NCI-60 Cancer Cell Line Panel Research Articles

Utilization of genomic signatures to identify phenotype-specific drugs.

Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 59 independent cells lines. Targets are cancer-relevant phenotypes represented as gene expression signatures that are used to identify cells within the NCI-60 panel reflecting the signature phenotype and then connect to compounds that are selectively active against those cells. As a proof-of-concept, we show that this strategy effectively identifies compounds with selectivity to the RAS or PI3K pathways. We have then extended this strategy to identify compounds that have activity towards cells exhibiting the basal phenotype of breast cancer, a clinically-important breast cancer characterized as ER-, PR-, and Her2- that lacks viable therapeutic options. One of these compounds, Simvastatin, has previously been shown to inhibit breast cancer cell growth in vitro and importantly, has been associated with a reduction in ER-, PR- breast cancer in a clinical study. We suggest that this approach provides a novel strategy towards identification of therapeutic agents based on clinically relevant phenotypes that can augment the conventional strategies of target-based screens.

Topoisomerase I levels in the NCI-60 cancer cell line panel determined by validated ELISA and microarray analysis and correlation with indenoisoquinoline sensitivity

Topoisomerase I (Top1) is a proven target for cancer therapeutics, and the level of Top1 in tumors has been used as a biomarker for chemotherapeutic efficacy. In this study, we report the development and validation of a two-site enzyme chemiluminescent immunoassay for Top1, which we used to measure Top1 levels in the NCI-60 cancer cell line panel. Top1 levels ranged from 0.9 to 9.8 ng/mL/microg protein extract in these cell lines. Levels varied both within and between cancer types but were generally highest in colon cancer and leukemia cell lines and lowest in central nervous system and renal cancer cell lines. Top1 mRNA levels in the NCI-60 cell lines were also measured by microarray; mRNA values generally showed a good correlation with protein levels (Pearson correlation = 0.8). When these expression levels were compared with the activity of the indenoisoquinoline class of Top1 inhibitors across the NCI-60 cell panel, low levels of Top1 were associated with increased resistance to these drugs. The results of our studies indicate that our Top1 assay can be used to quantify Top1 levels in untreated cells as well as cells treated with Top1 inhibitors and that the assay has the potential to be adapted for use in predicting clinical response to Top1-active antineoplastic agents.

HIGHLIGHTING RELATIONSHIPS BETWEEN HETEROGENEOUS BIOLOGICAL DATA THROUGH GRAPHICAL DISPLAYS BASED ON REGULARIZED CANONICAL CORRELATION ANALYSIS

Biological data produced by high throughput technologies are becoming more and more abundant and are arousing many statistical questions. This paper addresses one of them; when gene expression data are jointly observed with other variables with the purpose of highlighting significant relationships between gene expression and these other variables. One relevant statistical method to explore these relationships is Canonical Correlation Analysis (CCA). Unfortunately, in the context of postgenomic data, the number of variables (gene expressions) is usually greater than the number of units (samples) and CCA cannot be directly performed: a regularized version is required.We applied regularized CCA on data sets from two different studies and show that its interpretation evidences both previously validated relationships and new hypothesis. From the first data sets (nutrigenomic study), we generated interesting hypothesis on the transcription factor pathways potentially linking hepatic fatty acids and gene expression. From the second data sets (pharmacogenomic study on the NCI-60 cancer cell line panel), we identified new ABC transporter candidate substrates which relevancy is illustrated by the concomitant identification of several known substrates.In conclusion, the use of regularized CCA is likely to be relevant to a number and a variety of biological experiments involving the generation of high throughput data. We demonstrated here its ability to enhance the range of relevant conclusions that can be drawn from these relatively expensive experiments.

Comparison of structure fingerprint and molecular interaction field based methods in explaining biological similarity of small molecules in cell-based screens

In this work, we calculated the pair wise chemical similarity for a subset of small molecules screened against the NCI60 cancer cell line panel. Four different compound similarity calculation methods were used: Brutus, GRIND, Daylight and UNITY. The chemical similarity scores of each method were related to the biological similarity data set. The same was done also for combinations of methods. In the end, we had an estimate of biological similarity for a given chemical similarity score or combinations thereof. The data from above was used to identify chemical similarity ranges where combining two or more methods (data fusion) led to synergy. The results were also applied in ligand-based virtual screening using the DUD data set. In respect to their ability to enrich biologically similar compound pairs, the ranking of the four methods in descending performance is UNITY, Daylight, Brutus and GRIND. Combining methods resulted always in positive synergy within a restricted range of chemical similarity scores. We observed no negative synergy. We also noted that combining three or four methods had only limited added advantage compared to combining just two. In the virtual screening, using the estimated biological similarity for ranking compounds produced more consistent results than using the methods in isolation.

Drug Sensitivity Prediction by CpG Island Methylation Profile in the NCI-60 Cancer Cell Line Panel

Aberrant promoter hypermethylation and associated gene silencing are epigenetic hallmarks of tumorigenesis. It has been suggested that aberrant DNA methylation can affect the sensitivity of cancers to antineoplastic agents by altering expression of genes critical to drug response. To study this issue, we used bisulfite PCR to assess DNA methylation of 32 promoter-associated CpG islands in human cancer cell lines from the National Cancer Institute (NCI) drug-screening panel (NCI-60 panel). The frequency of aberrant hypermethylation of these islands ranged from 2% to 81% in NCI-60 cancer cells, and provided a database that can be analyzed for the sensitivity to approximately 30,000 drugs tested in this panel. By correlating drug activity with DNA methylation, we identified a list of methylation markers that predict sensitivity to chemotherapeutic drugs. Among them, hypermethylation of the p53 homologue p73 and associated gene silencing was strongly correlated with sensitivity to alkylating agents. We used small interfering RNA to down-regulate p73 expression in multiple cell lines, including the resistant cell lines TK10 (renal cancer) and SKMEL28 (melanoma). Down-regulating p73 substantially increased sensitivity to commonly used alkylating agents, including cisplatin, indicating that epigenetic silencing of p73 directly modulates drug sensitivity. Our results confirm that epigenetic profiles are useful in identifying molecular mediators for cancer drug sensitivity (pharmaco-epigenomics).

Regulators of Mitotic Arrest and Ceramide Metabolism Are Determinants of Sensitivity to Paclitaxel and Other Chemotherapeutic Drugs

Cytotoxic drug resistance is a major cause of cancer treatment failure. We report an RNA interference screen to identify genes influencing sensitivity of different cancer cell types to chemotherapeutic agents. A set of genes whose targeting leads to resistance to paclitaxel is identified, many of which are involved in the spindle assembly checkpoint. Silencing these genes attenuates paclitaxel-induced mitotic arrest and induces polyploidy in the absence of drug. We also identify a ceramide transport protein, COL4A3BP or CERT, whose downregulation sensitizes cancer cells to multiple cytotoxic agents, potentiating endoplasmic reticulum stress. COL4A3BP expression is increased in drug-resistant cell lines and in residual tumor following paclitaxel treatment of ovarian cancer, suggesting that it could be a target for chemotherapy-resistant cancers.

Control Genes and Variability: Absence of Ubiquitous Reference Transcripts in Diverse Mammalian Expression Studies

Control genes, commonly defined as genes that are ubiquitously expressed at stable levels in different biological contexts, have been used to standardize quantitative expression studies for more than 25 yr. We analyzed a group of large mammalian microarray datasets including the NCI60 cancer cell line panel, a leukemia tumor panel, and a phorbol ester induction time course as well as human and mouse tissue panels. Twelve housekeeping genes commonly used as controls in classical expression studies (including GAPD, ACTB, B2M, TUBA, G6PD, LDHA, and HPRT) show considerable variability of expression both within and across microarray datasets. Although we can identify genes with lower variability within individual datasets by heuristic filtering, such genes invariably show different expression levels when compared across other microarray datasets. We confirm these results with an analysis of variance in a controlled mouse dataset, showing the extent of variability in gene expression across tissues. The results show the problems inherent in the classical use of control genes in estimating gene expression levels in different mammalian cell contexts, and highlight the importance of controlled study design in the construction of microarray experiments.