Conceptualizing a tool to optimize therapy based on dynamic heterogeneity

Towards Multidrug Adaptive Therapy

A new ecologically inspired paradigm in cancer treatment known as "adaptive therapy" capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent "cycles" of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary "absorbing region" reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy. SIGNIFICANCE: Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.

Metronomic therapy in pediatric oncology: A snapshot

Metronomic chemotherapy transitioned from the bench to bedside in the early 2000s and since then has carved a niche for itself in pediatric oncology. It has been used solely or in combination with other modalities such as radiotherapy, maximum tolerated dose chemotherapy, and targeted agents in adjuvant, palliative, as well as maintenance settings. No wonder, the resulting medical literature is extremely heterogeneous. In this review, the authors review and synthesize the published literature in pediatric metronomics giving a glimpse of its history, varied applications, and evolution of this genre of chemotherapy in pediatric cancers. Limitations, future prospects, and grey areas are also highlighted.

Chemotherapeutic Dose Scheduling Based on Tumor Growth Rates Provides a Case for Low-Dose Metronomic High-Entropy Therapies

We extended the classical tumor regression models such as Skipper's laws and the Norton-Simon hypothesis from instantaneous regression rates to the cumulative effect over repeated cycles of chemotherapy. To achieve this end, we used a stochastic Moran process model of tumor cell kinetics coupled with a prisoner's dilemma game-theoretic cell-cell interaction model to design chemotherapeutic strategies tailored to different tumor growth characteristics. Using the Shannon entropy as a novel tool to quantify the success of dosing strategies, we contrasted MTD strategies as compared with low-dose, high-density metronomic strategies (LDM) for tumors with different growth rates. Our results show that LDM strategies outperformed MTD strategies in total tumor cell reduction. This advantage was magnified for fast-growing tumors that thrive on long periods of unhindered growth without chemotherapy drugs present and was not evident after a single cycle of chemotherapy but grew after each subsequent cycle of repeated chemotherapy. The evolutionary growth/regression model introduced in this article agrees well with murine models. Overall, this model supports the concept of designing different chemotherapeutic schedules for tumors with different growth rates and develops quantitative tools to optimize these schedules for maintaining low-volume tumors. Cancer Res; 77(23); 6717-28. ©2017 AACR.

What's Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Nondeterminism

The field of single-cell biology has morphed from a philosophical digression at its inception, to a playground for quantitative biologists, to a major area of biomedical research. The last several years have witnessed an explosion of new technologies, allowing us to apply even more of the modern molecular biology toolkit to single cells. Conceptual progress, however, has been comparatively slow. Here, we provide a framework for classifying both the origins of the differences between individual cells and the consequences of those differences. We discuss how the concept of "random" differences is context dependent, and propose that rigorous definitions of inputs and outputs may bring clarity to the discussion. We also categorize ways in which probabilistic behavior may influence cellular function, highlighting studies that point to exciting future directions in the field.

Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance

Therapies targeting signaling molecules mutated in cancers can often have striking short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures^1,2. Resistance can result from a secondary mutations^3,4, but other times there is no clear genetic cause, raising the possibility of non-genetic rare cell variability^5–11. Here, we show that melanoma cells can display profound transcriptional variability at the single cell level that predicts which cells will ultimately resist drug treatment. This variability involves infrequent, semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of drug then induces epigenetic reprogramming in these cells, converting the transient transcriptional state to a stably resistant state. This reprogramming begins with a loss of SOX10-mediated differentiation followed by activation of new signaling pathways, partially mediated by activity of Jun-AP-1 and TEAD. Our work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics in single cells. We find that other cell types also exhibit sporadic expression of many of these same marker genes, suggesting the existence of a general rare-cell expression program.

Stochastic phenotypic interconversion in tumors can generate heterogeneity

Phenotype variations define heterogeneity in biological and molecular systems, and play a crucial mechanistic role, and heterogeneity has been demonstrated in tumor cells. In this work, cells from blood of patients affected by colon cancer were analyzed and sorted using a microfluidic assay based on galactose-active moieties and incubated for culturing in severe combined immunodeficiency (SCID) mice. Based on the results of these experiments, a model based on Markov theory is implemented and discussed to explain the equilibrium existing between phenotypes of cell subpopulations sorted using the microfluidic assay. In combination with the experimental results, the model has many implications for tumor heterogeneity; For example, it displays interconversion of phenotypes, confirming the experiments. Such interconversion generates metastatic cells and implies that targeting circulating tumor cells (CTC) will not be an efficient method for prevention of tumor recurrence. Most importantly, understanding the transitions between cell phenotypes in the cell population can improve understanding of tumor generation and growth.

Non-genetic cancer cell plasticity and therapy-induced stemness in tumour relapse: 'What does not kill me strengthens me'

Therapy resistance and tumour relapse after drug therapy are commonly explained by Darwinian selection of pre-existing drug-resistant, often stem-like cancer cells resulting from random mutations. However, the ubiquitous non-genetic heterogeneity and plasticity of tumour cell phenotype raises the question: are mutations really necessary and sufficient to promote cell phenotype changes during tumour progression? Cancer therapy inevitably spares some cancer cells, even in the absence of resistant mutants. Accumulating observations suggest that the non-killed, residual tumour cells actively acquire a new phenotype simply by exploiting their developmental potential. These surviving cells are stressed by the cytotoxic treatment, and owing to phenotype plasticity, exhibit a variety of responses. Some are pushed into nearby, latent attractor states of the gene regulatory network which resemble evolutionary ancient or early developmental gene expression programs that confer stemness and resilience. By entering such stem-like, stress-response states, the surviving cells strengthen their capacity to cope with future noxious agents. Considering non-genetic cell state dynamics and the relative ease with which surviving but stressed cells can be tipped into latent attractors provides a foundation for exploring new therapeutic approaches that seek not only to kill cancer cells but also to avoid promoting resistance and relapse that are inherently linked to the attempts to kill them.

Conceptualizing cancer drugs as classifiers

Cancer and healthy cells have distinct distributions of molecular properties and thus respond differently to drugs. Cancer drugs ideally kill cancer cells while limiting harm to healthy cells. However, the inherent variance among cells in both cancer and healthy cell populations increases the difficulty of selective drug action. Here we formalize a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. More specifically, this discrimination should be performed on the basis of measurable cell markers. We divide the problem into three parts which we explore with examples. First, molecular markers should discriminate cancer cells from healthy cells at the single-cell level. Second, the effects of drugs should be statistically predicted by these molecular markers. Third, drugs should be optimized for classification performance. We find that expression levels of a handful of genes suffice to discriminate well between individual cells in cancer and healthy tissue. We also find that gene expression predicts the efficacy of some cancer drugs, suggesting that these cancer drugs act as suboptimal classifiers using gene profiles. Finally, we formulate a framework that defines an optimal drug, and predicts drug cocktails that may target cancer more accurately than the individual drugs alone. Conceptualizing cancer drugs as solving a discrimination problem in the high-dimensional space of molecular markers promises to inform the design of new cancer drugs and drug cocktails.

Why model?

Next generation sequencing technologies are bringing about a renaissance of mining approaches. A comprehensive picture of the genetic landscape of an individual patient will be useful, for example, to identify groups of patients that do or do not respond to certain therapies. The high expectations may however not be satisfied if the number of patient groups with similar characteristics is going to be very large. I therefore doubt that mining sequence data will give us an understanding of why and when therapies work. For understanding the mechanisms underlying diseases, an alternative approach is to model small networks in quantitative mechanistic detail, to elucidate the role of gene and proteins in dynamically changing the functioning of cells. Here an obvious critique is that these models consider too few components, compared to what might be relevant for any particular cell function. I show here that mining approaches and dynamical systems theory are two ends of a spectrum of methodologies to choose from. Drawing upon personal experience in numerous interdisciplinary collaborations, I provide guidance on how to model by discussing the question "Why model?"

Mosaic physiology from developmental noise: within-organism physiological diversity as an alternative to phenotypic plasticity and phenotypic flexibility

A key problem in organismal biology is to explain the origins of functional diversity. In the context of organismal biology, functional diversity describes the set of phenotypes, across scales of biological organization and through time, that a single genotype, or genome, or organism, can produce. Functional diversity encompasses many phenomena: differences in cell types within organisms; physiological and morphological differences among tissues and organs; differences in performance; morphological shifts in external phenotype; and changes in behavior. How can single genomes produce so many different phenotypes? Modern biology proposes two general mechanisms. The first is developmental programs, by which single cells and their single genomes diversify, via relatively deterministic processes, into the sets of cell types, tissues and organs that we see in most multicellular organisms. The second general mechanism is phenotypic modification stemming from interactions between organisms and their environments – modifications known either as phenotypic plasticity or as phenotypic flexibility, depending on the time scale of the response and the degree of reversibility. These two diversity-generating mechanisms are related because phenotypic modifications may sometimes arise as a consequence of environments influencing developmental programs. Here, I propose that functional diversity also arises via a third fundamental mechanism: stochastic developmental events giving rise to mosaics of physiological diversity within individual organisms. In biological systems, stochasticity stems from the inherently random actions of small numbers of molecules interacting with one another. Although stochastic effects occur in many biological contexts, available evidence suggests that they can be especially important in gene networks, specifically as a consequence of low transcript numbers in individual cells. I briefly review known mechanisms by which organisms control such stochasticity, and how they may use it to create adaptive functional diversity. I then fold this idea into modern thinking on phenotypic plasticity and flexibility, proposing that multicellular organisms exhibit ‘mosaic physiology’. Mosaic physiology refers to sets of diversified phenotypes, within individual organisms, that carry out related functions at the same time, but that are distributed in space. Mosaic physiology arises from stochasticity-driven differentiation of cells, early during cell diversification, which is then amplified by cell division and growth into macroscopic phenotypic modules (cells, tissues, organs) making up the physiological systems of later life stages. Mosaic physiology provides a set of standing, diversified phenotypes, within single organisms, that raise the likelihood of the organism coping well with novel environmental challenges. These diversified phenotypes can be distinct, akin to polyphenisms at the organismal level; or they can be continuously distributed, creating a kind of standing, simultaneously expressed reaction norm of physiological capacities.

Kinetics of MDR transport in tumor-initiating cells

Multidrug resistance (MDR) driven by ABC (ATP binding cassette) membrane transporters is one of the major causes of treatment failure in human malignancy. MDR capacity is thought to be unevenly distributed among tumor cells, with higher capacity residing in tumor-initiating cells (TIC) (though opposite finding are occasionally reported). Functional evidence for enhanced MDR of TICs was previously provided using a "side population" assay. This assay estimates MDR capacity by a single parameter - cell's ability to retain fluorescent MDR substrate, so that cells with high MDR capacity ("side population") demonstrate low substrate retention. In the present work MDR in TICs was investigated in greater detail using a kinetic approach, which monitors MDR efflux from single cells. Analysis of kinetic traces obtained allowed for the estimation of both the velocity (V max) and affinity (K M) of MDR transport in single cells. In this way it was shown that activation of MDR in TICs occurs in two ways: through the increase of V max in one fraction of cells, and through decrease of K M in another fraction. In addition, kinetic data showed that heterogeneity of MDR parameters in TICs significantly exceeds that of bulk cells. Potential consequences of these findings for chemotherapy are discussed.