Mark Robertson-Tessi

Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes.

Histopathologic knowledge that extensive heterogeneity exists between and within tumors has been confirmed and deepened recently by molecular studies. However, the impact of tumor heterogeneity on prognosis and treatment remains as poorly understood as ever. Using a hybrid multiscale mathematical model of tumor growth in vascularized tissue, we investigated the selection pressures exerted by spatial and temporal variations in tumor microenvironment and the resulting phenotypic adaptations. A key component of this model is normal and tumor metabolism and its interaction with microenvironmental factors. The metabolic phenotype of tumor cells is plastic, and microenvironmental selection leads to increased tumor glycolysis and decreased pH. Once this phenotype emerges, the tumor dramatically changes its behavior due to acid-mediated invasion, an effect that depends on both variations in the tumor cell phenotypes and their spatial distribution within the tumor. In early stages of growth, tumors are stratified, with the most aggressive cells developing within the interior of the tumor. These cells then grow to the edge of the tumor and invade into the normal tissue using acidosis. Simulations suggest that diffusible cytotoxic treatments, such as chemotherapy, may increase the metabolic aggressiveness of a tumor due to drug-mediated selection. Chemotherapy removes the metabolic stratification of the tumor and allows more aggressive cells to grow toward blood vessels and normal tissue. Antiangiogenic therapy also selects for aggressive phenotypes due to degradation of the tumor microenvironment, ultimately resulting in a more invasive tumor. In contrast, pH buffer therapy slows down the development of aggressive tumors, but only if administered when the tumor is still stratified. Overall, findings from this model highlight the risks of cytotoxic and antiangiogenic treatments in the context of tumor heterogeneity resulting from a selection for more aggressive behaviors.

Abscopal Benefits of Localized Radiotherapy Depend on Activated T-cell Trafficking and Distribution between Metastatic Lesions

It remains unclear how localized radiotherapy for cancer metastases can occasionally elicit a systemic antitumor effect, known as the abscopal effect, but historically, it has been speculated to reflect the generation of a host immunotherapeutic response. The ability to purposefully and reliably induce abscopal effects in metastatic tumors could meet many unmet clinical needs. Here, we describe a mathematical model that incorporates physiologic information about T-cell trafficking to estimate the distribution of focal therapy-activated T cells between metastatic lesions. We integrated a dynamic model of tumor-immune interactions with systemic T-cell trafficking patterns to simulate the development of metastases. In virtual case studies, we found that the dissemination of activated T cells among multiple metastatic sites is complex and not intuitively predictable. Furthermore, we show that not all metastatic sites participate in systemic immune surveillance equally, and therefore the success in triggering the abscopal effect depends, at least in part, on which metastatic site is selected for localized therapy. Moreover, simulations revealed that seeding new metastatic sites may accelerate the growth of the primary tumor, because T-cell responses are partially diverted to the developing metastases, but the removal of the primary tumor can also favor the rapid growth of preexisting metastatic lesions. Collectively, our work provides the framework to prospectively identify anatomically defined focal therapy targets that are most likely to trigger an immune-mediated abscopal response and therefore may inform personalized treatment strategies in patients with metastatic disease.

The role of toll-like receptors in colorectal cancer progression: evidence for epithelial to leucocytic transition

Toll-like receptors (TLRs) are expressed by immune cells, intestinal epithelium, and tumor cells. In the homeostatic setting, they help to regulate control over invading pathogens and maintain the epithelial lining of the large and small intestines. Aberrant expression of certain TLRs by tumor cells can induce growth inhibition while others contribute to tumorigenesis and progression. Activation of these TLRs can induce inflammation, tumor cell proliferation, immune evasion, local invasion, and distant metastasis. These TLR-influenced behaviors have similarities with properties observed in leukocytes, suggesting that tumors may be hijacking immune programs to become more aggressive. The concept of epithelial to leucocytic-transition (ELT) is proposed, akin to epithelial to mesenchymal transition, in which tumors develop the ability to activate leucocytic traits otherwise inaccessible to epithelial cells. Understanding the mechanisms of ELT could lead to novel therapeutic strategies for inhibiting tumor metastasis.

Defining cancer subpopulations by adaptive strategies rather than molecular properties provides novel insights into intratumoral evolution

Ongoing intratumoral evolution is apparent in molecular variations among cancer cells from different regions of the same tumor, but genetic data alone provide little insight into environmental selection forces and cellular phenotypic adaptations that govern the underlying Darwinian dynamics. In three spontaneous murine cancers (prostate cancers in TRAMP and PTEN mice, pancreatic cancer in KPC mice), we identified two subpopulations with distinct niche construction adaptive strategies that remained stable in culture: (i) invasive cells that produce an acidic environment via upregulated aerobic glycolysis; and (ii) noninvasive cells that were angiogenic and metabolically near-normal. Darwinian interactions of these subpopulations were investigated in TRAMP prostate cancers. Computer simulations demonstrated invasive, acid-producing (C2) cells maintain a fitness advantage over noninvasive, angiogenic (C3) cells by promoting invasion and reducing efficacy of immune response. Immunohistochemical analysis of untreated tumors confirmed that C2 cells were invariably more abundant than C3 cells. However, the C2 adaptive strategy phenotype incurred a significant cost due to inefficient energy production (i.e., aerobic glycolysis) and depletion of resources for adaptations to an acidic environment. Mathematical model simulations predicted that small perturbations of the microenvironmental extracellular pH (pHe) could invert the cost/benefit ratio of the C2 strategy and select for C3 cells. In vivo, 200 mmol/L NaHCO3 added to the drinking water of 4-week-old TRAMP mice increased the intraprostatic pHe by 0.2 units and promoted proliferation of noninvasive C3 cells, which remained confined within the ducts so that primary cancer did not develop. A 0.2 pHe increase in established tumors increased the fraction of C3 cells and signficantly diminished growth of primary and metastatic tumors. In an experimental tumor construct, MCF7 and MDA-MB-231 breast cancer cells were coinjected into the mammary fat pad of SCID mice. C2-like MDA-MB-231 cells dominated in untreated animals, but C3-like MCF7 cells were selected and tumor growth slowed when intratumoral pHe was increased. Overall, our data support the use of mathematical modeling of intratumoral Darwinian interactions of environmental selection forces and cancer cell adaptive strategies. These models allow the tumor to be steered into a less invasive pathway through the application of small but selective biological force. Cancer Res; 77(9); 2242-54. ©2017 AACR.

Big Bang and context-driven collapse

Heterogeneity is the single most important factor driving cancer progression and treatment failure, yet little is understood about how and when this heterogeneity arises. A new study shows that colorectal cancers acquire their dominant mutations early in development and that subsequent mutations, even if they confer greater fitness, are unlikely to sweep through the tumor.

Stochasticity in the Genotype-Phenotype Map: Implications for the Robustness and Persistence of Bet-Hedging

Nongenetic variation in phenotypes, or bet-hedging, has been observed as a driver of drug resistance in both bacterial infections and cancers. Here, we study how bet-hedging emerges in genotype–phenotype (GP) mapping through a simple interaction model: a molecular switch. We use simple chemical reaction networks to implement stochastic switches that map gene products to phenotypes, and investigate the impact of structurally distinct mappings on the evolution of phenotypic heterogeneity. Bet-hedging naturally emerges within this model, and is robust to evolutionary loss through mutations to both the expression of individual genes, and to the network itself. This robustness explains an apparent paradox of bet-hedging—why does it persist in environments where natural selection necessarily acts to remove it? The structure of the underlying molecular mechanism, itself subject to selection, can slow the evolutionary loss of bet-hedging to ensure a survival mechanism against environmental catastrophes even when they are rare. Critically, these properties, taken together, have profound implications for the use of treatment-holidays to combat bet-hedging-driven resistant disease, as the efficacy of breaks from treatment will ultimately be determined by the structure of the GP mapping.

Non-linear tumor-immune interactions arising from spatial metabolic heterogeneity

A hybrid multiscale mathematical model of tumor growth is used to investigate how tumoral and microenvironmental heterogeneity affect the response of the immune system. The model includes vascular dynamics and evolution of metabolic tumor phenotypes. Cytotoxic T cells are simulated, and their effect on tumor growth is shown to be dependent on the structure of the microenvironment and the distribution of tumor phenotypes. Importantly, no single immune strategy is best at all stages of tumor growth.A hybrid multiscale mathematical model of tumor growth is used to investigate how tumoral and microenvironmental heterogeneity affect treatment outcomes. A key component of this model is normal and tumor metabolism and its interaction with microenvironmental factors. In early stages of growth, tumors are stratified, with the most aggressive cells developing within the interior of the tumor. Simulations suggest that in some cases chemotherapy may increase the metabolic aggressiveness of a tumor due to drug-mediated selection.

Systematic Screening of Chemokines to Identify Candidates to Model and Create Ectopic Lymph Node Structures for Cancer Immunotherapy

The induction of ectopic lymph node structures (ELNs) holds great promise to augment immunotherapy against multiple cancers including metastatic melanoma, in which ELN formation has been associated with a unique immune-related gene expression signature composed of distinct chemokines. To investigate the therapeutic potential of ELNs induction, preclinical models of ELNs are needed for interrogation of these chemokines. Computational models provide a non-invasive, cost-effective method to investigate leukocyte trafficking in the tumor microenvironment, but parameterizing such models is difficult due to differing assay conditions and contexts among the literature. To better achieve this, we systematically performed microchemotaxis assays on purified immune subsets including human pan-T cells, CD4+ T cells, CD8+ T cells, B cells, and NK cells, with 49 recombinant chemokines using a singular technique, and standardized conditions resulting in a dataset representing 238 assays. We then outline a groundwork computational model that can simulate cellular migration in the tumor microenvironment in response to a chemoattractant gradient created from stromal, lymphoid, or antigen presenting cell interactions. The resulting model can then be parameterized with standardized data, such as the dataset presented here, and demonstrates how a computational approach can help elucidate developing ELNs and their impact on tumor progression.

Hybrid Automata Library

The Hybrid Automata Library (HAL) is a Java Library made of simple, efficient, generic components that can be used to model complex spatial systems. HAL’s components can broadly be classified into: on- and off-lattice agent containers, finite difference diffusion fields, a Gui building system, and additional tools and utilities for computation and data collection. These components are designed to operate independently and are standardized to make them easy to interface with one another. As a demonstration of how modeling can be simplified using our approach, we have included a complete example of a hybrid model (a spatial model with interacting agent-based and PDE components, commonly used for oncology modeling). HAL is a useful asset for researchers who wish to build efficient 1D, 2D and 3D hybrid models in Java, while not starting entirely from scratch. It is available on github at https://github.com/torococo/HAL under the MIT License. HAL requires at least Java 8 or later to run, and the java jdk version 1.8 or later to compile the source code.

Evolutionary exploitation of PD-L1 expression in hormone receptor positive breast cancer

Based on clinical data from hormone positive breast cancer patients, we determined that there is a potential tradeoff between reducing tumor burden and altering metastatic potential when administering combination therapy of aromatase inhibitors and immune checkpoint inhibitors. While hormone-deprivation therapies serve to reduce tumor size in the neoadjuvant setting pre-surgery, they may induce tumors to change expression patterns towards a metastatic phenotype. We used mathematical modeling to explore how the timing of the therapies affects tumor burden and metastatic potential with an eye toward developing a dynamic prognostic score and reducing both tumor size and risk of metastasis.