Dataset Information

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells.

ABSTRACT: Measuring the potencies of small-molecule drugs in cell lines is a critical aspect of preclinical pharmacology. Such experiments are also prototypical of high-throughput experiments in multi-well plates. The procedure is simple in principle, but many unrecognized factors can affect the results, potentially making data unreliable. The procedures for measuring drug response described here were developed by the NIH LINCS program to improve reproducibility. Key features include maximizing uniform cell growth during the assay period, accounting for the effects of cell density on response, and correcting sensitivity measures for differences in proliferation rates. Two related protocols are described: one involves an endpoint measure well-suited to large-scale studies and the second is a time-dependent measurement that reveals changes in response over time. The methods can be adapted to other types of plate-based experiments. © 2017 by John Wiley & Sons, Inc.

SUBMITTER: Niepel M

PROVIDER: S-EPMC5538315 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells.

Niepel Mario M Hafner Marc M Chung Mirra M Sorger Peter K PK

Current protocols in chemical biology 20170619 2

Measuring the potencies of small-molecule drugs in cell lines is a critical aspect of preclinical pharmacology. Such experiments are also prototypical of high-throughput experiments in multi-well plates. The procedure is simple in principle, but many unrecognized factors can affect the results, potentially making data unreliable. The procedures for measuring drug response described here were developed by the NIH LINCS program to improve reproducibility. Key features include maximizing uniform ce ...[more]

PMID: 28628199

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Project description:Platinum-based chemotherapeutic regimens are ultimately unsuccessful due to intrinsic or acquired drug resistance. Understanding the molecular basis for platinum drug sensitivity/resistance is necessary for the development of new drugs and therapeutic regimens. In an effort to identify such determinants, we evaluated the expression of approximately 4000 genes using cDNA microarray screening in a panel of 14 unrelated human ovarian cancer cell lines derived from patients who were either untreated or treated with platinum-based chemotherapy. These data were analysed relative to the sensitivities of the cells to four platinum drugs (cis-diamminedichloroplatinum (cisplatin), carboplatin, DACH-(oxalato)platinum (II) (oxaliplatin) and cis-diamminedichloro (2-methylpyridine) platinum (II) (AMD473)) as well as the proliferation rate of the cells. Correlation analysis of the microarray data with respect to drug sensitivity and resistance revealed a significant association of Stat1 expression with decreased sensitivity to cisplatin (r=0.65) and AMD473 (r=0.76). These results were confirmed by quantitative RT-PCR and Western blot analyses. To study the functional significance of these findings, the full-length Stat1 cDNA was transfected into drug-sensitive A2780 human ovarian cancer cells. The resulting clones that exhibited increased Stat1 expression were three- to five-fold resistant to cisplatin and AMD473 as compared to the parental cells. The effect of inhibiting Jak/Stat signalling on platinum drug sensitivity was investigated using the Janus kinase inhibitor, AG490. Pretreatment of platinum-resistant cells with AG490 resulted in significant increased sensitivity to AMD473, but not to cisplatin or oxaliplatin. Overall, the results indicate that cDNA microarray analysis may be used successfully to identify determinants of drug sensitivity/resistance and future functional studies of other candidate genes from this database may lead to an increased understanding of the drug resistance phenotype.

Project description:BACKGROUND:Predicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop and validate computational approaches for the prediction of drug responses. Most of current approaches predict drug sensitivity by building prediction models with individual genes, which suffer from low reproducibility due to biologic variability and difficulty to interpret biological relevance of novel gene-drug associations. As an alternative, pathway activity scores derived from gene expression could predict drug response of cancer cells. METHOD:In this study, pathway-based prediction models were built with four approaches inferring pathway activity in unsupervised manner, including competitive scoring approaches (DiffRank and GSVA) and self-contained scoring approaches (PLAGE and Z-score). These unsupervised pathway activity inference approaches were applied to predict drug responses of cancer cells using data from Cancer Cell Line Encyclopedia (CCLE). RESULTS:Our analysis on all the 24 drugs from CCLE demonstrated that pathway-based models achieved better predictions for 14 out of the 24 drugs, while taking fewer features as inputs. Further investigation on indicated that pathway-based models indeed captured pathways involving drug-related genes (targets, transporters and metabolic enzymes) for majority of drugs, whereas gene-models failed to identify these drug-related genes, in most cases. Among the four approaches, competitive scoring (DiffRank and GSVA) provided more accurate predictions and captured more pathways involving drug-related genes than self-contained scoring (PLAGE and Z-Score). Detailed interpretation of top pathways from the top method (DiffRank) highlights the merit of pathway-based approaches to predict drug response by identifying pathways relevant to drug mechanisms. CONCLUSION:Taken together, pathway-based modeling with inferred pathway activity is a promising alternative to predict drug response, with the ability to easily interpret results and provide biological insights into the mechanisms of drug actions.

Dataset Information

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells.

Publications

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells.

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