Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer

Beckman, Robert A.; Schemmann, Gunter S.; Yeang, Chen‐Hsiang

doi:10.1073/pnas.1203559109

Cited by 100 publications

(140 citation statements)

References 38 publications

(43 reference statements)

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“…Some studies used differential equation models formulated as deterministic optimal control problems (13)(14)(15)(16), whereas others used stochastic birth-death process models (17)(18)(19)(20)(21), to examine treatment regimens for tumors comprising a sensitive population along with a few resistant subpopulations. However, these studies, many of which dealt only with generic scenarios, focused primarily on drug scheduling, investigating the effects of frequency and dose intensity of drugs on resistance potential.…”

mentioning

confidence: 99%

Intratumor heterogeneity alters most effective drugs in designed combinations

Zhao

Hemann

Lauffenburger

2014

Proc. Natl. Acad. Sci. U.S.A.

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The substantial spatial and temporal heterogeneity observed in patient tumors poses considerable challenges for the design of effective drug combinations with predictable outcomes. Currently, the implications of tissue heterogeneity and sampling bias during diagnosis are unclear for selection and subsequent performance of potential combination therapies. Here, we apply a multiobjective computational optimization approach integrated with empirical information on efficacy and toxicity for individual drugs with respect to a spectrum of genetic perturbations, enabling derivation of optimal drug combinations for heterogeneous tumors comprising distributions of subpopulations possessing these perturbations. Analysis across probabilistic samplings from the spectrum of various possible distributions reveals that the most beneficial (considering both efficacy and toxicity) set of drugs changes as the complexity of genetic heterogeneity increases. Importantly, a significant likelihood arises that a drug selected as the most beneficial single agent with respect to the predominant subpopulation in fact does not reside within the most broadly useful drug combinations for heterogeneous tumors. The underlying explanation appears to be that heterogeneity essentially homogenizes the benefit of drug combinations, reducing the special advantage of a particular drug on a specific subpopulation. Thus, this study underscores the importance of considering heterogeneity in choosing drug combinations and offers a principled approach toward designing the most likely beneficial set, even if the subpopulation distribution is not precisely known.systems biology | cancer | combination therapy G enetic intratumor heterogeneity has long been appreciated as present in cancer patients (1). Recent sequencing studies further revealed the extent of this tumor diversity, arising from highly complex clonal evolutionary processes (2). This phenomenon has been observed in many solid (3-5) and hematopoietic cancers (6-8). Moreover, treatments also may have dramatic effects on tumor composition-with a preexisting subclone at diagnosis often becoming dominant at relapse (9-12). To meet this challenge, rational drug combination treatments must be designed so as to account for intratumor heterogeneity to better predict reoccurrence.The history of theoretical studies aimed at drug optimization in cancer therapy is long. Some studies used differential equation models formulated as deterministic optimal control problems (13-16), whereas others used stochastic birth-death process models (17-21), to examine treatment regimens for tumors comprising a sensitive population along with a few resistant subpopulations. However, these studies, many of which dealt only with generic scenarios, focused primarily on drug scheduling, investigating the effects of frequency and dose intensity of drugs on resistance potential. Practical applications of such results have been limited, although some of these strategies recently were combined with experimental validation to examin...

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mentioning

confidence: 99%

Intratumor heterogeneity alters most effective drugs in designed combinations

Zhao

Hemann

Lauffenburger

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…91,92 Beckman et al . 93 illustrated that the potential effects on drug response and resistance acquisition induced by single cell heterogeneity and cellular dynamics can be mathematically modeled and, therefore, should be included in personalized treatment strategies. They demonstrated that a strategy targeting all cancer subpopulations including the precursors of resistant clones, rather than treatment targeting the clone predominantly present which therefore initially leads to the largest reduction in tumour size, may be more effective.…”

Section: Cancer Systems Biology Approachesmentioning

confidence: 99%

Cancer Systems Biology: a peek into the future of patient care?

2014

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Traditionally, scientific research has focused on studying individual events, such as single mutations, gene function or the effect of the manipulation of one protein on a biological phenotype. A range of technologies, combined with the ability to develop robust and predictive mathematical models, is beginning to provide information that will enable a holistic view of how the genomic and epigenetic aberrations in cancer cells can alter the homeostasis of signalling networks within these cells, between cancer cells and the local microenvironment, at the organ and organism level. This systems biology process needs to be integrated with an iterative approach wherein hypotheses and predictions that arise from modelling are refined and constrained by experimental evaluation. Systems biology approaches will be vital for developing and implementing effective strategies to deliver personalized cancer therapy. Specifically, these approaches will be important to select those patients most likely to benefit from targeted therapies as well as for the development and implementation of rational combinatorial therapies. Systems biology can help to increase therapy efficacy or bypass the emergence of resistance, thus converting the current (often short term) effects of targeted therapies into durable responses, ultimately to improve quality of life and provide a cure.

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“…In the spirit of Beckman et al (39), who also investigated the impact of heterogeneity on different therapies, we assume that tumour growth is unbounded and that tumour heterogeneity is due to differential drug response among different subclones. This is of course a gross simplification, but allows us to focus on the relevant aspects of clonal evolution and the acquisition of resistance.…”

Section: Population Dynamicsmentioning

confidence: 99%

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

Gerlee

Kim

Anderson

2015

Seminars in Cancer Biology

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In this review we summarize our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only scratch the surface of in this review. We will focus on models that use neural networks as a means of connecting these different biological scales, since they allow us to easily create heterogeneity for selection to act upon and importantly this heterogeneity can be implemented at different biological scales. More specifically, we consider three different neural networks that bridge different aspects of these scales and the dialogue with the micro-environment, (i) the impact of the microenvironment on evolutionary dynamics, (ii) the mapping from genotype to phenotype under drug-induced perturbations and (iii) pathway activity in both normal and cancer cells under different micro-environmental conditions.

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Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer

Cited by 100 publications

References 38 publications

Intratumor heterogeneity alters most effective drugs in designed combinations

Intratumor heterogeneity alters most effective drugs in designed combinations

Cancer Systems Biology: a peek into the future of patient care?

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

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