Mathematical Oncology Systems Analysis Imaging Center (MOSAIC)

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SUMMARY: OVERALL: MATHEMATICAL ONCOLOGY SYSTEMS ANALYSIS IMAGING CENTER Glioblastoma (GBM), the most aggressive primary brain cancer, is amongst the most heterogeneous of cancers, both intra- and inter-tumorally. GBMs are an admixture of neoplastic glioma cells and non-neoplastic / reactive brain parenchyma that contribute to the overall imageable tumor mass. As such, cellular content, including both cellular density and cellular composition, is critically important for understanding the status and evolution of a given tumor. Although MRI provides excellent soft tissue contrast and can noninvasively characterize anatomy, no methods exist to integrate a spatial and temporal understanding of the cellular components of the tumor inferred from imaging in vivo. It has become increasingly clear that precision oncology strategies rely on a quantitative and predictive understanding of the state of the cancer complex system evolving within each patient. Recent findings from our group have revealed two key opportunities we seek to leverage in our proposed Mathematical Oncology Systems Analysis Imaging Center (MOSAIC). First, molecular analysis of a cohort of our image-localized biopsies of GBM have inspired the concept of Glioma Tissue States as a composite classification of tissue samples. Our findings from single nucleus RNAseq reveal that specific subpopulations and cellular phenotypes of neoplastic and non- neoplastic cells show distinct patterns of co-habitation constraining potential cross-talk signaling. Second, we have found mathematical modeling and machine learning analyses of clinical MRI features of GBM biopsies are able to predict loco-regional features of GBM biology in vivo. These image-based models provide the promise to track aspects of intra- and inter-tumoral heterogeneity previously unattainable during patient care. Our overall center vision is to build a conceptual framework to understand tissue state-associated cellular composition transitions that happen in glioma and the ways to interpret MRI relative to those changes for these key cellular phenotypes. Specifically, in Project 1 we will explore strategies to target unfavorable (unresponsive) tissue states to navigate transitions of the cancer complex system towards more favorable (responsive) tissue states. In Project 2 we will leverage mathematical modeling and machine learning approaches to fuse MRI and image-localized biopsy quantified tissue states to enable tracking tissue state changes in patient receiving standard of care and immunotherapy strategies. Thus, our MOSAIC perfectly aligns with the CSBC initiative, integrating experimental biology with computational modeling, using methods from imaging physics, mathematical tumor growth modeling, image-guided biopsies, molecular biology, machine learning, and integrative bioinformatics to develop validated advances in cancer systems biology.