A Quantitative Approach for Assessing Multidrug Resistance in Cancer

Multidrug resistance (MDR) is a primary barrier to successful cancer treatment with small molecule drugs. One mechanism of resistance is through transporters which pump drug out of cancer cells before they exert any therapeutic effect. The most studied transporter is P-glycoprotein (Pgp), a member of the ATP Binding Cassette (ABC) superfamily of drug pumps. Prior research has focused on determining whether drugs are substrates of Pgp. Pgp specificity for FDA approved drugs is currently unclear due to technical variability in assays that quantify enzyme kinetics using non-cellular experimental models. Unfortunately, there is extreme variability in enzyme parameters for drugs that are characterized and gaps in literature for drugs that have not been characterized. Thus, our overall goal is to develop live cell methods to obtain quantitative scores for substrates in cells and fill gaps in the literature. Studying Pgp in the context of MDR, we improve Pgp specificity scores by leveraging new Pgp expression (cell lines, tissues) and function (drug screening) datasets. We experimentally and computationally integrate functional dataset information to better understand Pgp specificity using an approach based on underlying Michaelis-Menten enzyme kinetics. We obtain consensus scores for Pgp specificity across [~]150 FDA approved oncology drugs and validate them experimentally in a subset of 76 substrates selected to represent the spectrum of drugs for Pgp specificity. These scores can be used to calibrate clinical diagnostics (Pgp expression), and our experimental platform can be used to quantify Pgp function in clinical samples. Overall, we develop a parallel computational and experimental procedure to estimate Pgp selectivity in live cells. This procedure can be expanded to other drug transporters which contribute to MDR to further characterize this phenotype quantitatively. Significance StatementMDR is facilitated through the action of drug transporters which are upregulated in cancer. Even though selective inhibitors have been designed to decrease drug efflux, they failed clinically. So, current clinical practice is to select non-substrates. However, non-substrates are difficult to define. Here, we have developed a quantitative platform for characterizing MDR in cancer that factors in gene expression and enzyme kinetics. Our computational and experimental platform successfully identified the spectrum of substrates for Pgp from the FDA approved oncology drug library. This quantitative approach reflects the multigene phenotype of cancer drug resistance and can be applied to other MDR transporter genes to optimize drug selection clinically.