SMB2023 FollowMonday during the "CT01" time block. Room assignment: Griffin East Ballroom (#2133) in The Ohio Union.
University of Oxford
"Investigating the impact of growth arrest mechanisms on tumour responses to combinations of radiotherapy and hyperthermia"
Developing targeted and effective treatment strategies for patients is at the forefront of cancer research today. Recently, there has been increased focus on combination therapies, which aim to exploit different anti-cancer effects while minimising detrimental side-effects. Hyperthermia (HT) is a promising candidate for enhancing the efficacy of radiotherapy (RT). However, incomplete understanding of their interactions is hindering their combined use in the clinic. In this work, we investigate tumour responses to fractionated RT and HT individually and in combination, focussing on how two different mechanisms for growth control may impact tumour sensitivity to these treatments. To do so, we extend an existing ordinary differential equation model of tumour growth, which distinguishes between growth arrest due to nutrient insufficiency and competition for space and exhibits three growth regimes: nutrient limited (NL), space limited (SL) and bistable (BS), where both mechanisms for growth arrest coexist. We adopt a time-dependent description of RT that accounts for two different types of tumour cell damage (sub-lethal vs. lethal), damage repair and cell death following insufficient repair. We also model the time-dependent effects of HT, distinguishing between mild HT, which re-oxygenates the tumour via vasodilation and a reduction in the oxygen consumption rates of tumour cells, and, high HT, which causes tumour and endothelial cell damage and an associated angiogenic response. We construct three virtual populations of tumours in the NL, SL and BS regimes and, for each population, we study tumour responses to conventional fractionation schedules for RT, HT, and combined RT and HT. We determine the average response in each regime and assess the biological processes that may explain positive and negative treatment outcomes. We also investigate the impact of the fraction dose and dosing frequency on tumour responses. This allows us to evaluate which treatment (RT, HT or RT+HT) and dosing regimen maximise the reduction in tumour burden. We find that mild HT cannot significantly enhance the tumour responses to RT. In particular, while the addition of mild HT leads to a modest improvement in the RT response of tumours in the SL regime, it typically worsens the RT response of tumours in NL and BS regimes. By contrast, high HT can act as a potent radiosensitiser for all tumours in each virtual cohort, provided that the HT-induced angiogenic response is sufficiently weak. When there is a strong HT-induced angiogenic response, high HT enhances the RT response of a few tumours in the NL and SL regimes, while it worsens the RT response of most tumours in the BS regime. Finally, in cases where combination treatments are advisable, higher doses of HT applied with lower doses of RT maximise treatment efficacy.
Additional authors: Philip K. Maini, Mathematical Institute, University of Oxford; Helen M. Byrne, Mathematical Institute, University of Oxford
Massachusetts General Hospital and Harvard Medical School
"AMBER (Agent-based Modeling of Biophysical Evolution after Radiotherapy): a computational tumor model as the first step to simulate radiation response."
Intro: Cancer evolution and responses to radiotherapy treatments depend on a complex interplay among physical, chemical, and biological processes. In order to capture this and optimize treatments, novel strategies are required. This work aims to develop a multiscale model capable of predicting tumor fates using a bottom-up approach. While existing tumor models address radiation delivery using the linear-quadratic response to radiation, mechanistic models connecting cell- with tumor-scale radiation effects are yet scarce. Our model aims to incorporate radiosensitivity-modifier factors such as angiogenesis, different cell types, hypoxic and necrotic regions, and oxygen concentration. Additionally, our model is intended to include radiation dose in different areas of the tumor calculated through the TOPAS Monte Carlo toolkit.
Methods: We developed a hybrid model including agent-based components and continuum-like aspects in a voxelized geometry. Our model uses tumor mechanical and vascular properties and simulates tumor growth and necrosis evolution following a set of mechanistic rules for (i) cell proliferation and migration, (ii) oxygenation, (iii) angiogenesis, and (iv) cell fates, as follows: (i) within each voxel, cells reproduce with division times following a Gamma distribution and diffuse to neighboring voxels, depending on the crowding degree in each voxel. (ii) A pre-generated healthy vasculature provides oxygen to each voxel at the beginning of the simulations. To decide the oxygen each cell takes, we performed sub-voxel size simulations depending on crowding and the density of vessels to calibrate a Beta distribution representative of the cell-wise oxygen distribution within a voxel. (iii) If a cell is not supplied with enough oxygen, it releases VEGF, which triggers angiogenesis. Tumor-induced angiogenesis replaces healthy vasculature through a directed random walk considering crowding and VEGF gradient. The step size in the random walk also depends on local pressure and VEGF gradient. When the step size reaches a lower-bound threshold, the vessel stops growing. The radius of each vessel is updated using Murray’s law with a linear correction for the pressure exerted by the cells. (iv) Finally, at each time step, a simple vitality function determines if cancer cells are cycling, quiescent, or going through apoptosis/necrosis.
Results: Our model is capable of mimicking the genesis and early development of a generic tumor, including a necrotic core. The order of magnitude of the highest microvascular density (h-MVD) in our simulations matches values found from in vivo measurements done by Forster et al.  of 20-200 mm-2. Our simulated tumors show chronic hypoxia due to the intra-voxel oxygen distribution and acute hypoxia resulting from increased pressure due to tumor growth leading to vasculature damage. The density of cells in the tumor center and the tumor radius increase exponentially until reaching a plateau caused by insufficient oxygen supply. At this point, the simulation of angiogenesis is necessary to allow for further tumor growth. If angiogenesis is turned off, the cells stop dividing and go through necrosis due to the lack of oxygen supply, leading to a shrinkage of the tumor. When adding angiogenesis, the oxygen concentration reaches a stable value allowing for the number of cancer cells and the radius of the tumor to keep increasing linearly. The pressure in the center of the tumor acts as a barrier to angiogenesis, creating a necrotic core.
Conclusion: We present AMBER, a flexible and modular model simulating the evolution of a tumor. To match the observations described earlier with in-vivo data corresponding to the anatomical region and the type of cancer of interest, one should try to tune the model parameters linked to angiogenesis and cell-specific characteristics, such as VEGF production rate, cell doubling time, necrosis threshold or necrosis probability.
Our model is designed to account for radiation dose by the future addition of radiation bioeffect models. Its design will allow for a wide range of potential applications in radiotherapy, including external beam as well as radiopharmaceutical therapies or brachytherapy. The model's adaptability will allow for seamless integration with existing models for radiation delivery, such as TOPAS and TOPAS-nBio, and with models for DNA damage and DNA repair.
 Forster, Jake, Wendy Harriss-Phillips, Michael Douglass, and Eva Bezak. “A Review of the Development of Tumor Vasculature and Its Effects on the Tumor Microenvironment.” Hypoxia Volume 5 (April 2017): 21–32. https://doi.org/10.2147/HP.S133231.
Additional authors: Mohammad Reza Nikmaneshi; Ibrahim Chamseddine; Jan Schuemann; Lance L. Munn; Harald Paganetti; Alejandro Bertolet. All affiliated with Massachusetts General Hospital and Harvard Medical School
Moffitt Cancer Center
"Modeling competition between subpopulations with variable DNA content in resource limited microenvironments"
Resource limitations shape the outcome of competitions between genetically heterogeneous pre-malignant cells. One example of such heterogeneity is in the ploidy (DNA content) of pre-malignant cells. A whole-genome duplication (WGD) transforms a diploid cell into tetraploid one and has been detected in 28-56% of human cancers. If a tetraploid subclone expands, it consistently does so early in tumor evolution, when cell density is still low and competition for nutrients is comparatively weak – an observation confirmed for several tumor types. WGD+ cells need more resources, to synthesize increasing amounts of DNA, RNA and proteins. To quantify resource limitations and how they relate to ploidy we performed a PAN cancer analysis of WGD, PET/CT and MRI scans. Segmentation of >20 different organs from >900 PET/CT scans was performed with MOOSE. We observed a strong correlation between organ-wide population-average estimates of Oxygen and the average ploidy of cancers growing in the respective organ (Pearson R = 0.66; P= 0.001). In-vitro experiments using near-diploid and near-tetraploid lineages derived from a breast cancer cell line supported the hypothesis that DNA-content influences Glucose- and Oxygen dependent proliferation-, death- and migration rates. To model how subpopulations with variable DNA-content compete in the resource limited environment of the human brain we developed a stochastic state space model of the brain (S3MB). The model discretizes the brain into voxels, whereby the state of each voxel is defined by 8+ variables that are updated over time: stiffness, oxygen, phosphate, glucose, vasculature, dead cells, migrating cells and proliferating cells of various DNA content, and treatment conditions such as radiotherapy and chemotherapy. Fokker-Planck partial differential equations govern the distribution of resources and cells across voxels. We applied S3MB on sequencing and imaging data obtained from a primary GBM patient. We performed whole genome sequencing (WGS) of four surgical specimens collected during the 1st and 2nd surgeries of the GBM and used HATCHET to quantify its clonal composition and how it changes between the two surgeries. HATCHET identified two aneuploid subpopulations of ploidy 1.98 and 2.29 respectively. The low-ploidy clone was dominant at the time of the first surgery and became even more dominant upon recurrence. MRI images were available before and after each surgery and registered to MNI space. The S3MB domain was initiated from 4mm^3 voxels of the MNI space. T1 post and T2 flair scans acquired after the 1st surgery informed tumor cell densities per voxel. Magnetic Resonance Elastography scans and PET/CT scans informed stiffness and Glucose access per voxel. We performed a parameter search to recapitulate the GBM’s tumor cell density and ploidy composition before the 2nd surgery. Results suggest that the high-ploidy subpopulation had a higher Glucose-dependent proliferation rate, but a lower Glucose-dependent death rate, resulting in spatial differences in the distribution of the two subpopulations. Understanding how genomics and microenvironments interact to shape cell fate decisions can help pave the way to therapeutic strategies that mimic prognostically favorable microenvironments.
Additional authors: Parag Katira, Frederika Rentzeperis, Richard Beck, Ana Forero, Zuzanna Nowicka, Vural Tagal, Jamie Teer, Elisa Scanu, Stefano Pasetto, Thomas Veith, Giada Fiandaca, Yi Fritz, Karen Devine, Jill Barnholtz-Sloan, Jack Farinhas, Ana Gomes, Noemi Andor
"A new grammar for real-time and reproducible modeling of multicellular interactions in cancer"
Cancer is driven by complex and multiscale interactions among a community of malignant epithelial, stromal, and immune cells. Within these cancer ecosystems, cells exchange a vast array of chemical and physical signals to drive behavioral responses. Agent-based models (ABMs)--which simulate individual cells as software agents with independent states and “rules” that represent how biophysical cues drive their behaviors--are frequently used to simulate and explore these complex systems, but creating these models requires expertise in both biology and computational modeling. ABMs generally require modelers to (1) formulate biological hypotheses on how biophysical signals drive individual behavioral responses, (2) transform these biological hypotheses into mathematics, and (3) implement these mathematical statements into computational code in C++, Python, Java, or other languages. Development of robust models requires close collaboration between computational scientists and experimental and clinical scientists with biological domain expertise. To facilitate collaborative development and assessment of computational models through modeling loops, we have designed a new modeling approach which encourages interaction between teams of multidisciplinary scientists. We present a new modeling grammar based on interpretable hypotheses of how chemical and physical signals affect cell behavior. (e.g., “In M0 macrophages, necrotic cell debris increases transformation to M1 macrophages. In malignant epithelial cells, doxorubicin increases apoptosis.”) These hypotheses statements are directly mapped onto mathematical response functions to automatically generate model code in real time. In our approach, new and prior model hypotheses are automatically merged and annotated for reproducibility. Moreover, we can combine knowledge-based rules with data-driven rules learned through high-throughput genomics, text mining, and other techniques. When combined with a graphical model editor, multidisciplinary teams can formulate, run, visualize, and refine complex cancer models in real time, accelerating the modeling loop from months to minutes. We will demonstrate the grammar with examples in cancer hypoxia, cancer-immune interactions, and response of cancer and immune cells to therapeutic compounds. We will give a live demonstration to develop, run, and refine a cancer model on-the-fly, and discuss implications of this approach for reproducible team science in multicellular cancer systems biology.
Additional authors: Jeanette Johnson, Johns Hopkins University; Genevieve Stein-O’Brien, Johns Hopkins University; Randy Heiland, Indiana University; Furkan Kurtoglu, Indiana University; Max Booth, Johns Hopkins University; Elmar Bucher, Indiana University; Atul Deshpande, Johns Hopkins University; Michael Getz, Indiana University; Ines Godet, Memorial Sloan Kettering Cancer Center; John Metzcar, Indiana UniversityHeber Rocha, Indiana University; Aneequa Sundus, Indiana University; Daniele Gilkes, Johns Hopkins University; Elizabeth Jaffee, Johns Hopkins University; Lei Zheng, Johns Hopkins University; Jacquelyn Zimmerman, Johns Hopkins University; Young Hwan Chang, Oregon Health and Science University; Lisa Coussens, Oregon Health and Science University; Joe Gray, Oregon Health and Science University; Laura Heiser, Oregon Health and Science University; Elana J Fertig, Johns Hopkins University