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

Thursday, June 17 at 11:30am (PDT)
Thursday, June 17 at 07:30pm (BST)
Friday, June 18 03:30am (KST)

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

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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.

Darren Tyson

(Vanderbilt University, US)
"The many dimensions of anticancer drug response—quantifying cell population dynamics at single-cell resolution using automated live-cell microscopy"
A tumor in a human patient is an evolving system of interacting components, including different cell types containing various genetic alterations, adjacent stromal cells, and many different types of cell–cell and cell–matrix interactions. In addition, many parameters affect how therapeutic drugs can (ideally) kill all the tumor cells while sparing normal adjacent cells, including pharmacodynamic/pharmacokinetic properties of the drugs, the specificity with which they target tumor cells, specific genetic alterations that affect how a cell responds to the drug, etc. I will demonstrate how human cancer cell can be analyzed for their responses to anticancer drugs in high throughput using automated fluorescence microscopy to enable the direct visualization of many features of individual cells over time and how we have modeled the dynamics of cell population-level changes by simultaneously estimating the rates of cell division, death and entry into a non-dividing state from the single-cell measurements. This model facilitates the interpretation of how single-cell fate decisions affect the overall cell population dynamics in a drug concentration- and time-dependent manner that removes biases inherent in more traditional end-point measurements. I will describe how we have used this basic model as a framework to develop more detailed models to interrogate different aspects of tumor cell biology, including: 1) transitions into more drug-tolerant cell states; 2) potential synergistic action of combinations of drugs at different concentration; and 3) effects of matrix stiffness on cellular responses to drugs.

Victor Perez-Garcia

(University of Castilla-La Mancha, Spain)
"From metabolic imaging to biomarkers through mathematical models in cancer"
Tumor initiation and progression are evolutionary processes driven by the accumulation of genetic alterations, some of which confer selective fitness advantages to cancer cells. Selective pressures induced by microenvironmental conditions, treatments, the immune system and other effects have a role in the complex evolutionary dynamics in tumors. However, although the situation is changing fast, it is still very difficult to obtain longitudinal biological data of evolutionary dynamics of tumors in individual patients. Metabolic imaging provides a global perspective of the tumor metabolism and proliferation status and can be performed sequentially to assess tumor dynamics and response to treatments. In this talk I will present a extension of the Fisher-Kolmogorov classical model displaying evolutionary dynamics. The analysis of the model predicts a displacement of the location of metabolic hotspots from the tumor core to its periphery during its natural history. This fact allows to define a novel metabolic imaging biomarker based on the distance from the metabolic hotspot to the tumor centroid, that is found to correlate with tumor aggressiveness and patient survival for different tumor histologies. Moreover, further analysis of the model shows that the maximum metabolic activity (SUVmax) grows with tumor size following a scaling law with power 1/4. A fact that was confirmed in different metabolic imaging datasets. Deviations from this scaling law allow to define another biomarker related to the relation between observed peak activity and the value expected from the scaling law. That provides another biomarker with a strong prognostic factor in breast cancer, lung cancer, head and neck cancer and glioblastoma. The metric found outperformed classical metabolic prognostic variables used in nuclear medicine. In conclusion, mathematical models with evolutionary dynamics suggests how to construct different metabolic imaging biomarkers with a strong prognostic value and thus clinical utility for different tumor histologies.

Jana Lipkova

(Brigham and Women’s Hospital, Harvard Medical School, US)
"Personalized Radiotherapy Design for Glioblastoma:Integrating Mathematical Tumor Models,Multimodal Scans and Bayesian Inference"
Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high resolution MRI scans and highly specific FET-PET metabolic maps to infer patient-specific tumor cell density, which in turn allow design of personalized radiotherapy plans. Initial clinical study showed that the proposed treatment plans spare more healthy tissue, this reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

Guillermo Lorenzo

(University of Pavia, Italy)
"Personalized image-based modeling of organ-confined prostate cancer: exploring the mechanical interactions between tumor growth and coexisting benign prostatic hyperplasia"
Prostate cancer (PCa) is a public health burden and a major concern among ageing men worldwide, with high rates of incidence and mortality. Thanks to regular screening and risk-group triaging most patients are currently diagnosed and successfully treated when the tumor is in early stage and confined within the prostate. Benign prostatic hyperplasia (BPH) is another common pathology in ageing men that causes the prostate to gradually enlarge over time, which may produce bothersome lower urinary tract symptoms. PCa originating in men with larger prostates tend to present more favorable pathological features, but the fundamental mechanisms that explain this interaction between BPH and prostate cancer are largely unknown. Here, we propose a mechanical explanation for this phenomenon: the mechanical stress fields that originate as tumors grow are known to slow down their dynamics, and BPH contributes to these mechanical stress fields, hence further restraining PCa growth. To explore this hypothesis, we run a qualitative simulation study using a mechanically-coupled mathematical model of PCa growth. We run our study leveraging a patient-specific geometric model of the prostate and tumor extracted from magnetic resonance imaging data. Our simulations show that the mechanical stress fields accumulated in the prostate by BPH over time impede prostatic tumor growth and limit its invasiveness. We further explore the effect on tumor growth of a type of BPH drugs that are being investigated for the chemoprevention of PCa: 5-alpha reductase inhibitors (e.g., finasteride, dutasteride), which reduce the size of the prostate (thereby treating BPH symptoms) and might promote apoptosis in the tumor. Depending on the intensity of these two mechanisms, our simulations show different tumor growth dynamics ranging from long-term inhibition of PCa growth to rapidly growing large tumors, which may evolve towards advanced disease. The latter case may provide a mechanistic explanation for the controversial advanced PCa cases found in chemoprevention trials of these drugs. In the future, we think that our computational technology can contribute to further investigate the biophysical mechanisms underlying PCa and BPH, and ultimately assist physicians in the clinical management of these diseases by forecasting pathological and therapeutic outcomes on an organ-scale, patient-specific basis.

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Virtual conference of the Society for Mathematical Biology, 2021.