Histology-guided mathematical model of tumor oxygenation: sensitivity analysis of physical and computational parameters.

A hybrid off-lattice agent-based model has been developed to reconstruct the tumor tissue oxygenation landscape based on histology images and simulated interactions between vasculature and cells with microenvironment metabolites. Here, we performed a robustness sensitivity analysis of that model's physical and computational parameters. We found that changes in the domain boundary conditions, the initial conditions, and the Michaelis constant are negligible and, thus, do not affect the model outputs. The model is also not sensitive to small perturbations of the vascular influx or the maximum consumption rate of oxygen. However, the model is sensitive to large perturbations of these parameters and changes in the tissue boundary condition, emphasizing an imperative aim to measure these parameters experimentally.

Visualizing the Spatio-Temporal Dynamics of Clonal Evolution with LinG3D software.

Cancer clonal evolution, especially following anti-cancer treatments, depends on the locations of the mutated cells within the tumor tissue. Cells near the vessels, exposed to higher concentrations of drugs, will undergo a different evolutionary path than cells residing far from the vasculature in the areas of lower drug levels. However, classical representations of cell lineage trees do not account for this spatial component of emerging cancer clones. Here, we propose the LinG3D (Lineage Graphs in 3D) algorithms to trace clonal evolution in space and time. These are an open-source collection of routines (in MATLAB, Python, and R) that enables spatio-temporal visualization of clonal evolution in a two-dimensional tumor slice from computer simulations of the tumor evolution models. These routines draw traces of tumor clones in both time and space, with an option to include a projection of a selected microenvironmental factor, such as the drug or oxygen distribution within the tumor. The utility of LinG3D has been demonstrated through examples of simulated tumors with different number of clones and, additionally, in experimental colony growth assay. This routine package extends the classical lineage trees, that show cellular clone relationships in time, by adding the space component to show the locations of cellular clones within the 2D tumor tissue patch from computer simulations of tumor evolution models.

Exploring chronic and transient tumor hypoxia for predicting the efficacy of hypoxia-activated pro-drugs.

Hypoxia, a low level of oxygen in the tissue, arises due to an imbalance between the vascular oxygen supply and oxygen demand by the surrounding cells. Typically, hypoxia is viewed as a negative marker of patients' survival, because of its implication in the development of aggressive tumors and tumor resistance. Several drugs that specifically target the hypoxic cells have been developed, providing an opportunity for exploiting hypoxia to improve cancer treatment. Here, we consider combinations of hypoxia-activated pro-drugs (HAPs) and two compounds that transiently increase intratumoral hypoxia: a vasodilator and a metabolic sensitizer. To effectively design treatment protocols with multiple compounds we used mathematical micro-pharmacology modeling and determined treatment schedules that take advantage of heterogeneous and dynamically changing oxygenation in tumor tissue. Our model was based on data from murine pancreatic cancers treated with evofosfamide (as a HAP) and either hydralazine (as a vasodilator), or pyruvate (as a metabolic sensitizer). Subsequently, this model was used to identify optimal schedules for different treatment combinations. Our simulations showed that schedules of HAPs with the vasodilator had a bimodal distribution, while HAPs with the sensitizer showed an elongated plateau. All schedules were more successful than HAP monotherapy. The three-compound combination had three local optima, depending on the HAPs clearance from the tissue interstitium, each two-fold more effective than baseline HAP treatment. Our study indicates that the three-compound therapy administered in the defined order will improve cancer response and that designing complex schedules could benefit from the use of mathematical modeling.

Dynamics of Fibril Collagen Remodeling by Tumor Cells: A Model of Tumor-Associated Collagen Signatures.

Many solid tumors are characterized by a dense extracellular matrix (ECM) composed of various ECM fibril proteins. These proteins provide structural support and a biological context for the residing cells. The reciprocal interactions between growing and migrating tumor cells and the surrounding stroma result in dynamic changes in the ECM architecture and its properties. With the use of advanced imaging techniques, several specific patterns in the collagen surrounding the breast tumor have been identified in both tumor murine models and clinical histology images. These tumor-associated collagen signatures (TACS) include loosely organized fibrils far from the tumor and fibrils aligned either parallel or perpendicular to tumor colonies. They are correlated with tumor behavior, such as benign growth or invasive migration. However, it is not fully understood how one specific fibril pattern can be dynamically remodeled to form another alignment. Here, we present a novel multi-cellular lattice-free (MultiCell-LF) agent-based model of ECM that, in contrast to static histology images, can simulate dynamic changes between TACSs. This model allowed us to identify the rules of cell-ECM physical interplay and feedback that guided the emergence and transition among various TACSs.

Targeting myeloid-derived suppressor cells with gemcitabine to enhance efficacy of adoptive cell therapy in bladder cancer.

Background: New therapeutics in development for bladder cancer need to address the recalcitrant nature of the disease. Intravesical adoptive cell therapy (ACT) with tumor infiltrating lymphocytes (TIL) can potentially induce durable responses in bladder cancer while maximizing T cells at the tumor site. T cells infused into the bladder directly encounter immunosuppressive populations, such as myeloid derived suppressor cells (MDSCs), that can attenuate T cell responses. Intravesical instillation of gemcitabine can be used as a lymphodepleting agent to precondition the bladder microenvironment for infused T cell products. Methods: Urine samples from bladder cancer patients and healthy donors were analyzed by flow cytometry and cytometric bead array for immune profiling and cytokine quantification. MDSCs were isolated from the urine and cocultured with stimulated T cells to assess effects on proliferation. An orthotopic murine model of bladder cancer was established using the MB49-OVA cell line and immune profiling was performed. MDSCs from tumor-bearing mice were cocultured with OT-I splenocytes to assess T cell proliferation. Mice received intravesical instillation of gemcitabine and depletion of immune cells was measured via flow cytometry. Bladder tumor growth of mice treated with intravesical gemcitabine, OT-I transgenic T cells, or combination was monitored via ultrasound measurement. Results: In comparison to healthy donors, urine specimen from bladder cancer patients show high levels of MDSCs and cytokines associated with myeloid chemotaxis, T cell chemotaxis, and inflammation. T cells isolated from healthy donors were less proliferative when cocultured with MDSCs from the urine. Orthotopic murine bladder tumors also presented with high levels of MDSCs along with enrichment of cytokines found in the patient urine samples. MDSCs isolated from spleens of tumor-bearing mice exerted suppressive effects on the proliferation of OT-I T cells. Intravesical instillation of gemcitabine reduced overall immune cells, MDSCs, and T cells in orthotopic bladder tumors. Combination treatment with gemcitabine and OT-I T cells resulted in sustained anti-tumor responses in comparison to monotherapy treatments. Conclusion: MDSCs are enriched within the microenvironment of bladder tumors and are suppressive to T cells. Gemcitabine can be used to lymphodeplete bladder tumors and precondition the microenvironment for intravesical ACT.

TIM-3 blockade enhances IL-12-dependent antitumor immunity by promoting CD8+ T cell and XCR1+ dendritic cell spatial co-localization.

BACKGROUND: T cell immunoglobulin and mucin domain containing-3 (TIM-3) blocking antibodies are currently being evaluated in clinical trials for solid and hematological malignancies. Despite its identification on T cells, TIM-3 is predominantly expressed by myeloid cells, including XCR1+ type I conventional dendritic cells (cDC1s). We have recently shown that TIM-3 blockade promotes expression of CXCR3 chemokine ligands by tumor cDCs, but how this drives a CD8+ T cell-dependent response to therapy is unclear. METHODS: T cell infiltration, effector function, and spatial localization in relation to XCR1+ cDC1s were evaluated in a murine orthotopic mammary carcinoma model during response to TIM-3 blockade and paclitaxel chemotherapy. Mixed bone marrow chimeras and diphtheria toxin depletion were used to determine the role of specific genes in cDC1s during therapeutic responses. RESULTS: TIM-3 blockade increased interferon-γ expression by CD8+ T cells without altering immune infiltration. cDC1 expression of CXCL9, but not CXCL10, was required for response to TIM-3 blockade. CXCL9 was also necessary for the increased proximity observed between CD8+ T cells and XCR1+ cDC1s during therapy. Tumor responses were dependent on cDC1 expression of interleukin-12, but not MHCI. CONCLUSIONS: TIM-3 blockade increases exposure of intratumoral CD8+ T cells to cDC1-derived cytokines, with implications for the design of therapeutic strategies using antibodies against TIM-3.

Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations.

Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell-level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, models simulating individual cells' behavior are needed. Here, we provide a link between the average data values recorded for radiologic images and the cellular and vascular architecture of the corresponding tissues. Using hybrid agent-based modeling, we generate a set of tissue morphologies capable of reproducing oxygenation levels observed in radiologic images. We then use these in silico tissues to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies show that intravascular changes in oxygen supply reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, larger-magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in a biologically feasible manner. Additionally, we develop a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications, this approach can be used to generate a set of tissues comparable with radiology images and to simulate tumor responses to various anti-cancer treatments at the tissue-scale level.

Collective Cell Migration in a Fibrous Environment: A Hybrid Multiscale Modelling Approach.

The specific structure of the extracellular matrix (ECM), and in particular the density and orientation of collagen fibres, plays an important role in the evolution of solid cancers. While many experimental studies discussed the role of ECM in individual and collective cell migration, there are still unanswered questions about the impact of nonlocal cell sensing of other cells on the overall shape of tumour aggregation and its migration type. There are also unanswered questions about the migration and spread of tumour that arises at the boundary between different tissues with different collagen fibre orientations. To address these questions, in this study we develop a hybrid multi-scale model that considers the cells as individual entities and ECM as a continuous field. The numerical simulations obtained through this model match experimental observations, confirming that tumour aggregations are not moving if the ECM fibres are distributed randomly, and they only move when the ECM fibres are highly aligned. Moreover, the stationary tumour aggregations can have circular shapes or irregular shapes (with finger-like protrusions), while the moving tumour aggregations have elongate shapes (resembling to clusters, strands or files). We also show that the cell sensing radius impacts tumour shape only when there is a low ratio of fibre to non-fibre ECM components. Finally, we investigate the impact of different ECM fibre orientations corresponding to different tissues, on the overall tumour invasion of these neighbouring tissues.

High School Internship Program in Integrated Mathematical Oncology (HIP IMO): Five-Year Experience at Moffitt Cancer Center.

Modern cancer research, and the wealth of data across multiple spatial and temporal scales, has created the need for researchers that are well versed in the life sciences (cancer biology, developmental biology, immunology), medical sciences (oncology) and natural sciences (mathematics, physics, engineering, computer sciences). College undergraduate education traditionally occurs in disciplinary silos, which creates a steep learning curve at the graduate and postdoctoral levels that increasingly bridge multiple disciplines. Numerous colleges have begun to embrace interdisciplinary curricula, but students who double major in mathematics (or other quantitative sciences) and biology (or medicine) remain scarce. We identified the need to educate junior and senior high school students about integrating mathematical and biological skills, through the lens of mathematical oncology, to better prepare students for future careers at the interdisciplinary interface. The High school Internship Program in Integrated Mathematical Oncology (HIP IMO) at Moffitt Cancer Center has so far trained 59 students between 2015 and 2019. We report here on the program structure, training deliverables, curriculum and outcomes. We hope to promote interdisciplinary educational activities early in a student's career.

Comparison of Drug Inhibitory Effects ([Formula: see text]) in Monolayer and Spheroid Cultures.

Traditionally, the monolayer (two-dimensional) cell cultures are used for initial evaluation of the effectiveness of anticancer drugs. In particular, these experiments provide the [Formula: see text] curves that determine drug concentration that can inhibit growth of a tumor colony by half when compared to the cells grown with no exposure to the drug. Low [Formula: see text] value means that the drug is effective at low concentrations, and thus will show lower systemic toxicity when administered to the patient. However, in these experiments cells are grown in a monolayer, all well exposed to the drug, while in vivo tumors expand as three-dimensional multicellular masses, where inner cells have a limited access to the drug. Therefore, we performed computational studies to compare the [Formula: see text] curves for cells grown as a two-dimensional monolayer and a cross section through a three-dimensional spheroid. Our results identified conditions (drug diffusivity, drug action mechanisms and cell proliferation capabilities) under which these [Formula: see text] curves differ significantly. This will help experimentalists to better determine drug dosage for future in vivo experiments and clinical trials.

Drug-Induced Resistance in Micrometastases: Analysis of Spatio-Temporal Cell Lineages.

Resistance to anti-cancer drugs is a major cause of treatment failure. While several intracellular mechanisms of resistance have been postulated, the role of extrinsic factors in the development of resistance in individual tumor cells is still not fully understood. Here we used a hybrid agent-based model to investigate how sensitive tumor cells develop drug resistance in the heterogeneous tumor microenvironment. We characterized the spatio-temporal evolution of lineages of the resistant cells and examined how resistance at the single-cell level contributes to the overall tumor resistance. We also developed new methods to track tumor cell adaptation, to trace cell viability trajectories and to examine the three-dimensional spatio-temporal lineage trees. Our findings indicate that drug-induced resistance can result from cells adaptation to the changes in drug distribution. Two modes of cell adaptation were identified that coincide with microenvironmental niches-areas sheltered by cell micro-communities (protectorates) or regions with limited drug penetration (refuga or sanctuaries). We also recognized that certain cells gave rise to lineages of resistant cells (precursors of resistance) and pinpointed three temporal periods and spatial locations at which such cells emerged. This supports the hypothesis that tumor micrometastases do not need to harbor cell populations with pre-existing resistance, but that individual tumor cells can adapt and develop resistance induced by the drug during the treatment.

Hybrid modeling frameworks of tumor development and treatment.

Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments. This article is characterized under: Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Therapeutic Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Morphophenotypic classification of tumor organoids as an indicator of drug exposure and penetration potential.

The dynamics of tumor progression is driven by multiple factors, which can be exogenous to the tumor (microenvironment) or intrinsic (genetic, epigenetic or due to intercellular interactions). While tumor heterogeneity has been extensively studied on the level of cell genetic profiles or cellular composition, tumor morphological diversity has not been given as much attention. The limited analysis of tumor morphophenotypes may be attributed to the lack of accurate models, both experimental and computational, capable of capturing changes in tumor morphology with fine levels of spatial detail. Using a three-dimensional, agent-based, lattice-free computational model, we generated a library of multicellular tumor organoids, the experimental analogues of in vivo tumors. By varying three biologically relevant parameters-cell radius, cell division age and cell sensitivity to contact inhibition, we showed that tumor organoids with similar growth dynamics can express distinct morphologies and possess diverse cellular compositions. Taking advantage of the high-resolution of computational modeling, we applied the quantitative measures of compactness and accessible surface area, concepts that originated from the structural biology of proteins. Based on these analyses, we demonstrated that tumor organoids with similar sizes may differ in features associated with drug effectiveness, such as potential exposure to the drug or the extent of drug penetration. Both these characteristics might lead to major differences in tumor organoid's response to therapy. This indicates that therapeutic protocols should not be based solely on tumor size, but take into account additional tumor features, such as their morphology or cellular packing density.

Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue.

Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.

Targeting Ligand Specificity Linked to Tumor Tissue Topological Heterogeneity via Single-Cell Micro-Pharmacological Modeling.

Targeted therapy has held promise to be a successful anticancer treatment due to its specificity towards tumor cells that express the target receptors. However, not all targeting drugs used in the clinic are equally effective in tumor eradication. To examine which biochemical and biophysical properties of targeted agents are pivotal for their effective distribution inside the tumor and their efficient cellular uptake, we combine mathematical micro-pharmacological modeling with in vivo imaging of targeted human xenograft tumors in SCID mice. The mathematical model calibrated to experimental data was used to explore properties of the targeting ligand (diffusion and affinity) and ligand release schemes (rates and concentrations) with a goal to identify the properties of cells and ligands that enable high receptor saturation. By accounting for heterogeneities typical of in vivo tumors, our model was able to identify cell- and tissue-level barriers to efficient drug uptake. This work provides a base for utilizing experimentally measurable properties of a ligand-targeted agent and patient-specific attributes of the tumor tissue to support the development of novel targeted imaging agents and for improvement in their delivery to individual tumor cells.

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues.

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

Limiting the development of anti-cancer drug resistance in a spatial model of micrometastases.

While chemoresistance in primary tumors is well-studied, much less is known about the influence of systemic chemotherapy on the development of drug resistance at metastatic sites. In this work, we use a hybrid spatial model of tumor response to a DNA damaging drug to study how the development of chemoresistance in micrometastases depends on the drug dosing schedule. We separately consider cell populations that harbor pre-existing resistance to the drug, and those that acquire resistance during the course of treatment. For each of these independent scenarios, we consider one hypothetical cell line that is responsive to metronomic chemotherapy, and another that with high probability cannot be eradicated by a metronomic protocol. Motivated by experimental work on ovarian cancer xenografts, we consider all possible combinations of a one week treatment protocol, repeated for three weeks, and constrained by the total weekly drug dose. Simulations reveal a small number of fractionated-dose protocols that are at least as effective as metronomic therapy in eradicating micrometastases with acquired resistance (weak or strong), while also being at least as effective on those that harbor weakly pre-existing resistant cells. Given the responsiveness of very different theoretical cell lines to these few fractionated-dose protocols, these may represent more effective ways to schedule chemotherapy with the goal of limiting metastatic tumor progression.

Circulating Tumor Cells: When a Solid Tumor Meets a Fluid Microenvironment.

Solid tumor dissemination from the primary site to the sites of metastasis involves tumor cell transport through the blood or lymph circulation systems. Once the tumor cells enter the bloodstream, they encounter a new hostile microenvironment. The cells must withstand hemodynamic forces and overcome the effects of fluid shear. The cells are exposed to immunological signaling insults from leukocytes, to collisions with erythrocytes, and to interactions with platelets or macrophages. Finally, the cells need to attach to the blood vessel walls and extravasate to the surrounding stroma to form tumor metastases. Although only a small fraction of invasive cells is able to complete the metastatic process, most cancer-related deaths are the result of tumor metastasis. Thus, investigating the intracellular properties of circulating tumor cells and the extracellular conditions that allow the tumor cells to survive and thrive in this microenvironment is of vital interest. In this chapter, we discuss the intravascular microenvironment that the circulating tumor cells must endure. We summarize the current experimental and computational literature on tumor cells in the circulation system. We also illustrate various aspects of the intravascular transport of circulating tumor cells using a mathematical model based on immersed boundary principles.

Microenvironmental Niches and Sanctuaries: A Route to Acquired Resistance.

A tumor vasculature that is functionally abnormal results in irregular gradients of metabolites and drugs within the tumor tissue. Recently, significant efforts have been committed to experimentally examine how cellular response to anti-cancer treatments varies based on the environment in which the cells are grown. In vitro studies point to specific conditions in which tumor cells can remain dormant and survive the treatment. In vivo results suggest that cells can escape the effects of drug therapy in tissue regions that are poorly penetrated by the drugs. Better understanding how the tumor microenvironments influence the emergence of drug resistance in both primary and metastatic tumors may improve drug development and the design of more effective therapeutic protocols. This chapter presents a hybrid agent-based model of the growth of tumor micrometastases and explores how microenvironmental factors can contribute to the development of acquired resistance in response to a DNA damaging drug. The specific microenvironments of interest in this work are tumor hypoxic niches and tumor normoxic sanctuaries with poor drug penetration. We aim to quantify how spatial constraints of limited drug transport and quiescent cell survival contribute to the development of drug resistant tumors.

Pathology to enhance precision medicine in oncology: lessons from landscape ecology.

A major goal of modern medicine is increasing patient specificity so that the right treatment is administered to the right patient at the right time with the right dose. While current cancer studies have largely focused on identification of genetic or epigenetic properties of tumor cells, emerging evidence has clearly demonstrated substantial genetic heterogeneity between tumors in the same patient and within subclones of a single tumor. Thus, molecular analysis from populations of cells (either a whole tumor or small biopsy of that tumor) is, at best, an incomplete representation of the underlying biology. These observations indicate a significant need to define intratumoral evolutionary dynamics that yield the observed spatial variations in cellular properties. It is generally accepted that genetic heterogeneity among cancer cells is a manifestation of intratumoral evolution, and this is typically viewed as a consequence of random mutations generated by genomic instability within the cancer cells. We suggest that this represents an incomplete view of Darwinian dynamics, which typically are governed by phenotypic variations in response to spatial and temporal heterogeneity in environmental selection forces. We propose that pathologic feature analysis can provide precise information regarding regional variations in environmental selection forces and phenotypic adaptations. These observations can be integrated using quantitative, spatially explicit methods developed in landscape ecology to interrogate heterogenous biological processes in tumors within individual patients. The ability to investigate tumor heterogeneity has been shown to inform physicians regarding critical aspects of cancer progression including invasion, metastasis, drug resistance, and disease relapse.