Mutation-selection balance and compensatory mechanisms in tumour evolution.

Intratumour heterogeneity and phenotypic plasticity, sustained by a range of somatic aberrations, as well as epigenetic and metabolic adaptations, are the principal mechanisms that enable cancers to resist treatment and survive under environmental stress. A comprehensive picture of the interplay between different somatic aberrations, from point mutations to whole-genome duplications, in tumour initiation and progression is lacking. We posit that different genomic aberrations generally exhibit a temporal order, shaped by a balance between the levels of mutations and selective pressures. Repeat instability emerges first, followed by larger aberrations, with compensatory effects leading to robust tumour fitness maintained throughout the tumour progression. A better understanding of the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection and prevention of cancer as well as development of efficient therapeutic strategies.

A gene for all seasons: The evolutionary consequences of HIF-1 in carcinogenesis, tumor growth and metastasis.

Oxygen played a pivotal role in the evolution of multicellularity during the Cambrian Explosion. Not surprisingly, responses to fluctuating oxygen concentrations are integral to the evolution of cancer-a disease characterized by the breakdown of multicellularity. Poorly organized tumor vasculature results in chaotic patterns of blood flow characterized by large spatial and temporal variations in intra-tumoral oxygen concentrations. Hypoxia-inducible growth factor (HIF-1) plays a pivotal role in enabling cells to adapt, metabolize, and proliferate in low oxygen conditions. HIF-1 is often constitutively activated in cancers, underscoring its importance in cancer progression. Here, we argue that the phenotypic changes mediated by HIF-1, in addition to adapting the cancer cells to their local environment, also "pre-adapt" them for proliferation at distant, metastatic sites. HIF-1-mediated adaptations include a metabolic shift towards anaerobic respiration or glycolysis, activation of cell survival mechanisms like phenotypic plasticity and epigenetic reprogramming, and formation of tumor vasculature through angiogenesis. Hypoxia induced epigenetic reprogramming can trigger epithelial to mesenchymal transition in cancer cells-the first step in the metastatic cascade. Highly glycolytic cells facilitate local invasion by acidifying the tumor microenvironment. New blood vessels, formed due to angiogenesis, provide cancer cells a conduit to the circulatory system. Moreover, survival mechanisms acquired by cancer cells in the primary site allow them to remodel tissue at the metastatic site generating tumor promoting microenvironment. Thus, hypoxia in the primary tumor promoted adaptations conducive to all stages of the metastatic cascade from the initial escape entry into a blood vessel, intravascular survival, extravasation into distant tissues, and establishment of secondary tumors.

The harsh microenvironment in early breast cancer selects for a Warburg phenotype.

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected low-glycolytic breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA sequencing analysis of WE clones identified the transcription factor KLF4 as potential inducer of the WE phenotype. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a WE phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation.

MOTIVATION: Time-lapse microscopy is a powerful technique that relies on images of live cells cultured ex vivo that are captured at regular intervals of time to describe and quantify their behavior under certain experimental conditions. This imaging method has great potential in advancing the field of precision oncology by quantifying the response of cancer cells to various therapies and identifying the most efficacious treatment for a given patient. Digital image processing algorithms developed so far require high-resolution images involving very few cells originating from homogeneous cell line populations. We propose a novel framework that tracks cancer cells to capture their behavior and quantify cell viability to inform clinical decisions in a high-throughput manner. RESULTS: The brightfield microscopy images a large number of patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to 6 days. We developed a robust and user-friendly pipeline CancerCellTracker that detects cells in co-culture, tracks these cells across time and identifies cell death events using changes in cell attributes. We validated our computational pipeline by comparing the timing of cell death estimates by CancerCellTracker from brightfield images and a fluorescent channel featuring ethidium homodimer. We benchmarked our results using a state-of-the-art algorithm implemented in ImageJ and previously published in the literature. We highlighted CancerCellTracker's efficiency in estimating the percentage of live cells in the presence of bone marrow stromal cells. AVAILABILITY AND IMPLEMENTATION: https://github.com/compbiolabucf/CancerCellTracker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ecoevolutionary biology of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC), the most common histological subtype of pancreatic cancer, is an aggressive disease predicted to be the 2nd cause of cancer mortality in the US by 2040. While first-line therapy has improved, 5-year overall survival has only increased from 5 to ∼10%, and surgical resection is only available for ∼20% of patients as most present with advanced disease, which is invariably lethal. PDAC has well-established highly recurrent mutations in four driver genes including KRAS, TP53, CDKN2A, and SMAD4. Unfortunately, these genetic drivers are not currently therapeutically actionable. Despite extensive sequencing efforts, few additional significantly recurrent and druggable drivers have been identified. In the absence of targetable mutations, chemotherapy remains the mainstay of treatment for most patients. Further, the role of the above driver mutations on PDAC initiation and early development is well-established. However, these mutations alone cannot account for PDAC heterogeneity nor discern early from advanced disease. Taken together, management of PDAC is an example highlighting the shortcomings of the current precision medicine paradigm. PDAC, like other malignancies, represents an ecoevolutionary process. Better understanding the disease through this lens can facilitate the development of novel therapeutic strategies to better control and cure PDAC. This review aims to integrate the current understanding of PDAC pathobiology into an ecoevolutionary framework.

Artificial selection for host resistance to tumour growth and subsequent cancer cell adaptations: an evolutionary arms race.

BACKGROUND: Cancer progression is governed by evolutionary dynamics in both the tumour population and its host. Since cancers die with the host, each new population of cancer cells must reinvent strategies to overcome the host's heritable defences. In contrast, host species evolve defence strategies over generations if tumour development limits procreation. METHODS: We investigate this "evolutionary arms race" through intentional breeding of immunodeficient SCID and immunocompetent Black/6 mice to evolve increased tumour suppression. Over 10 generations, we injected Lewis lung mouse carcinoma cells [LL/2-Luc-M38] and selectively bred the two individuals with the slowest tumour growth at day 11. Their male progeny were hosts in the subsequent round. RESULTS: The evolved SCID mice suppressed tumour growth through biomechanical restriction from increased mesenchymal proliferation, and the evolved Black/6 mice suppressed tumour growth by increasing immune-mediated killing of cancer cells. However, transcriptomic changes of multicellular tissue organisation and function genes allowed LL/2-Luc-M38 cells to adapt through increased matrix remodelling in SCID mice, and reduced angiogenesis, increased energy utilisation and accelerated proliferation in Black/6 mice. CONCLUSION: Host species can rapidly evolve both immunologic and non-immunologic tumour defences. However, cancer cell plasticity allows effective phenotypic and population-based counter strategies.

The Effects of Anesthetics and Perioperative Medications on Immune Function: A Narrative Review.

Preclinical and clinical studies have sought to better understand the effect of anesthetic agents, both volatile and intravenous, and perioperative adjuvant medications on immune function. The immune system has evolved to incorporate both innate and adaptive components, which are delicately interwoven and essential for host defense from pathogens and malignancy. This review summarizes the complex and nuanced relationship that exists between each anesthetic agent or perioperative adjuvant medication studied and innate and adaptive immune function with resultant clinical implications. The most commonly used anesthetic agents were chosen for review including volatile agents (sevoflurane, isoflurane, desflurane, and halothane), intravenous agents (propofol, ketamine, etomidate, and dexmedetomidine), and perioperative adjuvant medications (benzodiazepines, opioids, nonsteroidal anti-inflammatory drugs [NSAIDs], and local anesthetic agents). Patients who undergo surgery experience varying combinations of the aforementioned anesthetic agents and adjuncts, depending on the type of surgery and their comorbidities. Each has unique effects on immunity, which may be more or less ideal depending on the clinical situation. Further study is needed to better understand the clinical effects of these relationships so that patient-specific strategies can be developed to improve surgical outcomes.

Special Collection on Ecological and Evolutionary Approaches to Cancer Control: Cancer Finds a Conceptual Home.

Despite a century of intense investigation, cancer biology and treatment remain plagued by unanswered questions. Even basic questions regarding the fundamental forces driving the formation of cancer remain controversial. Recent approaches view cancer in the context of a complex web of interactions among cancer cells of the tumor, together with their interactions with the many cells and constituents of the complex and highly dynamic tumor microenvironment. As seen in this special collection, we believe that viewing cancer as a process of evolution driven by ongoing ecological processes playing out within a dynamic environment offers many insights and potential new pathways for cancer control.

Insights From the Ecology of Information to Cancer Control.

Uniquely in nature, living systems must acquire, store, and act upon information. The survival and replicative fate of each normal cell in a multicellular organism is determined solely by information obtained from its surrounding tissue. In contrast, cancer cells as single-cell eukaryotes live in a disrupted, heterogeneous environment with opportunities and hazards. Thus, cancer cells, unlike normal somatic cells, must constantly obtain information from their environment to ensure survival and proliferation. In this study, we build upon a simple mathematical modeling framework developed to predict (1) how information promotes population persistence in a highly heterogeneous environment and (2) how disruption of information resulting from habitat fragmentation increases the probability of population extinction. Because (1) tumors grow in a highly heterogeneous microenvironment and (2) many cancer therapies fragment tumors into isolated, small cancer cell populations, we identify parallels between these 2 systems and develop ideas for cancer cure based on lessons gleaned from Anthropocene extinctions. In many Anthropocene extinctions, such as that of the North American heath hen (Tympanuchus cupido cupido), a large and widespread population was initially reduced and fragmented owing to overexploitation by humans (a "first strike"). After this, the small surviving populations are vulnerable to extinction from environmental or demographic stochastic disturbances (a "second strike"). Following this analogy, after a tumor is fragmented into small populations of isolated cancer cells by an initial therapy, additional treatment can be applied with the intent of extinction (cure). Disrupting a cancer cell's ability to acquire and use information in a heterogeneous environment may be an important tactic for causing extinction following an effective initial therapy. Thus, information, from the scale of cells within tumors to that of species within ecosystems, can be used to identify vulnerabilities to extinction and opportunities for novel treatment strategies.

The Intersection of Regional Anesthesia and Cancer Progression: A Theoretical Framework.

The surgical stress and inflammatory response and volatile anesthetic agents have been shown to promote tumor metastasis in animal and in-vitro studies. Regional neuraxial anesthesia protects against these effects by decreasing the surgical stress and inflammatory response and associated changes in immune function in animals. However, evidence of a similar effect in humans remains equivocal due to the high variability and retrospective nature of clinical studies and difficulty in directly comparing regional versus general anesthesia in humans. We propose a theoretical framework to address the question of regional anesthesia as protective against metastasis.This theoretical construct views the immune system, circulating tumor cells, micrometastases, and inflammatory mediators as distinct populations in a highly connected system. In ecological theory, highly connected populations demonstrate more resilience to local perturbations but are prone to system-wide shifts compared with their poorly connected counterparts. Neuraxial anesthesia transforms the otherwise system-wide perturbations of the surgical stress and inflammatory response and volatile anesthesia into a comparatively local perturbation to which the system is more resilient. We propose this framework for experimental and mathematical models to help determine the impact of anesthetic choice on recurrence and metastasis and create therapeutic strategies to improve cancer outcomes after surgery.

Mutations, evolution and the central role of a self-defined fitness function in the initiation and progression of cancer.

The origin and progression of cancer is widely viewed as "somatic evolution" driven by the accumulation of random genetic changes. This theoretical model, however, neglects fundamental conditions for evolution by natural selection, which include competition for survival and a local environmental context. Recent observations that the mutational burden in different cancers can vary by 2 orders of magnitude and that multiple mutations, some of which are "oncogenic," are observed in normal tissue suggests these neglected Darwinian dynamics may play a critical role in modifying the evolutionary consequences of molecular events. Here we discuss evolutionary principles in normal tissue focusing on the dynamical tension between different evolutionary levels of selection. Normal somatic cells within metazoans do not ordinarily evolve because their survival and proliferation are governed by tissue signals and internal controls (e.g. telomere shortening) that maintain homeostatic function. The fitness of each cell is, thus, identical to the whole organism, which is the evolutionary level of selection. For a cell to evolve, it must acquire a self-defined fitness function so that its survival and proliferation is determined entirely by its own heritable phenotypic properties. Cells can develop independence from normal tissue control through randomly accumulating mutations that disrupt its ability to recognize or respond to all host signals. A self-defined fitness function can also be gained non-genetically when tissue control signals are lost due to injury, inflammation, or infection. Accumulating mutations in cells without a self-defined fitness function will produce no evolution - consistent with reports showing mutations, including some that would ordinarily be oncogenic, are present in cells from normal tissue. Furthermore, once evolution begins, Darwinian forces will promote mutations that increase fitness and eliminate those that do not. Thus, cancer cells will typically have a mutational burden similar to adjacent normal cells and many (perhaps most) mutations observed in cancer cells occurred prior to somatic evolution and may not contribute to the cell's malignant phenotype. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

A Mathematical Dissection of the Adaptation of Cell Populations to Fluctuating Oxygen Levels.

The disordered network of blood vessels that arises from tumour angiogenesis results in variations in the delivery of oxygen into the tumour tissue. This brings about regions of chronic hypoxia (i.e. sustained low oxygen levels) and regions with alternating periods of low and relatively higher oxygen levels, and makes it necessary for cancer cells to adapt to fluctuating environmental conditions. We use a phenotype-structured model to dissect the evolutionary dynamics of cell populations exposed to fluctuating oxygen levels. In this model, the phenotypic state of every cell is described by a continuous variable that provides a simple representation of its metabolic phenotype, ranging from fully oxidative to fully glycolytic, and cells are grouped into two competing populations that undergo heritable, spontaneous phenotypic variations at different rates. Model simulations indicate that, depending on the rate at which oxygen is consumed by the cells, dynamic nonlinear interactions between cells and oxygen can stimulate chronic hypoxia and cycling hypoxia. Moreover, the model supports the idea that under chronic-hypoxic conditions lower rates of phenotypic variation lead to a competitive advantage, whereas higher rates of phenotypic variation can confer a competitive advantage under cycling-hypoxic conditions. In the latter case, the numerical results obtained show that bet-hedging evolutionary strategies, whereby cells switch between oxidative and glycolytic phenotypes, can spontaneously emerge. We explain how these results can shed light on the evolutionary process that may underpin the emergence of phenotypic heterogeneity in vascularised tumours.

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.

Evolutionary dynamics of competing phenotype-structured populations in periodically fluctuating environments.

Living species, ranging from bacteria to animals, exist in environmental conditions that exhibit spatial and temporal heterogeneity which requires them to adapt. Risk-spreading through spontaneous phenotypic variations is a known concept in ecology, which is used to explain how species may survive when faced with the evolutionary risks associated with temporally varying environments. In order to support a deeper understanding of the adaptive role of spontaneous phenotypic variations in fluctuating environments, we consider a system of non-local partial differential equations modelling the evolutionary dynamics of two competing phenotype-structured populations in the presence of periodically oscillating nutrient levels. The two populations undergo heritable, spontaneous phenotypic variations at different rates. The phenotypic state of each individual is represented by a continuous variable, and the phenotypic landscape of the populations evolves in time due to variations in the nutrient level. Exploiting the analytical tractability of our model, we study the long-time behaviour of the solutions to obtain a detailed mathematical depiction of the evolutionary dynamics. The results suggest that when nutrient levels undergo small and slow oscillations, it is evolutionarily more convenient to rarely undergo spontaneous phenotypic variations. Conversely, under relatively large and fast periodic oscillations in the nutrient levels, which bring about alternating cycles of starvation and nutrient abundance, higher rates of spontaneous phenotypic variations confer a competitive advantage. We discuss the implications of our results in the context of cancer metabolism.

The 2019 mathematical oncology roadmap.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

The Role of Cell Membrane Information Reception, Processing, and Communication in the Structure and Function of Multicellular Tissue.

Investigations of information dynamics in eukaryotic cells focus almost exclusively on heritable information in the genome. Gene networks are modeled as "central processors" that receive, analyze, and respond to intracellular and extracellular signals with the nucleus described as a cell's control center. Here, we present a model in which cellular information is a distributed system that includes non-genomic information processing in the cell membrane that may quantitatively exceed that of the genome. Within this model, the nucleus largely acts a source of macromolecules and processes information needed to synchronize their production with temporal variations in demand. However, the nucleus cannot produce microsecond responses to acute, life-threatening perturbations and cannot spatially resolve incoming signals or direct macromolecules to the cellular regions where they are needed. In contrast, the cell membrane, as the interface with its environment, can rapidly detect, process, and respond to external threats and opportunities through the large amounts of potential information encoded within the transmembrane ion gradient. Our model proposes environmental information is detected by specialized protein gates within ion-specific transmembrane channels. When the gate receives a specific environmental signal, the ion channel opens and the received information is communicated into the cell via flow of a specific ion species (i.e., K+, Na+, Cl-, Ca2+, Mg2+) along electrochemical gradients. The fluctuation of an ion concentration within the cytoplasm adjacent to the membrane channel can elicit an immediate, local response by altering the location and function of peripheral membrane proteins. Signals that affect a larger surface area of the cell membrane and/or persist over a prolonged time period will produce similarly cytoplasmic changes on larger spatial and time scales. We propose that as the amplitude, spatial extent, and duration of changes in cytoplasmic ion concentrations increase, the information can be communicated to the nucleus and other intracellular structure through ion flows along elements of the cytoskeleton to the centrosome (via microtubules) or proteins in the nuclear membrane (via microfilaments). These dynamics add spatial and temporal context to the more well-recognized information communication from the cell membrane to the nucleus following ligand binding to membrane receptors. Here, the signal is transmitted and amplified through transduction by the canonical molecular (e.g., Mitogen Activated Protein Kinases (MAPK) pathways. Cytoplasmic diffusion allows this information to be broadly distributed to intracellular organelles but at the cost of loss of spatial and temporal information also contained in ligand binding.

Chiral Monomers Ensure Orientational Specificity of Monomer Binding During Polymer Self-Replication.

Biomolecular homochirality is universally observed in living systems but the molecular and evolutionary dynamics that led to its emergence are unknown. In fact, there are significant disadvantages in using chiral monomers for polymerization, which include enantiomeric cross-inhibition in racemic medium and under-utilization of available resources for self-replication in the primordial environment. Nevertheless, most investigations of homochirality in living systems assume that the individual primordial monomers were chiral prior to the formation of self-replicating polymer and therefore focus on identifying a symmetry-breaking mechanism that might choose one enantiomer over the other in a racemic medium. Within the premise that the extant biomolecules are products of molecular evolution, we ask a related but distinct question: why is an achiral monomer molecule disfavored? Here we identify an evolutionary advantage for molecular evolution to choose chiral over achiral monomers to construct primordial self-replicating polymers. We argue that when polymerization is constrained to proceed in only one direction along the template, as in DNA, evolution favors chiral monomers and homochiral polymers. This evolutionary advantage stems from the ability of a chiral monomer to bond with the template in only one orientation relative to the template monomer, along the direction of polymerization. An achiral monomer, on the other hand, offers more than one possible orientation for bonding with the template monomer, due to the presence of symmetry elements in its structure, which would lead to inhibition of polymerization. We show that the requirement of orientational specificity leads to monomer chirality, by using a known relationship between rotational and reflection symmetry elements, within the constraint that the resultant polymers are helical.

Evolutionary advantage of directional symmetry breaking in self-replicating polymers.

Due to the asymmetric nature of the nucleotides, the extant informational biomolecule, DNA, is constrained to replicate unidirectionally on a template. As a product of molecular evolution that sought to maximize replicative potential, DNA's unidirectional replication poses a mystery since symmetric bidirectional self-replicators obviously would replicate faster than unidirectional self-replicators and hence would have been evolutionarily more successful. Here we carefully examine the physico-chemical requirements for evolutionarily successful primordial self-replicators and theoretically show that at low monomer concentrations that possibly prevailed in the primordial oceans, asymmetric unidirectional self-replicators would have an evolutionary advantage over bidirectional self-replicators. The competing requirements of low and high kinetic barriers for formation and long lifetime of inter-strand bonds respectively are simultaneously satisfied through asymmetric kinetic influence of inter-strand bonds, resulting in evolutionarily successful unidirectional self-replicators. Within our model, circular strands, the configuration prefered by primitive life forms, have higher replicative potential compared to linear strands.

Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer.

In metastatic castrate resistant prostate cancer (mCRPC), abiraterone is conventionally administered continuously at maximal tolerated dose until treatment failure. The majority of patients initially respond well to abiraterone but the cancer cells evolve resistance and the cancer progresses within a median time of 16 months. Incorporating techniques that attempt to delay or prevent the growth of the resistant cancer cell phenotype responsible for disease progression have only recently entered the clinical setting. Here we use evolutionary game theory to model the evolutionary dynamics of patients with mCRPC subject to abiraterone therapy. In evaluating therapy options, we adopt an optimal control theory approach and seek the best treatment schedule using nonlinear constrained optimization. We compare patient outcomes from standard clinical treatments to those with other treatment objectives, such as maintaining a constant total tumor volume or minimizing the fraction of resistant cancer cells within the tumor. Our model predicts that continuous high doses of abiraterone as well as other therapies aimed at curing the patient result in accelerated competitive release of the resistant phenotype and rapid subsequent tumor progression. We find that long term control is achievable using optimized therapy through the restrained and judicious application of abiraterone, maintaining its effectiveness while providing acceptable patient quality of life. Implementing this strategy will require overcoming psychological and emotional barriers in patients and physicians as well as acquisition of a new class of clinical data designed to accurately estimate intratumoral eco-evolutionary dynamics during therapy.