Integrating quantitative imaging and mechanistic modeling to characterize tumor growth and therapeutic response

Wednesday, June 16 at 11:30am (PDT)
Wednesday, June 16 at 07:30pm (BST)
Thursday, June 17 03:30am (KST)

SMB2021 SMB2021 Follow Wednesday (Thursday) during the "MS14" time block.
Note: this minisymposia has multiple sessions. The second session is MS20-ONCO (click here).

Share this


Guillermo Lorenzo (University of Pavia, Italy), David Hormuth (The University of Texas at Austin, US), Angela Jarrett (The University of Texas at Austin, US), Thomas Yankeelov (The University of Texas at Austin, US)


The overall goal of this symposium is to present and discuss recent developments in (1) the integration of imaging data in mechanistic models to investigate cancer development and therapeutic response both in vitro and in vivo, (2) translating pre-clinical image-based models to clinical disease, and (3) assessing image-based and model-inspired biomarkers and response metrics to improve clinical decision-making and to advance (pre)clinical research. Cancers are highly heterogeneous diseases supported by diverse biological mechanisms occurring, interacting, and evolving at multiple spatial and temporal scales. Quantitative imaging provides a noninvasive means to characterize this heterogeneous, multiscale nature by providing a wealth of temporally and spatially resolved data about morphology, architecture, vascularity, growth dynamics, and response to therapy. Hence, quantitative imaging is being increasingly used to improve cancer diagnosis, monitoring, and treatment planning. Additionally, these imaging technologies are accelerating in vitro and in vivo research on the biological mechanisms underlying the development and therapeutic response of tumors. Quantitative imaging data can be further exploited to constrain biophysical models of tumor growth and treatment response both in preclinical and clinical settings. These models can then be leveraged to test hypotheses, produce individualized tumor forecasts to guide clinical decision-making, and, ultimately, to design optimized therapies.

Andrea Gardner

(The University of Texas at Austin, US)
"Quantification of interactions between epithelial-like and mesenchymal-like subpopulations in a triple-negative breast cancer cell line ecosystem"
Many cancer cell lines once thought to be relatively homogeneous are composed of distinct subpopulations. Informed by single-cell RNA sequencing of the triple-negative breast cancer cell line MDA-MB-231, we discovered a surface marker which effectively separates epithelial-like (EL) cells from mesenchymal-like (ML) cells from this population. Growth characteristics of EL and ML subpopulations were determined in monoculture and in co-culture and under varying environmental conditions. We find that while the ML cells are neutral to the presence of EL cells, the growth rate of the already faster EL cells is further boosted in the presence of ML cells. One would expect this phenomenon to lead to the extinction of the ML cells, however, these cells co-exist over many generations in vitro. To investigate this paradox, experimental data from live-cell imaging was integrated with an extended Lotka-Volterra competition model to quantify the intrinsic properties and interactions of these two-subpopulations and we present our findings here.

Haley Bowers

(Wake Forest School of Medicine, US)
"Image Data-Driven Biophysical Mathematical Model Based Characterization of Multicellular Tumor Spheroids"
Multicellular tumor spheroid (MCTS) systems provide an in vitro cell culture model system which replicates many of the complexities of an in vivo solid tumor and its tumor microenvironment. MCTS systems are often used to study cancer cell growth and drug efficacy. In this work, we present a coupled experimental-computational framework to estimate phenotypic growth and biophysical tumor microenvironment properties. This novel framework utilizes standard microscopy imaging of MCTS systems to drive a biophysical mathematical model of MCTS growth and mechanical interactions. This work is an extension of our previous in vivo mechanically-coupled reaction-diffusion modeling framework we developed a microscopy image processing framework capable of mechanistic characterization of MCTS systems. Using fluorescently labeled MDA-MB-231 breast cancer MCTS, we estimated biophysical parameters of cellular diffusion, rate of cellular proliferation, and cellular tractions forces. We found significant differences in between untreated and treated MCTS systems using these model-based biophysical parameters throughout the treatment time course, whereas traditional morphometric parameters were inconclusive. This experimental-computational framework estimates mechanistic MCTS growth and invasion parameters with significant potential to assist in better and more precise assessment of in vitro drug efficacy through the development of computational analysis methodologies for three-dimensional cell culture systems to improve the development and evaluation of antineoplastic drugs.

Anum Kazerouni

(University of Washington, US)
"Characterizing tumor heterogeneity using quantitative MRI habitats in breast cancer in vivo"
Within a tumor exists a dynamic interplay of spatially-varying cell populations and tissue microenvironments that both contributes to tumor progression and influences therapeutic response. For example, the uneven distribution of vasculature across a tumor can yield nonuniform drug delivery. Additionally, phenotypic diversity across cancer cells can result in variable response to treatment. These aspects of tumor heterogeneity provide a major challenge in the clinical treatment of breast cancer and result in significant diversity of outcomes across a patient population, emphasizing the need for personalized approaches to cancer treatment. Methods to characterize the spatiotemporal evolution of an individual tumor and its resulting heterogeneity can lend improved understanding of a patient’s tumor pathology and their potential response to therapy. Quantitative magnetic resonance imaging (MRI) is noninvasive and can, therefore, longitudinally detect changes in physiological characteristics across a tumor volume. In particular, diffusion-weighted (DW-) MRI and dynamic contrast-enhanced (DCE-) MRI provide quantitative assessment of tissue cellularity and vascularity, respectively—key tumor attributes that are affected by therapy. Accordingly, quantitative MRI measures have demonstrated promise as biomarkers of breast cancer treatment response in both preclinical and clinical settings. Recent work has investigated methods to spatially resolve intratumoral heterogeneity using quantitative MRI through an approach known as habitat imaging6. With this technique, physiologically distinct tumors subregions (i.e., habitats) are identified by clustering multiparametric image data, thus facilitating quantitative characterization of microenvironmental heterogeneity for individual tumors. In this presentation, we will describe how DW- and DCE-MRI data can be leveraged to spatially resolve physiologically distinct tumor habitats in vivo with biological validation ex vivo. Using preclinical models of breast cancer, we will demonstrate how this approach can be used to measure longitudinal alterations in the tumor microenvironment in response to treatment and identify tumor imaging phenotypes with differing therapeutic sensitivities. Additionally, we will describe the translation of this approach to the clinical setting and its promise in identifying breast cancer patients with increased likelihood of neoadjuvant therapy response based on tumor habitat composition. These tumor-specific characterizations of microenvironmental heterogeneity could provide a means to more accurately guide individualized patient treatment strategies.

David Hormuth

(The University of Texas at Austin, US)
"Image-driven modeling of radiation therapy response in gliomas"
Radiotherapy is a fundamental component of the treatment and management of high-grade gliomas. The efficacy of radiotherapy can vary from tumor to tumor due to spatial and temporal heterogeneity in (for example) cellularity, blood volume, and perfusion. A rigorous understanding of the dynamics of tumor heterogeneity could enable the personalization of radiotherapy for individual tumors. Quantitative imaging techniques such as diffusion weighted (DW-) magnetic resonance imaging (MRI) and dynamic contrast-enhanced (DCE-) MRI provide an opportunity to longitudinally, and non-invasively, observe the dynamics of tumor heterogeneity in 3D. We have developed an experimental and computational framework to integrate these longitudinal measurements of tumor heterogeneity into mathematical models of tumor growth and response. Seven animals implanted intra-cranially with the U87 glioblastoma cell line were imaged before, during, and after the delivery of radiotherapy. We then initialized and calibrated a family of 18 models of response to radiotherapy for each animal using DW-MRI and DCE-MRI. Using the calibrated model parameters we assessed the error in response predictions at the local and global levels. At the global level, we observed less than 16.2% error in tumor volume predictions while at the local level we observed a Pearson correlation coefficient of greater than 0.87 for each animal. This effort demonstrates the strength of using longitudinal MRI data for personalization of models predicting the response of brain tumors to radiotherapy.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.