scholarly journals A Transient Metabolic State In Melanoma Persister Cells Mediated By Chemotherapeutic Treatments

2021 ◽  
Author(s):  
Prashant Karki ◽  
Vahideh Angardi ◽  
Juan C. Mier ◽  
Mehmet A. Orman
Keyword(s):  
Cancer Cell ◽  
Cell Population ◽  
Krebs Cycle ◽  
Chemotherapeutic Agents ◽  
Health Concern ◽  
Cell Physiology ◽  
Persister Cells ◽  
Diverse Range ◽  
Metabolic Remodeling ◽  
Cancer Cell Population

ABSTRACTPersister cells are defined as the small fraction of quiescent cells in a bulk cancer cell population that can tolerate unusually high levels of drugs. Persistence is a transient state that poses an important health concern in cancer therapy. The mechanisms associated with persister phenotypes are highly diverse and complex, and many aspects of persister cell physiology remain to be explored. We applied a melanoma cell line and panel of chemotherapeutic agents to show that melanoma persister cells are not necessarily preexisting dormant cells or stem cells; in fact, they may be induced by cancer chemotherapeutics. Our metabolomics analysis and phenotype microarray assays further demonstrated that the levels of Krebs cycle molecules are significantly lower in the melanoma persister subpopulation than in the untreated bulk cell population due to increased utilization rates in persisters. Our data indicate that this observed metabolic remodeling is transient, as the consumption rates of Krebs cycle metabolites are significantly reduced in the progenies of persisters. Given that the mitochondrial electron transport chain (ETC) is more active in the persister subpopulation than in the bulk cancer cell population, we also verified that targeting ETC activity can reduce melanoma persistence. The reported metabolic remodeling feature seems to be a conserved characteristic of melanoma persistence, as it has been observed in various melanoma persister subpopulations derived from a diverse range of chemotherapeutics. Elucidating a global metabolic mechanism that contributes to persister survival and reversible switching will ultimately foster the development of novel cancer therapeutic strategies.

scholarly journals Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy

2020 ◽  
Vol 15 ◽  
pp. 14 ◽  
Author(s):  
Rebecca E.A. Stace ◽  
Thomas Stiehl ◽  
Mark A.J. Chaplain ◽  
Anna Marciniak-Czochra ◽  
Tommaso Lorenzi
Keyword(s):  
Cancer Cell ◽  
Cell Population ◽  
Evolutionary Dynamics ◽  
Analytical Description ◽  
Chemotherapeutic Agents ◽  
Cell Populations ◽  
Theoretical Ground ◽  
Individual Based Model ◽  
Nonlocal Parabolic Equation ◽  
Epigenetic Drug

We present a stochastic individual-based model for the phenotypic evolution of cancer cell populations under chemotherapy. In particular, we consider the case of combination cancer therapy whereby a chemotherapeutic agent is administered as the primary treatment and an epigenetic drug is used as an adjuvant treatment. The cell population is structured by the expression level of a gene that controls cell proliferation and chemoresistance. In order to obtain an analytical description of evolutionary dynamics, we formally derive a deterministic continuum counterpart of this discrete model, which is given by a nonlocal parabolic equation for the cell population density function. Integrating computational simulations of the individual-based model with analysis of the corresponding continuum model, we perform a complete exploration of the model parameter space. We show that harsher environmental conditions and higher probabilities of spontaneous epimutation can lead to more effective chemotherapy, and we demonstrate the existence of an inverse relationship between the efficacy of the epigenetic drug and the probability of spontaneous epimutation. Taken together, the outcomes of the model provide theoretical ground for the development of anticancer protocols that use lower concentrations of chemotherapeutic agents in combination with epigenetic drugs capable of promoting the re-expression of epigenetically regulated genes.

A SIMPLE EVOLUTIONARY MODEL FOR CANCER CELL POPULATION AND ITS IMPLICATIONS ON CANCER THERAPY

2009 ◽  
Vol 23 (25) ◽  
pp. 2999-3011 ◽  
Author(s):  
PENG YAO ◽  
SHUTANG WEN ◽  
BAOSHUN LI ◽  
YUXIAO LI
Keyword(s):  
Cancer Therapy ◽  
Cancer Cell ◽  
Cell Population ◽  
Fitness Landscape ◽  
Evolutionary Model ◽  
Evolutionary Games ◽  
Cellular Interactions ◽  
Malignant Cells ◽  
Quasispecies Model ◽  
Cancer Cell Population

We established a simple evolutionary model based on the cancer stem cell hypothesis. By taking cellular interactions into consideration, we introduced the evolutionary games theory into the quasispecies model. The fitness values are determined by both genotypes and cellular interactions. In the evolutionary model, a cancer cell population can evolve in different patterns. For single peak intrinsic fitness landscape, the evolution pattern can transit with increasing differentiation probability from malignant cells to benign cells in four different modes. For a large enough value of differentiation probability, the evolution is always the case that the malignant cells extinct ultimately, which might give some implications on cancer therapy.

scholarly journals The CEA−/lo colorectal cancer cell population harbors cancer stem cells and metastatic cells

Oncotarget ◽  
2016 ◽  
Vol 7 (49) ◽  
pp. 80700-80715 ◽  
Author(s):  
Chang Yan ◽  
Yibing Hu ◽  
Bo Zhang ◽  
Lei Mu ◽  
Kaiyu Huang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Cancer Cell ◽  
Cell Population ◽  
Colorectal Cancer Cell ◽  
Metastatic Cells ◽  
Cancer Cell Population

scholarly journals The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

2020 ◽  
Author(s):  
Michael Raatz ◽  
Saumil Shah ◽  
Guranda Chitadze ◽  
Monika Brüggemann ◽  
Arne Traulsen
Keyword(s):  
Growth Rate ◽  
Cancer Cell ◽  
Cell Population ◽  
Phenotypic Heterogeneity ◽  
Therapy Options ◽  
Rate Dependent ◽  
Trait Distribution ◽  
Intratumour Heterogeneity ◽  
Treatment Type ◽  
Cancer Cell Population

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.Author summaryThe individual cells within a cancer cell population are not all equal. The heterogeneity among them can strongly affect disease progression and treatment success. Recent diagnostic advances allow measuring how the characteristics of this heterogeneity change over time. To match these advances, we developed deterministic and stochastic trait-based models that capture important characteristics of the intratumour heterogeneity and allow to evaluate different treatment types that either do or do not interact with this heterogeneity. We focus on growth rate as the decisive characteristic of the intratumour heterogeneity. We find that by shifting the trait distribution of the cancer cell population, the growth rate-dependent treatment delays an eventual relapse compared to the growth rate-independent treatment. As a downside, however, we observe a refuge effect where slower-growing subpopulations are less affected by the growth rate-dependent treatment, which may decrease the likelihood of successful therapy. We find that navigating along this trade-off may be achieved by sequentially combining both treatment types, which agrees qualitatively with current clinical practice. Interestingly, even rather large intervals between treatment changes allow for close-to-optimal treatment results, which again hints towards a practical applicability.

scholarly journals Finsler-type estimators for the cancer cell population dynamics

2015 ◽  
Vol 98 (112) ◽  
pp. 53-69
Author(s):  
Vladimir Balan ◽  
Jelena Stojanov
Keyword(s):  
Dynamical System ◽  
Population Growth ◽  
Cancer Cell ◽  
Cell Population ◽  
Biological Models ◽  
Cell Population Dynamics ◽  
Finsler Structure ◽  
Cell Population Growth ◽  
Cancer Cell Population ◽  
The Difference

We introduce a Finslerian model related to the classical Garner dynamical system, which models the cancer cell population growth. The Finsler structure is determined by the energy of the deformation field-the difference of the fields, which describe the reduced and the proper biological models. It is shown that a certain locally-Minkowski anisotropic Randers structure, obtained by means of statistical fitting, is able to provide a Zermelo-type drift of the overall cancer cell population growth, which occurs due to significant changes within the cancerous process. The geometric background, the applicative advantages and perspective openings of the constructed geometric structure are discussed.

scholarly journals Allele Frequency Spectrum in a Cancer Cell Population

2017 ◽  
Author(s):  
H. Ohtsuki ◽  
H. Innan
Keyword(s):  
Population Genetics ◽  
Cell Division ◽  
Allele Frequency ◽  
Frequency Spectrum ◽  
Cancer Cell ◽  
Cell Population ◽  
Large Cell ◽  
Parental Cancer ◽  
Allele Frequency Spectrum ◽  
Cancer Cell Population

ABSTRACTA cancer grows from a single cell, thereby constituting a large cell population. In this work, we are interested in how mutations accumulate in a cancer cell population. We provided a theoretical framework of the stochastic process in a cancer cell population and obtained near exact expressions of allele frequency spectrum or AFS (only continuous approximation is involved) from both forward and backward treatments under a simple setting; all cells undergo cell division and die at constant rates, b and d, respectively, such that the entire population grows exponentially. This setting means that once a parental cancer cell is established, in the following growth phase, all mutations are assumed to have no effect on b or d (i.e., neutral or passengers). Our theoretical results show that the difference from organismal population genetics is mainly in the coalescent time scale, and the mutation rate is defined per cell division, not per time unit (e.g., generation). Except for these two factors, the basic logic are very similar between organismal and cancer population genetics, indicating that a number of well established theories of organismal population genetics could be translated to cancer population genetics with simple modifications.

scholarly journals A mechanically-induced colon cancer cell population shows increased metastatic potential

2014 ◽  
Vol 13 (1) ◽  
pp. 131 ◽  
Author(s):  
Xin Tang ◽  
Theresa B Kuhlenschmidt ◽  
Qian Li ◽  
Shahjahan Ali ◽  
Stephane Lezmi ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Cell ◽  
Cell Population ◽  
Metastatic Potential ◽  
Colon Cancer Cell ◽  
Cancer Cell Population
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