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Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.

ABSTRACT: Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC) niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-?B signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted therapy in HER2-positive breast cancer.

SUBMITTER: Sehl ME

PROVIDER: S-EPMC4580445 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.

Sehl Mary E ME Shimada Miki M Landeros Alfonso A Lange Kenneth K Wicha Max S MS

PloS one 20150923 9

Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochas ...[more]

PMID: 26397099

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Project description:Drug resistance in breast cancer cell populations has been shown to arise through phenotypic transition of cancer cells to a drug-tolerant state, for example through epithelial-to-mesenchymal transition or transition to a cancer stem cell state. However, many breast tumors are a heterogeneous mixture of cell types with numerous epigenetic states in addition to stem-like and mesenchymal phenotypes, and the dynamic behavior of this heterogeneous mixture in response to drug treatment is not well-understood. Recently, we showed that plasticity between differentiation states, as identified with intracellular markers such as cytokeratins, is linked to resistance to specific targeted therapeutics. Understanding the dynamics of differentiation-state transitions in this context could facilitate the development of more effective treatments for cancers that exhibit phenotypic heterogeneity and plasticity. In this work, we develop computational models of a drug-treated, phenotypically heterogeneous triple-negative breast cancer (TNBC) cell line to elucidate the feasibility of differentiation-state transition as a mechanism for therapeutic escape in this tumor subtype. Specifically, we use modeling to predict the changes in differentiation-state transitions that underlie specific therapy-induced changes in differentiation-state marker expression that we recently observed in the HCC1143 cell line. We report several statistically significant therapy-induced changes in transition rates between basal, luminal, mesenchymal, and non-basal/non-luminal/non-mesenchymal differentiation states in HCC1143 cell populations. Moreover, we validate model predictions on cell division and cell death empirically, and we test our models on an independent data set. Overall, we demonstrate that changes in differentiation-state transition rates induced by targeted therapy can provoke distinct differentiation-state aggregations of drug-resistant cells, which may be fundamental to the design of improved therapeutic regimens for cancers with phenotypic heterogeneity.

Project description:New strategies of autologous hematopoietic stem cell transplantation (auto-HSCT) have gained much interest for the treatment of type 1 diabetes mellitus. However, assessing the clinical response and residual ?-cell function still has limitations. The aim of the study was to select the optimal quantitative index to assess pre-existing ?-cell function and to explore its predictive function for clinical response after auto-HSCT therapy. In this study, all of the patients who had undergone auto-HSCT were clustered into a responder group (??-score > 0) and a nonresponder group (??-score ? 0). We compared their quantitative metabolic indexes at baseline and performed receiver-operating characteristic (ROC) analysis to analyze the correlations between the indexes and clinical response. Kaplan-Meier analysis was conducted to compare the cumulative response durations in each quartile of the selected indexes. In an average of 15.13 ± 6.15 months of follow-up, 44 of 112 patients achieved a clinical response. The responder group had lower levels of fasting plasma glucose and quantitative insulin sensitivity check index (QUICKI) but higher levels of fasting C-peptide, fasting insulin, and homeostasis model assessments for insulin resistance (HOMA-IR). ROC analysis showed that HOMA-IR had the largest area under the curve (0.756), which was similar to that of QUICKI. Kaplan-Meier analysis further confirmed that the third quartile (1.3371-1.7018) of HOMA-IR or the second quartile (0.3523-0.3657) of QUICKI was preferential for a prolonged response. In conclusion, HOMA-IR and QUICKI could be optimal measurements for ?-cell reserves, and they were predictive for the clinical response after auto-HSCT.The ?-score was comprehensive and reliable in evaluating clinical response after autologous hematopoietic stem cell transplantation (HSCT). The homeostasis model assessments for insulin resistance and the quantitative insulin sensitivity check index could serve as precise assessments for residual ?-cell function and good predictors of clinical response. They might be used to select optimal clinical trial participants or predict the clinical response after auto-HSCT.

Dataset Information

Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.

Publications

Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.

Similar Datasets

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