HISTORY: Between 1975 and 1989 compounds were screened for anticancer activity by the Developmental Therapeutics Program (DTP) of the National Cancer Institute (NCI) using P388 and L1210 murine leukemias. This approach identified compounds effective against leukemias but was not effective at identifying compounds effective against solid tumors. Between 1985 and 1989 the NCI-60 protocol was developed.

WHAT IS THE NCI 60?: The NCI 60 is a panel of 60 cell lines (59 presently available) derived from several different cancer types. These include leukemias, melanomas, ovarian, renal, prostate, colon, lung and CNS cancers. These cell lines have been characterized pharmacologically by exposure to more that 100,000 compounds.

SCREENING METHODS: Most of the compounds screened have no antiproliferative activity (up to 85%). In order to avoid screening inactive compounds across all the cell lines, a prescreen in done using 3 highly sensitive cell lines (Breast MCF-7, Lung NCI-H640, CNS SF-268). Antiproliferative activity must be seen in these cell lines in order to continue to the 60 cell line panel. The 59 different human tumor lines are incubated with 5 different doses of compound and a sulforhodamine blue (SRB) assay is performed after 48 hours to determine cytotoxicity. From the 5 point curve, the following concentrations are extrapolated: GI50 (inhibits growth by 50%), TGI (totally inhibits growth), LC50 (kills 50% of cells). For the specific screening methods from the DTP website click here. Compounds shown to have anticancer activity in cell lines within the NCI 60 panel may then move on to animal trials and if successful, may eventually move on to be tested in clinical trials.

EVOLUTION OF THE NCI 60 PANEL: The original hypothesis was that NCI-60 screening would identify compounds with activity against cancers within a particular organ. This has not been convincingly demonstrated but the NCI 60 panel has evolved into a system for profiling the compounds and cell lines. It is now being used more and more to screen molecular targets to identify compounds active against cells with a certain molecular profile.

DATABASES: Three databases have been created to classify the multitude of information that has been gathered about these cell lines. The Activity (A) Database shows that patterns of activity of tested compounds across the 60 cell lines. The Structure (S) Database is a database of the structural characteristics of the tested compounds. The Target (T) Database contains DNA, mRNA, protein and functional data on individual molecules across the 60 cell lines.

The information in these databases can be mapped into one another to identify such things structure-activity relationships and to identify compounds that are active against a certain molecular profile. In the paper presented, molecular target data (ABC transporter mRNA levels) is mapped into the activity database in order to identify which transporters confer resistance to which compounds.

Transporter proteins are very important in normal cellular processes. Cancer cells can utilize these transporter proteins to enhance survival by chemoresistance. Members of the ATP binding cassette (ABC) family are the best characterized mediators of resistance to anticancer drugs. The ABC transporters couple ATP hydrolysis to move drugs out of the cell.

The first ABC transporter identified and shown to confer resistance to anticancer drugs was p-glycoprotein or ABCB1 (encoded by MDR1 gene). P-glycoprotein was also the first molcular target analyzed using the NCI 60 molecular target database (T database). (Wu et al. 1992; Lee et al., 1994, Izquierdo et al., 1996). In 1997, John Weinstein et al. generated a clustered image map combining the A and T databases. They were able to show that known MDR-1 substrates cluster together. In the upper right quadrant of the clustered image map there is a small blue square that correlates to these compounds. Blue represents a negative correlation to drug activity (i.e. resistance).

Click here to see Weinstein's clustered image map.

MDR-1 is associated with multi-drug resistance to therapeutic agents such as anthracyclines, etoposide, vinca alkaloids (Lee et al., 1994). A correlation between MDR1 expression and poor outcome in AML has been shown (Leith et al., 1999) and expression of MDR1 in tumors led to ABCB1 inhibitor trials. The results were disappointing but prompted a search for additional drug transporters. This led to the discovery of MRP1/ABCC1 (Cole et al., 1992) and the discovery of homologs in human genome. The MRP family (a subfamily of ABC transporters) has 9 members, 8 of which are associated with cancer drug resistance (Kruh and Belinsky, 2003). Substrates of MRP1 include topoisomerase I inhibitors, cisplatin, methotraxate, nucleosides, nucleotides, fluoropyrimidines (Guo et al., 2003).

There are 48 ABC transporter proteins encoded by the human genome. Thirteen of these ABC transporters are currently known to be associated with resistance to chemotherapeutic drugs but relatively little is known about the rest of the family members (reviewed by Ross and Doyle, 2004). This paper shows the use of a pharmacogenomic approach to identify which of these transporters do or do not contribute to drug resistance and to which drugs. They first characterized ABC transporter gene expression in NCI-60. Since the NCI-60 have been profiled at the DNA, mRNA, protein, and functional levels, the expression of ABC transporters could reveal a relationship between ABC transporter function and other to other molecular properties of the cell lines. Also, the relationship between ABC transporter expression levels and sensitivity to drugs could yield insight into which family members are associated with resistance or sensitivity to groups of compounds. They used the NCI 60 cell panel to correlate ABC transporter gene expression with compound sensitivity.

RT-PCR was employed to quantify the mRNA of all the human ABC transporters. Supplemental Table S1 shows the complete RT-PCR results. A clustered image map (Figure 1) was generated from these results. Some expression patterns noted on the clustered image map include: the 10 melanoma cell lines cluster together; ABC1 is expressed ubiquitously; ABCB5 expressed in melanoma-derived cells; there is high expression of ABCA2 in brain; there is high expression of ABCA3 in lung; there is high expression of ABCB1 and ABCC4 in kidney; ABCA2 is ubiquitously expressed while ABCA3 is selectively expressed in H522M, A549, and EKVX (all lung cancer lines); distribution of ABC transporters on the dendrogram seem independent of sequence homology categories. No conclusions are made from these observations but the database findings serve as a 'hypothesis generator'.

Figure 2 shows the relationship between compounds with known mechanisms of action and ABCB1 expression. The blue bars represent known ABCB1 substrates while the red bars represent compounds that have been shown not to be ABCB1 substrates. The black bars represent compounds for which there is no data on ABCB1 interactions. This figure shows that NCI-60 and ABC transporter mRNA data correlates nicely with the information previously known about certain compounds. The known substrates for ABCB1 show a negative correlation with expression (the higher ABCB1 expression, the less active the drug is) and the substrates known not to be substrates of ABCB1 show a positive correlation with expression (these compounds are active against cells that express ABCB1 because they are not substrates).

To identify unknown substrates of ABCB1, this study was extended from the 118 compounds with known mechanisms of action to a larger activity data set of 1,429 compounds. Supplemental Figure S1 shows the 18 compounds that were predicted by this analysis to be ABCB1 substrates. These substrates identified through statistical screening all share similar chemical characteristics.

Szakas and colleagues wanted to verify that the NCI 60 approach really identified new substrates. Figure 3 shows some predictions of new substrates and then validates the predictions using a MTT assay that tests for cell proliferation. To do this they utilized a carcinoma cell line KB-3-1 and a derivative of this line that overexpresses MDR1, KB-V1 (Shen et al., 1986). For several compounds that had shown a negative correlation with MDR1 expression (indicating resistance), they were able to show that KB-V1 cells overexpressing MDR1 were, in fact, resistant to the cytotoxic effects as compared with KB-3-1 cells. In addition, they showed that a MDR1 antagonist could resensitize these cells to the compounds.

Szakas and colleagues performed similar experiments to validate predicted substrates for MRP2 and MRP8. These results are shown in Figure 4. They were able to show that cells overexpressing these transporters really were resistant to the predicted substrates.

One surprising finding was that expression of ABCB1 does not confer resistance to the compound NSC 73306, but increased ABCB1 expression is associated with increased activity of this drug. Figure 5 shows the prediction from the NCI 60 data that ABCB1 expression potentiates NSC 73306 and then verifies this prediction by MTT assay. Figure 5A s is a scatter plot of sensitivity to NSC 73306 against ABCB1 expression. This plot shows that as ABCB1 expression increases, so does the sensitivity of the cell to this compound. The Pearson correlation coefficient is positive (0.54). Figure 5B shows a dose-response curve graphing cell survival against drug concentration. This experiment is done in cell line KB-3-1 and in the same cell line overexpressing MDR1, KB-V1 in the absence and presence of the MDR1 antagonist, PSC 833. This figure shows that NSC 73306 is more toxic to KB-V1, the cell line overexpressing MDR1. This toxicity is ameliorated when the MDR1 antagonist is added back. These findings were further validated in KB HeLa cells expressing MDR1 under tetracycline control (results shown in Figure 5C).

(Supplementary data can be found here).

This work by Szakas and colleagues provides evidence that a pharmacogenomic approach can by used to study ABC transporter function. The RT-PCR database provides a means to discover new substrates and transporters involved in multidrug resistance. This database can also be used to identify compounds that are active in cells expressing MDR proteins and to find drugs that counteract the resistance conferred by MDR proteins.

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