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Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition.

ABSTRACT: BACKGROUND:Human tumor is a complex tissue with multiple heterogeneous hypoxic regions and significant cell-to-cell variability. Due to the complexity of the disease, the explanation of why anticancer therapies fail cannot be attributed to intrinsic or acquired drug resistance alone. Furthermore, there are inconsistent reports of hypoxia-induced kinase activities in different cancer cell-lines, where increase, decreases, or no change has been observed. Thus, we asked, why are there widely contrasting results in kinase activity under hypoxia in different cancer cell-lines and how does hypoxia play a role in anti-cancer drug sensitivity? RESULTS:We took a modeling approach to address these questions by analyzing the model simulation to explain why hypoxia driven signals can have dissimilar impact on tumor growth and alter the efficacy of anti-cancer drugs. Repeated simulations with varying concentrations of biomolecules followed by decision tree analysis reveal that the highly differential effects among heterogeneous subpopulation of tumor cells could be governed by varying concentrations of just a few key biomolecules. These biomolecules include activated serine/threonine-specific protein kinases (pRAF), mitogen-activated protein kinase kinase (pMEK), protein kinase B (pAkt), or phosphoinositide-4,5-bisphosphate 3-kinase (pPI3K). Additionally, the ratio of activated extracellular signal-regulated kinases (pERK) or pAkt to its respective total was a key factor in determining the sensitivity of pERK or pAkt to hypoxia. CONCLUSION:This work offers a mechanistic insight into how hypoxia can affect the efficacy of anti-cancer drug that targets tumor signaling and provides a framework to identify the types of tumor cells that are either sensitive or resistant to anti-cancer therapy.

SUBMITTER: Chen EP

PROVIDER: S-EPMC6802183 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition.

Chen Emile P EP Song Roy S RS Chen Xueer X

BMC bioinformatics 20191021 1

<h4>Background</h4>Human tumor is a complex tissue with multiple heterogeneous hypoxic regions and significant cell-to-cell variability. Due to the complexity of the disease, the explanation of why anticancer therapies fail cannot be attributed to intrinsic or acquired drug resistance alone. Furthermore, there are inconsistent reports of hypoxia-induced kinase activities in different cancer cell-lines, where increase, decreases, or no change has been observed. Thus, we asked, why are there widel ...[more]

PMID: 31638911

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Project description:The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a "mechanistic" model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells).

Project description:IntroductionOverexpression of the oxygen-responsive transcription factor hypoxia-inducible factor 1α (HIF-1α) correlates with poor prognosis in breast cancer patients. The mouse mammary tumor virus polyoma virus middle T (MMTV-PyMT) mouse is a widely utilized preclinical mouse model that resembles human luminal breast cancer and is highly metastatic. Prior studies in which the PyMT model was used demonstrated that HIF-1α is essential to promoting carcinoma onset and lung metastasis, although no differences in primary tumor end point size were observed. Using a refined model system, we investigated whether HIF-1α is directly implicated in the regulation of tumor-initiating cells (TICs) in breast cancer.MethodsMammary tumor epithelial cells were created from MMTV-PyMT mice harboring conditional alleles of Hif1a, followed by transduction ex vivo with either adenovirus β-galactosidase or adenovirus Cre to generate wild-type (WT) and HIF-1α-null (KO) cells, respectively. The impact of HIF-1α deletion on tumor-initiating potential was investigated using tumorsphere assays, limiting dilution transplantation and gene expression analysis.ResultsEfficient deletion of HIF-1α reduced primary tumor growth and suppressed lung metastases, prolonging survival. Loss of HIF-1α led to reduced expression of markers of the basal lineage (K5/K14) in cells and tumors and of multiple genes involved in the epithelial-to-mesenchymal transition. HIF-1α also enhanced tumorsphere formation at normoxia and hypoxia. Decreased expression of several genes in the Notch pathway as well as Vegf and Prominin-1 (CD133)was observed in response to Hif1a deletion. Immunohistochemistry confirmed that CD133 expression was reduced in KO cells and in tumorspheres. Tumorsphere formation was enhanced in CD133hi versus CD133neg cells sorted from PyMT tumors. Limiting dilution transplantation of WT and KO tumor cells into immunocompetent recipients revealed > 30-fold enrichment of TICs in WT cells.ConclusionThese results demonstrate that HIF-1α plays a key role in promoting primary mammary tumor growth and metastasis, in part through regulation of TICs. HIF-1α regulates expression of several members of the Notch pathway, CD133 and markers of the basal lineage in mammary tumors. Our results suggest that CD133, which has not been profiled extensively in breast cancer, may be a useful marker of TICs in the PyMT mouse model. These data reveal for the first time that HIF-1α directly regulates breast TIC activity in vivo.

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Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition.

Publications

Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition.

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