Kinetics of MDR transport in tumor-initiating cells

Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC)

Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify cell contours which are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow is only reliable if cells maintain their positions during the time-lapse measurements. If the cells move, the original cell contours become unsuitable for evaluating intracellular fluorescence and the CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report a CRRC workflow developed to be applicable to motile cells. The new workflow combines fluorescence microscopy with transmitted-light microscopy and utilizes a new automated tool for cell identification and tracking. A transmitted-light image is taken right before every fluorescence image to determine cell contours, and cell contours are tracked through the time-stack of transmitted-light images to account for cell movement. Each unique contour is used to determine fluorescence intensity of cells in the associated fluorescence image. Next, time dependencies of the intracellular fluorescence intensities are used to determine each cell's rate constant and construct a kinetic histogram "number of cells vs rate constant." The new workflow's robustness to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results. Additionally, the workflow could potentially monitor kinetics of varying biological processes at the single-cell level for sizable cell populations. Although our workflow was designed ad hoc for CRRC, this cell-segmentation/cell-tracking strategy also represents an entry-level, user-friendly option for a variety of biological assays (i.e., migration, proliferation assays, etc.). Importantly, no prior knowledge of informatics (i.e., training a model for deep learning) is required.

Dasatinib self-assembled nanoparticles decorated with hyaluronic acid for targeted treatment of tumors to overcome multidrug resistance

Multidrug resistance (MDR) and lack of targeting specificity are the main reasons why traditional drug therapies fail and produce toxic side effects in cancer chemotherapy. In order to increase targeting specificity and maximize therapeutic efficacy, new intelligent drug delivery systems are needed. In this study, we prepared the hyaluronic acid (HA) conjugated dasatinib (DAS) and D-α-tocopherol acid polyethylene glycolsuccinate (TPGS) copolymer nanoparticles (THD-NPs). The water solubility of the hydrophobic drug DAS was improved by chemically linking with HA. HA can bind to the over-expressed CD44 protein of tumor cells to increase targeting specificity, TPGS can inhibit the activity of P-glycoprotein (P-gp), and increase the intracellular accumulation of drugs. The prepared drug-loaded nanoparticle has a particle size of 82.23 ± 1.07 nm with good in vitro stability. Our in vitro studies showed that THD-NPs can be released more rapidly in a weakly acidic environment (pH = 5.5) than in a normal physiological environment (pH = 7.4), which can realize the selective release of nanoparticles in tumor cells. Compared to free drugs, THD-NPs showed more efficient cellular uptake, effectively increased the cytotoxic effect of DAS on nasopharyngeal carcinoma HNE1 cells drug resistance HNE1/DDP cells and increased the accumulation of drugs in HNE1/DDP cells, which may be due to the inhibitory effect of TPGS on the efflux function of P-gp. In vivo experiments showed that THD-NPs can effectively inhibit tumor growth without obvious side effects. In conclusion, the targeted and pH-sensitive nanosystem, we designed has great potential to overcome drug resistance and increase therapeutic effects in cancer treatment.

Analytical Challenges in Development of Chemoresistance Predictors for Precision Oncology

Chemoresistance, i.e., tumor insensitivity to chemotherapy, shortens life expectancy of cancer patients. Despite the availability of new treatment options, initial systemic regimens for solid tumors are dominated by a set of standard chemotherapy drugs, and alternative therapies are used only when a patient has demonstrated chemoresistance clinically. Chemoresistance predictors use laboratory parameters measured on tissue samples to predict the patient's response to chemotherapy and help to avoid application of chemotherapy to chemoresistant patients. Despite thousands of publications on putative chemoresistance predictors, there are only about a dozen predictors that are sufficiently accurate for precision oncology. One of the major reasons for inaccuracy of predictors is inaccuracy of analytical methods utilized to measure their laboratory parameters: an inaccurate method leads to an inaccurate predictor. The goal of this study was to identify analytical challenges in chemoresistance-predictor development and suggest ways to overcome them. Here we describe principles of chemoresistance predictor development via correlating a clinical parameter, which manifests disease state, with a laboratory parameter. We further classify predictors based on the nature of laboratory parameters and analyze advantages and limitations of different predictors using the reliability of analytical methods utilized for measuring laboratory parameters as a criterion. Our eventual focus is on predictors with known mechanisms of reactions involved in drug resistance (drug extrusion, drug degradation, and DNA damage repair) and using rate constants of these reactions to establish accurate and robust laboratory parameters. Many aspects and conclusions of our analysis are applicable to all types of disease biomarkers built upon the correlation of clinical and laboratory parameters.

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.

Cytometry of Reaction Rate Constant: Measuring Reaction Rate Constant in Individual Cells To Facilitate Robust and Accurate Analysis of Cell-Population Heterogeneity

Robust and accurate analysis of cell-population heterogeneity is challenging but required in many areas of biology and medicine. In particular, it is pivotal to the development of reliable cancer biomarkers. Here, we prove that cytometry of reaction rate constant (CRRC) can facilitate such analysis when the kinetic mechanism of a reaction associated with the heterogeneity is known. In CRRC, the cells are loaded with a reaction substrate, and its conversion into a product is followed by time-lapse fluorescence microscopy at the single-cell level. A reaction rate constant is determined for every cell, and a kinetic histogram "number of cells versus the rate constant" is used to determine quantitative parameters of reaction-based cell-population heterogeneity. Such parameters include, for example, the number and sizes of subpopulations. In this work, we applied CRRC to a reaction of substrate extrusion from cells by ATP-binding cassette (ABC) transporters. This reaction is viewed as a potential basis for predictive biomarkers of chemoresistance in cancer. CRRC proved to be robust (insensitive to variations in experimental settings) and accurate for finding quantitative parameters of cell-population heterogeneity. In contrast, a typical nonkinetic analysis, performed on the same data sets, proved to be both nonrobust and inaccurate. Our results suggest that CRRC can potentially facilitate the development of reliable cancer biomarkers on the basis of quantitative parameters of cell-population heterogeneity. A plausible implementation scenario of CRRC-based development, validation, and clinical use of a predictor of ovarian cancer chemoresistance to its frontline therapy is presented.

Cutaneous metastases of breast cancer during adjuvant chemotherapy correlates with increasing CD44+/CD24- and ALDH-1 expression: a case report and literature review

Cancer stem cells (CSCs) within a tumor are scarce and self-sustaining and they have the abilities for self-renewal and the potential of giving rise to diverse types of cells that compose the tumor. These cells are suggested to be associated with therapeutic failure, and therefore they remain as an important issue in this regard. We report the cases of two breast cancer patients diagnosed with rapid cutaneous metastases during adjuvant cytotoxic chemotherapy after curative mastectomy. For elucidating a relationship between CSCs and resistance to chemotherapy, we evaluated primary tumor and metastatic cutaneous lesions by CSC markers in immunohistochemical stains (CD44⁺/CD24^- and ALDH-1). Either CD44⁺/CD24^- or ALDH-1 expression increased compared to primary breast cancer during chemotherapy. This case report shows that CD44⁺/CD24^- or ALDH-1 expression in primary or cutaneous metastatic breast cancer may be associated with rapid onset chemoresistance.

Metabolic Suppression of a Drug-Resistant Subpopulation in Cancer Spheroid Cells

Inhibition of metabolic features which distinguish cancer cells from their non-malignant counterparts is a promising approach to cancer treatment. Energy support for drug extrusion in multidrug resistance (MDR) is a potential target for metabolic inhibition. Two major sources of ATP-based metabolic energy are partial (glycolysis) and complete (mitochondrial oxidative phosphorylation) oxidation of metabolic fuels. In cancer cells, the balance between them tends to be shifted toward glycolysis; this shift is considered to be characteristic of the cancer metabolic phenotype. Numerous earlier studies, conducted with cells cultured in a monolayer (2-D model), suggested inhibition of glycolytic ATP production as an efficient tool to suppress MDR in cancer cells. Yet, more recent work challenged the appropriateness of the 2-D model for such studies and suggested that a more clinically relevant approach would utilize a more advanced cellular model such as a 3-D model. Here, we show that the transition from the 2-D model (cultured monolayer) to a 3-D model (cultured spheroids) introduces essential changes into the concept of energetic suppression of MDR. The 3-D cell organization leads to the formation of a discrete cell subpopulation (not formed in the 2-D model) with elevated MDR transport capacity. This subpopulation has a specific metabolic phenotype (mixed glycolytic/oxidative MDR support) different from that of cells cultured in the 2-D model. Finally, the shift to the oxidative phenotype becomes greater when the spheroids are grown under conditions of lactic acidosis that are typical for solid tumors. The potential clinical significance of these findings is discussed.