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.