NIH Funding

Aim 1: Characterize the landscape of glioma tissue states of MRI-localized biopsies of pre- and post-treatment GBM. Hypothesis: Comprehensive analyses of cellular compositions and phenotypic alterations across multi-regional radiographic localization of glioma will leverage insight into the cohabitation of specific cellular states and lineages that define tissue states with distinct biological features that correspond to specific therapeutic vulnerabilities. Aim 1.1. Expand our genomic analysis of image localized biopsies using single nucleus sequencing and bulk RNA sequencing to assess changes in tissue organization during tumor progression in male and female patients. Aim 1.2. Validate the cellular composition of tissue states by RNAScope and multiplex-IHC. Aim 1.3 Identify and validate the expression of receptor-ligand signaling pairs that mediate cross-talk signaling between specific populations of cells that co-inhabit the tissue states

Aim 2: Investigate the cross-talk signal(s) between glioma cells and the microenvironment cells within a glioma tissue state that influences progression. Hypothesis: Cross-talk signals between non-tumor cells and glioma cells influences tissue translation states and therapeutic response. Aim 2.1. Determine whether the tissue dynamic state of the glioma microenvironment is dependent upon candidate cross-talk signaling in vivo. Aim 2.2. Evaluate the effect of cross-talk signaling in microglial cells in vitro. Aim 2.3. Determine effect of the microglial cells primed by purinergic stimulation or glioma conditioned medium on T-cell function. Aim 2.4 Determine effects of stimulating or inhibiting cross-talk signaling in acute slices generated from patient-derived GBM samples.

Aim 3: Determine the impact of the crosstalk perturbation on tissue state vulnerabilities in vivo.  Hypothesis:  Inhibition of the cross-talk signals between glioma cells and non-tumor cells will induce a transition to a tissue state with increased vulnerability to therapies. Aim 3.1. Determine the effect of ligand-receptor inhibition on therapeutic vulnerabilities in immunocompetent models. Aim 3.2. Determine the effect of inhibiting cross-talk signaling using inhibitory molecule(s) on tissue state transition in immunocompetent models.

With a dismal median survival of 16 months, glioblastoma (GBM) is the most common malignant primary brain tumor within adult patients. Response to the standard-of-care (SOC) is widely variable across patients. Identifying optimal targeted treatments traditionally relies on tissue sampling to identify patient-relevant targets. Yet, tissue sampling has many severe limitations and costs (time, money, and facility access), and ultimately provides only limited scope both spatially and temporally thus always leaving behind residual tumor cells that have not been sampled. Multi-parametric magnetic resonance imaging (MRI) measures an array of complementary physiologic biomarkers that correspond with diverse tumor phenotypes (e.g., proliferation, inflammation, angiogenesis), and it serves as the clinical mainstay for monitoring therapeutic response and disease progression.  As tumor cell signaling may be mediated through interactions (i.e.,“cross-talk”) with surrounding non-tumoral cells in the regional microenvironment, there is a critical need to define the degree to which this cross-talk influences local tissue state, phenotypic expression, and disease progression.  Understanding these associations should help refine the clinical interpretations of imaging phenotypes to improve guidelines for non-invasive diagnosis and disease monitoring. There is an urgent need for image-based radiomics tools that can 1) predict which patients will respond to a given treatment and 2) can observe/track that response over time.

Overall Hypothesis: Tissue states, represented as combinations of cellular constituents and phenotypes, can be resolved on clinical imaging to a level sufficient to identify transitions in these states with and without treatments in individual patients in vivo.

Our two aims in this project investigate this hypothesis in two separate settings, Aim 1) Standard of Care, Aim 2) Immunotherapy. In these aims, we will characterize the landscape of phenotypic states, build image-based models to predict tissue state from images, investigate how predicted tumor states correspond with outcomes, quantify dynamics of states from pre- to post-therapy, and finally build mechanistic models to understand the critical driving differences in the flow of cells in local phenotype state space leading to the overall tumor state.

The major objective of the Biospecimen Core is to obtain, process, image, and analyze glioblastoma (GBM) patients’ biopsies for use in both Projects of our Cedars-Sinai/Columbia CSBC Mathematical Oncology Systems Analysis Imaging Center (MOSAIC). Patient biopsies are central to our CSBC’s primary goal: building a conceptual framework to understand sensitive response to therapy through the represented tissue states in clinical and experimental settings. The duties of the Biospecimen Core are spread across both sites: Cedars-Sinai Medical Center (CSMC) and Columbia University Medical Center (CUMC).

The Biospecimen Core will assist the Center by operating under two specific aims.  In Aim 1, we collect, process, and analyze image-guided biopsies from pre-and post-treatment GBM. For Aim 2, we perform histopathological, immunohistochemical (multiplex-IHC), and molecular analyses (bulk RNA-seq, scRNA-seq) of image-guided biopsies.

A major challenge to any integrated, collaborative, multi-institutional effort is the centralized management and synthesis of shared data. The Data Integration and Computation Core will be central to every part of our Mayo/Columbia CSBC Mathematical Oncology Systems Analysis Imaging Center (MOSAIC), assimilating diverse data types and sources. The primary mission of this Core is to provide infrastructure for data resource management, including modeling tools and support.

The Data Integration Core will house and integrate the digital data generated by the Biospecimen Core while providing a central resource for modeling tools to support our CSBC and interacting with the Administration Core for ensuring IRB compliance. We will achieve the mission of our Data Integration and Computation Core through three specific aims. In Aim 1 we collect and process and store data across all CSBC sites. In Aim 2 we provide and integrate reproducible bioinformatics workflows. And in Aim 3 we provide computational modeling tools and techniques.

The Data Integration Core will provide support for both projects.

• Project 1: Targeting Glioma Tissue States will utilize the image-localized biopsy locations. They will further utilize the stored molecular data accessed through this core making use of the provided bioinformatics.

• Project 2: Imaging the Dynamic Tissue State in Patients In Vivo will utilize the stored molecular and imaging data along with all segmentations and generated image features. Project 2 will also make extensive use of the mechanistic modeling and calibration support along with the bioinformatics support.

The administrative core is the primary touchpoint for all pieces of the grant, overseeing that work is done in an efficient way and providing administrative support to coordinate all efforts. The central work for this grant revolves around extensive analysis of image-localized biopsies that are collected, processed, and stored by Biospecimen Core, analyzed by the Data Integration and Computation Core, and shared between Projects 1 and 2. This highly integrated analysis of image-localized biopsies is actively managed by the Administrative Core to ensure a smooth experimental flow with an emphasis on quality and reproducibility.  Additionall, the administrative Core manages center logistics and communication, administers Intra-Center Pilot Projects, and creates and supports the External Advisory Committee for the grant.