bridge the temporal scales. PS-OP investigators are focusing on the most cellular immune population in glioblastoma, microglia/macrophages, that constitute as much as 50% of the cellular content of tumor specimens.

By fusing MRI with the biological heterogeneity found in image-localized biopsies through such radiomics approaches provides an opportunity to individualize the understanding of the tumor-immune environment, broadly benefiting scientists across the fields of oncology and immunology.

In addition to providing a deeper understanding of the tumor at every imaging time point, the radiomics maps can also be used to parameterize dynamic mechanistic models of tumor growth to allow for prediction of future dynamics. These spatio-temporal models allow researchers to test hypotheses about causal relationships between different cell types and microenvironmental factors, as well as to verify whether the radiomics maps provide early dynamic insights into tumor response that can impact


Kristin Swanson, Ph.D.

Kristin Swanson, Ph.D.
Mayo Clinic in Arizona

Dr. Kristin Swanson received her BS in Mathematics in 1996 from Tulane University followed by her MS (1998) and PhD (1999) in Mathematical Biology from the University of Washington. Following a postdoctoral fellowship in Mathematical Medicine at UCSF, she joined the faculty at the University of Washington in 2000, with appointments in both Neuropathology and Applied Mathematics. In 2015, she joined Mayo Clinic in Arizona as Professor and Vice Chair of the department of Neurological Surgery. She also holds an appointment at Arizona State University.

Dr. Swanson's research lab has served to pioneer the burgeoning field of mathematical neuro-oncology generating compelling data to support the practical application of patient-specific bio-mathematical models of glioma to assess, predict and optimize treatment. Her research efforts have been supported through funding by the NIH, numerous foundations, the James D. Murray Endowed Chair at the University of Washington, TGen and the Mayo Clinic.

Peter Canoll, M.D., Ph.D.

Peter Canoll, M.D., Ph.D.
Columbia University

Dr. Peter Canoll is a Professor of Pathology and Cell Biology, and Director of Neuropathology at Columbia University. He has 20 years of experience in diagnostic and experimental neuropathology and serves as the head neuropathologist on multiple clinical trials in the Columbia Brain Tumor Center and Neuro-Oncology program. For the last seven years he has been leading a multi-disciplinary effort at Columbia to collect and analyze MRI-localized biopsies from pre- and post-treatment Glioblastoma to characterize the molecular and cellular heterogeneity of these tumors and to correlate these findings with radiographic features.

His laboratory is applying cutting-edge methods to quantify the abundance, distribution and phenotypes of different cellular populations within the highly cellular core and infiltrative margins of glioma, including transformed glioma cells and untransformed cells in the brain tumor microenvironment.

Leland S. Hu, M.D.

Leland Hu, M.D.
Mayo Clinic in Arizona

Dr. Leland Hu is an Assistant Professor in Radiology at the Mayo Clinic College of Medicine and serves as an attending Neuroradiologist at Mayo Clinic in Phoenix, Arizona. He received his medical degree at the University of Texas – Southwestern Medical School, where he also completed his medical internship and residency training in Diagnostic Radiology. After completing his two-year clinical fellowship in Diagnostic Neuroradiology at Barrow Neurological Institute, he joined the medical faculty at Mayo Clinic in 2008.

Dr. Hu’s research focuses on the development and implementation of advanced imaging methods to improve diagnosis, treatment planning, and treatment monitoring in brain tumors. His initial work has sought to improve the accuracy of surveillance imaging in glioma, and his group published one of the first studies that validated the accuracy of Dynamic Susceptibility-weighted Contrast-enhanced (DSC) perfusion MRI (pMRI) to distinguish high-grade glioma recurrence from post-treatment radiation effects (e.g., pseudoprogression, radiation necrosis). He and his group have utilized image-guided tissue analysis and stereotactic coregistration to help overcome the challenges of intratumoral heterogeneity. Dr. Hu has recently published studies that have developed MRI and texture-based biomarkers of regional tumor cell invasion and intratumoral genetic heterogeneity in glioblastoma. He currently serves on the Imaging Committee for the Alliance for Clinical Trials in Oncology.

Nhan Tran, Ph.D.

Nhan Tran, Ph.D.
Mayo Clinic in Arizona

Dr. Nhan L. Tran is a Professor in the Department of Research and Cancer Biology at Mayo Clinic Arizona. Dr. Tran’s research has been focused on determining the cellular and biochemical mechanisms of action of candidate genes expressed in highly invasive glioblastoma cells and their matrix of aberrant signaling to discover points of convergence that can serve as targets of vulnerability for therapeutic intervention.

His laboratory focuses on the identification and characterization of certain members of the super family of cytokine receptors, the tumor necrosis factor receptors (TNFR) and their downstream signals via RhoGTPases to play important roles in modulating glioblastoma cell adhesion, invasion and cell survival. In addition, Dr. Tran’s expertise also lies in high-throughput assay development and applying molecular chemical libraries screens to exploit novel GBM targets as an innovative strategy to treat invasive glioblastoma. In addition, his research also focuses in characterizing GBM intratumor heterogeneity and genomic aberration of invasive glioblastoma cells by implementing range of genomic technologies (whole genome, exome, RNA sequencing and methylation) to study therapeutic resistance and drug delivery.

Jing Li, Ph.D.

Jing Li, Ph.D.
Georgia Institute of Technology

Dr. Jing Li’s research is machine learning for multi-modality data fusion in predictive modeling, sparse learning of high-dimensional data, and transfer learning. Her lab has been closely collaborating with domain experts and clinicians and developing machine learning algorithms to support imaging-based diagnosis and prognosis, as well as fusion of imaging, genomics, and clinical datasets to develop patient-specific models for brain cancer and other neurological diseases.

She has 10+ years of experiences of collaborating with researchers and clinicians on glioblastoma and developing radiomic/radiogenomic models to quantify intra-tumor heterogeneity based on machine learning integration of imaging and histologic/genomic datasets.

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