Modeling the Transfer of Drug Resistance in Solid Tumors

ABC efflux transporters are a key factor leading to multidrug resistance in cancer. Overexpression of these transporters significantly decreases the efficacy of anti-cancer drugs. Along with selection and induction, drug resistance may be transferred between cells, which is the focus of this paper. Specifically, we consider the intercellular transfer of P-glycoprotein (P-gp), a well-known ABC transporter that was shown to confer resistance to many common chemotherapeutic drugs. In a recent paper, Durán et al. (Bull Math Biol 78(6):1218–1237, 2016) studied the dynamics of mixed cultures of resistant and sensitive NCI-H460 (human non-small lung cancer) cell lines. As expected, the experimental data showed a gradual increase in the percentage of resistance cells and a decrease in the percentage of sensitive cells. The experimental work was accompanied with a mathematical model that assumed P-gp transfer from resistant cells to sensitive cells, rendering them temporarily resistant. The mathematical model provided a reasonable fit to the experimental data. In this paper, we develop a new mathematical model for the transfer of drug resistance between cancer cells. Our model is based on incorporating a resistance phenotype into a model of cancer growth (Greene et al. in J Theor Biol 367:262–277, 2015). The resulting model for P-gp transfer, written as a system of integro-differential equations, follows the dynamics of proliferating, quiescent, and apoptotic cells, with a varying resistance phenotype. We show that this model provides a good match to the dynamics of the experimental data of Durán et al. (2016). The mathematical model shows a better fit when resistant cancer cells have a slower division rate than the sensitive cells.     

Mechanisms of intrinsic and acquired resistance to kinase‐targeted therapies

Cancer drugs that target pivotal signaling molecules required for malignant cell survival and growth have demonstrated striking antitumor activities in appropriately selected patient populations. Unfortunately, however, therapeutic responses are often of limited duration, typically 6–12 months, because of emergence of drug‐resistant subclones of tumor cells. In this review, we highlight several of the mechanisms of emergent resistance to several kinase‐targeted small molecule therapies used in melanoma, non‐small cell lung cancer (NSCLC) and other solid tumors as illustrative examples. We discuss the implications of these findings for the development of new treatment strategies to delay or prevent the onset of drug resistance.     

Evolution of resistance during clonal expansion

Acquired drug resistance is a major limitation for cancer therapy. Often, one genetic alteration suffices to confer resistance to an otherwise successful therapy. However, little is known about the dynamics of the emergence of resistant tumor cells. In this article, we consider an exponentially growing population starting from one cancer cell that is sensitive to therapy. Sensitive cancer cells can mutate into resistant ones, which have relative fitness alpha prior to therapy. In the special case of no cell death, our model converges to the one investigated by Luria and Delbrück. We calculate the probability of resistance and the mean number of resistant cells once the cancer has reached detection size M. The probability of resistance is an increasing function of the detection size M times the mutation rate u. If Mu < 1, then the expected number of resistant cells in cancers with resistance is independent of the mutation rate u and increases with M in proportion to M(1-1/alpha) for advantageous mutants with relative fitness alpha>1, to l nM for neutral mutants (alpha = 1), but converges to an upper limit for deleterious mutants (alpha<1). Further, the probability of resistance and the average number of resistant cells increase with the number of cell divisions in the history of the tumor. Hence a tumor subject to high rates of apoptosis will show a higher incidence of resistance than expected on its detection size only.     

CRISPR technology: A versatile tool to model,screen, and reverse drug resistance in cancer

BackgroundDrug resistance is a serious challenge in cancer treatment that can render chemotherapy a failure. Understanding the mechanisms behind drug resistance and developing novel therapeutic approaches are cardinal steps in overcoming this issue. Clustered regularly interspaced short palindrome repeats (CRISPR) gene-editing technology has proven to be a useful tool to study cancer drug resistance mechanisms and target the responsible genes. In this review, we evaluated original research studies that used the CRISPR tool in three areas related to drug resistance, namely screening resistance-related genes, generating modified models of resistant cells and animals, and removing resistance by genetic manipulation. We reported the targeted genes, study models, and drug groups in these studies. In addition to discussing different applications of CRISPR technology in cancer drug resistance, we analyzed drug resistance mechanisms and provided examples of CRISPR’s role in studying them. Although CRISPR is a powerful tool for examining drug resistance and sensitizing resistant cells to chemotherapy, more studies are required to overcome its disadvantages, such as off-target effects, immunotoxicity, and inefficient delivery of CRISPR/cas9 into the cells.     

Timing the Emergence of Resistance to Anti-HIV Drugs with Large Genetic Barriers

New antiretroviral drugs that offer large genetic barriers to resistance, such as the recently approved inhibitors of HIV-1 protease, tipranavir and darunavir, present promising weapons to avert the failure of current therapies for HIV infection. Optimal treatment strategies with the new drugs, however, are yet to be established. A key limitation is the poor understanding of the process by which HIV surmounts large genetic barriers to resistance. Extant models of HIV dynamics are predicated on the predominance of deterministic forces underlying the emergence of resistant genomes. In contrast, stochastic forces may dominate, especially when the genetic barrier is large, and delay the emergence of resistant genomes. We develop a mathematical model of HIV dynamics under the influence of an antiretroviral drug to predict the waiting time for the emergence of genomes that carry the requisite mutations to overcome the genetic barrier of the drug. We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments. Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately. Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.     

Expression of phenotypic traits following modulation of colchicine resistance in J774.2 cells

Development of resistance to colchicine in the mouse macrophage-like cell line J774.2 coincides with the expression of a variety of phenotypic traits. A cloned subline (J7/CLC-20), maintained in 20 microM colchicine, exhibits reduced steady-state association with drug, increased presence of a 140,000-145,000 dalton (140-145 kD) phosphoglycoprotein associated with the plasma membrane, double minute chromosomes and cross-resistance to other drugs. While similar phenotypic traits are observed in J774.2 cells resistant to taxol and vinblastine, differences in the electrophoretic mobilities of the resistance-specific glycoproteins in each of the three sublines suggest that multi-drug resistant sublines exhibit specificity for individual drugs. In an attempt to elucidate the relationships between the phenotypic traits associated with colchicine resistance, the degree of colchicine resistance in J7/CLC-20 cells was modulated and the levels of expression of the phenotypic traits were quantitated. In the absence of colchicine in the growth medium, J7/CLC-20 cells reverted to drug sensitivity within 35 days. A decrease in the level of resistance coincided with coordinate changes in both the quantity of the resistance-specific glycoprotein and the average number of double minute chromosomes. We propose that the emergence and disappearance of the resistance-specific glycoprotein and double minute chromosomes may be closely linked. However, J7/CLC-20 cells which had regained their drug sensitivity after growth in drug-free medium maintained a reduced level of steady-state drug association. The persistence of reduced drug association in cells that have reverted to a drug-sensitive state suggests that this phenomenon, although related to colchicine resistance, need not be the primary or only mechanism of drug resistance.     

Stochastic modeling of drug resistance in cancer

One of the main causes of failure in the treatment of cancer is the development of drug resistance by the cancer cells. Employing multi-drug therapeutic strategies is a promising way to prevent resistance and improve the chances of treatment success. We formulate and analyse a stochastic model for multi-drug resistance and investigate the dependence of treatment outcomes on the initial tumor load, mutation rates and the turnover rate of cancerous cells. We elucidate the general principles of the emergence and evolution of resistant cells inside the tumor, before and after the start of treatment. We discover that for non-mutagenic drugs, pre-existence contributes more to resistance generation than the treatment phase; this result holds for the case where all drugs are applied simultaneously, and is not applicable for sequential therapy models. The application of mathematical modelling to aspects of adjuvant chemotherapy scheduling. J. Math. Biol. 48(4), 375-422]. Also, we find that treatment success is independent on the turnover rate for one drug, and it depends strongly on it for multi-drug therapies. For low-turnover rates, increasing the number of drugs will increase the probability of successful therapy. For very high-turnover rates, increasing the number of drugs used does not significantly increase the chances of treatment success.     

Different modalities of intercellular membrane exchanges mediate cell-to-cell p-glycoprotein transfers in MCF-7 breast cancer cells

Multi-drug resistance (MDR) is a phenomenon by which tumor cells exhibit resistance to a variety of chemically unrelated chemotherapeutic drugs. The classical form of multidrug resistance is connected to overexpression of membrane P-glycoprotein (P-gp), which acts as an energy dependent drug efflux pump. P-glycoprotein expression is known to be controlled by genetic and epigenetic mechanisms. Until now processes of P-gp gene up-regulation and resistant cell selection were considered sufficient to explain the emergence of MDR phenotype within a cell population. Recently, however, "non-genetic" acquisitions of MDR by cell-to-cell P-gp transfers have been pointed out. In the present study we show that intercellular transfers of functional P-gp occur by two different but complementary modalities through donor-recipient cells interactions in the absence of drug selection pressure. P-glycoprotein and drug efflux activity transfers were followed over 7 days by confocal microscopy and flow cytometry in drug-sensitive parental MCF-7 breast cancer cells co-cultured with P-gp overexpressing resistant variants. An early process of remote transfer was established based on the release and binding of P-gp-containing microparticles. Microparticle-mediated transfers were detected after only 4 h of incubation. We also identify an alternative mode of transfer by contact, consisting of cell-to-cell P-gp trafficking by tunneling nanotubes bridging neighboring cells. Our findings supply new mechanistic evidences for the extragenetic emergence of MDR in cancer cells and indicate that new treatment strategies designed to overcome MDR may include inhibition of both microparticles and Tunneling nanotube-mediated intercellular P-gp transfers.     

Clinical Evaluation of Liposome Encapsulated Doxorubicin and the Modulation of Multidrug Resistance in Cancer Cells

Abstract

Doxorubicin is the cornerstone of some widely used combination chemotherapy regimens because of its high anticancer activity in a number of human neoplasms. However, its clinical use is highly compromised because of treatment-limiting acute and chronic toxicities of which cardiotoxicity has the most debilitating effect. Our laboratories have demonstrated that liposome encapsulated doxorubicin (LED) provides important advantages in regards to the attenuation of cardiotoxicity in rodents by altering pharmacokinetics and pharmacodynamics of the drug, provides effective protection from immunotoxicity and maintains full therapeutic activity of the drug in liposomes. A Phase I clinical trial of LED in cancer patients has establish the maximum tolerated dose of 90 mg/m² with granulocytopenia being the major treatment-limiting toxicity. We have performed a Phase II trial of LED in 20 recurrent breast cancer patients at a dose of 75 mg/m² as an intravenous infusion every three weeks. Objective responses were observed in 9/20 patients of which 5 demonstrated a complete response. Hematologic toxicity with LED consisted of only grade 1-2 granulocytopenia in some patients, whereas gastrointestinal toxicity, mucositis and venous sclerosis were markedly reduced. Alopecia was complete in all patients. Twelve patients received cumulative LED doses of more than 400 mg/m² and 8 of them received doses of over 500 mg/m². Five of these patients were followed by endomyocardial biopsies and 4 of them were found to be Billingham Grade 0 whereas one of them had Billingham Grade 1 toxicity (cumulative dose of 750 mg/m²). This Phase II trial demonstrates higher therapeutic efficacy of LED than free doxorubicin in recurrent breast cancer patients with strong indication of cardiotoxicity protection at doses of 500-800 mg/m².

The emergence of tumor cells resistant to major classes of cytotoxic agents is a predominant obstacle in cancer treatment. This resistance is frequently related to the expression of a plasma membrane P-glycoprotein (pgp) of 170 Kd that is encoded by a family of MDR genes. Support for the involvement of pgp in MDR has been shown by transaction of sensitive cells with an expression vector containing full length cDNA of the MDR1 gene, which results in the appearance of pgp and the sensitive cells convert to the drug-resistant phenotype. Our studies demonstrate that LED modulates very effectively the MDR phenotype in LZ cells, a Chinese hamster cell line made resistant to doxorubicin and the cellular drug uptake was 2 to 3 fold higher with LED exposure than with free drug. This modulation of drug resistance and enhanced cellular drug uptake is effected by the direct binding of liposomes to pgp on the surfaces of MDR phenotype cells. LED completely inhibited the photoaffinity labeling of pgp by azidopine in membrane vesicles of HL-60/VCR cells and in KB-GSV2 cells transfected with human MDR gene. These studies demonstrate that LED has unique effectiveness in overcoming MDR phenotype in cancer cells and appears to be a potentially attractive modality of treatment of human cancers.     

A Population Genetic Model for the Initial Spread of Partially Resistant Malaria Parasites under Anti-Malarial Combination Therapy and Weak Intrahost Competition

To develop public-health policies that extend the lifespan of affordable anti-malarial drugs as effective treatment options, it is necessary to understand the evolutionary processes leading to the origin and spread of mutations conferring drug resistance in malarial parasites. We built a population-genetic model for the emergence of resistance under combination drug therapy. Reproductive cycles of parasites are specified by their absolute fitness determined by clinical parameters, thus coupling the evolutionary-genetic with population-dynamic processes. Initial mutations confer only partial drug-resistance. Therefore, mutant parasites rarely survive combination therapy and within-host competition is very weak among parasites. The model focuses on the early phase of such unsuccessful recurrent mutations. This ends in the rare event of mutants enriching in an infected individual from which the successful spread of resistance over the entire population is initiated. By computer simulations, the waiting time until the establishment of resistant parasites is analysed. Resistance spreads quickly following the first appearance of a host infected predominantly by mutant parasites. This occurs either through a rare transmission of a resistant parasite to an uninfected host or through a rare failure of drugs in removing “transient” mutant alleles. The emergence of resistance is delayed with lower mutation rate, earlier treatment, higher metabolic cost of resistance, longer duration of high drug dose, and higher drug efficacy causing a stronger reduction in the sensitive and resistant parasites’ fitnesses. Overall, contrary to other studies’ proposition, the current model based on absolute fitness suggests that aggressive drug treatment delays the emergence of drug resistance.     

Modeling within-host HIV-1 dynamics and the evolution of drug resistance: trade-offs between viral enzyme function and drug susceptibility

There are many biological steps between viral infection of CD4(+) T cells and the production of HIV-1 virions. Here we incorporate an eclipse phase, representing the stage in which infected T cells have not started to produce new virus, into a simple HIV-1 model. Model calculations suggest that the quicker infected T cells progress from the eclipse stage to the productively infected stage, the more likely that a viral strain will persist. Long-term treatment effectiveness of antiretroviral drugs is often hindered by the frequent emergence of drug resistant virus during therapy. We link drug resistance to both the rate of progression of the eclipse phase and the rate of viral production of the resistant strain, and explore how the resistant strain could evolve to maximize its within-host viral fitness. We obtained the optimal progression rate and the optimal viral production rate, which maximize the fitness of a drug resistant strain in the presence of drugs. We show that the window of opportunity for invasion of drug resistant strains is widened for a higher level of drug efficacy provided that the treatment is not potent enough to eradicate both the sensitive and resistant virus.     

Hedging against Antiviral Resistance during the Next Influenza Pandemic Using Small Stockpiles of an Alternative Chemotherapy

Background

The effectiveness of single-drug antiviral interventions to reduce morbidity and mortality during the next influenza pandemic will be substantially weakened if transmissible strains emerge which are resistant to the stockpiled antiviral drugs. We developed a mathematical model to test the hypothesis that a small stockpile of a secondary antiviral drug could be used to mitigate the adverse consequences of the emergence of resistant strains.

Methods and Findings

We used a multistrain stochastic transmission model of influenza to show that the spread of antiviral resistance can be significantly reduced by deploying a small stockpile (1% population coverage) of a secondary drug during the early phase of local epidemics. We considered two strategies for the use of the secondary stockpile: early combination chemotherapy (ECC; individuals are treated with both drugs in combination while both are available); and sequential multidrug chemotherapy (SMC; individuals are treated only with the secondary drug until it is exhausted, then treated with the primary drug). We investigated all potentially important regions of unknown parameter space and found that both ECC and SMC reduced the cumulative attack rate (AR) and the resistant attack rate (RAR) unless the probability of emergence of resistance to the primary drug p_A was so low (less than 1 in 10,000) that resistance was unlikely to be a problem or so high (more than 1 in 20) that resistance emerged as soon as primary drug monotherapy began. For example, when the basic reproductive number was 1.8 and 40% of symptomatic individuals were treated with antivirals, AR and RAR were 67% and 38% under monotherapy if p _A = 0.01. If the probability of resistance emergence for the secondary drug was also 0.01, then SMC reduced AR and RAR to 57% and 2%. The effectiveness of ECC was similar if combination chemotherapy reduced the probabilities of resistance emergence by at least ten times. We extended our model using travel data between 105 large cities to investigate the robustness of these resistance-limiting strategies at a global scale. We found that as long as populations that were the main source of resistant strains employed these strategies (SMC or ECC), then those same strategies were also effective for populations far from the source even when some intermediate populations failed to control resistance. In essence, through the existence of many wild-type epidemics, the interconnectedness of the global network dampened the international spread of resistant strains.

Conclusions

Our results indicate that the augmentation of existing stockpiles of a single anti-influenza drug with smaller stockpiles of a second drug could be an effective and inexpensive epidemiological hedge against antiviral resistance if either SMC or ECC were used. Choosing between these strategies will require additional empirical studies. Specifically, the choice will depend on the safety of combination therapy and the synergistic effect of one antiviral in suppressing the emergence of resistance to the other antiviral when both are taken in combination.     

Optimization of leaf morphology in relation to leaf water status: A theory

The leaf economic traits such as leaf area, maximum carbon assimilation rate, and venation are all correlated and related to water availability. Furthermore, leaves are often broad and large in humid areas and narrower in arid/semiarid and hot and cold areas. We use optimization theory to explain these patterns. We have created a constrained optimization leaf model linking leaf shape to vein structure that is integrated into coupled transpiration and carbon assimilation processes. The model maximizes net leaf carbon gain (NPP_leaf) over the loss of xylem water potential. Modeled relations between leaf traits are consistent with empirically observed patterns. As the results of the leaf shape–venation relation, our model further predicts that a broadleaf has overall higher NPP_leaf compared to a narrowleaf. In addition, a broadleaf has a lower stomatal resistance compared to a narrowleaf under the same level of constraint. With the same leaf area, a broadleaf will have, on average, larger conduits and lower total leaf xylem resistance and thus be more efficient in water transportation but less resistant to cavitation. By linking venation structure to leaf shape and using water potential as the constraint, our model provides a physical explanation for the general pattern of the covariance of leaf traits through the safety–efficiency trade‐off of leaf hydraulic design.     

MicroRNAs: key players of taxane resistance and their therapeutic potential in human cancers

The successful long‐term use of taxane for cancer therapy is often prevented by the development of drug resistance in clinic. Thus, exploring the mechanisms involved is a first step towards rational strategies to overcome taxane resistance. Taxane resistance‐related microRNA (miRNAs) are under investigation and miRNAs could induce the taxane resistance of tumour cells by regulating cell cycle distribution, survival and/or apoptosis pathways, drug transports, epithelial–mesenchymal transition and cancer stem cell. This article summarizes current research involving miRNAs as regulators of key target genes for tanxanxe chemoresistance and discusses the complex regulatory networks of miRNAs. Also, the authors will envisage future developments towards the potential use of targeting miRNAs as a novel strategy for improving response of tumour patients to taxane. miRNAs play critical roles in taxane chemoresistance and the miRNA‐based therapies will be helpful for overcoming drug resistance and developing more effective personalized anti‐cancer treatment strategies. Further research studies should be performed to promote therapeutic–clinical use of taxane resistance‐related miRNAs in cancer patients, especially in those patients with taxane‐resistant cancers.     

Antiviral resistance and the control of pandemic influenza: the roles of stochasticity, evolution and model details

Antiviral drugs, most notably the neuraminidase inhibitors, are an important component of control strategies aimed to prevent or limit any future influenza pandemic. The potential large-scale use of antiviral drugs brings with it the danger of drug resistance evolution. A number of recent studies have shown that the emergence of drug-resistant influenza could undermine the usefulness of antiviral drugs for the control of an epidemic or pandemic outbreak. While these studies have provided important insights, the inherently stochastic nature of resistance generation and spread, as well as the potential for ongoing evolution of the resistant strain have not been fully addressed. Here, we study a stochastic model of drug resistance emergence and consecutive evolution of the resistant strain in response to antiviral control during an influenza pandemic. We find that taking into consideration the ongoing evolution of the resistant strain does not increase the probability of resistance emergence; however, it increases the total number of infecteds if a resistant outbreak occurs. Our study further shows that taking stochasticity into account leads to results that can differ from deterministic models. Specifically, we find that rapid and strong control cannot only contain a drug sensitive outbreak, it can also prevent a resistant outbreak from occurring. We find that the best control strategy is early intervention heavily based on prophylaxis at a level that leads to outbreak containment. If containment is not possible, mitigation works best at intermediate levels of antiviral control. Finally, we show that the results are not very sensitive to the way resistance generation is modeled.     

Spontaneous emergence of multiple drug resistance in tuberculosis before and during therapy

The emergence of drug resistance in M. tuberculosis undermines the efficacy of tuberculosis (TB) treatment in individuals and of TB control programs in populations. Multiple drug resistance is often attributed to sequential functional monotherapy, and standard initial treatment regimens have therefore been designed to include simultaneous use of four different antibiotics. Despite the widespread use of combination therapy, highly resistant M. tb strains have emerged in many settings. Here we use a stochastic birth-death model to estimate the probability of the emergence of multidrug resistance during the growth of a population of initially drug sensitive TB bacilli within an infected host. We find that the probability of the emergence of resistance to the two principal anti-TB drugs before initiation of therapy ranges from 10(-5) to 10(-4); while rare, this is several orders of magnitude higher than previous estimates. This finding suggests that multidrug resistant M. tb may not be an entirely "man-made" phenomenon and may help explain how highly drug resistant forms of TB have independently emerged in many settings.     

Targeting signal transduction pathways to eliminate chemotherapeutic drug resistance and cancer stem cells

Breast cancer is one of the most common cancers and affects nearly 1 in 7 women. We have demonstrated that targeting the CaM-K, Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR pathways may be a novel approach to treat drug resistant breast cancer and eliminate cancer stem cells. Common chemotherapeutic drugs, such as doxorubicin, induce the CaM-K pathway which in turn, leads to activation of anti-apoptotic pathways such as Raf/MEK/ERK and PI3K/Akt. Some drug resistant breast cancers exhibited increased expression of CaM-KIV. CaM-K inhibitors synergized with doxorubicin to induce the death of all drug resistant breast cancers examined. Since CaM-Ks are known to result in activation of the Raf/MEK/ERK and PI3K/Akt pathways, we investigated the roles that these pathways exert in breast cancer drug resistance. CaM-K inhibitors suppressed ERK activation in response to doxorubicin in both drug sensitive and resistant cells. CaM-K inhibitors also suppressed ERK activation in response to FBS in the drug resistant cells suggesting dependence on the CaM-K pathway for proliferation. Both the Raf/MEK/ERK and PI3K/Akt pathways are involved in breast cancer drug resistance as they were detected at elevated, activated levels in the drug resistant cells and introduction of constitutively activated forms of Raf-1 and Akt-1 resulted in drug resistance. Drug resistant CICs were often hypersensitive to MEK and mTOR inhibitors, implicating important roles of these pathways in drug resistance. In summary, targeting these pathways may enhance therapy of drug resistant breast cancer and eliminate CICs.Breast cancer therapy is often limited by the occurrence of drug resistance which may be due to the re-emergence of CICs. The studies outlined in this proposal may identify a potentially novel role for CaM-Ks in drug resistance and metastasis and may lead to improved approaches to treat breast tumors by eliminating CICs. Our proposed studies are highly innovative as we will determine the involvement of the CaM-K pathway in breast cancer drug resistance, metastasis and CIC formation. Similar approaches have not been previously performed. Our studies may result in the discovery of novel methods to treat breast cancer by targeting the CaM-K pathway in combination with currently used and approved chemotherapeutic regimens to eliminate CICs which may be responsible for both drug resistance and metastasis.     

Sequential Salinomycin Treatment Results in Resistance Formation through Clonal Selection of Epithelial-Like Tumor Cells

Acquiring therapy resistance is one of the major obstacles in the treatment of patients with cancer. The discovery of the cancer stem cell (CSC)–specific drug salinomycin raised hope for improved treatment options by targeting therapy-refractory CSCs and mesenchymal cancer cells. However, the occurrence of an acquired salinomycin resistance in tumor cells remains elusive. To study the formation of salinomycin resistance, mesenchymal breast cancer cells were sequentially treated with salinomycin in an in vitro cell culture assay, and the resulting differences in gene expression and salinomycin susceptibility were analyzed. We demonstrated that long-term salinomycin treatment of mesenchymal cancer cells resulted in salinomycin-resistant cells with elevated levels of epithelial markers, such as E-cadherin and miR-200c, a decreased migratory capability, and a higher susceptibility to the classic chemotherapeutic drug doxorubicin. The formation of salinomycin resistance through the acquisition of epithelial traits was further validated by inducing mesenchymal-epithelial transition through an overexpression of miR-200c. The transition from a mesenchymal to a more epithelial-like phenotype of salinomycin-treated tumor cells was moreover confirmed in vivo, using syngeneic and, for the first time, transgenic mouse tumor models. These results suggest that the acquisition of salinomycin resistance through the clonal selection of epithelial-like cancer cells could become exploited for improved cancer therapies by antagonizing the tumor-progressive effects of epithelial-mesenchymal transition.     

BCR-ABL Gene Expression Is Required for Its Mutations in a Novel KCL-22 Cell Culture Model for Acquired Resistance of Chronic Myelogenous Leukemia

Acquired resistance through genetic mutations is a common phenomenon in several cancer therapies using molecularly targeted drugs, best exemplified by the BCR-ABL inhibitor imatinib in treating chronic myelogenous leukemia (CML). Overcoming acquired resistance is a daunting therapeutic challenge, and little is known about how these mutations evolve. To facilitate understanding the resistance mechanisms, we developed a novel culture model for CML acquired resistance in which the CML cell line KCL-22, following initial response to imatinib, develops resistant T315I BCR-ABL mutation. We demonstrate that the emergence of BCR-ABL mutations do not require pre-existing BCR-ABL mutations derived from the original patient as the subclones of KCL-22 cells can form various BCR-ABL mutations upon imatinib treatment. BCR-ABL mutation rates vary from cell clone to clone and passages, in contrast to the relatively stable mutation rate of the hypoxanthine-guanine phosphoribosyltransferase gene. Strikingly, development of BCR-ABL mutations depends on its gene expression because BCR-ABL knockdown completely blocks KCL-22 cell relapse on imatinib and acquisition of mutations. We further show that the endogenous BCR-ABL locus has significantly higher mutagenesis potential than the transduced randomly integrated BCR-ABL cDNA. Our study suggests important roles of BCR-ABL gene expression and its native chromosomal locus for acquisition of BCR-ABL mutations and provides a new tool for further studying resistance mechanisms.