Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours

Brock, Amy; Chang, Hsie-Chia; Huang, Sui

doi:10.1038/nrg2556

Cited by 468 publications

(472 citation statements)

References 68 publications

Supporting

Mentioning

465

Contrasting

Unclassified

Order By: Relevance

“…Traditionally, genetic mutations causing oncogene activation or tumor suppressor loss were considered essential (2). However, accumulating evidence for tumor-promoting epigenetic, microenvironmental, and stochastic forms of heritable variation is challenging the traditional view (3,4). Genetically identical tumor cells show highly variable responses to apoptosis-inducing ligands (5) and chemotherapeutic drugs (6,7) attributable to cell-cell differences in gene expression and pathway activity.…”

mentioning

confidence: 99%

Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions

Lee

Farquhar

et al. 2014

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

135

166

View full text Add to dashboard Cite

The sources and consequences of nongenetic variability in metastatic progression are largely unknown. To address these questions, we characterized a transcriptional regulatory network for the metastasis suppressor Raf kinase inhibitory protein (RKIP). We previously showed that the transcription factor BACH1 is negatively regulated by RKIP and promotes breast cancer metastasis. Here we demonstrate that BACH1 acts in a double-negative (overall positive) feedback loop to inhibit RKIP transcription in breast cancer cells. BACH1 also negatively regulates its own transcription. Analysis of the BACH1 network reveals the existence of an inverse relationship between BACH1 and RKIP involving both monostable and bistable transitions that can potentially give rise to nongenetic variability. Single-cell analysis confirmed monostable and bistablelike behavior. Treatment with histone deacetylase inhibitors or depletion of the polycomb repressor enhancer of zeste homolog 2 altered relative RKIP and BACH1 levels in a manner consistent with a prometastatic state. Together, our results suggest that the mutually repressive relationship between metastatic regulators such as RKIP and BACH1 can play a key role in determining metastatic progression in cancer.ancer progression is an evolutionary process of variant cells competing to expand first locally and then distally within the human body. Ultimately, tumor evolution generates metastatic lesions that account for over 90% of cancer deaths (1). Whereas the requirement of heritably variant cells for tumor progression is undisputed, how they emerge is much less clear. Traditionally, genetic mutations causing oncogene activation or tumor suppressor loss were considered essential (2). However, accumulating evidence for tumor-promoting epigenetic, microenvironmental, and stochastic forms of heritable variation is challenging the traditional view (3, 4). Genetically identical tumor cells show highly variable responses to apoptosis-inducing ligands (5) and chemotherapeutic drugs (6, 7) attributable to cell-cell differences in gene expression and pathway activity. Tumor cells diversify in vitro (8) and in vivo (9) showing different levels of stem cell marker expression. However, the molecular mechanisms and interactions controlling such nongenetic forms of diversity are not fully known (10, 11).Multiple studies have implicated positive feedback loops in amplifying and maintaining stochastic fluctuations, creating heritable diversity in genetically homogenous cell populations (12, 13). This heritable nongenetic diversity then generates random subpopulations that can survive during various forms of environmental stress (14, 15), enabling subsequent evolutionary adaptation (16). Understanding how feedback loops and other network structures may affect nongenetic heterogeneity and contribute to metastatic cancer progression will be crucial for combating the disease (3, 10). However, the regulation of metastasis-related genes is incompletely understood, and the role of regulatory network-mediated n...

show abstract

mentioning

confidence: 99%

Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions

Lee

Farquhar

et al. 2014

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

135

166

View full text Add to dashboard Cite

show abstract

“…Cell lineage data are also becoming available in other systems, including hematopoietic stem cells (17) and carcinoma cell lines (18). Such detailed data should allow one to proceed beyond the classical measures of selection, both in microbiology and in other fields such as cancer biology where selection occurs within phenotypically heterogeneous populations of cells (19,20). We introduce here a theoretical framework that takes full advantage of individual-level temporal data that is typical of recent experiments, while simultaneously maintaining the intuitive aspects of Fisher's and Price's formulations of population evolution (6,21).…”

mentioning

confidence: 99%

Individual histories and selection in heterogeneous populations

Leibler

Kussell

2010

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

109

122

View full text Add to dashboard Cite

The strength of selection in populations has traditionally been inferred by measuring changes in bulk population parameters, such as mean reproductive rates. Untangling the effect of selection from other factors, such as specific responses to environmental fluctuations, poses a significant problem both in microbiology and in other fields, including cancer biology and immunology, where selection occurs within phenotypically heterogeneous populations of cells. Using "individual histories"-temporal sequences of all reproduction events and phenotypic changes of individuals and their ancestors-we present an alternative approach to quantifying selection in diverse experimental settings. Selection is viewed as a process that acts on histories, and a measure of selection that employs the distribution of histories is introduced. We apply this measure to phenotypically structured populations in fluctuating environments across different evolutionary regimes. Additionally, we show that reproduction events alone, recorded in the population's tree of cell divisions, may be sufficient to accurately measure selection. The measure is thus applicable in a wide range of biological systems, from microorganisms-including species for which genetic tools do not yet exist-to cellular populations, such as tumors and stem cells, where detailed temporal data are becoming available.phenotypic diversity | selection strength | statistical mechanics | stochastic switching | fundamental theorem of natural selection

show abstract

“…Ainsi, si les processus de transitions aléatoires semblent être répandus (également observés pour les cellules souches pluripotentes [27] et les cellules immunitaires [28]), les architectures génétiques sous-jacentes semblent très diverses. Enfin, il a été suggéré récemment que le bruit d'expression génétique et la diversification phénotypique jouent un rôle clé dans l'évolution des tumeurs [29]. Il a été démontré que les cellules extraites de tumeurs du sein sont non seulement hétérogènes, mais que cette hétérogénéité peut s'expliquer par un schéma de taux de transitions fixes entre tous les types observés [30].…”

Section: Diversification Phénotypique Chez Les Microbesunclassified

Hasard et destinée cellulaire

Nghe

2015

Med Sci (Paris)

View full text Add to dashboard Cite

> Les fluctuations thermiques à l'échelle moléculaire génèrent des fluctuations aléatoires d'expression des gènes, qui, lorsqu'elles sont associées à des circuits de différenciation, donnent lieu à une diversification phénotypique à l'échelle de populations de cellules. Dans cette synthèse, nous détaillerons les mécanismes moléculaires à l'origine de cette diversification et illustrerons leurs conséquences dans différents organismes. Chez les bactéries, la diversification phénotypique aléatoire permet d'anticiper des changements de l'environnement, autrement imprévisibles, en particulier d'optimiser les transitions métaboliques et la réponse au stress, induisant par exemple une forme transitoire de résistance aux traitements antibiotiques. Dans les organismes pluricellulaires, des mécanismes similaires permettent la maintenance des tissus sains, tels que les intestins, l'épiderme ou la rétine, mais semblent également jouer un rôle dans l'établissement et la maintenance de l'hétérogénéité tumorale. < indique que la variance d'un processus, répété indépendamment N fois, est de l'ordre N, si bien que l'amplitude des fluctuations (repré-sentée par l'écart-type) rapportée à la moyenne diminue comme la racine carré de N, ce qui donne, par exemple, une fluctuation de 1 % lorsque 10 000 molécules sont en jeu. Force est de constater que de nombreux objets biologiques se comptent en petit nombre par cellule, à commencer par les gènes qui sont généralement présents en une seule copie et transcrits par dizaines chez les bactéries, et par centaines chez les eucaryotes. Ainsi, la loi statistique énoncée ci-dessus, donne lieu à une variabilité relative de 30 % pour N = 10 transcrits. Comme nous le verrons, bien que ce modèle soit extrêmement simplifié, le résultat correspond bien à la variabilité du niveau d'expression génétique qui est mesuré en général. Cette variabilité se maintient-elle au niveau de l'expression des protéines qui sont, elles, exprimées par milliers ? Peut-elle avoir des conséquences physiologiques à l'échelle de la cellule, des tissus, voire des organismes entiers ? Ces questions ont été abordées expérimentalement à l'échelle de la cellule unique par des mesures quantitatives essentiellement basées sur la microscopie de fluorescence et le traitement d'image automatisé. Nous examinerons comment les fluctuations thermiques, associées aux différents processus moléculaires, génèrent des fluctuations aléatoires, ou bruit, dans l'expression génétique. Nous verrons également comment ce bruit se transmet à travers les réactions intracellulaires. Lorsqu'il est associé à des architectures génétiques programmant des états cellulaires multiples, ce bruit génère de la diversité phénotypique dans les populations de cellules. Cela peut, d'une part, provoquer une résistance aux conditions adverses, en particulier aux traitements, et, d'autre part, conduire à l'établissement de proportions contrôlées entre plusieurs types cellulaires. Nous illustrerons ces propriétés dans le cas des microbes, puis dans le cadre d'organisme...

show abstract

Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours

Cited by 468 publications

References 68 publications

Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions

Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions

Individual histories and selection in heterogeneous populations

Hasard et destinée cellulaire

Contact Info

Product

Resources

About