Concordance of Gene Expression and Functional Correlation Patterns across the NCI-60 Cell Lines and the Cancer Genome Atlas Glioblastoma Samples

Background

The NCI-60 is a panel of 60 diverse human cancer cell lines used by the U.S. National Cancer Institute to screen compounds for anticancer activity. We recently clustered genes based on correlation of expression profiles across the NCI-60. Many of the resulting clusters were characterized by cancer-associated biological functions. The set of curated glioblastoma (GBM) gene expression data from the Cancer Genome Atlas (TCGA) initiative has recently become available. Thus, we are now able to determine which of the processes are robustly shared by both the immortalized cell lines and clinical cancers.

Results

Our central observation is that some sets of highly correlated genes in the NCI-60 expression data are also highly correlated in the GBM expression data. Furthermore, a “double fishing” strategy identified many sets of genes that show Pearson correlation ≥0.60 in both the NCI-60 and the GBM data sets relative to a given “bait” gene. The number of such gene sets far exceeds the number expected by chance.

Conclusion

Many of the gene-gene correlations found in the NCI-60 do not reflect just the conditions of cell lines in culture; rather, they reflect processes and gene networks that also function in vivo. A number of gene network correlations co-occur in the NCI-60 and GBM data sets, but there are others that occur only in NCI-60 or only in GBM. In sum, this analysis provides an additional perspective on both the utility and the limitations of the NCI-60 in furthering our understanding of cancers in vivo.     

Impact of EGFR gene polymorphisms on anticancer drug cytotoxicity in vitro

BACKGROUND AND OBJECTIVE: The epidermal growth factor receptor (EGFR) plays a major role in cell proliferation of epithelial tissues, and its alterations frequently contribute to oncogenesis. Several common polymorphisms of the EGFR gene have been described, at the level of both coding and regulatory sequences. Some of these polymorphisms are associated with alterations of EGFR expression and/or activity and may have an impact on the activity of anticancer agents. This study aims to analyze the relationships between specific EGFR functional polymorphisms and anticancer drug activity. METHOD: We investigated, in the panel of 60 human tumor cell lines established by the National Cancer Institute (NCI-60), whether the EGFR polymorphisms -216G>T, -191C>A, Arg521Lys (R521K), Val592Ala (V592A), and Cys624Phe (C624F), and the intron 1 (CA)n repeat were associated with EGFR gene expression and the in vitro cytotoxicity of anticancer agents using data extracted from the NCI database. We also looked for mutations of exons 18-21, known to enhance the activity of tyrosine kinase inhibitors, and the deletion of exons 2-7, associated to the oncogenesis of glioblastomas. RESULTS: In the NCI-60 cell lines, only two mutations were observed, both in exon 19, in a leukemia and melanoma cell line. These mutations have not been described previously in clinical samples and their functional role is uncertain. The allele frequencies of the -216G>T, -191C>A, and R521K single nucleotide polymorphisms (SNPs) in the NCI-60 panel were 33%, 8.5%, and 27%, respectively; the V592A and C624F SNPs were not found in any NCI-60 cell line. The intron 1 CA repeat was highly variable in the cell lines; 32 cell lines having a total number of repeats below 35, and 27 having a total number of repeats above 35.The heterozygous and variant homozygous cell lines for the -216G>T SNP presented a significantly higher expression of the EGFR gene than the homozygous wild-type lines. In contrast, there was no association between the -191C>A or R521K SNPs and EGFR gene expression. No association could be detected between the number of CA repeats in intron 1 and the expression of EGFR.The cell lines having at least one variant T allele at the -216G>T SNP were more sensitive to erlotinib and less sensitive to geldanamycin, topoisomerase I and II inhibitors, and alkylating agents than those without a variant allele. No relationship was detected between anticancer drug sensitivity and the -191C>A SNP. The R521K SNP was associated to lower sensitivity to alkylating agents. The number of CA repeats was associated with significant differences in anticancer drug activity: a high total number of CA repeats (>35 per diploid genome) was associated to increased sensitivity to alkylating agents and topoisomerase I and II inhibitors. DISCUSSION: We provide evidence in this work that EGFR polymorphisms are associated with significant differences in the in vitro cytotoxicity of several types of DNA-interfering agents. Studies attempting a clinical validation of these clues are warranted.     

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.     

pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels

We recently described a methodology that reliably predicted chemotherapeutic response in multiple independent clinical trials. The method worked by building statistical models from gene expression and drug sensitivity data in a very large panel of cancer cell lines, then applying these models to gene expression data from primary tumor biopsies. Here, to facilitate the development and adoption of this methodology we have created an R package called pRRophetic. This also extends the previously described pipeline, allowing prediction of clinical drug response for many cancer drugs in a user-friendly R environment. We have developed several other important use cases; as an example, we have shown that prediction of bortezomib sensitivity in multiple myeloma may be improved by training models on a large set of neoplastic hematological cell lines. We have also shown that the package facilitates model development and prediction using several different classes of data.     

Correlation of PIK3Ca mutations with gene expression and drug sensitivity in NCI-60 cell lines

The gene that encodes the alpha-isoform of phosphatidylinositol 3-kinase (PIK3Ca) is frequently mutated in human cancers. We profiled the mutation status of the PIK3Ca gene in the National Cancer Institute (NCI)-60 panel of human cancer cell lines maintained by the Developmental Therapeutics Program of the NCI. Mutation hotspots on the gene were PCR amplified and sequenced, and the trace data were analyzed with software designed to detect mutations. Seven of the cell lines tested have PIK3Ca mutations: two lines derived from breast cancer, two from colon cancer, two from ovarian cancer, and one from lung cancer. BRAF and EGFR genes were normal in the PIK3Ca mutant lines. Two of the cell lines with mutant PIK3Ca also have a mutant version of the KRAS gene. The mutation status was correlated with array-based gene expression that is publicly available for the NCI-60 cell lines. We found increased expression levels for estrogen receptor (ER) and ERBB2 in PIK3Ca mutant lines. The PIK3Ca mutation status was also correlated with compound screening data for the cell lines. PIK3Ca-mutant cell lines were relatively more sensitive than PIK3Ca-normal cell lines to the ER inhibitor tamoxifen and the AKT inhibitor triciribine, among other compounds. The results provide insights into the role of mutant PIK3Ca in oncogenic signaling and allow preliminary identification of novel targets for therapeutic intervention in cancers harboring PIK3Ca mutations.     

Exploring effects of DNA methylation and gene expression on pan-cancer drug response by mathematical models

Since genetic alteration only accounts for 20%–30% in the drug effect-related factors, the role of epigenetic regulation mechanisms in drug response is gradually being valued. However, how epigenetic changes and abnormal gene expression affect the chemotherapy response remains unclear. Therefore, we constructed a variety of mathematical models based on the integrated DNA methylation, gene expression, and anticancer drug response data of cancer cell lines from pan-cancer levels to identify genes whose DNA methylation is associated with drug response and then to assess the impact of epigenetic regulation of gene expression on the sensitivity of anticancer drugs. The innovation of the mathematical models lies in: Linear regression model is followed by logistic regression model, which greatly shortens the calculation time and ensures the reliability of results by considering the covariates. Second, reconstruction of prediction models based on multiple dataset partition methods not only evaluates the model stability but also optimizes the drug-gene pairs. For 368,520 drug-gene pairs with P < 0.05 in linear models, 999 candidate pairs with both AUC ≥ 0.8 and P < 0.05 were obtained by logistic regression models between drug response and DNA methylation. Then 931 drug-gene pairs with 45 drugs and 491 genes were optimized by model stability assessment. Integrating both DNA methylation and gene expression markedly increased predictive power for 732 drug-gene pairs where 598 drug-gene pairs including 44 drugs and 359 genes were prioritized. Several drug target genes were enriched in the modules of the drug-gene-weighted interaction network. Besides, for cancer driver genes such as EGFR, MET, and TET2, synergistic effects of DNA methylation and gene expression can predict certain anticancer drugs’ responses. In summary, we identified potential drug sensitivity-related markers from pan-cancer levels and concluded that synergistic regulation of DNA methylation and gene expression affect anticancer drug response.     

Entry Tropism of BK and Merkel Cell Polyomaviruses in Cell Culture

Merkel Cell Polyomavirus (MCV or MCPyV) was recently discovered in an aggressive form of skin cancer known as Merkel cell carcinoma (MCC). Integration of MCV DNA into the host genome likely contributes to the development of MCC in humans. MCV infection is common and many healthy people shed MCV virions from the surface of their skin. MCV DNA has also been detected in samples from a variety of other tissues. Although MCC tumors serve as a record that MCV can infect the Merkel cell lineage, the true tissue tropism and natural reservoirs of MCV infection in the host are not known. In an effort to gain insight into the tissue tropism of MCV, and to possibly identify cellular factors responsible for mediating infectious entry of the virus, the infection potential of human cells derived from a variety of tissues was evaluated. MCV gene transfer vectors (pseudoviruses) carrying reporter plasmid DNA encoding GFP or luciferase genes were used to transduce keratinocytes and melanocytes, as well as lines derived from MCC tumors and the NCI-60 panel of human tumor cell lines. MCV transduction was compared to transduction with pseudoviruses based on the better-studied human BK polyomavirus (BKV). The efficiency of MCV and BKV transduction of various cell types occasionally overlapped, but often differed greatly, and no clear tissue type preference emerged. Application of native MCV virions to a subset of highly transducible cell types suggested that the lines do not support robust replication of MCV, consistent with recent proposals that the MCV late phase may be governed by cellular differentiation in vivo. The availability of carefully curated gene expression data for the NCI-60 panel should make the MCV and BKV transduction data for these lines a useful reference for future studies aimed at elucidation of the infectious entry pathways of these viruses.     

Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity prediction. We validated this approach in three independent clinical trial datasets, and obtained predictions equally good, or better than, gene signatures derived directly from clinical data.     

Gene expression profiles of the NCI-60 human tumor cell lines define molecular interaction networks governing cell migration processes

Although there is extensive information on gene expression and molecular interactions in various cell types, integrating those data in a functionally coherent manner remains challenging. This study explores the premise that genes whose expression at the mRNA level is correlated over diverse cell lines are likely to function together in a network of molecular interactions. We previously derived expression-correlated gene clusters from the database of the NCI-60 human tumor cell lines and associated each cluster with function categories of the Gene Ontology (GO) database. From a cluster rich in genes associated with GO categories related to cell migration, we extracted 15 genes that were highly cross-correlated; prominent among them were RRAS, AXL, ADAM9, FN14, and integrin-beta1. We then used those 15 genes as bait to identify other correlated genes in the NCI-60 database. A survey of current literature disclosed, not only that many of the expression-correlated genes engaged in molecular interactions related to migration, invasion, and metastasis, but that highly cross-correlated subsets of those genes engaged in specific cell migration processes. We assembled this information in molecular interaction maps (MIMs) that depict networks governing 3 cell migration processes: degradation of extracellular matrix, production of transient focal complexes at the leading edge of the cell, and retraction of the rear part of the cell. Also depicted are interactions controlling the release and effects of calcium ions, which may regulate migration in a spaciotemporal manner in the cell. The MIMs and associated text comprise a detailed and integrated summary of what is currently known or surmised about the role of the expression cross-correlated genes in molecular networks governing those processes.     

Fork head transcription factor is required for ovarian mature in the brown planthopper, Nilaparvata lugens (Stål)

Background

The NCI-60 is a collection of tumor cell lines derived from a variety of human adult cancer tissue types and is commonly used for genetic analysis and screening of potential chemotherapeutic agents. We wanted to understand the contributions of specific mechanisms of genomic instability to the etiology of cancers represented by the NCI-60.

Results

We screened the NCI-60 for dysregulated homologous recombination by using the gene cluster instability (GCI) assay we pioneered, and for defects in base excision repair by sensitivity to 5-hydroxymethyl-2'-deoxyuridine (hmdUrd). We identified subsets of the NCI-60 lines that either displayed the characteristic molecular signature of GCI or were sensitive to hmdUrd. With the exception of the NCI-H23 lung cancer line, these phenotypes were not found to overlap. None of the lines examined in either subset exhibited significant changes in the frequency of sister chromatid exchanges (SCE), neither did any of the lines in either subset exhibit microsatellite instability (MSI) indicative of defects in DNA mismatch repair.

Conclusions

Gene cluster instability, sensitivity to hmdUrd and sister chromatid exchange are mechanistically distinct phenomena. Genomic instability in the NCI-60 appears to involve only one mechanism of instability for each individual cell line.     

Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells

Microarray gene-expression profiles are generally validated one gene at a time by real-time RT-PCR. We describe here a different approach based on simultaneous mutual validation of large numbers of genes using two different expression-profiling platforms. The result described here for the NCI-60 cancer cell lines is a consensus set of genes that give similar profiles on spotted cDNA arrays and Affymetrix oligonucleotide chips. Global concordance is parameterized by a 'correlation of correlations' coefficient.     

Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.     

Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells

ABSTRACT: BACKGROUND: In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy. RESULTS: Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway. CONCLUSIONS: Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization.