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• W2129375080 abstract "Cancer cells within individual tumors often exist in distinct phenotypic states that differ in functional attributes. While cancer cell populations typically display distinctive equilibria in the proportion of cells in various states, the mechanisms by which this occurs are poorly understood. Here, we study the dynamics of phenotypic proportions in human breast cancer cell lines. We show that subpopulations of cells purified for a given phenotypic state return towards equilibrium proportions over time. These observations can be explained by a Markov model in which cells transition stochastically between states. A prediction of this model is that, given certain conditions, any subpopulation of cells will return to equilibrium phenotypic proportions over time. A second prediction is that breast cancer stem-like cells arise de novo from non-stem-like cells. These findings contribute to our understanding of cancer heterogeneity and reveal how stochasticity in single-cell behaviors promotes phenotypic equilibrium in populations of cancer cells. Cancer cells within individual tumors often exist in distinct phenotypic states that differ in functional attributes. While cancer cell populations typically display distinctive equilibria in the proportion of cells in various states, the mechanisms by which this occurs are poorly understood. Here, we study the dynamics of phenotypic proportions in human breast cancer cell lines. We show that subpopulations of cells purified for a given phenotypic state return towards equilibrium proportions over time. These observations can be explained by a Markov model in which cells transition stochastically between states. A prediction of this model is that, given certain conditions, any subpopulation of cells will return to equilibrium phenotypic proportions over time. A second prediction is that breast cancer stem-like cells arise de novo from non-stem-like cells. These findings contribute to our understanding of cancer heterogeneity and reveal how stochasticity in single-cell behaviors promotes phenotypic equilibrium in populations of cancer cells. Cancer cell populations interconvert between phenotypic states Cell-state transitions can be described with a stochastic Markov model Markov model predicts convergence to equilibrium phenotypic proportions Cancer stem cells can arise de novo from noncancer stem cells The regulation of cell-state decisions is critical for the survival of living systems. In unicellular organisms, cell-state changes occur in response to environmental stressors or nutrient changes. Direct sensing of environmental stimuli often involves stochastic cell-fate decisions, modulated by random noise in gene expression (Süel et al., 2006Süel G.M. Garcia-Ojalvo J. Liberman L.M. Elowitz M.B. An excitable gene regulatory circuit induces transient cellular differentiation.Nature. 2006; 440: 545-550Crossref PubMed Scopus (532) Google Scholar, Süel et al., 2007Süel G.M. Kulkarni R.P. Dworkin J. Garcia-Ojalvo J. Elowitz M.B. Tunability and noise dependence in differentiation dynamics.Science. 2007; 315: 1716-1719Crossref PubMed Scopus (346) Google Scholar). Such probabilistic behavior has been shown to be advantageous in certain environmental conditions (Kussell and Leibler, 2005Kussell E. Leibler S. Phenotypic diversity, population growth, and information in fluctuating environments.Science. 2005; 309: 2075-2078Crossref PubMed Scopus (852) Google Scholar, Thattai and van Oudenaarden, 2004Thattai M. van Oudenaarden A. Stochastic gene expression in fluctuating environments.Genetics. 2004; 167: 523-530Crossref PubMed Scopus (383) Google Scholar, Wolf et al., 2005Wolf D.M. Vazirani V.V. Arkin A.P. Diversity in times of adversity: probabilistic strategies in microbial survival games.J. Theor. Biol. 2005; 234: 227-253Crossref PubMed Scopus (205) Google Scholar). Noisy gene-expression levels can also stochastically influence cell-state decisions in eukaryotes (Di Talia et al., 2007Di Talia S. Skotheim J.M. Bean J.M. Siggia E.D. Cross F.R. The effects of molecular noise and size control on variability in the budding yeast cell cycle.Nature. 2007; 448: 947-951Crossref PubMed Scopus (316) Google Scholar). However, less is known about the role that stochasticity might play in regulating cell-state equilibria in populations of cells. Cell-state dynamics are of particular significance in tumor pathobiology. Even within individual tumors, cancer cells frequently exist in any of several possible phenotypic states. Cancer cells in distinct phenotypic states often exhibit important differences in functional properties. For example, subpopulations of stem-like cancer cells with increased tumor-seeding ability and drug resistance have been identified in a variety of tumor types (Al-Hajj et al., 2003Al-Hajj M. Wicha M.S. Benito-Hernandez A. Morrison S.J. Clarke M.F. Prospective identification of tumorigenic breast cancer cells.Proc. Natl. Acad. Sci. USA. 2003; 100: 3983-3988Crossref PubMed Scopus (7896) Google Scholar, Lapidot et al., 1994Lapidot T. Sirard C. Vormoor J. Murdoch B. Hoang T. Caceres-Cortes J. Minden M. Paterson B. Caligiuri M.A. Dick J.E. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice.Nature. 1994; 367: 645-648Crossref PubMed Scopus (3431) Google Scholar, Li et al., 2007Li C. Heidt D.G. Dalerba P. Burant C.F. Zhang L. Adsay V. Wicha M. Clarke M.F. Simeone D.M. Identification of pancreatic cancer stem cells.Cancer Res. 2007; 67: 1030-1037Crossref PubMed Scopus (2592) Google Scholar, Singh et al., 2004Singh S.K. Hawkins C. Clarke I.D. Squire J.A. Bayani J. Hide T. Henkelman R.M. Cusimano M.D. Dirks P.B. Identification of human brain tumour initiating cells.Nature. 2004; 432: 396-401Crossref PubMed Scopus (5681) Google Scholar, Smalley and Ashworth, 2003Smalley M. Ashworth A. Stem cells and breast cancer: A field in transit.Nat. Rev. Cancer. 2003; 3: 832-844Crossref PubMed Scopus (301) Google Scholar, Stingl and Caldas, 2007Stingl J. Caldas C. Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis.Nat. Rev. Cancer. 2007; 7: 791-799Crossref PubMed Scopus (350) Google Scholar). The proportion of cancer cells in the various states is related to both tumor type and grade (Chiou et al., 2008Chiou S.H. Yu C.C. Huang C.Y. Lin S.C. Liu C.J. Tsai T.H. Chou S.H. Chien C.S. Ku H.H. Lo J.F. Positive correlations of Oct-4 and Nanog in oral cancer stem-like cells and high-grade oral squamous cell carcinoma.Clin. Cancer Res. 2008; 14: 4085-4095Crossref PubMed Scopus (510) Google Scholar, Harris et al., 2008Harris M.A. Yang H. Low B.E. Mukherjee J. Guha A. Bronson R.T. Shultz L.D. Israel M.A. Yun K. Cancer stem cells are enriched in the side population cells in a mouse model of glioma.Cancer Res. 2008; 68: 10051-10059Crossref PubMed Scopus (123) Google Scholar). Additionally, because anticancer therapies preferentially kill specific cancer cell states, treatment can result in selective changes in phenotypic proportions within tumors (Creighton et al., 2009Creighton C.J. Li X. Landis M. Dixon J.M. Neumeister V.M. Sjolund A. Rimm D.L. Wong H. Rodriguez A. Herschkowitz J.I. et al.Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features.Proc. Natl. Acad. Sci. USA. 2009; 106: 13820-13825Crossref PubMed Scopus (1014) Google Scholar, Gupta et al., 2009Gupta P.B. Onder T.T. Jiang G. Tao K. Kuperwasser C. Weinberg R.A. Lander E.S. Identification of selective inhibitors of cancer stem cells by high-throughput screening.Cell. 2009; 138: 645-659Abstract Full Text Full Text PDF PubMed Scopus (1816) Google Scholar, Li et al., 2008Li X. Lewis M.T. Huang J. Gutierrez C. Osborne C.K. Wu M.F. Hilsenbeck S.G. Pavlick A. Zhang X. Chamness G.C. et al.Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy.J. Natl. Cancer Inst. 2008; 100: 672-679Crossref PubMed Scopus (1357) Google Scholar, Woodward et al., 2007Woodward W.A. Chen M.S. Behbod F. Alfaro M.P. Buchholz T.A. Rosen J.M. WNT/beta-catenin mediates radiation resistance of mouse mammary progenitor cells.Proc. Natl. Acad. Sci. USA. 2007; 104: 618-623Crossref PubMed Scopus (502) Google Scholar). Understanding how cancer cell states coexist and evolve within tumors is of fundamental interest and could facilitate the development of more effective therapies. Phenotypic equilibrium in cell-state proportions is observed in vivo and in cell lines adapted to in vitro culture. Under fixed conditions, both normal and cancerous epithelial lines display stable proportions of cells in stem-like, basal, or luminal states during propagation in culture. The mechanisms that stabilize phenotypic proportions within cellular populations remain unclear. Two general classes of mechanisms can be envisioned: (1) in the absence of interconversion between states, equilibrium proportions could be maintained through intercellular signals that modulate the proliferation rates of distinct states and (2) proliferation rates remain equal, but cancer cells could interconvert between different states in a manner that maintains equilibrium cell-state proportions. Here, we study the mechanisms that underlie phenotypic diversity in populations of cancer cells. We develop and validate a quantitative Markov model of phenotypic transitions that predicts evolution toward equilibrium proportions in cancer cell populations. The Markov model makes several unanticipated predictions about cell-state transitions and dynamics in cancer cell populations. In addition, the proposed model is useful in characterizing the effects of genetic and chemical perturbations on phenotypic proportions in cancer cell populations. To study cell-state dynamics in cancer cells, we used fluorescence-activated cell sorting (FACS) to isolate three mammary epithelial cell states that have been previously defined and characterized using cell-surface markers: stem-like (CD44hiCD24negEpCAMlo), basal (CD44hiCD24negEpCAMneg), and luminal (CD44loCD24hiEpCAMhi) (Fillmore and Kuperwasser, 2008Fillmore C.M. Kuperwasser C. Human breast cancer cell lines contain stem-like cells that self-renew, give rise to phenotypically diverse progeny and survive chemotherapy.Breast Cancer Res. 2008; 10: R25Crossref PubMed Scopus (807) Google Scholar, Shipitsin et al., 2007Shipitsin M. Campbell L.L. Argani P. Weremowicz S. Bloushtain-Qimron N. Yao J. Nikolskaya T. Serebryiskaya T. Beroukhim R. Hu M. et al.Molecular definition of breast tumor heterogeneity.Cancer Cell. 2007; 11: 259-273Abstract Full Text Full Text PDF PubMed Scopus (1071) Google Scholar). Using this system, we isolated each of these cellular fractions from two human breast cancer lines derived from primary tumors (SUM159 and SUM149), resulting in subpopulations that exhibited significant differences in morphology in culture (Figures 1A and 1B ). To confirm that these markers indeed define cells in the expected cell-differentiation states, we collected global gene expression data from sorted CD44hiCD24negEpCAMlo (stem-like), CD44hiCD24negEpCAMneg (basal), or CD44loCD24hiEpCAMhi (luminal) subpopulations of the SUM149 and SUM159 breast cancer lines. We next applied gene set enrichment analysis (GSEA) (Mootha et al., 2003Mootha V.K. Lindgren C.M. Eriksson K.F. Subramanian A. Sihag S. Lehar J. Puigserver P. Carlsson E. Ridderstråle M. Laurila E. et al.PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.Nat. Genet. 2003; 34: 267-273Crossref PubMed Scopus (5043) Google Scholar, Subramanian et al., 2005Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. 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Campbell L.L. Argani P. Weremowicz S. Bloushtain-Qimron N. Yao J. Nikolskaya T. Serebryiskaya T. Beroukhim R. Hu M. et al.Molecular definition of breast tumor heterogeneity.Cancer Cell. 2007; 11: 259-273Abstract Full Text Full Text PDF PubMed Scopus (1071) Google Scholar, Sleeman et al., 2006Sleeman K.E. Kendrick H. Ashworth A. Isacke C.M. Smalley M.J. CD24 staining of mouse mammary gland cells defines luminal epithelial, myoepithelial/basal and non-epithelial cells.Breast Cancer Res. 2006; 8: R7Crossref PubMed Scopus (246) Google Scholar, Sørlie et al., 2006Sørlie T. Wang Y. Xiao C. Johnsen H. Naume B. Samaha R.R. Børresen-Dale A.L. Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms.BMC Genomics. 2006; 7: 127Crossref PubMed Scopus (291) Google Scholar) (Table S1 available online). We observed that in both the SUM159 and SUM149 lines, the expression of genes associated with luminal differentiation was indeed enriched in the CD44loCD24hiEpCAMhi subpopulation (Figures 1C ans 1D and Table S1). For example, the luminal markers CD24, claudin 1 (CLDN1), and cytokeratins 6B and 8 were all specifically upregulated in CD44loCD24hiEpCAMhi cells (Figure 1C and Table S1). Similarly, we found a significant enrichment in the expression of genes associated with basal differentiation in the CD44hiCD24negEpCAMneg subpopulation (Figures 1C and 1D and Table S1); these genes included vimentin (VIM), Zeb1 (TCF8), membrane metallo-endopeptidase (CALLA/CD10), decorin (DCN), and plasminogen activator inhibitor type 1 (SERPINE2). Genes implicated in the self-renewal of embryonic stem cells, including EZH2, KLF5, KLF4, and SOX9 (Brambrink et al., 2008Brambrink T. Foreman R. Welstead G.G. Lengner C.J. Wernig M. Suh H. Jaenisch R. Sequential expression of pluripotency markers during direct reprogramming of mouse somatic cells.Cell Stem Cell. 2008; 2: 151-159Abstract Full Text Full Text PDF PubMed Scopus (631) Google Scholar, Ivanova et al., 2006Ivanova N. Dobrin R. Lu R. Kotenko I. Levorse J. DeCoste C. Schafer X. Lun Y. Lemischka I.R. Dissecting self-renewal in stem cells with RNA interference.Nature. 2006; 442: 533-538Crossref PubMed Scopus (764) Google Scholar, Parisi et al., 2008Parisi S. Passaro F. Aloia L. Manabe I. Nagai R. Pastore L. Russo T. Klf5 is involved in self-renewal of mouse embryonic stem cells.J. Cell Sci. 2008; 121: 2629-2634Crossref PubMed Scopus (106) Google Scholar), were significantly enriched in their expression in the CD44hiCD24negEpCAMlo fraction (Figures 1C and 1D and Table S1). The expression levels of genes associated with basal differentiation in the CD44hiCD24negEpCAMneg fraction was further confirmed via quantitative RT-PCR. Consistent with the microarray data, we observed that CD44hiCD24negEpCAMneg cells from SUM149 and SUM159 populations expressed elevated levels of Vimentin, N-cadherin, and Zeb1 compared to unsorted cells (Figure 1E). In contrast, CD44loCD24hiEpCAMhi cells, which are in a luminal state, exhibited a reduction in the expression levels of these basal genes and elevated levels of the luminal epithelial marker, E-cadherin (Figure 1E). Collectively, these data indicated that, as previously reported, the cell-surface expression of CD24, CD44, and EpCAM does indeed allow for an accurate fractionation of cells in distinct differentiation states. We next determined the proportions of the individual cell-states in the SUM159 and SUM149 lines and found that they contained distinct cell-state proportions: SUM159 populations exhibited predominantly basal (B) differentiation with minority subpopulations that were stem-like (S) or luminal (L) (proportion of B, S, and L = 97.3%, 1.9%, and 0.62%, respectively; Figure 2B ); in contrast, SUM149 populations exhibited predominantly luminal differentiation with stem-like and basal minority subpopulations (B, S, and L = 3.3%, 3.9%, and 92.8%, respectively; Figure 2B). Using FACS, we sorted stem-like, basal, or luminal cells from the SUM159 and SUM149 lines (resulting in subpopulations that were at least 96% pure based on reanalysis immediately after sorting). By isolating relatively pure subpopulations of cells in a given differentiation state and allowing them to expand in culture, we could monitor how cell-state dynamics evolve over time (Figure 2A). After 6 days of growth in culture, we assessed the proportions of stem-like, basal, and luminal cells (Figure 2C). For each isolated subpopulation, we observed a rapid progression toward equilibrium proportions (Figure 2C). Two lines of evidence indicated that this progression was not due to differential growth rates of cells in the basal, stem-like, or luminal states but rather to interconversion between states. First, we observed no difference in the proliferation rates of the stem-like, basal, or luminal subpopulations sorted from either SUM159 or SUM149 (Figure S1). Second, given the purity of the sorted populations and the rapid rate of return to equilibrium proportions, some minority subpopulations would need to divide more than three times per day to achieve the observed proportions through differential growth alone. Such a high proliferation rate is implausible because even the most rapidly dividing human cells—embryonic stem cells—require at least 24 hr to complete a proliferation cycle (Cowan et al., 2004Cowan C.A. Klimanskaya I. McMahon J. Atienza J. Witmyer J. Zucker J.P. Wang S. Morton C.C. McMahon A.P. Powers D. Melton D.A. Derivation of embryonic stem-cell lines from human blastocysts.N. Engl. J. Med. 2004; 350: 1353-1356Crossref PubMed Scopus (796) Google Scholar). Given that interconversion between cell states was occurring, we modeled these observations as a stochastic process in which cancer cells transition randomly between states with each proliferative cycle. We additionally made the assumption that cell transitions follow a Markov process—that is, transition probabilities depend only on a cell's current state, not on its prior states (see the Experimental Procedures). Under a Markov model, it is possible to use data from short-term cell culture of isolated subpopulations to infer the probabilities of transition between any two cell states (for example, PB→S denotes the probability per generation of a basal cell transitioning into a stem-like state). By revealing whether certain transitions are allowed or forbidden, these inferred probabilities could, in principle, provide significant insights into the cell-state dynamics in cell lines. The transition probabilities for the SUM159 and SUM149 lines inferred from the experiment above (Figure 2D and Table 1) revealed several interesting similarities and distinctions between the cell lines:(1)For both lines, we found that stem-like cells could either self-renew or transition into either of the basal or luminal states (Figure 2D and Table 1). While the self-renewal probabilities are comparable (PS→S = 0.58, 0.61), stem-like cells were more likely to transition into a luminal state in the SUM149 line (PS→L = 0.30, PS→B = 0.09), but into a basal state in the SUM159 line (PS→L = 0.07, PS→B = 0.35).(2)In both cell lines, basal cells exhibited a high probability of self-renewing divisions (PB→B = 0.99, 0.90). In contrast, the behavior of luminal cells differed strongly between the cell lines. Luminal cells in the SUM149 line displayed a high probability of self-renewal (PL→L = 0.99), while luminal cells in the SUM159 line exhibited roughly equal probabilities of either remaining in the luminal state or transitioning to basal state (PL→L = 0.47; PL→B = 0.49). These distinctions help explain the higher proportion of basal cells observed at equilibrium in the SUM159 line relative to the SUM149 line.Table 1Cell-State Transition Probabilities for Control or TBX3-Inhibited Human Breast Cancer CellsControlshTBX3Transition ProbabilitiesSBLSBLSUM159Stem0.580.350.070.500.490.01Basal0.010.990.000.010.990.00Luminal0.040.490.470.060.160.78SUM149Stem0.610.090.300.630.070.30Basal0.010.900.080.020.860.12Luminal0.010.000.990.010.000.99S, B and L correspond to stem-like, basal, and luminal states, respectively. The rows and columns correspond to initial (pretransition) and final (posttransition) states, respectively. Transition probabilities are shown per cell division. Open table in a new tab S, B and L correspond to stem-like, basal, and luminal states, respectively. The rows and columns correspond to initial (pretransition) and final (posttransition) states, respectively. Transition probabilities are shown per cell division. The inferred Markov transition probabilities make it possible to quantitatively predict how a population of cells evolves over time given the initial proportion of cell states. Examples are shown in Figure 3 for several initial cell state proportions of SUM159 populations. The model makes several unanticipated predictions. First, even though a subpopulation of sorted stem-like cells from SUM159 has a low proportion of luminal cells both immediately after sorting and at 6 days postsort (0.6%), the model predicts that the proportion of luminal cells will actually show a sharp transient rise to ∼7.3% at 1 day postsort (arrow in Figure 3). We tested this prediction and indeed observed an increase in the proportion of luminal cells (to 6.5%) at 1 day postsort. This unexpected prediction could not have been made without a quantitative model of the underlying cell-state dynamics. A second striking prediction of the model is that basal and luminal cells have a non-zero probability of transitioning to a stem-like state—that is, that cancer stem-like cells can arise from non-stem-like cells. This notion stands in stark contrast to the classical understanding of the concept of “stem cells” in normal tissues, which posits the existence of a rigid lineage-hierarchy in which stem cells can give rise to nonstem cells but not vice versa. To test this prediction, we evaluated the ability of sorted SUM159 subpopulations to seed tumors in mice, either after their initial isolation or after propagation in culture. Consistent with previous observations, the stem-like fraction could efficiently seed tumors, but neither the luminal nor basal fraction was capable of doing so (Table 2). The lack of tumor-seeding potential of luminal and basal subpopulations could be due to either an inherent inability to give rise to cancer stem cells (CSCs) in vivo or simply an inability to survive long enough at the site of implantation to do so. We speculated that admixture with irradiated carrier cells could allow sorted subpopulations to survive longer in vivo following injection, as has been previously shown in the context of hematopoietic reconstitution (Bonnet et al., 1999Bonnet D. Bhatia M. Wang J.C. Kapp U. Dick J.E. Cytokine treatment or accessory cells are required to initiate engraftment of purified primitive human hematopoietic cells transplanted at limiting doses into NOD/SCID mice.Bone Marrow Transplant. 1999; 23: 203-209Crossref PubMed Scopus (109) Google Scholar).Table 2Incidence and Phenotype Analyses of Tumors Arising from Sorted SUM159 SubpopulationsSUM159 SubpopulationsAnalysis of Formed TumorsBasalStem-likeLuminalTumor IncidenceViable cells (%)GFP-neg H2K-neg (%)Basal (%)Stem-like (%)Luminal (%)Direct Injection+––0/4–+–4/417.1149.3493.386.030.59––+0/4With GFP + Irrad. SUM159–––0/4+––4/558.1 ± 2.156.0 ± 6.081.7 ± 5.911.4 ± 3.56.9 ± 2.4–+–4/553.4 ± 4.756.7 ± 7.767.2 ± 11.424.1 ± 8.08.6 ± 3.5––+4/565.7 ± 6.357.7 ± 7.282.4 ± 8.112." @default.
• W2129375080 created "2016-06-24" @default.
• W2129375080 creator A5001889895 @default.
• W2129375080 creator A5020748592 @default.
• W2129375080 creator A5024400042 @default.
• W2129375080 creator A5041410362 @default.
• W2129375080 creator A5068295256 @default.
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• W2129375080 date "2011-08-01" @default.
• W2129375080 modified "2023-10-18" @default.
• W2129375080 title "Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells" @default.
• W2129375080 cites W1614092612 @default.
• W2129375080 cites W1700916660 @default.
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