Abstract A10: Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics

Abstract

Abstract Intratumor functional heterogeneity is the presence of multiple subpopulations or localized regions with different physiological properties that affect cancer biology (e.g., motility, invasion) and/or response to systemic therapy. Functional heterogeneity may reflect unique aspects of the tumor microenvironment or cellular genetic diversity including, but not limited to, the consequences of different localized patterns of vascular perfusion, stromal infiltration, somatic mutation, epigenetic modifications. Given the continuing improvements in tumor imaging technologies and the biological importance of tumor vascularization as a major driver of functional heterogeneity, the goal of the present work was to develop novel mathematical models to study vascular heterogeneity and its changes in tumors using data from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). DCE-MRI provides a noninvasive in vivo method to evaluate tumor vasculature architectures based on contrast accumulation and washout. Understanding the role of vascular heterogeneity in tumors has significant implications for advancing individualized cancer diagnosis and treatment. To improve vascular characterization, we developed multi-tissue compartment modeling (MTCM). Notably, MTCM is a fully unsupervised method for deconvoluting dynamic imaging series data from heterogeneous tumors that contain unknown numbers of distinct vascular compartments. The pixel-wise tracer concentration in a particular vascular compartment is modeled as being proportional to the local volume transfer constant of the vascular compartment. However, the imaging data often contain significant numbers of partial-volume pixels. To address this limitation, MTCM estimates pharmacokinetic parameters (flux rate constants) using the time-courses of pure-volume pixels, i.e., the signals from those pixels highly enriched in a particular vascular compartment. A convex analysis of mixtures scheme is applied to identify those pure-volume pixels present at the vertices of the clustered pixel time series scatter simplex, without any knowledge of compartment distribution. Thus, MTCM offers an unsupervised approach to characterize intratumor heterogeneity. Applying MTCM to dynamic contrast-enhanced MRI of breast cancers revealed characteristic intratumor vascular heterogeneity and therapeutic responses that were otherwise undetectable. We identified differential and heterogeneous changes in tissue-specific vascular pharmacokinetics in tumors during treatment that were undetected using standard analysis, including tumor islands of persistent enhancement that have escaped the effects of therapy. While it is not yet possible to assign causality, these in vivo results allowed us to propose new hypotheses regarding the complex relationships between intratumor heterogeneity, clonal repopulation, cancer stem-cell, and therapeutic efficacy. Citation Format: Li Chen, peter Choyke, Robert Clarke, Zaver Bhujwalla, Yue Wang. Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr A10. doi:10.1158/1538-7445.CHTME14-A10