Cancer Research CTRC-AACR San Antonio Breast Cancer Symposium Tumor Immunology: New Perspectives
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Meeting Abstracts Online
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Amundson, S. A.
Right arrow Articles by Fornace, A. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Amundson, S. A.
Right arrow Articles by Fornace, A. J., Jr.
[Cancer Research 60, 6101-6110, November 1, 2000]
© 2000 American Association for Cancer Research

Regular Articles

An Informatics Approach Identifying Markers of Chemosensitivity in Human Cancer Cell Lines1

Sally A. Amundson2, Timothy G. Myers3, Dominic Scudiero, Shinichi Kitada, John C. Reed and Albert J. Fornace, Jr.

NIH, National Cancer Institute, Biological Research Laboratory [S. A. A., A. J. F.] and Developmental Therapeutics Program [T. G. M.], Bethesda, Maryland 20892; Frederick Cancer Research Facility, Frederick, Maryland 21702 [D. S.]; and The Burnham Institute, La Jolla, California 92037 [S. K., J. C. R.]


   ABSTRACT
Top
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
We have used a sensitive and reproducible method of measuring mRNA expression to compare basal levels of 10 transcripts in the 60 cell lines of the National Cancer Institute’s in vitro anticancer drug screen (NCI-ACDS) under conditions of exponential growth. The strongest correlation among these target genes was between levels of CIP1/WAF1 and BAX. Levels of the three major growth arrest and DNA damage-inducible gene transcripts, (GADD34, GADD45, and GADD153), which are coordinately regulated in response to many stresses, were also correlated across the 60 cell lines. Although the stress induction of several of the transcripts studied here has been shown to be dependent on wild-type p53 status, basal levels of only CIP1/WAF1 and BAX were found to correlate with p53 status. As expected, basal expression of O6 alkyl guanine alkyl-transferase correlated well with resistance to O6-alkylating agents (r = -0.44) but not with resistance to alkylators with different mechanisms of action (r = -0.04). When basal expression levels of the 10 genes across the NCI-ACDS panel were compared with sensitivities to a panel of 122 standard chemotherapy agents, the most striking relationship was a strong negative correlation (r = -0.3) between basal BCL-X levels and sensitivity to drugs in all of the mechanistic classes except one class of antimetabolites. Sensitivities to a maximally diverse sample of 1200 from 70,000 compounds tested in the NCI-ACDS of agents were also negatively correlated with BCL-X levels. A novel application of factor analysis revealed that the newly discovered associations were independent of previously demonstrated sensitivity factors such as p53 mutation status and native population doubling time. A similar pattern of correlation was seen for Bcl-XL protein levels. Conversely, BAX and BCL2, two other genes associated with regulation of apoptosis, showed no overall correlation with drug sensitivities. This suggests that BCL-X may play a unique role in general resistance to cytotoxic agents, with the cell lines demonstrating relative resistance to 70,000 cytotoxic agents in the NCI-ACDS being characterized by high BCL-X expression.


   INTRODUCTION
Top
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The NCI-ACDS4 panel (1 , 2) consists of 60 cell lines selected from human tumors of different tissues of origin (breast, central nervous system, colon, bone marrow, lung, skin, ovary, prostate, and kidney). Nearly 100,000 chemical compounds have been tested for cytotoxicity in this panel using a 2-day assay to determine the GI50 in each cell line (1) , with more compounds being tested continually. In addition to identification of potential antineoplastic drugs, these 60 cell lines are also being characterized on a molecular level to define alterations in genes that may contribute to carcinogenesis. Appropriate data mining of such databases may in turn aid in the development of compounds with specific cytotoxicity directed against cancer cells with particular molecular characteristics (3) .

Although expression at the protein level (4) may be a more accurate predictor of activity in the cell, mRNA expression can be a very useful end point for comparison between cell lines. When mRNA levels are informative, their measurement would have the practical advantage of requiring a relatively small sample as well as being more quantitative, rapid, and sensitive. It is also possible to use RNA analysis for newly identified target genes in advance of antibody availability. Microarray technologies promise to yield quantitative measurement of thousands of mRNA levels at once (5 , 6) , which paves the way for such applications as molecular tumor profiling and precise tailoring of individual chemotherapy regimens.

Accurate measurements of relative basal mRNA levels in human cancer cell lines may provide insight into mechanisms of molecular regulation, interactions, and drug action when this information is considered in the context of the other data in the NCI-ACDS. For instance, our laboratory recently identified a subset of the p53 wild-type cell lines with reduced or absent induction of GADD45 after {gamma}-irradiation (7) . When the NCI-ACDS database was searched for differential sensitivity of this subset of cell lines to cytotoxic drugs, topoisomerase II inhibitors were found to be significantly less toxic in the cell lines with defective GADD45 induction (P < 0.0001). This difference was confirmed by the demonstration that etoposide 16 toxicity and DNA-protein cross-links were decreased when GADD45 expression was blocked with an antisense vector and led to discovery of a role for Gadd45 in regulating chromatin accessibility (8) . This example demonstrates how mechanistic insight can be gained by exploring hypotheses suggested from analysis of a large, complex, but well-controlled biological survey.

We have performed a careful quantitation of the relative basal levels of 10 transcripts chosen for their known roles in cellular damage responses or cancer biology and as potential modifiers of toxicity (O6AT, CIP1/WAF1, GADD34, GADD45, GADD153, cMYC, MDM2, BAX, BCL2, and BCL-XL) in the 60 cell lines of the NCI-ACDS and looked for correlations between levels of these transcripts, p53 status, and cytotoxicity of the tested compounds in the NCI-ACDS database. Many statistically significant and interesting associations with existing molecular target profiles were observed. In this report, we highlight those we found most provocative, but other correlations can be explored.5 One of the most striking findings in the present study was the overall negative correlation between BCL-X levels and sensitivity to the 122 "standard" chemotherapy agents with the exception of one group of antimetabolites. This correlation was stronger than the positive correlation with cytotoxicity previously reported for p53 across diverse mechanisms of drug action (9) and suggests the importance of endogenous BCL-X levels in the cellular response to chemotherapeutic agents, independent of p53 status.


   MATERIALS AND METHODS
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Cell Culture.
Cells from the NCI-ACDS cancer cell panel (9) were grown in RPMI 1640 supplemented with 10% fetal bovine serum and 100 units/ml penicillin (Sigma) and 100 µg/ml streptomycin (Sigma). All of the cultures were maintained at 37°C in a humidified 5% CO2 atmosphere. Cells were lysed for RNA harvest at 40–60% confluence. Suspension cell lines were maintained between 1 and 10 x 105 cells/ml and were harvested in log phase growth at 4–5 x 105 cells/ml. All of the cultures were fed with fresh medium the day before harvest.

Quantitative Expression Analysis.
mRNA was isolated by the guanidine thiocyanate method of Chomczynski and Sacchi (10) , followed by poly(A) purification using oligodeoxythymidylate cellulose as previously described (11) . cDNA probes for GADD34, GADD45, and GADD153 were obtained by excising the insert from pHu34B (12) , pHul45 (12) , and pHul75 (13) , respectively. Other cDNA plasmids were obtained from Oncor (c-MYC) or were generously provided by B. Vogelstein of The Johns Hopkins University, Baltimore, MD (MDM2), S. Korsmeyer of the Washington University School of Medicine, St. Louis, MO (BAX and BCL2), W. El-Deiry of the University of Pennsylvania School of Medicine, Philadelphia, PA (CIP1/WAF1), and L. Boise of the University of Miami School of Medicine, Miami, FL (BCL-X). The cDNA inserts were labeled with 32P using the PrimeIt RT system (Stratagene) according to the directions of the supplier. Twofold serial dilutions of the mRNAs were fixed on nylon membranes, with six copies of each filter being made from the same RNA dilution at one time. For each transcript, hybridization to the labeled cDNA probe was carried out on a complete filter set (which included RNA samples from all of the cell lines in the screen) in the same hybridization mix. High-stringency hybridization and wash conditions were as previously described (11) . Hybridization was quantitated on a phosphorimager (Molecular Dynamics). Relative signal levels, normalized to the poly(A) content of each sample, were determined using the RNA Analysis program as previously described (14) . Relative protein levels of Bcl-XL and Bax were measured using standard Western blotting techniques as previously described (15) .

Statistical Methods.
Growth inhibition patterns (IC50 values for 60 human tumor cell lines) were those available from the NCI Developmental Therapeutics Program.5 Values were reexpressed as potency values by using the negative log of the molar concentration calculated in the NCI screen. The dependence of drug potency on gene expression levels was gauged using either the Spearman correlation coefficient for continuous value gene expression measurements, such as GADD45 expression, or the Wilcoxon Rank Sum test for binary gene expression measurements, such as Mer-/Mer+ or p53 wild-type/mutant. Positive correlations occurred when relatively high levels of gene expression were found in relatively sensitive cell lines. Negative correlations occurred when relatively high levels of gene expression were found in resistant cell lines.

Partial correlations were calculated using SAS Proc Corr (SAS Institute, Cary, NC); e.g., to find the residual correlation between chemosensitivity and BCL-X after "removing" any contributions attributable to a correlation between chemosensitivity and measured doubling time, Proc Corr was executed with drug potency and BCL-X levels listed as the variables to test correlation and with doubling time listed as the partial variable. In this way, the potential role of previously demonstrated sensitivity factors ("molecular targets") as underlying factors responsible for the present observed correlations could be examined statistically.

To visualize a large number of correlation or Wilcoxon test results simultaneously, we first calculated the asymptotic P from the test statistic for each test using normal approximation and then plotted the results for all of the tests in a histogram or color-coded table. The histogram reveals the number of drug sensitivity correlations that would have been considered statistically significant had each drug’s correlation with gene expression been evaluated alone. The same histogram can reveal positive or negative trends that might not have been detected by a simple count of individual results above a particular significance threshold. The colorized matrix (or in some cases a single column) of P also highlights the statistical significance of correlation test results as well as positive or negative trends but allows the display of results according to a logical (drug mechanism of action) or empirical (cluster) order.

The P reported here correspond to the single-tailed test, in which the alternative hypothesis is that the correlation is negative and the null hypothesis is that the correlation is zero. As such, a significant negative correlation ({alpha} = 0.05) will be reported on our one-sided P scale as 0.05, whereas a significant positive correlation ({alpha} = 0.05) will be reported as 0.95. The latter case can be understood as there being a 95% probability of finding a more negative correlation by chance, whereas more positive correlations happen because of chance alone only 5% of the time, indicating with reasonable certainty that the correlation is positive.

Unidrug Pattern Data Set.
To reduce the bias in the drug screen database toward the overrepresentation of some chemical structure and activity classes, a k-means, algorithm-based clustering process was developed.6 A series of iterative clustering optimizations was performed on the NCI-ACDS IC50 patterns to find 1200 clusters that were well populated but maximally diverse in pattern. Exemplar compounds, nonconfidential compounds closest to the center of each cluster, were taken to represent the entire database. In theory and as first demonstrated by early applications of the COMPARE program7 and many examples since, highly correlated groups of compounds will share the same physicochemical properties and more importantly, often share mechanism of action properties. This phenomenon was named by the late Ken Paull the "COMPARE effect." Appropriately, the sets of compounds represented by the database exemplars are termed compare effect clusters. When a molecular target pattern comprising some cell characteristic measured in the NCI-ACDS is used to search for correlating activity patterns, a search against the redundancy-reduced set of compare effect cluster exemplars is expected to provide a more concise and accurate survey of the molecular target’s role in measured cytotoxicity.


   RESULTS
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Accurate Measurement of Basal mRNA Levels.
Many of the transcripts measured in this study previously have been shown to be induced by alteration in growth conditions, including confluence and starvation stress (16, 17, 18, 19, 20) , so maintaining consistent growth conditions was crucial for meaningful and reproducible measurement of basal RNA levels. Culture conditions were especially important in lymphoid and myeloid lines, a point that is often overlooked as the concept of confluence does not apply to such cell lines. For instance, when the lymphoid cell line Molt4 was deliberately grown past exponential growth phase without the addition of fresh medium, GADD45, CIP1/WAF1, and MDM2 were all induced 10-fold or more over the levels present several days previously in the same culture while in exponential growth (data not shown). In the same experiment, however, no change was found in the levels of GADD34, GADD153, BAX, BCL-XL, cMYC, or O6AT. To avoid the potential confounding effects of starvation, glucose deprivation, or other overgrowth-related stresses in various cell lines, all of the cultures were maintained in exponential growth, with suspension lines kept at densities of 2–10 x 105 cells/ml. All of the cell lines were fed with fresh medium the day before harvest of RNA. Attached cell lines were harvested at 40–60% confluence, whereas suspension lines were harvested at densities of 4–5 x 105 cells/ml.

In previous studies with inducible genes (21, 22, 23) our laboratory has developed an accurate and rapid approach for comparative measurements of low-abundance transcripts (11) that is less labor intensive than RNase protection and that avoids the problems of normalization inherent in Northern blot analysis of weakly expressed transcripts. Serial dilutions of poly(A) RNA are hybridized to a radioactive probe and used to generate a standard calibration curve. This compensates for the nonlinearity (pseudo-first-order kinetics) of signal to mRNA content, which can be encountered in hybridizations, such as when the probe is not in excess (11 , 24) . Replicate membranes made with the same serially diluted RNA samples are hybridized to a polyuridylic acid probe to control for differences in the poly(A) RNA content between different samples or for any loading differences between lanes. The poly(A) content of different samples is generally within ~25%, although greater variations did sometimes occur between different cell lines. Hybridization to a polyuridylic acid probe effectively corrects for such variations (14) . This method is also much more reliable than normalization to so-called housekeeping genes, such as GAPDH or ß-actin, which can show considerable variability between different cell lines. Overall, this technique has been shown to yield accurate determinations of low-abundance transcripts (on the order of 1/105 ) and to reliably detect differences of 1.5-fold or greater (11 , 14 , 25) with results comparable with those of RNase protection (26) . Such sensitivity and accuracy are critical for the meaningful comparison of uninduced basal levels of transcripts in different cell lines and exceed the degree of accuracy possible for measurements of relative protein levels or for many other methods of measuring relative mRNA levels, including most array applications. Using this approach, we determined the relative basal levels of the mRNAs for the genes GADD34, GADD45, GADD153, BAX, BCL2, BCL-XL, MDM2, CIP1/WAF1, O6AT, and cMYC in the 60 human cancer cell lines of the NCI-ACDS. RNA samples from all of the 60 cell lines were hybridized together at high stringency in the same hybridization mix at the same time to avoid variations in hybridization conditions. The results of quantitative hybridization of these 10 transcripts are presented in Table 1Citation as basal levels relative to the levels in BT549 or in the case of BCL2, which was not detectable in this cell line, relative to the levels in MDA-N. Although some transcripts were more variable than others, in most cases variation was less than 10-fold across the NCI-ACDS.


View this table:
[in this window]
[in a new window]
  		Table 1 Relative basal mRNA and protein levels in cell lines of the NCI-ACDS

 
The basal transcript levels in a subset of the lines were rechecked by comparing the RNA samples from the original isolation side by side with newly isolated RNA from freshly grown cells. Cell lines representing both the median and the most extreme expression levels were chosen for each probe. The values of the repeated measurements agreed well with the original values, showing only slight variations (Fig. 1)Citation .



View larger version (22K):
[in this window]
[in a new window]
  		Fig. 1. Reproducibility of measurements. Bars, average ratio of repeated determinations of the expression level of the genes measured in three or more independently grown cultures of untreated, exponentially growing Molt4, CCRF-CEM, SKOV3, T47D, UACC62, HCT116, RKO, and NCI-H226. Error bars, SE.

 
For a number of the transcripts measured in this study, previous measurements of relative protein levels were available in the NCI-ACDS database. Comparison of our quantitation of basal mRNA levels with these protein data sets (Table 1)Citation gives an additional indication of the robust nature of our measurements. As might be expected, there is a strong positive correlation between these two measurements where relative protein levels are available for comparison (BAX: r = 0.41, P = 0.001; BCL-XL: r = 0.55, P = 0.000006). These mRNA and protein level measurements are in good agreement, but there is also substantial variation in the protein level that is not explained by the mRNA measurements. Some of this unexplained variation may be attributable to posttranslational protein regulation, whereas some may reflect the different sources of error in the very different quantitation techniques necessarily used and the inherent difficulties in normalizing relative protein content. Although mRNA levels may not be a direct substitute for protein measurements, they can nonetheless be of predictive value.

Correlations between Basal RNA Levels of Different Genes.
We first looked for correlations between the basal mRNA levels of the 10 genes measured in the cell lines of the NCI-ACDS. The strongest correlation between these targets is for CIP1/WAF1 and BAX (Fig. 2Citation ; r = 0.687; P = 0.0001). CIP1/WAF1 basal levels also correlated well with the basal levels of GADD45 (r = 0.471; P = 0.0001), a gene similarly regulated during cell growth arrest and stress responses (27) . This may suggest that when both of these genes are expressed in a cell, they tend to maintain a relatively constant ratio with respect to each other. Previous experiments had suggested a compensatory mechanism whereby under stress conditions, a cell could offset a lack of GADD45 induction with an unusually prolonged and large magnitude CIP1/WAF1 response (7) . Because the basal levels of these two genes correlate positively with each other rather than negatively, such compensation would appear to be engaged only during stress response, however, not during normal growth. The basal levels of the three major GADD genes as measured here also correlate with each other (GADD45 and GADD153: r = 0.36; P = 0.004; GADD45 and GADD34: r = 0.42; P = 0.0007; GADD153 and GADD34: r = 0.52; P < 0.00005), suggesting coordinate regulation of these genes under normal growth conditions and during stress response (12) .



View larger version (17K):
[in this window]
[in a new window]
  		Fig. 2. Scatter plot showing the relative expression levels of BAX and CIP1/WAF1 in each of the cell lines in the NCI-ACDS with wild-type ({circ}) and mutant ({lozenge}) p53 function as previously determined (9) .

 
Despite reports that both basal (28) and stress-induced levels (29) of the GADD genes are down-regulated by overexpression of c-Myc, basal levels of the GADD genes do not correlate well with endogenous c-MYC mRNA levels (r = 0.13; P > 0.25). This may indicate that for Myc, mRNA is not a good predictor of protein levels or that additional regulators of the GADD genes may be more important in determining basal levels of these genes during exponential cell growth. It should also be noted that the previously reported down-regulation of the gadd genes by c-Myc occurred only in the presence of Myc overexpression, so it is possible that the effect of c-Myc levels on gadd gene regulation is not significant within the normal endogenous range of c-MYC expression.

Relationship of Basal RNA Levels to p53 Status.
The most significant relationship between p53 status and any of the basal mRNA levels measured here was for CIP1/WAF1 (P = 0.002; Fig. 3ACitation ). Induction of CIP1/WAF1 by ionizing radiation previously has been shown to be dependent on wild-type p53 function (30 , 31) . p53 may also play an important role in the basal regulation of CIP1/WAF1, because among p53 mutant cell lines, basal expression was extremely low in most cases. Basal levels of BAX, another gene with p53-regulated stress response (32 , 33) , also tended to be higher among p53 wild-type cell lines (P = 0.003; Fig. 3BCitation ). Interestingly, no significant correlation was found between p53 status and basal levels of GADD45, despite the dependence of ionizing radiation induction of this gene on wild-type p53 function (34 , 35) and the positive correlation of basal levels of GADD45 with those of CIP1/WAF1. Induction of GADD45 by other DNA-damaging stresses, such as UV radiation or alkylating agents, is regulated by both p53-dependent and -independent pathways (34 , 36) . The present results perhaps suggest a greater role for p53-independent mechanisms in maintenance of basal GADD45 levels.



View larger version (45K):
[in this window]
[in a new window]
  		Fig. 3. A, ranked order of relative expression levels of CIP1/WAF1 mRNA in exponentially growing, untreated cells. {blacksquare}, cell lines with wild-type p53; {square}, cell lines with mutant p53. B, ranked order of relative expression levels of BAX mRNA in exponentially growing, untreated cells. {blacksquare}, cell lines with wild-type p53; {square}, cell lines with mutant p53.

 
Induced Responses Do Not Compensate for Differences in Basal Levels.
The role of genes such as GADD45, CIP1/WAF1, and MDM2 in stress response has been well characterized both in our laboratory and others (18 , 20 , 37 , 38) , and the ionizing radiation response of all three genes has been found to be dependent on wild-type p53. Although in this study the basal level of only BAX and CIP1/WAF1 correlated with p53 status, we were interested to see if there was a relationship between basal levels and ionizing radiation induction of these genes or if induction raised the absolute expression to similar levels in all of the cell lines, irrespective of basal levels. The relative induction of mRNA by {gamma}-rays was measured previously for these three genes in the 60 cell lines of the NCI-ACDS (9) . Comparing the basal levels of these genes with their absolute induced levels (the product of the basal level and the relative fold-induction in each line) there is a trend of increased absolute induced levels with increasing basal expression both within the p53 wild-type subset of cell lines (Fig. 4)Citation and across the entire NCI-ACDS panel (GADD45: r = 0.71; CIP1/WAF1: r = 0.63; MDM2: r = 0.94; all P < 0.00005), despite the relative lack of induction in the p53 mutant cell lines. This indicates that the stress response of these genes does not compensate for their variations in basal expression. Rather, the relative basal expression levels of these genes are an important determinant of absolute stress-induced expression levels, regardless of the fold-induction in a particular cell line. The finding that the relative relationship of gene expression levels among the cell lines is similar before and after genotoxic stress, irrespective of p53 status, supports the use of basal expression level data as an informative marker for predicting genotoxic response.



View larger version (12K):
[in this window]
[in a new window]
  		Fig. 4. A, relationship between the relative basal level of CIP1/WAF1 mRNA expression in growing, untreated cells and the absolute ionizing radiation-induced level [defined as the relative basal level multiplied by the overall fold induction 4 h after treatment with 20 Gy {gamma}-rays (9) ] in the p53 wild-type cell lines of the NCI-ACDS. B, relationship between the relative basal level of GADD45 mRNA expression in growing, untreated cells and the absolute ionizing radiation-induced level in the p53 wild-type cell lines of the NCI-ACDS. C, relationship between the relative basal level of MDM2 mRNA expression in growing, untreated cells and the absolute ionizing radiation-induced level in the p53 wild-type cell lines of the NCI-ACDS. r, coefficient of correlation the p53 wild-type subset (A–C).

 
Correlations with Drug Sensitivities.
We next compared basal expression levels of the 10 genes quantitated here with sensitivity to a set of 122 standard chemotherapy agents in the NCI-ACDS (Table 2)Citation . Loss of detectable expression of O6AT at either the mRNA or protein level is estimated to occur in ~15–25% of primary human tumors (39) , resulting in the Mer- phenotype. A slightly higher fraction, 18/60 (30%), of the cell lines in the NCI-ACDS panel expressed undetectable levels of O6AT in our study (Table 1)Citation . To test the ability of our gene expression measurements to identify mechanisms of drug action, we first examined the correlation of Mer+/Mer- phenotype with activity of O6 -alkylating agents (r = -0.44; P < 0.05). This contrasted with the lack of correlation between the Mer phenotype and toxicity of other alkylators (r = -0.04). This would be expected because the O6AT DNA repair protein is specific for the reversal of the O6 lesions generated by these agents but is not active against other lesions or other mechanisms of toxicity (Fig. 5A and B)Citation . As a specific example, a comparison of the GI50 values for PCNU, one of the agents in this mechanistic class, is illustrated in Fig. 5CCitation for cell lines expressing (Mer+) or not expressing (Mer-) O6AT. Expression of O6AT has previously been shown to be a good predictor of sensitivity of a cell line to nitrosourea compounds such as this (39) . These results provide a positive control illustrating the identification of drugs by mechanism of action through informatic analysis of RNA expression data and provide a validation of the methodology used here.


View this table:
[in this window]
[in a new window]
  		Table 2 Mechanisms of action of the standard set of anticancer agents included in the NCI-ADS

 


View larger version (28K):
[in this window]
[in a new window]
  		Fig. 5. A, plot of P for the significance of correlations between the toxicity of O6-alkylating agents and the binary expression of O6AT in the cell lines of the NCI-ACDS. First column of the histogram: P < 0.05 indicates a significant negative correlation between gene expression and toxicity or a protective effect of gene expression; dashed lines: location of the nitrosourea subgroup of the alkylating agents in the overall P matrix for O6AT (right of the histograms). Agents with similar mechanisms are grouped together, and a dark purple block represents those with significant negative correlations with O6AT expression. (The color key for the entire P spectrum is shown in Fig. 6Citation ). B, plot of the P for the agents tested that act via mechanisms other than O6-alkylation. Although some of these agents show a significant negative correlation with O6AT expression, the P matrix to the right reveals that these significant values are scattered, not clustered within one mechanism of action, as in the case of the nitrosoureas shown in A. C, relationship between O6AT expression and sensitivity of the cell lines in the NCI-ACDS to a representative nitrosourea compound indicated by the arrow in the P matrix: PCNU (CAS 13909029). The cell lines have been divided into two groups: Mer+ (those expressing measurable levels of O6AT) and Mer- (those not expressing detectable levels of O6AT). {square}, each point represents the sensitivity of an individual cell line expressed as GI50; bars, median for each category. A -log (GI50) value of 4 corresponds to a concentration of 10-4 M.

 
One of the most striking findings in this analysis is a broad negative correlation (r = -0.3) between basal BCL-XL levels and overall drug sensitivity to all of the categories of drugs tested, with the exception of one subgroup, which includes antimetabolites such as thioguanine, thiopurine, and 5-azacytidine (Fig. 6A)Citation . This pattern is almost an exact mirror image of the pattern of drug sensitivities correlated with p53 status (Fig. 6BCitation ; an overall positive correlation of ~0.23; Ref. 9 ). Bcl-XL is a member of the Bcl2 family of apoptosis-regulating proteins, which has been shown to protect against p53-mediated apoptosis (40 , 41) . An alternately spliced form of the same transcript codes for Bcl-XS, which antagonizes the activity of the major Bcl-XL protein to promote apoptosis (42) , but this message is expressed at very low levels in most cell lines examined. Overexpression of BCL-X in murine lymphoid cells has been reported to have a protective effect against the toxicity of diverse agents including cyclosporin A, rapamycin, FK-506 (43) , bleomycin, cisplatin, etoposide, vincristine, hygromycin B, mycophenolic acid (44) , vinblastine, teniposide, methotrexate, fluorouracil, hydroxyurea, and {gamma}-irradiation (45) . Conversely, suppression of Bcl-XL levels in a human cancer cell line by use of either BCL-X antisense or overexpression of its antagonist Bak was found to sensitize the cells to apoptosis after treatment with 5-fluorouracil or cisplatin (46) .



View larger version (46K):
[in this window]
[in a new window]
  		Fig. 6. Histograms and Spearman P matrix relating the activity of the set of 122 standard chemotherapy agents to expression levels of BCL-X and other genes with known roles in apoptosis. The values have been color coded according to the scale shown. Values < 0.05 indicate a significant negative correlation (protective effect of gene expression), whereas values > 0.95 indicate a significant positive correlation (sensitizing effect of gene expression). The proposed major mechanism of action of each group of compounds is shown along the borders of the matrix (the term "alkylating agents" is used broadly to include platinating agents). Each column within the matrix represents the significance of correlation between gene expression and toxicity of an individual drug within the mechanistic classes. Histograms show the distribution of P for correlations with relative levels of BCL-X expression (A), binary p53 status (Pearson P; B), BCL2 expression (C), BAX expression (D) , and cell generation time (E), as determined by population doubling time. The distribution of P associated with correlations of drug sensitivities with expression of BCL-X after correction for contributions of doubling time (F) or p53 status of the cells (G) are also shown. P for correlation of drug sensitivities and BCL-X expression among only the p53 mutant cell lines (H). Comparisons of the rows in the P matrix with the corresponding histogram show the distribution of significant associations among the various mechanistic classes.

 
Although overexpression of Bcl2, another major antiapoptotic protein, has also been shown to protect against killing by many different agents (47 , 48) , the basal levels of BCL2 mRNA in untreated cells show no correlation with overall sensitivity to the chemotherapy agents in this study (Fig. 6C)Citation , possibly indicating Bcl-XL as a more important regulator of cell death. In addition, levels of BAX, which codes for an apoptosis-promoting protein, do not show a comprehensive correlation with drug toxicity across the NCI-ACDS (Fig. 6D)Citation as might have been expected. This may suggest that Bax-independent mechanisms of Bcl-XL action may play a predominant role in determining the survival of cancer cells exposed to chemotherapeutic agents. On the other hand, because only basal expression levels were measured here, we cannot exclude the possibility that drug-inducible levels of BAX or BCL-2 may correlate with tumor cell line responses to anticancer compounds. The pattern of correlations between BCL-X basal expression and cytotoxicity does appear to be specific to expression of this gene, however, and not merely a general hallmark of apoptosis-regulating genes.

Because BCL-X previously had been shown to be induced by ionizing radiation in a small subset of cancer cell lines (49) , we measured {gamma}-ray induction of this gene in an additional 31 of the 60 cell lines (data not shown). We found a relationship between basal and absolute induced levels similar to (r = 0.79; P < 0.0001) that found for the previously measured genes GADD45, CIP1/WAF1, and MDM2 discussed above, indicating that the relative relationship of BCL-X levels are also very similar before and after treatment. The relative inductions of BCL-X were furthermore not predictive of cytotoxicity in either the Standard set of 122 agents or the larger Unidrug set.8

Previous measurements of doubling time for the cell lines of the NCI-ACDS (9) also show a significant negative correlation across all of the drug classes examined (Fig. 6E)Citation . This indicates, as might be expected, general increased chemoresistance the more slowly a cell divides. This pattern was similar to that found for BCL-X, so it was possible that BCL-X expression levels were acting as a marker for cellular growth rate rather than chemosensitivity. The correlation between doubling time and BCL-X expression is not significant, however (P > 0.05). Furthermore, examining the partial correlation of BCL-X levels and drug sensitivity by "removing" the statistical effect of doubling time still demonstrates a highly significant effect for BCL-X (Fig. 6F)Citation , indicating an effect independent of doubling time. Similarly, the protective effect of BCL-X also appears to be independent of p53 status because there was no difference in distribution of BCL-X levels among the p53 wild-type and p53 mutant cell lines in the NCI-ACDS. The significance of correlations between drug sensitivities and BCL-X expression was also unaffected by p53 status of the cell lines, as demonstrated by either partial correlation (Fig. 6G)Citation or by the exclusion of the p53 wild-type cell lines in determining the drug sensitivity correlations (Fig. 6H)Citation .


   DISCUSSION
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The NCI-ACDS screen for drug activity, the associated molecular target databases, and correlation analysis tools make a potent combination to identify markers associated with chemoresistance and other parameters relevant to cancer treatment. The novel factor analysis described here, which uses partial correlations to remove potential confounding effects of previously described sensitivity factors, increases the utility of this approach. We have used these analyses to find correlations between the parameters in the NCI-ACDS database and the basal expression of 10 genes involved in the cellular response to stress. Although many interesting relationships were uncovered, the most striking was the overall protective effect found for expression of BCL-XL.

It is interesting that the protective effect of Bcl-XL extends across all of the cancer cell types in the NCI-ACDS panel and is not restricted to lymphoid and myeloid cell lines, which are especially prone to undergo apoptosis. BCL-XL induction by ionizing radiation previously has been shown to occur primarily in cell lines with both wild-type p53 function and a propensity to undergo rapid radiation-induced apoptosis (49) . In contrast to the specificity of cell type required for BCL-X induction, the protective effect of high basal levels of BCL-X extends across the NCI-ACDS, depending neither on wild-type p53 function, nor on cell type. In a smaller set of cell lines, relatively low basal levels of BCL-XL have been shown to correlate with a greater degree of {gamma}-ray-induced apoptosis (49) , suggesting the protective effect of high basal levels may extend also to ionizing radiation. Although no quantitative measurement of radiation-induced apoptosis has been made for the 60 cell lines of the NCI-ACDS, a comparison of the basal BCL-X levels in the lymphoid/myeloid panel (lines very prone to apoptosis; mean, 0.34 ± 0.10) with levels in all of the other cell lines (mean, 0.93 ± 0.08) indicates a similar trend. This implies that basal levels of BCL-X can play a major role in the determination of cellular response to a wide variety of chemotherapeutic agents, perhaps through modulation of apoptosis.

Unlike the sensitizing effect on cell killing previously seen for wild-type p53 (9) , the protective effect of BCL-X extends beyond the more classical cancer therapeutics. This is illustrated with the "Unidrug 1247," a set of 1247 agents from the NCI-ACDS database having maximally diverse activity patterns, and in theory, 1247 distinguishable biochemical mechanisms of cell killing.6 Although the dependence of toxicity on p53 status is no longer evident in this larger survey of agents, the (inverse) association between toxicity and BCL-X expression (Fig. 7, A and B)Citation remains essentially unchanged from that seen across the smaller set of drug mechanisms. A similar pattern of correlations is also observed between drug activity and measurements of Bcl-XL protein expression (Fig. 7, C and D)Citation . A {chi}2 comparison of the drug activity patterns in the Unidrug 1247 for Bcl-XL measured as mRNA versus protein had a P < 0.0001, again indicating good agreement between the two measurements. In contrast, comparison between BAX expression at either the mRNA or protein level and sensitivity to this larger drug set shows no pattern of overall correlation (data not shown), consistent with the results in the initial set of 122 agents (Fig. 6C)Citation . Unlike the case with BCL-X, when the bias introduced by multiple drugs sharing a small number of cytotoxic mechanisms is removed by considering the diverse mechanisms included in the Unidrug set, the strongly significant dependence of drug toxicity on wild-type p53 status (as seen in Fig. 6BCitation ) is no longer strikingly apparent (Fig. 7E)Citation , indicating that the protective effect of BCL-X expression may be much broader than the sensitization by wild-type p53.



View larger version (22K):
[in this window]
[in a new window]
  		Fig. 7. A, distribution of Spearman correlations between BCL-X mRNA expression and sensitivity to a larger set of 1247 drugs with more diverse mechanisms of action; B, the associated P indicating the significance of the correlations. C, distribution of Spearman correlations between BCL-XL protein expression levels and sensitivity to the Unidrug 1247, and D, their associated P. High levels of BCL-X expression appear to be broadly protective against a great diversity of chemotoxic agents. E, distribution of P for the Pearson correlations between p53 wild-type status and sensitivity to the Unidrug set of 1247 agents. Compare the shape of this distribution with that for the smaller set of 122 standard chemotherapy agents seen in Fig. 6Citation B.

 
The broad nature of the association between endogenous BCL-X expression and protection from chemotoxicity implicates this gene as an extremely important general determinant of cell death and suggests a p53-independent mechanism of Bcl-XL action as a major component of chemoresistance in cancer cells. In addition, the lack of dependence on wild-type p53 function increases the attractiveness of Bcl-XL as a potential target of cancer therapy, because the majority of human cancers have lost p53 function (50 , 51) . Such p53-mutant tumors are generally more refractory to chemotherapy, as reflected in the increased chemoresistance of cell lines with aberrant p53 (9) . A therapeutic approach targeting Bcl-XL may be able to circumvent the protective effect conferred by loss of normal p53 function in many tumors.


   ACKNOWLEDGMENTS
 
We thank Edward A. Sausville for his contributions to the NCI-ACDS database.


   FOOTNOTES
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 Supported in part by NIH Grants GM60554 and CA78040 (to J. C. R. and S. K.). Back

2 To whom requests for reprints should be addressed, at NIH, National Cancer Institute, 37 Convent Drive, Building 37, Room 5C09, Bethesda, MD 20892. Back

3 Present address: Large Scale Proteomics Corp., Rockville, MD 20850. Back

4 The abbreviations used are: NCI-ACDS, National Cancer Institute’s anticancer drug screen; GI50, 50% growth-inhibitory concentration; GADD, growth arrest and DNA damage-inducible; poly(A), polyadenylate; PCNU, 1-(2-chloroethyl)3-(2,6-dioxo-3-piperidyl)-1-nitrosourea. Back

5 Internet address to access the NCI-ACDS database and the NCI Developmental Therapeutics Program: http://dtp.nci.nih.gov/. Back

6 T. G. Myers, unpublished results. Details are available at http://www.chemodb.org. Back

7 Written by K. Paull in the DTP of the NCI. Details are available at http://dtp.nci.nih.gov/docs/compare/compare.html. Back

8 Details are available at http://rex.nci.nih.gov/RESEARCH/basic/lbc/fornace.html and http://www.chemodb.org. Back

Received 2/25/00. Accepted 8/24/00.


   REFERENCES
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Monks A., Scudiero D., Skehan P., Shoemaker R., Paull K., Vistica D., Hose C., Langley J., Cronise P., Vaigro-Wolff A., et al Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines. J. Natl. Cancer Inst., 83: 757-766, 1991.[Abstract/Free Full Text]
  2. Grever M. R., Schepartz S. A., Chabner B. A. The National Cancer Institute: cancer drug discovery and development program. Semin. Oncol., 19: 622-638, 1992.[Medline]
  3. Weinstein J. N., Myers T. G., O’Connor P. M., Friend S. H., Fornace A. J., Jr.,, Kohn K. W., Fojo T., Bates S. E., Rubinstein L. V., Anderson N. L., Buolamwini J. K., Osdol W. W., Monks A. P., Scudiero D. A., Sausville E. A., Zaharevitz D. W., Bunow B., Johnson G. S., Wittes R. E., Paull K. D. An information intensive approach to the molecular pharmacology of cancer. Science (Washington DC), 275: 343-349, 1997.[Abstract/Free Full Text]
  4. Myers T. G., Anderson N. L., Waltham M., Li G., Buolamwini J. K., Scudiero D. A., Paull K. D., Sausville E. A., Weinstein J. N. A protein expression database for the molecular pharmacology of cancer. Electrophoresis, 18: 647-653, 1997.[Medline]
  5. Amundson S. A., Bittner M., Chen Y. D., Trent J., Meltzer P., Fornace A. J., Jr. cDNA microarray hybridization reveals complexity and heterogeneity of cellular genotoxic stress responses. Oncogene, 18: 3666-3672, 1999.[Medline]
  6. DeRisi J., Penland L., Brown P. O., Bittner M. L., Meltzer P. S., Ray M., Chen Y., Su Y. A., Trent J. M. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet., 14: 457-460, 1996.[Medline]
  7. Bae I., Smith M. L., Sheikh M. S., Zhan Q., Scudiero D. A., Friend S. H., O’Connor P. M., Fornace A. J., Jr. An abnormality in the p53 pathway following {gamma}-irradiation in many wild-type p53 human melanoma lines. Cancer Res., 56: 840-847, 1996.[Abstract/Free Full Text]
  8. Carrier F., Georgel P. T., Pourquier P., Blake M., Kontny H. U., Antinore M. J., Gariboldi M., Myers T. G., Weinstein J., Pommier Y., Fornace A. J., Jr. Gadd45, a p53-responsive stress protein, modifies DNA accessibility on damaged chromatin. Mol. Cell. Biol., 19: 1673-1685, 1999.[Abstract/Free Full Text]
  9. O’Connor P. M., Jackman J., Bae I., Myers T. G., Fan S., Mutoh M., Scudiero D., Monks A., Sausville E. A., Weinstein J. N., Friend S., Fornace A. J., Jr.,, Kohn K. W. Characterization of the p53 tumor suppressor pathway in cell lines of the National Cancer Institute anticancer drug screen and correlations with the growth-inhibitory potency of 123 anticancer agents. Cancer Res., 57: 4285-4300, 1997.[Abstract/Free Full Text]
  10. Chomczynski P., Sacchi N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem., 162: 156-159, 1987.[Medline]
  11. Hollander M. C., Fornace A. J., Jr. Estimation of relative mRNA content by filter hybridization to a polythymidylate probe. Biotechniques, 9: 174-179, 1990.[Medline]
  12. Jackman J., Alamo I., Fornace A. J., Jr. Genotoxic stress confers preferential and coordinate mRNA stability on the five gadd genes. Cancer Res., 54: 5656-5662, 1994.[Abstract/Free Full Text]
  13. Luethy J. D., Fargnoli J., Park J. S., Fornace A. J., Jr.,, Holbrook N. J. Isolation and characterization of the hamster gadd153 gene. Activation of promoter activity by agents that damage DNA.. J. Biol. Chem., 265: 16521-16526, 1990.[Abstract/Free Full Text]
  14. Koch-Paiz C. A., Momenan R., Amundson S. A., Lamoreaux E., Fornace A. J., Jr. Estimation of relative mRNA content by filter hybridization to a polyuridylic probe. Biotechniques, 29: 706-714, 2000.[Medline]
  15. Kitada S., Andersen J., Akar S., Zapata J. M., Takayama S., Krajewski S., Wang H. G., Zhang X., Bullrich F., Croce C. M., Rai K., Hines J., Reed J. C. Expression of apoptosis-regulating proteins in chronic lymphocytic leukemia: correlations with in vitro and in vivo chemoresponses. Blood, 91: 3379-3389, 1998.[Abstract/Free Full Text]
  16. Fornace A. J., Jr.,, Nebert D. W., Hollander M. C., Luethy J. D., Papathanasiou M., Fargnoli J., Holbrook N. J. Mammalian genes coordinately regulated by growth arrest signals and DNA-damaging agents. Mol. Cell. Biol., 9: 4196-4203, 1989.[Abstract/Free Full Text]
  17. Fornace A. J., Jr.,, Jackman J., Hollander M. C., Hoffman-Liebermann B., Liebermann D. A. Genotoxic-stress-response genes and growth-arrest genes; the gadd, MyD, and other genes induced by treatments eliciting growth arrest. Ann. NY Acad. Sci., 663: 139-153, 1992.[Abstract]
  18. Zhan Q., Carrier F., Fornace A. J., Jr., Induction of cellular p53 activity by DNA-damaging agents and growth arrest. Mol. Cell. Biol., 13: 4242-4250, 1993.[Abstract/Free Full Text]
  19. Zhang W., Grasso L., McClain C. D., Gambel A. M., Cha Y., Travali S., Deisseroth A. B., Mercer W. E. p53-independent induction of WAF1/CIP1 in human leukemia cells is correlated with growth arrest accompanying monocyte/macrophage differentiation. Cancer Res., 55: 668-674, 1995.[Abstract/Free Full Text]
  20. Smith M. L., Fornace A. J., Jr., Mammalian DNA damage-inducible genes associated with growth arrest and apoptosis. Mutat. Res., 340: 109-124, 1996.[Medline]
  21. Fornace A. J., Jr.,, Alamo I. J., Hollander M. C., Lamoreaux E. Induction of heat shock protein transcripts and B2 transcripts by various stresses in Chinese hamster cells. Exp. Cell Res., 182: 61-74, 1989.[Medline]
  22. Fornace A. J., Jr.,, Alamo I. J., Hollander M. C., Lamoreaux E. Ubiquitin mRNA is a major stress-induced transcript in mammalian cells. Nucleic Acids Res., 17: 1215-1230, 1989.[Abstract/Free Full Text]
  23. Fornace A. J., Jr.,, Alamo I. J., Hollander M. C. DNA damage-inducible transcripts in mammalian cells. Proc. Natl. Acad. Sci. USA, 85: 8800-8804, 1988.[Abstract/Free Full Text]
  24. Kafatos F. C., Jones C. W., Efstratiadis A. Determination of nucleic acid sequence homologies and relative concentrations by a dot hybridization procedure. Nucleic Acids Res., 7: 1542-1552, 1979.
  25. Hollander M. C., Fornace A. J., Jr., Induction of fos RNA by DNA-damaging agents. Cancer Res., 49: 1687-1692, 1989.[Abstract/Free Full Text]
  26. Zhan Q., Bae I., Kastan M. B., Fornace A. J., Jr., The p53-dependent {gamma}-ray response of GADD45. Cancer Res., 54: 2755-2760, 1994.[Abstract/Free Full Text]
  27. Zhan Q., El-Deiry W., Bae I., Alamo I., Jr.,, Kastan M. B., Vogelstein B., Fornace A. J., Jr., Similarity of the DNA-damage responsiveness and growth-suppressive properties of WAF1 to GADD45. Int. J. Oncol., 6: 937-946, 1995.
  28. Marhin W. W., Chen S., Facchini L. M., Fornace A. J., Jr.,, Penn L. Z. Myc represses the growth arrest gene gadd45. Oncogene, 14: 2825-2834, 1997.[Medline]
  29. Amundson S. A., Zhan Q., Penn L. Z., Fornace A. J., Jr., Myc suppresses induction of the growth arrest genes gadd34, gadd45 and gadd153 by DNA-damaging agents. Oncogene, 17: 2149-2154, 1998.[Medline]
  30. El-Deiry W. S., Tokino T., Velculescu V. E., Levy D. B., Parsons R., Trent J. M., Lin D., Mercer W. E., Kinzler K. W., Vogelstein B. WAF1, a potential mediator of p53 tumor suppression. Cell, 75: 817-825, 1993.[Medline]
  31. El-Deiry W. S., Harper J. W., O’Connor P. M., Velculescu V. E., Canman C. E., Jackman J., Pietenpol J. A., Burrell M., Hill D. E., Wang Y., et al WAF1/CIP1 is induced in p53-mediated G1 arrest and apoptosis. Cancer Res., 54: 1169-1174, 1994.[Abstract/Free Full Text]
  32. Miyashita T., Krajewski S., Krajewska M., Wang H. G., Lin H. K., Liebermann D. A., Hoffman B., Reed J. C. Tumor suppressor p53 is a regulator of bcl-2 and bax gene expression in vitro and in vivo.. Oncogene, 9: 1799-1805, 1994.[Medline]
  33. Zhan Q., Fan S., Bae I., Guillouf C., Liebermann D. A., O’Connor P. M., Fornace A. J., Jr. Induction of BAX by genotoxic stress in human cells correlates with normal p53 status and apoptosis. Oncogene, 9: 3743-3751, 1994.[Medline]
  34. Kastan M. B., Zhan Q., El-Deiry W. S., Carrier F., Jacks T., Walsh W. V., Plunkett B. S., Vogelstein B., Fornace A. J., Jr. A mammalian cell cycle checkpoint utilizing p53 and GADD45 is defective in ataxia telangiectasia. Cell, 71: 587-597, 1992.[Medline]
  35. Hollander M. C., Alamo I., Jackman J., McBride O. W., Fornace A. J., Jr. Sequence conservation and DNA damage-responsiveness of the mammalian gadd45 gene. J. Biol. Chem., 268: 24385-24393, 1993.[Abstract/Free Full Text]
  36. Zhan Q., Fan S., Smith M. L., Bae I., Yu K., Alamo I., Jr.,, O’Connor P. M., Fornace A. J., Jr., Abrogation of p53 function affects the response of gadd genes to DNA base damaging agents and medium starvation. DNA Cell Biol., 15: 805-815, 1996.[Medline]
  37. Ko L. J., Prives C. p53: puzzle and paradigm. Genes Dev., 10: 1054-1072, 1996.[Free Full Text]
  38. Gorospe M., Martindale J. L., Sheikh M. S., Fornace A. J., Jr.,, Holbrook N. J. Regulation of p21CIP1/WAF1 expression by cellular stress: p53-dependent and p53-independent mechanisms. Mol. Cell. Differ., 4: 47-65, 1996.
  39. Pegg A. E., Byers T. L. Repair of DNA containing O6-alkylguanine. FASEB J., 6: 2302-2310, 1992.[Abstract]
  40. Schott A. F., Apel I. J., Nunez G., Clarke M. F. Bcl-XL protects cancer cells from p53-mediated apoptosis. Oncogene, 11: 1389-1394, 1995.[Medline]
  41. Cheng M. H-Y.,, Levine B., Boise L. H., Thompson C. B., Hardwick J. M. Bax-independent inhibition of apoptosis by Bcl-XL. Nature (Lond.), 379: 554-556, 1996.[Medline]
  42. Minn A. J., Boise L. H., Thompson C. B. Expression of Bcl-xL and loss of p53 can cooperate to overcome a cell cycle checkpoint induced by mitotic spindle damage. Genes Dev., 10: 2621-2631, 1996.[Abstract/Free Full Text]
  43. Gottschalk A. R., Boise L. H., Thompson C. B., Quintans J. Identification of immunosuppressant-induced apoptosis in a murine B-cell line and its prevention by bcl-x but not bcl-2. Proc. Natl. Acad. Sci. USA, 91: 7350-7354, 1994.[Abstract/Free Full Text]
  44. Minn A. J., Rudin C. M., Boise L. H., Thompson C. B. Expression of bcl-xL can confer a multidrug resistance phenotype. Blood, 86: 1903-1910, 1995.[Abstract/Free Full Text]
  45. Simonian P. L., Grillot D. A., Nunez G. Bcl-2 and Bcl-XL can differentially block chemotherapy-induced cell death. Blood, 90: 1208-1216, 1997.[Abstract/Free Full Text]
  46. Kondo S., Shinomura Y., Kanayama S., Higashimoto Y., Kiyohara T., Zushi S., Kitamura S., Ueyama H., Matsuzawa Y. Modulation of apoptosis by endogenous Bcl-xL expression in MKN-45 human gastric cancer cells. Oncogene, 17: 2585-2591, 1998.[Medline]
  47. Selvakumaran M., Lin H. K., Sjin R. T. T., Reed J. C., Liebermann D., Hoffman B. The novel primary response gene MyD118 and proto-oncogenes myb, myc, and bcl2 modulate TGFß1 induced apoptosis of myeloid leukemia cells. Mol. Cell. Biol., 14: 2352-2356, 1994.[Abstract/Free Full Text]
  48. Park D. S., Stefanis L., Yan C. Y. I., Farinelli S. E., Greene L. A. Ordering the cell death pathway. Differential effects of BCL2, an interleukin-1-converting enzyme family protease inhibitor, and other survival agents on JNK activation in serum/nerve growth factor-deprived PC12 cells. J. Biol. Chem., 271: 21898-21905, 1996.[Abstract/Free Full Text]
  49. Zhan Q., Alamo I., Jr.,, Yu K., Boise L. H., O’Connor P. M., Fornace A. J., Jr., The apoptosis-associated {gamma}-ray response of BCL-XL depends on normal p53 function. Oncogene, 13: 2287-2293, 1996.[Medline]
  50. Levine A. J., Momand J., Finlay C. A. The p53 tumor suppressor gene. Nature (Lond.), 351: 453-456, 1991.[Medline]
  51. Hollstein M., Sidransky D., Vogelstein B., Harris C. C. p53 mutations in human cancers. Science (Washington DC), 253: 49-53, 1991.[Abstract/Free Full Text]



This article has been cited by other articles:


Home page
 J. Biol. Chem.Home page
P. Cloutier, J. Toutant, L. Shkreta, S. Goekjian, T. Revil, and B. Chabot
Antagonistic Effects of the SRp30c Protein and Cryptic 5 ' Splice Sites on the Alternative Splicing of the Apoptotic Regulator Bcl-x
J. Biol. Chem., August 1, 2008; 283(31): 21315 - 21324.
[Abstract] [Full Text] [PDF]

Home page
 Cancer Res.Home page
S. A. Amundson, K. T. Do, L. C. Vinikoor, R. A. Lee, C. A. Koch-Paiz, J. Ahn, M. Reimers, Y. Chen, D. A. Scudiero, J. N. Weinstein, et al.
Integrating Global Gene Expression and Radiation Survival Parameters across the 60 Cell Lines of the National Cancer Institute Anticancer Drug Screen
Cancer Res., January 15, 2008; 68(2): 415 - 424.
[Abstract] [Full Text] [PDF]

Home page
 Genes Dev.Home page
J. I-J. Leu and D. L. George
Hepatic IGFBP1 is a prosurvival factor that binds to BAK, protects the liver from apoptosis, and antagonizes the proapoptotic actions of p53 at mitochondria
Genes & Dev., December 1, 2007; 21(23): 3095 - 3109.
[Abstract] [Full Text] [PDF]

Home page
 Sci SignalHome page
S. J. Weintraub and B. E. Deverman
Chronoregulation by Asparagine Deamidation
Sci. Signal., October 23, 2007; 2007(409): re7 - re7.
[Abstract] [Full Text] [PDF]

Home page
 Molecular Cancer TherapeuticsHome page
P. S. Schwartz, M. K. Manion, C. B. Emerson, J. S. Fry, C. M. Schulz, I. R. Sweet, and D. M. Hockenbery
2-Methoxy antimycin reveals a unique mechanism for Bcl-xL inhibition
Mol. Cancer Ther., July 1, 2007; 6(7): 2073 - 2080.
[Abstract] [Full Text] [PDF]

Home page
 BloodHome page
S. Trudel, Z. H. Li, J. Rauw, R. E. Tiedemann, X. Y. Wen, and A. K. Stewart
Preclinical studies of the pan-Bcl inhibitor obatoclax (GX015-070) in multiple myeloma
Blood, June 15, 2007; 109(12): 5430 - 5438.
[Abstract] [Full Text] [PDF]

Home page
 Molecular Cancer TherapeuticsHome page
G. N.C. Chiu, L. A. Edwards, A. I. Kapanen, M. M. Malinen, W. H. Dragowska, C. Warburton, G. G. Chikh, K. Y.Y. Fang, S. Tan, J. Sy, et al.
Modulation of cancer cell survival pathways using multivalent liposomal therapeutic antibody constructs
Mol. Cancer Ther., March 1, 2007; 6(3): 844 - 855.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
I. V. Lebedeva, I. Washington, D. Sarkar, J. A. Clark, R. L. Fine, P. Dent, D. T. Curiel, N. J. Turro, and P. B. Fisher
Strategy for reversing resistance to a single anticancer agent in human prostate and pancreatic carcinomas
PNAS, February 27, 2007; 104(9): 3484 - 3489.