A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer

Development of adaptive anoikis resistance promotes metastasis that can be overcome by CDK8/19 Mediator kinase inhibition

Anoikis resistance or evasion of cell death triggered by cell detachment into suspension is a hallmark of cancer that is concurrent with cell survival and metastasis. The effects of frequent matrix detachment encounters on the development of anoikis resistance in cancer remains poorly defined. Here we show using a panel of ovarian cancer models, that repeated exposure to suspension stress in vitro followed by attached recovery growth leads to the development of anoikis resistance paralleling in vivo development of anoikis resistance in ovarian cancer ascites. This resistance is concurrent with enhanced invasion, chemoresistance and the ability of anoikis adapted cells to metastasize to distant sites. Adapted anoikis resistant cells show a heightened dependency on oxidative phosphorylation and can also evade immune surveillance. We find that such acquired anoikis resistance is not genetic, as acquired resistance persists for a finite duration in the absence of suspension stress. Transcriptional reprogramming is however essential to this process, as acquisition of adaptive anoikis resistance in vitro and in vivo is exquisitely sensitive to inhibition of CDK8/19 Mediator kinase, a pleiotropic regulator of transcriptional reprogramming. Our data demonstrate that growth after recovery from repeated exposure to suspension stress is a direct contributor to metastasis and that inhibition of CDK8/19 Mediator kinase during such adaptation provides a therapeutic opportunity to prevent both local and distant metastasis in cancer.

Cell population growth kinetics in the presence of stochastic heterogeneity of cell phenotype

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the uniform exponential growth model for the initial growth ("take-off"). Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.

From resistance to resilience: Uncovering chemotherapeutic resistance mechanisms; insights from established models

Despite the tremendous advances in cancer treatment, resistance to chemotherapeutic agents impedes higher success rates and accounts for major relapses in cancer therapy. Moreover, the resistance of cancer cells to chemotherapy is linked to low efficacy and high recurrence of cancer. To stand up against chemotherapy resistance, different models of chemotherapy resistance have been established to study various molecular mechanisms of chemotherapy resistance. Consequently, this review is going to discuss different models of induction of chemotherapy resistance, highlighting the most common mechanisms of cancer resistance against different chemotherapeutic agents, including overexpression of efflux pumps, drug inactivation, epigenetic modulation, and epithelial-mesenchymal transition. This review aims to open a new avenue for researchers to lower the resistance to the existing chemotherapeutic agents, develop new therapeutic agents with low resistance potential, and establish possible prognostic markers for chemotherapy resistance.

Cytotoxic and chemomodulatory effects of Phyllanthus niruri in MCF-7 and MCF-7ADR breast cancer cells

The members of the genus Phyllanthus have long been used in the treatment of a broad spectrum of diseases. They exhibited antiproliferative activity against various human cancer cell lines. Breast cancer is the most diagnosed cancer and a leading cause of cancer death among women. Doxorubicin (DOX) is an anticancer agent used to treat breast cancer despite its significant cardiotoxicity along with resistance development. Therefore, this study was designed to assess the potential cytotoxicity of P. niruri extracts (and fractions) alone and in combination with DOX against naïve (MCF-7) and doxorubicin-resistant breast cancer cell lines (MCF-7^ADR). The methylene chloride fraction (CH₂Cl₂) showed the most cytotoxic activity among all tested fractions. Interestingly, the CH₂Cl₂-fraction was more cytotoxic against MCF-7^ADR than MCF-7 at 100 µg/mL. At sub-cytotoxic concentrations, this fraction enhanced the cytotoxic effect of DOX against the both cell lines under investigation (IC₅₀ values of 0.054 µg/mL and 0.14 µg/mL vs. 0.2 µg/mL for DOX alone against MCF-7) and (1.2 µg/mL and 0.23 µg/mL vs. 9.9 µg/mL for DOX alone against MCF-7^ADR), respectively. Further, TLC fractionation showed that B2 subfraction in equitoxic combination with DOX exerted a powerful synergism (IC₅₀ values of 0.03 µg/mL vs. 9.9 µg/mL for DOX alone) within MCF-7^ADR. Untargeted metabolite profiling of the crude methanolic extract (MeOH) and CH₂Cl₂ fraction exhibiting potential cytotoxicity was conducted using liquid chromatography diode array detector-quadrupole time-of-flight mass spectrometry (LC-DAD-QTOF). Further studies are needed to separate the active compounds from the CH₂Cl₂ fraction and elucidate their mechanism(s) of action.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part III

The development of in vitro/in vivo translational methods and a clinical trial framework for synergistically acting drug combinations are needed to identify optimal therapeutic conditions with the most effective therapeutic strategies. We performed physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) modelling and virtual clinical trial simulations for siremadlin, trametinib, and their combination in a virtual representation of melanoma patients. In this study, we built PBPK/PD models based on data from in vitro absorption, distribution, metabolism, and excretion (ADME), and in vivo animals' pharmacokinetic-pharmacodynamic (PK/PD) and clinical data determined from the literature or estimated by the Simcyp simulator (version V21). The developed PBPK/PD models account for interactions between siremadlin and trametinib at the PK and PD levels. Interaction at the PK level was predicted at the absorption level based on findings from animal studies, whereas PD interaction was based on the in vitro cytotoxicity results. This approach, combined with virtual clinical trials, allowed for the estimation of PK/PD profiles, as well as melanoma patient characteristics in which this therapy may be noninferior to the dabrafenib and trametinib drug combination. PBPK/PD modelling, combined with virtual clinical trial simulation, can be a powerful tool that allows for proper estimation of the clinical effect of the above-mentioned anticancer drug combination based on the results of in vitro studies. This approach based on in vitro/in vivo extrapolation may help in the design of potential clinical trials using siremadlin and trametinib and provide a rationale for their use in patients with melanoma.

Assessing chemotherapy dosing strategies in a spatial cell culture model

Predicting patient responses to chemotherapy regimens is a major challenge in cancer treatment. Experimental model systems coupled with quantitative mathematical models to calculate optimal dose and frequency of drugs can enable improved chemotherapy regimens. Here we developed a simple approach to track two-dimensional cell colonies composed of chemo-sensitive and resistant cell populations via fluorescence microscopy and coupled this to computational model predictions. Specifically, we first developed multiple 4T1 breast cancer cell lines resistant to varying concentrations of doxorubicin, and demonstrated how heterogeneous populations expand in a two-dimensional colony. We subjected cell populations to varied dose and frequency of chemotherapy and measured colony growth. We then built a mathematical model to describe the dynamics of both chemosensitive and chemoresistant populations, where we determined which number of doses can produce the smallest tumor size based on parameters in the system. Finally, using an in vitro model we demonstrated multiple doses can decrease overall colony growth as compared to a single dose at the same total dose. In the future, this system can be adapted to optimize dosing strategies in the setting of heterogeneous cell types or patient derived cells with varied chemoresistance.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part I

Translation of the synergy between the Siremadlin (MDM2 inhibitor) and Trametinib (MEK inhibitor) combination observed in vitro into in vivo synergistic efficacy in melanoma requires estimation of the interaction between these molecules at the pharmacokinetic (PK) and pharmacodynamic (PD) levels. The cytotoxicity of the Siremadlin and Trametinib combination was evaluated in vitro in melanoma A375 cells with MTS and RealTime-Glo assays. Analysis of the drug combination matrix was performed using Synergy and Synergyfinder packages. Calculated drug interaction metrics showed high synergy between Siremadlin and Trametinib: 23.12%, or a 7.48% increase of combined drug efficacy (concentration-independent parameter β from Synergy package analysis and concentration-dependent δ parameter from Synergyfinder analysis, respectively). In order to select the optimal PD interaction parameter which may translate observed in vitro synergy metrics into the in vivo setting, further PK/PD studies on cancer xenograft animal models coupled with PBPK/PD modelling are needed.

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin

The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that treatment with doxorubicin induces a compartmentalization of the breast cancer cell population into surviving cells, which continue proliferating after treatment, and irreversibly damaged cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor cell dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the cytotoxic effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.

A mechanistic model captures the emergence and implications of non-genetic heterogeneity and reversible drug resistance in ER+ breast cancer cells

Resistance to anti-estrogen therapy is an unsolved clinical challenge in successfully treating ER+ breast cancer patients. Recent studies have demonstrated the role of non-genetic (i.e. phenotypic) adaptations in tolerating drug treatments; however, the mechanisms and dynamics of such non-genetic adaptation remain elusive. Here, we investigate coupled dynamics of epithelial–mesenchymal transition (EMT) in breast cancer cells and emergence of reversible drug resistance. Our mechanism-based model for underlying regulatory network reveals that these two axes can drive one another, thus enabling non-genetic heterogeneity in a cell population by allowing for six co-existing phenotypes: epithelial-sensitive, mesenchymal-resistant, hybrid E/M-sensitive, hybrid E/M-resistant, mesenchymal-sensitive and epithelial-resistant, with the first two ones being most dominant. Next, in a population dynamics framework, we exemplify the implications of phenotypic plasticity (both drug-induced and intrinsic stochastic switching) and/or non-genetic heterogeneity in promoting population survival in a mixture of sensitive and resistant cells, even in the absence of any cell–cell cooperation. Finally, we propose the potential therapeutic use of mesenchymal–epithelial transition inducers besides canonical anti-estrogen therapy to limit the emergence of reversible drug resistance. Our results offer mechanistic insights into empirical observations on EMT and drug resistance and illustrate how such dynamical insights can be exploited for better therapeutic designs.

Multi-drug-resistance efflux in cisplatin-naive and cisplatin-exposed A2780 ovarian cancer cells responds differently to cell culture dimensionality

A primary reason for chemotherapy failure is chemoresistance, which is driven by various mechanisms. Multi-drug resistance (MDR) is one such mechanism that is responsible for drug extrusion from the intracellular space. MDR can be intrinsic and thus, may pre-exist the first application of chemotherapy. However, MDR may also be acquired during tumor exposure to chemotherapeutic agents. To understand whether cell clustering can influence intrinsic and acquired MDR, the present study assessed cultured monolayers (representing individual cells) and spheroids (representing clusters) formed by cisplatin-naïve (intrinsic MDR) and cisplatin-exposed (acquired MDR) lines of ovarian cancer A2780 cells by determining the cytometry of reaction rate constant (CRRC). MDR efflux was characterized using accurate and robust cell number vs. MDR efflux rate constant (k_MDR) histograms. Both cisplatin-naïve and cisplatin-exposed monolayer cells presented unimodal histograms; the histogram of cisplatin-exposed cells was shifted towards a higher k_MDR value suggesting greater MDR activity. Spheroids of cisplatin-naïve cells presented a bimodal histogram indicating the presence of two subpopulations with different MDR activity. In contrast, spheroids of cisplatin-exposed cells presented a unimodal histogram qualitatively similar to that of the monolayers of cisplatin-exposed cells but with a moderate shift towards greater MDR activity. A flow-cytometry assessment of multidrug resistance-associated protein 1 transporter levels in monolayers and dissociated spheroids revealed distributions similar to those of k_MDR, thus, suggesting a plausible molecular mechanism for the observed differences in MDR activity. The observed greater effect of cell clustering on intrinsic rather than in acquired MDR can help guide the development of new therapeutic strategies targeting clusters of circulating tumor cells.

LncRNA CBR3-AS1 regulates of breast cancer drug sensitivity as a competing endogenous RNA through the JNK1/MEK4-mediated MAPK signal pathway

Background

Adriamycin (ADR) resistance is one of the main obstacles to improving the clinical prognosis of breast cancer patients. Long noncoding RNAs (lncRNAs) can regulate cell behavior, but the role of these RNAs in the anti-ADR activity of breast cancer remains unclear. Here, we aim to investigate the imbalance of a particular long noncoding RNA, lncRNA CBR3 antisense RNA 1 (CBR3-AS1), and its role in ADR resistance.

Methods

Microarray analysis of ADR-resistant breast cancer cells was performed to identify CBR3-AS1. CCK-8 and colony formation assays were used to detect the sensitivity of breast cancer cells to ADR. Dual-luciferase reporter, RNA pulldown, IHC and western blot analyses were used to verify the relationship between the expression of CBR3-AS1, miRNA and target genes. For in vivo experiments, the effect of CBR3-AS1 on breast cancer resistance was observed in a xenograft tumor model. The role of CBR3-AS1 in influencing ADR sensitivity was verified by clinical breast cancer specimens from the TCGA, CCLE, and GDSC databases.

Results

We found that CBR3-AS1 expression was significantly increased in breast cancer tissues and was closely correlated with poor prognosis. CBR3-AS1 overexpression promoted ADR resistance in breast cancer cells in vitro and in vivo. Mechanistically, we identified that CBR3-AS1 functioned as a competitive endogenous RNA by sponging miR-25-3p. MEK4 and JNK1 of the MAPK pathway were determined to be direct downstream proteins of the CBR3-AS1/miR-25-3p axis in breast cancer cells.

Conclusions

In summary, our findings demonstrate that CBR3-AS1 plays a critical role in the chemotherapy resistance of breast cancer by mediating the miR-25-3p and MEK4/JNK1 regulatory axes. The potential of CBR3-AS1 as a targetable oncogene and therapeutic biomarker of breast cancer was identified.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-01844-7.

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

LINC00160 mediated paclitaxel-And doxorubicin-resistance in breast cancer cells by regulating TFF3 via transcription factor C/EBPβ

Chemoresistance represents a major challenge in breast cancer (BC) treatment. This study aimed to probe the roles of LINC00160 in paclitaxel‐ and doxorubicin‐resistant BC cells. Three pairs of BC and adjacent normal tissue were used for lncRNA microarray analysis. Paclitaxel‐resistant MCF‐7 (MCF‐7/Tax) and doxorubicin‐resistant BT474 (BT474/Dox) cells were generated by exposure of parental drug‐sensitive MCF‐7 or BT474 cells to gradient concentrations of drugs. Correlation between LINC00160 expression and clinical response to paclitaxel in BC patients was examined. Short interfering RNAs specifically targeting LINC00160 or TFF3 were designed to construct LINC00160‐ and TFF3‐depleted BC cells to discuss their effects on biological episodes of MCF‐7/Tax and BT474/Dox cells. Interactions among LINC00160, transcription factor C/EBPβ and TFF3 were identified. MCF‐7/Tax and BT474/Dox cells stable silencing of LINC00160 were transplanted into nude mice. Consequently, up‐regulated LINC00160 led to poor clinical response to paclitaxel in BC patients. LINC00160 knockdown reduced drug resistance in MCF‐7/Tax and BT474/Dox cells and reduced cell migration and invasion. LINC00160 recruited C/EBPβ into the promoter region of TFF3 and increased TFF3 expression. LINC00160‐depleted MCF‐7/Tax and BT474/Dox cells showed decreased tumour growth rates in nude mice. Overall, we identified a novel mechanism of LINC00160‐mediated chemoresistance via the C/EBPβ/TFF3 axis, highlighting the potential of LINC00160 for treating BC with chemoresistance.

Spheroid-Based Approach to Assess the Tissue Relevance of Analysis of Dispersed-Settled Tissue Cells by Cytometry of the Reaction Rate Constant

Cytometry of Reaction Rate Constant (CRRC) uses time-lapse fluorescence microscopy to measure a rate constant of a catalytic reaction in individual cells and, thus, facilitate accurate size determination for cell subpopulations with distinct efficiencies of this reaction. Reliable CRRC requires uniform exposure of cells to the reaction substrate followed by their uniform imaging, which in turn, requires that a tissue sample be disintegrated into a suspension of dispersed cells, and these cells settle on the support surface before being analyzed by CRRC. We call such cells "dispersed-settled" to distinguish them from cells cultured as a monolayer. Studies of the dispersed-settled cells can be tissue-relevant only if the cells maintain their 3D tissue state during the multi-hour CRRC procedure. Here, we propose an approach for assessing tissue relevance of the CRRC-based analysis of the dispersed-settled cells. Our approach utilizes cultured multicellular spheroids as a 3D cell model and cultured cell monolayers as a 2D cell model. The CRRC results of the dispersed-settled cells derived from spheroids are compared to those of spheroids and monolayers in order to find if the dispersed-settled cells are representative of the spheroids. To demonstrate its practical use, we applied this approach to a cellular reaction of multidrug resistance (MDR) transport, which was followed by extrusion of a fluorescent substrate from the cells. The approach proved to be reliable and revealed long-term maintenance of MDR transport in the dispersed-settled cells obtained from cultured ovarian cancer spheroids. Accordingly, CRRC can be used to determine accurately the size of a cell subpopulation with an elevated level of MDR transport in tumor samples, which makes CRRC a suitable method for the development of MDR-based predictors of chemoresistance. The proposed spheroid-based approach for validation of CRRC is applicable to other types of cellular reactions and, thus, will be an indispensable tool for transforming CRRC from an experimental technique into a practical analytical tool.

Phototoxic effectiveness of zinc phthalocyanine tetrasulfonic acid on MCF-7 cells with overexpressed P-glycoprotein

The development of multidrug resistance is often associated with the over-expression of P-glycoprotein (P-gp). This protein prevents drug accumulation and extrudes them out of the cell before they reach the intended target. The aim of this study was to develop an in vitro MCF-7 cell line with increased expression of P-gp and test the phototoxicity of a novel photoactivated zinc phthalocyanine tetrasulfonic acid (ZnPcS4) on these cells. The over-expressed P-gp MCF-7 cells (MCF-7/DOX) were developed from wildtype (WT) MCF-7 cells by a stepwise continuous exposure of the WT cells to different concentrations of Doxorubicin (DOX) (0.1 - 1 μM) over a period of 4 months. The P-gp expression was measured using flow cytometry, immunofluorescence and enzyme immunoassay. To verify whether zinc phthalocyanine-mediated photodynamic therapy (ZnPcS4 - PDT) is effective in MCF-7/DOX, we studied the subcellular localization, phototoxicity and nuclear damage. The flow cytometry result showed two distinct peaks of P-gp positive and negative expression in MCF-7/DOX cell population, which correlates with the ELISA-based assay (p˂0.001). The ME16C (Normal breast cells) was used as control. The localization studies showed that ZnPcS4 have greater affinity for lysosome than mitochondria. Phototoxicity results indicated that photoactivated zinc phthalocyanine decreased the cell proliferation and viability as the drug and laser light dosages increased to 16 μM and 20 J/cm² respectively. PDT-induced cytotoxicity using lactose dehydrogenase (LDH) enzyme leakage as measure did not increase likewise. The ZnPcS4-induced PDT was less effective for MCF-7/DOX cells which could be attributed to decreased retention of ZnPcS4 in major cellular organelles due to the presence of increased drug efflux P-gp. The current findings suggest that, increased P-gp expression, a characteristic of multidrug resistance together with other related intrinsic mechanisms might contribute to render MCF-7/DOX cells less sensitive to ZnPcS4-induced phototoxicity.

Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer

Development of drug resistance in cancer has major implications for patients’ outcome. It is related to processes involved in the decrease of drug efficacy, which are strongly influenced by intratumor heterogeneity and changes in the microenvironment. Heterogeneity arises, to a large extent, from genetic mutations analogously to Darwinian evolution, when selection of tumor cells results from the adaptation to the microenvironment, but could also emerge as a consequence of epigenetic mutations driven by stochastic events. An important exogenous source of alterations is the action of chemotherapeutic agents, which not only affects the signalling pathways but also the interactions among cells. In this work we provide experimental evidence from in vitro assays and put forward a mathematical kinetic transport model to describe the dynamics displayed by a system of non-small-cell lung carcinoma cells (NCI-H460) which, depending on the effect of a chemotherapeutic agent (doxorubicin), exhibits a complex interplay between Darwinian selection, Lamarckian induction and the nonlocal transfer of extracellular microvesicles. The role played by all of these processes to multidrug resistance in cancer is elucidated and quantified.