Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma

Glioblastoma (GBM) exhibits profound intratumoral molecular heterogeneity that contributes to treatment resistance and poor survival. Specifically, each tumor comprises multiple molecularly-distinct subpopulations with different treatment sensitivities. This heterogeneity not only portends the pre-existence of resistant molecular subpopulations, but also the communications between neighboring subpopulations that further modulate tumorigenicity and resistance. In fact, a minority tumor subpopulation with EGFRvIII mutation has been shown to potentiate a majority subpopulation with wild-type EGFR to increase tumor growth, cell survival, and drug resistance. This type of cooperativity presents clear implications for improving GBM treatment. Yet compared to other tumor types, the interactions in GBM remain critically understudied.

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A significant barrier to studying the interactions between molecularly-distinct subpopulations is the challenge of tissue sampling in GBM. In particular, contrast-enhanced MRI (CE-MRI) routinely guides surgical biopsy and resection of the MRI enhancing core, but fails to address the diverse subpopulations of the surrounding non-enhancing parenchyma (so called “brain around tumor” or BAT). These unresected residual subpopulations in BAT represent the main contributors to tumor recurrence, which can exhibit different therapeutic targets (and interactions) compared with enhancing biopsies. To address the limitations of tissue sampling, imaging techniques can help quantitatively characterize tumors in their entirety, including unresected BAT regions. Our group has used multi-parametric MRI and image-guided biopsies to develop and validate machine-learning (ML) models of intratumoral genomic heterogeneity, with particular focus on the BAT zone.

In Aim 1, will we collect and molecularly profile a large set of image-recorded stereotactic biopsies in primary GBM patients to quantify the diversity of molecularly-distinct subpopulations, as well as their phenotypic niches, throughout the BAT zone. We will assess local heterogeneity at the biopsy level and also co-localize regional patterns and rates of recurrence on serial MRI. In Aim 2, we will use these biopsies and spatially matched MRI metrics to refine our existing ML predictive models. We will use these ML models to co-localize spatial patterns of molecularly-distinct subpopulations (and their phenotypic niches) to quantify their risk of regional recurrence. In Aim 3, we will functionally validate the subpopulation interactions observed in Aims 1 and 2 using patient derived xenograft (PDX) models. We will also validate these interactions in human GBM using a subset of spatially matched biopsies from primary and recurrent tumors in the same patients.

This proposal leverages our unique expertise in image-guided tissue analysis and MRI-based computational modeling to study the diversity of molecularly-distinct subpopulations and the evolving competitive landscapes in human GBM. This work will help risk stratify patients in future targeted clinical drug trials and should also facilitate new strategies (e.g., adaptive therapy) to exploit subpopulation co-dependency for therapeutic benefit.

Collaborating Institutions

Mayo Clinic

Arizona State University

Barrow Neurological Institute


Kristin R. Swanson, PhD Professor Swanson is MPI of this CSBC U01 Project. Dr. 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 post-doctoral 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 is co-Director of the Precision NeuroTherapeutics Innovation Program and Director of the Mathematical NeuroOncology Lab at Mayo Clinic. She also holds appointments at Arizona State University and the Translational Genomics Institute. Dr. Swanson’s research lab has worked to pioneer the highly integrative field of Mathematical Neuro-Oncology which is focused on developing clinical-translational tools to enable improving the lives of patients with brain cancer. She has over 15 years of experience developing and analyzing mathematical models to describe biomedical phenomena specifically relating to routine and novel imaging modalities.

Leland S. Hu, MD Professor Hu is MPI of this CSBC U01 Project. Dr. Hu received his MD in 2001 and completed his residency in Diagnostic Radiology in 2006 at the University of Texas – Southwestern Medical Center. He completed a Neuroradiology fellowship at Barrow Neurological Institute in 2008. Currently, Dr. Hu is a board certified Neuroradiologist and an Assistant Professor of Radiology at Mayo Clinic. He has accumulated over 10 years of translational research experience with advanced imaging techniques in human brain tumors. His research has focused on the development and validation of image-based biomarkers to quantify histologic and molecular heterogeneity in glioblastoma (GBM). He has led unique multi-disciplinary teams of clinicians and researchers to quantify diverse aspects of intratumoral heterogeneity and the microenvironment in GBM. Dr. Hu has a primary clinical appointment at Mayo Clinic in Arizona and an adjunct appointment at Barrow Neurological Institute (BNI) and has clinical privileges to help oversee patient recruitment, multi-parametric MRI acquisition, and biopsy collection at both medical institutions.

J. Ross Mitchell, PhD Professor Mitchell is MPI of this CSBC U01 Project. Dr. Mitchell received his BSc Hons (1986) and MSc (1989) in Computer Science from University of Regina and completed his PhD (1995) in Medical Biophysics from Western University in London, Ontario. Dr. Mitchell is a Professor of Radiology with Mayo Clinic in Arizona. He has 25 years of experience developing radiomics algorithms – methods to identify, extract and combine information from medical images and signals to predict disease state and/or clinical outcomes. This often requires sophisticated tools (visualization, computer vision, machine learning and deep learning). He has extensive experience translating academic research to clinical products – he is the Founding Scientist and a co-founder of Calgary Scientific Inc. (CSI). The prototype for CSI’s FDA Class II cleared product for mobile tele-radiology was developed in his lab. This product is now available in 12 languages and 33 countries worldwide. He and his laboratory provide expertise in the development and/or implementation of the radiomics and machine learning methods employed in the proposed work.

Nhan L. Tran, PhD Professor Tran is MPI of this CSBC U01 Project. Dr. Tran completed his PhD in 2002 at the University of Arizona followed by fellowships in NeuroOncology at Barrow Neurological Institute and Translational Genomics Research Institute (TGen) from 2003 to 2007. He served as cancer biology faculty at TGen from 2007 until 2016. In 2016, Dr. Tran joined Mayo Clinic in Arizona as Professor of Cancer Biology and Neurosurgery. Dr. Tran’s research strengths lie in the area of signal transduction and the application of molecular genetics techniques toward the elucidation of the mechanisms of aberrant signaling in the context of tumor growth and invasion. Dr. Tran has extensive background and training in tumor cell biology, genomic and molecular technology, biochemistry, and immune detection techniques for signal transduction. Additionally, he also has experiences with microRNA profiling, aCGH analysis, whole genome sequencing, RNA sequencing, and expression profiling and validation of candidate genes in cell biology in both frozen and FFPE specimens.

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