Impact of P53 Knockout and Topotecan Treatment on Gene Expression Profiles in Human Colon Carcinoma Cells: a Pharmacogenomic Study

[CANCER RESEARCH 63, 2782–2793, June 1, 2003] Impact of p53 Knockout and Topotecan Treatment on Gene Expression Profiles in Human Colon Carcinoma Cells: A Pharmacogenomic Study

Sayed S. Daoud,1 Peter J. Munson, William Reinhold, Lynn Young, Vinay V. Prabhu, Qiang Yu, Jihyun LaRose, Kurt W. Kohn, John N. Weinstein, and Yves Pommier2 Department of Pharmaceutical Sciences, Washington State University, Pullman, Washington 99164-6510 [S. S. D.], and Mathematical and Statistical Computing Laboratory, Center for Information Technology [P. J. M., L. Y., V. V. P.] and Laboratory of Molecular Pharmacology, Center for Cancer Research [W. R., Q. Y., J. L., K. W. K., J. N. W., Y. P.], National Cancer Institute, NIH, Bethesda, Maryland 20892-4255

ABSTRACT through sequence-specific binding of its central domain to cis-acting elements within the promoters or introns of responsive genes. At To uncover transcriptional stress responses related to p53, we used present, more than 20 genes are known to be activated by p53, most cDNA microarrays (National Cancer Institute Oncochips comprising 6500 of them in growth arrest or apoptotic pathways (5, 6). Other promoters different genes) to characterize the gene expression profiles of wild-type p53 HCT-116 cells and an isogenic p53 knockout counterpart after treat- (many of which are viral or growth stimulatory) are repressed by p53 ment with topotecan, a specific topoisomerase I inhibitor. The use of the (7). Consequently, the downstream effects of activating p53 are com- p53 knockout cells had the advantage over p53-overexpressing systems in plex, and no single pathway mediates the full range of functions that p53 activation is mediated physiologically. RNA was extracted after of p53. low (0.1 ␮M)- and high (1 ␮M)-dose topotecan at multiple time points To analyze the p53 dependence of molecular events after DNA within the first6hoftreatment. To facilitate simultaneous study of the damage, we compared gene expression changes in a p53 wild-type p53 status and pharmacological effects on gene expression, we developed human colon carcinoma cell line, HCT-116 (p53ϩ/ϩ), with those in a novel “cross-referenced network” experimental design and used multi- an isogenic p53 knockout (p53Ϫ/Ϫ; Ref. 8) after treatment with the ple linear least squares fitting to optimize estimates of relative transcript topoisomerase I inhibitor topotecan. Topotecan (Hycamtin), a semi- levels in the network of experimental conditions. Approximately 10% of synthetic water-soluble derivative of camptothecin, is a clinically the transcripts were up- or down-regulated in response to topotecan in the p53؉/؉ cells, whereas only 1% of the transcripts changed in the p53؊/؊ useful agent. It is approved for first-line therapy of cisplatin-refractory cells, indicating that p53 has a broad effect on the transcriptional response ovarian cancer and second-line therapy of small cell lung cancer (9). to this stress. Individual transcripts and their relationships were analyzed Like other camptothecins, topotecan converts topoisomerase I into a using clustered image maps and by a novel two-dimensional analysis/ cellular poison by trapping topoisomerase I in a covalent complex visualization, gene expression map, in which each gene expression level is with DNA. The cytotoxic lesions result from breaks, generated by represented as a function of both the genotypic/phenotypic difference (i.e., collision of the complexes with DNA or RNA polymerase (10). We p53 status) and the treatment effect (i.e., of topotecan dose and time of chose to use the p53 knockout in this study, rather than an overex- exposure). Overall, drug-induced p53 activation was associated with a pressing p53 transfectant, so that the p53 expression would be phys- coherent genetic program leading to cell cycle arrest and apoptosis. We iological. identified novel p53-induced and DNA damage-induced genes (the pro- To simultaneously study the effects of p53 status and topotecan apoptotic SIVA gene and a set of transforming growth factor ␤-related genes). Genes induced independently of p53 included the antiapoptotic treatment at different concentrations and time points, we developed cFLIP gene and known stress genes related to the mitogen-activated (and present here) a new experimental design for microarray studies. protein kinase pathway and the Fos/Jun pathway. Genes that were neg- We term it a cross-referenced network (Fig. 1). The most frequently atively regulated by p53 included members of the antiapoptotic protein used design for two-color microarray experiments simply compares chaperone heat shock protein 70 family. Finally, among the p53-depend- each sample with a single internal reference sample by cohybridiza- ent genes whose expression was independent of drug treatment was tion. The network design uses internal reference samples, but it also ؉ S100A4, a small Ca2 -binding protein that has recently been implicated in provides the additional, global set of comparisons indicated in Fig. 1. p53 binding and regulation. The new experimental design and gene ex- Given this design, we were able to analyze the entire network of data pression map analysis introduced here are applicable to a wide range of by multiple regression in an appropriately weighted fashion to in- studies that encompass both treatment effects and genotypic or pheno- crease the statistical reliability of the results and conclusions in terms typic differences. of statistical consistency test. The data were then displayed using a novel visualization, GEM,3 to identify transcripts that differed in INTRODUCTION expression level in relation to p53 status and/or drug treatment. The p53 protein is a central transcription factor activated in response to a variety of cellular stresses, including DNA damage, MATERIALS AND METHODS mitotic spindle damage, heat shock, metabolic changes, hypoxia, viral infection, and oncogene activation (1, 2). p53 can induce growth arrest Cell Culture and Drug Treatment. Isogenic p53-null (p53Ϫ/Ϫ) and and apoptosis, events that prevent the survival of damaged cells. p53 wild-type (p53ϩ/ϩ) human HCT-116 colon carcinoma cells, kindly provided can also promote early senescence in response to unregulated mito- by Dr. Bert Vogelstein (8), were grown as monolayers in DMEM (Life genic signaling (3, 4). The transactivation function of p53 is mediated Technologies, Inc., Gaithersburg, MD) supplemented with 10% fetal bovine serum (Hyclone, Logan, UT). Exponentially growing cultures at 80% conflu- ence were treated for 1 h with 0.1 ␮M (LD) or 1 ␮M (HD) topotecan obtained Received 8/30/02; accepted 3/31/03. The costs of publication of this article were defrayed in part by the payment of page from the Developmental Therapeutics Program, National Cancer Institute, NIH charges. This article must therefore be hereby marked advertisement in accordance with (Bethesda, MD). Cells were then washed twice with PBS (pH 7.4) resuspended 18 U.S.C. Section 1734 solely to indicate this fact. in drug-free medium and harvested 0, 1.5, 3, or 6 h later. Total RNA was 1 To whom requests for reprints may be addressed, at Department of Pharmaceutical Sciences Cancer Prevention & Research Center Washington State University, 259 Wegner Hall, Pullman, WA 99164-6510. E-mail: [email protected]. 3 The abbreviations used are: GEM, gene expression map; TGF, transforming growth 2 To whom requests for reprints may be addressed, at Laboratory of Molecular factor; LD, low dose; HD, high dose; CIM, clustered image map; RMS, root mean square; Pharmacology Center for Cancer Research National Cancer Institute/NIH Building 37, RT-PCR, reverse transcription-PCR; PCNA, proliferating cell nuclear antigen; HSP, heat Room 5068, Bethesda, MD 20892-4255. E-mail: [email protected]. shock protein. 2782

Experimental Design. The 12 distinct cDNA preparations were as shown in Table 1. Each chip was hybridized using two cDNA preparations, each labeled with either Cy3 or Cy5. Every such comparison of preparation i with preparation j was also performed using reciprocal labeling, and the pair was replicated n (i, j) times. To compensate for any possible systematic bias caused by the chemical labeling difference between the two dyes, the experimental log-ratio, LЉ(i, j, k), and that from the reciprocal experiment, Ϫ LЉ(j, i, k), for each replicate k were averaged to give the final estimate of the log-expression ratio y(i, j) for that comparison.

n͑i, j͒ Fig. 1. Experimental design of the cDNA hybridization experiments. Total RNA from ͸ ͑LЉ͑i, j, k͒ Ϫ LЉ͑ j, i, k͒͒ p53 wild-type (positive) cells [p, HCT-116 (p53ϩ/ϩ)] and p53 knockout (minus) cells [m, HCT-116 (p53Ϫ/Ϫ)] was collected at the beginning of treatment (t ϭ 0) or at the kϭ1 ϭ ϭ y͑i, j͒ ϭ (A) indicated time point (t 1.5, 3, or 6 h) after1hoftreatment with either a low (LD 0.1 2*n͑i, j͒ ␮M) or high concentration (HD ϭ 1 ␮M) of topotecan. As indicated by arrows, pairs of conditions were directly compared by labeling test samples with Cy3 or Cy5 and reference samples with Cy5 or Cy3 and by cohybridizing equal amounts of the product to a single When an observation fell below quality control cutoffs (based on the number array slide. The arrows are double-headed to indicate that hybridizations were done in of pixels in a spot and the intensity level above background), it was dropped pairs with reversal of the colors to eliminate any dye-related bias. Initially, all times and from the average. When the reciprocally labeled data were compared with doses were compared with the t ϭ 0 samples. Cell types (p, m) were then compared at t ϭ 0,3,and6hatHD.Thenetwork of comparisons was used mathematically (by replicate data for the p6LD versus p0 comparison, we noted that agreement weighted multiple linear least-squares regression) to calculate the relative expression between reciprocally labeled data were better, in general, than would be based differences between all pairs of conditions, not just those cohybridized on the same array. expected from the agreement of similarly labeled replicates. This fact supports Expression was assessed relative to m0 for subsequent analysis. the effectiveness of the block normalization/color correction procedure used in this study. Initially, each sample was hybridized against its corresponding zero-time isolated from the cells using the RNeasy Midi Kit (Qiagen, Valencia, CA) and sample, producing 10 pairwise comparisons. Subsequently, the p53 wild-type diluted to a concentration of 5 ␮g/␮l in diethyl pyrocarbonate-treated water cells (p) and p53 null cells (m) were compared with each other directly at t ϭ 0, (Invitrogen, Carlsbad, CA). The RNA samples were then aliquoted and stored 3,and6hatHD,bringing the total to 13 pair-wise comparisons. The resulting at Ϫ80°C until used for cDNA hybridization. network of comparisons (Fig. 1) can be used to deduce the relative expression cDNA Microarray Hybridization. cDNA microarray hybridization was difference between any two treatment conditions in the study, not just those carried out based on a protocol developed in the Laboratory of Cancer cohybridized on the same array slide. Genetics, NHGRI,4 with minor modifications. In cDNA labeling reactions, 90 Initial Selection of Significant Differences using a “Consistency Test.” and 78 ␮g of total RNA were used for Cy5 and Cy3 labeling, respectively. A gene was judged to differ significantly for a particular comparison of Seventy-five ␮mol of Cy3-dUTP or Cy5-dUTP were used in each cDNA preparation i with preparation j if the minimum log-ratio over all replicates, k, labeling reaction. Cy3- and Cy5-labeled cDNA samples (one experimental, the min{LЉ(i, j, k), Ϫ LЉ(j, i, k)} exceeded a fixed cutoff, c, or the maximum over other a reference sample) were mixed and hybridized to pin-spotted cDNA k, max{LЉ(i, j, k), Ϫ LЉ(j, i, k)} ϽϪc. For this study, we established a log-ratio microarray chips (Human Oncochips) developed in the Microarray Facility of cutoff of 0.2, corresponding to about a 1.6-fold (i.e., 60%) difference. Using a the National Cancer Institute’s Advanced Technology Center. The microarrays newly devised consistency test, it became possible to calculate the probability contained 6720 cDNA clones representing approximately 6500 individual of exceeding that cutoff. In our study, about 2.5% of genes exceeded the cancer-related genes. The complete gene list is posted at online.5 The hybrid- selected cutoff (in absolute value) in a single comparison. If there were indeed ization data were acquired using a GenePix 4000 fluorescence scanner (Axon no true changes of expression for any genes, the variation of the observed Instruments, Inc., Union City, CA). expression would arise only from random experimental noise (the null hypoth- Image Processing. Images were analyzed using a software package (F- esis). Assuming that the noise acted independently in duplicate experiment, SCAN) written by some of the authors [P. J. M., L. Y., and V. V. P. (11); one would expect (under the null hypothesis) less than (2.5%)2 ϭ 0.06% of the Center for Information Technology, NIH]. The F-SCAN software is freely genes to exceed the cutoff in duplicate comparisons, (2.5%)3 ϭ 0.000015 in available online.6 The output of F-SCAN includes signal and background triplicates, and so forth. For duplicate and quadruplicate experiments with intensities for each spot in each channel, as well as several quality control 6700 genes, one therefore expects fewer than 4.0 and 0.003 false positives, measures. The JMP 4.0 statistical package (SAS Inc., Cary, NC) was used to respectively. In most comparisons reported here, the false discovery rate (the inspect the data for artifacts, apply gene selection criteria, and calculate the number of false positives expected/number detected) was less than 20% by this hierarchical clustering. approach. Altogether, 809 of 6720 genes were seen to change by 60% or more Normalization and Color Correction. Measured raw signal intensities in all replicates of at least 1 of the 13 comparisons. One should interpret these ϭ were log-transformed: I1, I2 log10 (raw intensity) for channels 1 and 2, false discovery rates with caution because the replicate hybridization some- respectively. Log-intensity ratios related to spot colors were calculated as ϭ Ϫ L I1 I2. Systematic drift in color was occasionally seen across the 16 blocks of the array, corresponding to the 16 different pins used in the printing process. To ameliorate this effect, the median log-intensity within each block Table 1 cDNA preparations was subtracted and a pin-effect corrected log-intensity, LЈ, was computed. For Time after Concentrations ϭ Ј ϭ Preparation treatment of topotecan a particular spot, S, printed by pin p pin (S), LS LS – median{LT:pin no., i Designation p53 status (h) (␮M) (T) ϭ pin (S)}. Because LЈ sometimes varied as a function of spot intensity, it was necessary to reduce this effect by the following procedure. In the plot of 1m0 Ϫ/Ϫ 00 Ј ϭ ϩ 2 m1.5LD Ϫ/Ϫ 1.5 0.1 L versus average log-intensity, I (I1 I2)/2, a smooth, running median 3 m3LD Ϫ/Ϫ 3 0.1 curve modeled the color trend as a function of intensity. For a particular spot 4 m6LD Ϫ/Ϫ 6 0.1 Љ ϭ Ј Ϫ Ϫ S, the corrected log-ratio value was computed as LS LS – running_median 5 m3HD / 31 (I ), where running_median (I) is the value on the smooth curve corresponding 6 m6HD Ϫ/Ϫ 61 S 7p0 ϩ/ϩ 00 to average log-intensity I. 8 p1.5LD ϩ/ϩ 1.5 0.1 9 p3LD ϩ/ϩ 3 0.1 ϩ ϩ 4 http://www.nhgri.nih.gov/DIR/LCG/15K/HTML/protocol.html. 10 p6LD / 6 0.1 5 11 p3HD ϩ/ϩ 31 http://nciarray.nci.nih.gov/cgi-bin/gipo for array lot Hs-ATC 6.5k-4p6-071300. ϩ ϩ 6 http://abs.cit.nih.gov/fscan. 12 p6HD / 61 2783

Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2003 American Association for Cancer Research. PHARMACOGENOMIC OF p53 AND TOPOTECAN IN COLON CANCER times used cell lysates from the same flasks, although with different labeling, finds in the network of treatment comparisons. The RMS error is computed as and thus might not have been fully statistically independent. shown below. Multivariate Analysis. Using the full network of treatment comparisons ͑ Ϫ ␤ˆ ͒T͑ Ϫ ␤ˆ ͒ (Fig. 1), we determined log10 relative expression levels relative to a single Y X Y X ϭ RMS error ϭ ͱ (F) preparation, m0, the p53-null cells at t 0. The experimental design contained 2 multiple, redundant comparisons that are reflected as cycles in the graph. For example, p0 can be compared directly with m0 or, alternatively, can be Multivariate Gene Selection. No single criterion has been found that can compared with m0 indirectly through p3HD and m3HD. This redundancy effectively remove all outlying or bad data in the experiment. Therefore, each provides an opportunity to refine or “average” the results further. To reduce the gene in the set of 809 that showed significant differences in at least one 13 averaged comparison measurements to estimates of relative log-ratios, we pairwise comparison was further required to meet three conditions: (a)a used least-squares estimation with weighting by the number of available sufficient number of the comparisons schematized in Fig. 1 were present for replicates. that gene to determine a solution ␤ˆ to the least-squares equations (1); (b) the Let ␤(i) be the concentration of a particular mRNA species in the ith cDNA RMS error for the gene fell below a specified level, in other words, the gene preparation, i.e., ␤(1) ϭ m0 log-concentration, ␤(2) ϭ m1.5LD log-concen- must give self-consistent results across all available comparisons; and (c) the tration, and so forth. We can write a system of equations y(i, j) ϭ ␤(j) Ϫ ␤(i) responsiveness (SD of the expression pattern) of the gene exceeded a specified for each comparison of preparation i with preparation j. The least-squares level, i.e., the gene must clearly be among those that responded to the treatment estimation procedure can best be presented in the following vector notation. stimuli. The RMS cutoff was set so that genes with significantly more than We define Y to be the vector of the 13 log ratios for each comparison, indexed 30% error would be rejected. The upper 95% quantile for a ␹2 distribution with by preparation numbers, 2 degrees of freedom is 5.99. The corresponding cutoff for the RMS error is ϭ therefore 5.99*log10(1.3) 0.68, which was rounded to 0.7. The responsive- Y ϭ ͓ y͑2, 1͒, y͑3, 1͒, y͑4, 1͒, y͑5, 1͒, y͑6, 1͒, y͑8, 7͒, y͑9, 7͒, ness was measured as the SD of the relative concentration estimates, as shown below. y͑10, 7͒, y͑11, 7͒, y͑12, 7͒, y͑7, 1͒, y͑11, 5͒, y͑12, 6͔͒T (B) 2 12 12 and ␤, the vector of 12 unknown log-concentrations in each cDNA preparation, 1 ϭ ͸ ␤2 Ϫ ͸ ␤ SD ͱ i ΂ i΃ ΋11 (G) 12 ␤ ϭ ͓m0, m15LD, m3LD, m6LD, iϭ1 iϭ1

m3HD, m6HD, p0, p15LD, p3LD, p6LD, p3HD, p6HD͔T (C) The responsiveness cutoff was empirically set at 0.12, corresponding to about a 30% variation across the cDNA preparations. Although somewhat arbitrary The system of equations relating Y to ␤ can be written compactly as follows in nature, these filters effectively removed many obvious outliers and much of after defining the design Matrix X with 13 rows and 12 columns (showing only the lower quality data, leading to a set of 167 consistently and significantly the non-zero entries): changing, highly responsive, high quality spots, which formed the basis of all further analyses. Ϫ11...... Treatment Effect. The experimental design (Fig. 1) included three factors: Ϫ11 . p53 presence or absence; drug treatment at low concentration (LD) or high Ϫ11 . concentration (HD); and duration of treatment (0, 1.5, 3, or 6 h). The full ␤ Ϫ11 .pattern of response is given by the vector calculated previously. Complex Ϫ11 .patterns of response could be expected (e.g., overexpression at 1.5 h followed by underexpression at 3 h, and so forth). Various linear combinations of the . Ϫ11 . estimated ␤ parameters are likely to be more interpretable than the original ϭ Ϫ X . 11 . vector. In general, the vector space of possible patterns now spans an 11- Ϫ . 11. dimensional subspace of a 12-dimensional space. We defined an orthogonal . Ϫ11.rotation of this space to that after rotation; the second dimension represents the ΄ Ϫ ΅ . 11p53 effect, the next five dimensions include all treatment (dose and time) Ϫ11.effects, and the remaining five dimensions define interactions between p53 and . Ϫ11.treatment effects. The selected orthogonal rotation matrix E is given by: . ... . Ϫ1 . ....1

The system of equations is simply Y ϭ X␤. Because this system is not of full 111111111111 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ rank, we must add a constraint on ␤ to obtain a unique solution. This can be 1 1 1 1 1 1111111 accomplished by setting ␤(1) ϭ m0 ϭ 0, which is equivalent to treating all Ϫ511111Ϫ511111 other ␤(i) values relative to that for m0. Operationally, we augment X with a 0 Ϫ2 Ϫ2 Ϫ2330Ϫ2 Ϫ2 Ϫ233 single row havinga1inthefirst column only and augment the vector Y with 0 Ϫ211000Ϫ21100 00Ϫ110000Ϫ1100 a single element, zero. Finally, to account for the different numbers of E ϭ D* replicates, n(i, j), contributing to each Y value, we define the diagonal weight 0000Ϫ110000Ϫ11 matrix 5 Ϫ1 Ϫ1 Ϫ1 Ϫ1 Ϫ1 Ϫ511111 ΄ 0222Ϫ3 Ϫ30Ϫ2 Ϫ2 Ϫ233΅ W ϭ 2*diag͓͑n͑2, 1͒, n͑3, 1͒, n͑4, 1͒, n͑5, 1͒, n͑6, 1͒, n͑8, 7͒, n͑9, 7͒, 02Ϫ1 Ϫ1000Ϫ21100 001Ϫ10000Ϫ1100 ͑ ͒ ͑ ͒ ͑ ͒ ͑ ͒ ͑ ͒ ͑ ͔͒͒ n 10,7 , n 11, 7 , n 12, 7 , n 7, 1 , n 11, 5 , n 12, 6 (D) 00001Ϫ10000Ϫ11 The factor of 2 reflects the presence of two reciprocally labeled chips in each where D ϭ sqrt(inv(diag([12, 12, 60, 60, 12, 4, 4, 60, 60, 12, 4, 4]))), a replicate. The least-squares solution to the system of equations is given by: diagonal normalization matrix. The new vector of effects is computed as an orthogonal rotation of the Ϫ ␤ ϭ ͑XTWX͒ 1XTWY, (E) original vector ␤. a 12-component least-squares estimate of the desired set of relative log- Effect ϭ E*␤ (H) concentrations. The least-squares computation is carried out for each gene represented on the chip. The RMS error from the least squares regression is a Names may be given to these effects as follows: Mean; p53; treatment; HD; measure of the “lack of fit” of the model, i.e., how much inconsistency one early time at LD; late time at LD; early time at HD; p53-treatment interaction; 2784

Table 2 Number of topotecan-responsive genes detected as a function of drug sion patterns close together in the figure. The reordered matrix was then concentration, time of exposure, and p53 status color-coded, with red representing overexpression and green representing HCT-116 (p53ϩ/ϩ) HCT-116 (p53Ϫ/Ϫ) underexpression in comparison with m0. The present application of the CIM visualization (12, 13) is novel in that all expression ratios were first averaged Treatment Induced Repressed Induced Repressed over reciprocally labeled, replicate comparisons and then reconciled, and each LD, 1.5 h 13 0 0 2 treatment condition was compared with a single control condition. The under- LD,3h 2 24a 416a 11 11a 14 LD, 6 h 12} 13} 0} 1} lying data were obtained from 35 separate pairwise comparisons but were HD, 3 h 48 142 16 39 reconciled into just 12 expression ratios/gene, thus reducing the noise inherent 178a 483a 20 49a HD, 6 h 154} 387 } 4} 15} in separate measurements. Finally, each gene name was annotated with the a a Any treatment 186 487 31 53 region of the GEM in which the gene fell. There was good correspondence a Summary numbers are not simple sums because the same gene sometimes responds between these two representations of the expression profiles for the entire at different times and doses. study. Real-time RT-PCR. The expression levels of selected transcripts were verified by real-time RT-PCR using an ABI Prism7700 Sequence Detection p53-HD interaction; p53-early time at LD interaction; p53-late time at LD System (PE Applied Biosystems, Foster City, CA). TaqMan primers and interaction; and p53-early time at HD interaction; respectively. The p53 effect FAM-TAMRA-labeled probes were designed for each gene of interest using is given by Effect(2), or equivalent, the difference in expression between Primer Express 1.0 software (PE Applied Biosystems) with the manufacturer- p53ϩ/ϩ and p53Ϫ/Ϫ cells given the same treatment: specified parameters. Immunobloting. Cells were incubated with 0, 0.1, or 1 ␮M topotecan and

6 then washed twice with PBS (pH 7.4) and harvested. Cell lysates were 1 ϭ ͸͑␤ Ϫ ␤ ͒ prepared as described previously (14). One hundred-␮g protein samples were p53 effect iϩ6 i (I) ͱ12 then separated by SDS-PAGE (12% polyacrylamide gel) and transferred into iϭ1 Immobilon membranes (Millipore, Bradford, MA). p53 and PCNA proteins The root-12 appears here to normalize the result and make it comparable in were identified using anti-p53 and anti-PCNA primary antibodies (Dako, scale with the other effects. Carpinteria, CA). The reactive bands were then visualized using an enhanced Because the treatment effects are spread over the next five dimensions chemiluminescence detection system (New England Nuclear Life Products, [Effect(3) through Effect(7)], we chose to define a composite treatment effect Boston, MA). S100A4 was detected using anti-S100A4 primary antibody whose magnitude was the RMS of the five treatment effects, and its sign was (Dako). determined by Effect(3). RESULTS 7 Treatment effect ϭ sign͑Effect͑3͒͒* ͱ͸ Effect͑i͒2΋5 (J) The cDNAs obtained from all cell samples, HCT-116 (p53ϩ/ϩ) Ϫ Ϫ iϭ3 and HCT-116 (p53 / ), treated and untreated, were hybridized to microarrays in reciprocal pairs as indicated by the arrows in Fig. 1. The complex interaction components were neglected because most of the We analyzed the data using two approaches. The first was a pairwise variability of the genes in this study could be interpreted as a function of the analysis in which genes were selected based on a consistent difference two variable treatment effect and p53 effect. (Ͼ1.6-fold) between samples for at least one pair of conditions (as GEM. Depicting the results of the study in only two variables [p53 effect and treatment (Rx) effect] allows one to view the overall landscape of gene explained in “Materials and Methods”). This approach led to the expression for the entire set of genes being studied. We call this depiction (Fig. selection of 809 genes. The second approach was a multivariate 5) a GEM. By analogy with cartographic depictions of the world, the roles of analysis in which the 809 genes selected were further analyzed based longitude and latitude are played by p53 effect and treatment effect, respec- on consistency of results, as described in “Materials and Methods.” tively. Genes found in a particular region of the map have similar expression This approach led to the selection of 167 genes, which were then profiles and may also be associated in a more fundamental, physiological way, classified based on both p53 and treatment dependence. perhaps in some cases occurring in a common pathway. This map helps us to Pairwise Analysis of Differential Expression. The data summa- identify the most prominent landmark genes, which are found at the periphery rized in Table 2 show the number of genes that were expressed of the map. Genes with little or no response to the treatment and no p53 differentially as a function of drug concentration and time of expo- dependence are found close to the center of the map and are omitted to simplify sure. When p53ϩ/ϩ cells were treated with LD topotecan, a total of the picture. Territories of Expression Behavior. The genes can be classified accord- 24 genes were induced, and 16 genes were repressed within the6hof Ϫ Ϫ ing to whether their expression shows a positive, negligible, or negative p53 drug treatment. In the p53 / cells, 11 genes were induced, and only effect (i.e., in terms of p53ϩ/ϩ minus p53Ϫ/Ϫ levels). Likewise, a positive, 4 genes were repressed. This difference was even more pronounced negligible, or negative treatment effect indicates the behavior of gene expres- when the cells were treated with HD topotecan; 178 genes were sion as a result of topotecan treatment (at various doses and times studied here). The territories, named for convenience according to the points of the compass, are: (a) N, p53 independent, positive treatment effect; (b) NE, positive p53 effect, positive treatment effect; (c) E, positive p53 effect, treatment independent; (d) SE, positive p53 effect, negative treatment effect; (e) S, p53 independent, negative treatment effect; (f) SW, negative p53 effect, negative treatment effect; (g) W, negative p53 effect, treatment independent; and (h) NW, negative p53 effect, positive treatment effect. Gene expression levels found in N and S are essentially independent of p53 status but responsive to treatment, whereas gene expression levels found in E and W are essentially independent of treatment but differ between the p53ϩ/ϩ and p53Ϫ/Ϫ cells. CIM. Results for the 167 selected genes, rendered as a 167 ϫ 12 matrix of log-ratio comparisons with the m0 condition, were hierarchically row-clustered Fig. 2. Time- and concentration-dependent up-regulation of p53 in wild-type HCT-116 cells in response to topotecan treatment. HCT-116 (p53ϩ/ϩ) cells were treated with LD using Ward’s method implemented in JMP (SAS Inc.). Clustering the rows of (0.1 ␮M)orHD(1␮M) topotecan for 1.5, 3, or 6 h. Cell lysates were subjected to Western the matrix (corresponding to genes) had the effect of bringing similar expres- blot analysis with antibodies directed against p53 and PCNA (loading control). 2785

Fig. 3. CIM of expression patterns of 167 differentially expressed transcripts. Log-transformed expression ratios for the genes that showed consistently differential expression were clustered as described in “Materials and Methods.” The colored CIM encodes the relative expression levels (compared with those of p53Ϫ/Ϫ cells at t ϭ 0, m0). Red and green indicate higher and lower expression, respectively. The greatest increase or decrease was approximately 10-fold. Col- umns 1–6 correspond to 0, 1.5, 3, and6hatlow concentration (LD) and 3 and6hathigh concentration (HD), respectively. Genes were ordered on the vertical axis by agglomerative hierarchical clustering (Ward’s method, implemented in the JMP statistical package), which brings together genes with similar expression patterns. The first column indicates in which region of Fig. 5 the gene is found. The full gene descriptions are provided in Table 3 in the same order as in Fig. 3. Clusters ␣, ␤, and ␥ are shown in detail in Fig. 4. Genes selected for real-time RT-PCR analysis are indicated by a blue arrow.

induced and 483 were repressed in p53ϩ/ϩ cells, but only 20 were controlled at the transcriptional level directly or indirectly by p53. It induced and 49 were repressed in p53Ϫ/Ϫ cells. Thus, the number of is also noteworthy that the number of genes induced by HD treatment differentially expressed genes was much greater in the p53ϩ/ϩ cells in p53ϩ/ϩ cells relative to p53Ϫ/Ϫ cells was much greater at 6 h than than in the p53Ϫ/Ϫ cells, indicating that a large number of genes are at 3 h. The same was true for the repressed genes, indicating that 2786

Fig. 4. Expanded view of three gene clusters from Fig. 3. ␣,A portion of the p53-associated cluster that includes genes whose expression tended to decrease with drug treatment. ␤, Some p53-responsive genes that increased expression with treatment. ␥, HSP70-associated genes, whose expression increased in p53Ϫ/Ϫ cells treated with LD topotecan.

p53-dependent expression/repression (as reflected at the RNA level) p53-responsive transcript CDKN1A/p21Waf1/Cip1 (spot 58), as well as takes some time (Ͼ3 h) to develop fully. some members of the TGF-␤ superfamily [PLAB (three spots; spots We next asked whether the observed changes in gene expression 59, 60, and 62) and MADH6/SMAD6 (spot 61)] and the proapoptotic were, in fact, correlated with differences in p53 protein expression. gene SIVA (spot 63), whose p53-dependence has not been reported The Western blots in Fig. 2 show that p53 protein levels increased by previously. 1.5 h with LD topotecan without further detectable increase at 3 or Cluster ␥ consists of transcripts expressed at higher levels in the 6 h. The level of p53 protein was much higher after treatment with p53Ϫ/Ϫ cells than in the p53ϩ/ϩ cells after LD topotecan exposure. HD, and the maximum level of p53 protein was detected at 6 h, After HD exposure, the expression levels of these genes were uni- indicating that the change in p53 protein expression after topotecan formly low. Expression in the p53Ϫ/Ϫ cells after LD exposure peaked treatment is correlated with that of RNA expression. at 1.5 or 3 h and then declined (Fig. 4). Cluster ␥ contains genes CIM. The 809 selected genes were filtered further to yield 167 related to the HSP70 family [HSPA1A (spots 96 and 97), HSPA10 genes that showed substantial changes under some conditions of (spot 100), and HSPA1L (spots 101 and 102)]. drug concentration, time of exposure, or p53 status and also To test the microarray results, we performed real-time RT-PCR for showed overall consistency of responses. The results are displayed a cluster of three selected genes (indicated by a blue arrow in Fig. 3) in the form of the CIMs (12, 13) shown in Figs. 3 and 4. These 167 whose expression was drug dependent and greater in the p53Ϫ/Ϫ differentially expressed genes are also listed in Table 3 in the order cells than in the p53ϩ/ϩ cells. The same RNA batches were used for in which they appear in the CIM (Fig. 3). The most striking feature both microarrays and real-time RT-PCR. As indicated in Table 4, the of the CIM is the difference in gene expression patterns between two methods for expression analysis were in good agreement. the two cell types, clearly demonstrating that the presence of GEM Analysis. For concurrent examination of the two experimen- functional p53 profoundly affected the stress response profiles of a tal variables, namely, drug treatment and p53 status, we devised a large number of genes. novel approach for analysis and visualization. The results are dis- Fig. 4 focuses on the gene clusters marked ␣, ␤, and ␥ in Fig. 3. The played as a two-dimensional GEM (see “Materials and Methods”). expression patterns of these genes clearly distinguish the responses of The GEM (Fig. 5) shows how the 167 differentially expressed genes p53ϩ/ϩ and p53Ϫ/Ϫ cells. The transcripts in cluster ␣ were more can be grouped on the basis of treatment effect and p53 effect. The strongly expressed in p53ϩ/ϩcells, and in these cells, some tran- GEM regions where the individual genes fall are listed in the second scripts tended to decrease slightly with topotecan treatment, e.g., column in Table 3. The last two columns in Table 3 contain the HNRPK (spot 25), SLC7A5 (spot 32), and ENO1 (spot 37). coordinates of gene locations in the GEM of Fig. 5. Cluster ␤ includes p53-dependent transcripts whose expression The Northeast (NE) region contains genes that were expressed at increased with treatment. Genes in this cluster include the known higher levels in p53ϩ/ϩ cells than in p53Ϫ/Ϫ cells or that tended to 2787

Table 3 Effects of p53 status and topotecan treatment on gene expression for the 167 most consistently responsive genes Region Clone ID UniGene ID Gene Description p53 Effect Rx effect 1 E 306771 Hs.56145 TMSNB Thymosin, ␤, identified in neuroblastoma cells 0.39 0.07 2 NE 2184182 Hs.79037 HSPD1 Heat shock Mr 60,000 protein 1 (chaperonin) 0.49 0.11 3 E 742132 Hs.833 ISG15 Interferon-induced Mr 17,000 protein 0.28 0.07 4 E 742132 Hs.833 ISG15 Interferon-induced Mr 17,000 protein 0.28 0.07 5 E 262053 Hs.279923 E2IG3 Putative nucleotide-binding protein, estradiol-induced 0.33 0.07 6 E 325606 Hs.74346 ESTsa 0.36 0.08 7 NE 198453 Hs.181392 HLA-E MHC class I ϭ HLA-E 0.33 0.11 8 E 324873 Hs.180919 ID2 Id2 ϭ Id2H ϭ inhibitor of DNA binding 2 0.21 0.09 9 NE 300944 Hs.75878 EDR2 HPH2 ϭ polyhomeotic 2 homologue 0.44 0.11 10 E 358214 Hs.75462 BTG2 BTG2 ϭ p53-dependent inducible anti-proliferative gene 0.44 0.1 11 NE 73596 Hs.35086 USP1 Ubiquitin-specific protease 1 0.37 0.11 12 E 115295 Hs.77602 DDB2 Damage-specific DNA-binding protein 2 0.4 0.07 13 NE 243882 Hs.100980 PPM1D Protein phosphatase Wip1 0.27 0.2 14 NE 299745 Hs.82359 TNFRSF6 CD95 ϭ Fas 0.29 0.15 15 NE 752631 Hs.1420 FGFR3 FGFR3 ϭ fibroblast growth factor receptor 3 0.21 0.13 16 NE 810999 Hs.76686 GPX1 Glutathione peroxidase 1 0.25 0.11 17 NE 460673 Hs.87465 HEAB ATP/GTP-binding protein 0.28 0.17 18 NE 358162 Hs.23642 HSU79266 Protein predicted by clone 23627 0.25 0.12 19 NE 744917 Hs.11342 NINJ1 Ninjurin 1 0.24 0.13 20 NE 139681 Hs.117546 NNAT Neuronatin 0.27 0.13 21 NE 739192 Hs.153640 CNK PRK ϭ putative serine/threonine protein kinase 0.2 0.16 22 N 269295 Hs.79197 CD83 CD83 ϭ B-cell activation protein 0.08 0.14 23 N 769890 Hs.75514 NP Nucleoside phosphorylase 0.07 0.14 24 N 68950 Hs.9700 CCNE1 Cyclin E1 Ϫ0.05 0.16 25 SE 415700 Hs.129548 HNRPK Heterogeneous nuclear ribonucleoprotein K 0.29 Ϫ0.12 26 E 809639 Hs.2175 CSF3R G-CSF receptor 0.45 0.02 27 E 24415 Hs.1846 TP53 p53 0.42 Ϫ0.04 28 E 429234 Hs.171957 TRIO LAR transmembrane tyrosine phosphatase-binding protein 0.38 Ϫ0.04 29 E 45544 Hs.75725 TAGLN2 Transgelin 2 0.15 0.09 30 NE 485171 Hs.77502 MAT2A Methionine adenosyltransferase ␣ subunit 0.32 0.14 31 E 868368 Hs.75968 TMSB4X Thymosin, ␤ 4, X chromosome 0.31 0.09 32 SE 755578 Hs.184601 SLC7A5 Solute carrier family 7 0.16 Ϫ0.15 33 E 502085 Hs.62661 GBP1 Guanylate binding protein 1 interferon-inducible 0.66 0.03 34 E 24415 Hs.1846 TP53 p53 0.44 Ϫ0.05 35 E 446556 Hs.181163 HMG17 HMG-17 ϭ non-histone chromosomal protein 0.48 0.03 36 E 841340 Hs.158164 ABCB2 TAP1 ϭ peptide transporter 0.67 Ϫ0.06 37 SE 512247 Hs.254105 ENO1 40S ribosomal protein S2 0.68 Ϫ0.17 38 E 502669 Hs.3352 HDAC2 Histone deacetylase 2 0.58 Ϫ0.04 39 E 46070 Unknown 0.46 Ϫ0.1 40 N 784126 Hs.248267 TST Rhodanese 0.08 0.11 41 N 51363 Hs.79015 MOX2 MRC OX-2 0.01 0.09 42 N 491565 Hs.82071 CITED2 Cbp/p300-interacting transactivator Ϫ0.01 0.1 43 NE 328550 Hs.96 PMAIP1 APR ϭ immediate-early-response gene 0.15 0.16 44 N 204515 Hs.395 CCR2 C-C chemokine receptor 2 0.05 0.11 45 NE 924165 Hs.144700 EFNB1 LERK-2 ϭ EPLG2 ϭ tyrosine kinase receptor 0.12 0.13 46 N 343871 Hs.250870 MAP2K5 MEK5 ϭ MAP kinase kinase 5 0.08 0.13 47 N 809707 Hs.198951 JUNB jun-B Ϫ0.04 0.1 48 N 682817 Hs.170027 MDM2 Mouse double minute 2 Ϫ0.06 0.1 49 N 309864 Hs.198951 JUNB jun B proto-oncogene Ϫ0.02 0.11 50 N 809707 Hs.198951 JUNB jun-B Ϫ0.03 0.11 51 N 79022 Hs.75678 FOSB FosB ϭ G0S3 0.03 0.15 52 NW 701532 Hs.165249 ESTs Ϫ0.14 0.19 53 NE 51448 Hs.460 ATF3 ATF-3 ϭ activating transcription factor 3 0.38 0.17 54 NE 51448 Hs.460 ATF3 ATF-3 ϭ activating transcription factor 3 0.22 0.15 55 E 122428 Hs.198951 JUNB jun B proto-oncogene 0.16 0.1 56 E 472180 Hs.81256 S100A4 S100 calcium binding protein A4 1.4 Ϫ0.06 57 NE 470074 Hs.74621 PRNP PrP ϭ prion protein 0.58 0.17 58 NE 272499 Hs.179665 CDKN1A Cyclin-dependent kinase inhibitor 1A (p21, Cip1) 0.88 0.2 59 NE 788832 Hs.116577 PLAB Prostate differentiation factor 1.08 0.2 60 NE 256895 Hs.239189 KIAA0838 Glutaminase C 1.07 0.15 61 E 429356 Hs.153863 MADH6 Smad6 ϭ JV15-1 ϭ negative regulator of TGF-␤ signaling 0.6 0.1 62 NE 549383 Hs.116577 PLAB TGF-␤ superfamily protein 0.78 0.15 63 NE 501643 Hs.112058 SIVA CD27-binding protein ϭ Siva ϭ proapoptotic protein 0.79 0.14 64 S 262996 Hs.22670 CHD1 Nuclear protein with chromo and SNF2-related helicase/ATPase domains Ϫ0.04 Ϫ0.1 65 S 304908 Hs.1189 E2F3 E2F-3 ϭ pRB-binding transcription factor ϭ KIAA0075 Ϫ0.05 Ϫ0.12 66 SW 789253 Hs.25363 PSEN2 PRESENILIN 2 Ϫ0.19 Ϫ0.11 67 S 824393 Hs.29285 ZYG ZYG homologue Ϫ0.08 Ϫ0.09 68 S 345525 Hs.191356 GTF2H2 General transcription factor IIH Ϫ0.03 Ϫ0.08 69 S 50437 Hs.86386 MCL1 MCL1 ϭ myeloid cell differentiation protein 0.01 Ϫ0.19 70 S 68320 Hs.155421 AFP ␣-Fetoprotein Ϫ0.06 Ϫ0.12 71 S 278622 Hs.44450 SP3 Sp3 ϭ SPR-2 Ϫ0.08 Ϫ0.14 72 S 814792 Hs.78829 USP10 Ubiquitin-specific protease 10 Ϫ0.02 Ϫ0.11 73 SW 344759 Hs.172865 CSTF1 Cleavage stimulation factor Ϫ0.11 Ϫ0.13 74 S 240367 Hs.57419 CTCF CCCTC-binding factor (zinc finger protein) 0.04 Ϫ0.12 75 S 342378 Hs.2128 DUSP5 DUSP5 ϭ dual specificity phosphatase Ϫ0.07 Ϫ0.13 76 SW 825861 Hs.211608 NUP153 Nuclear pore complex protein Ϫ0.19 Ϫ0.15 77 SE 812965 Hs.79070 MYC c-myc 0.11 Ϫ0.12 78 SE 417226 Hs.79070 MYC c-myc 0.21 Ϫ0.12 79 SE 108851 Hs.269432 ESTs 0.19 Ϫ0.15 80 SE 366815 Hs.83484 SOX4 SRY (sex-determining region Y)-box 4 0.17 Ϫ0.16 81 SE 789049 Hs.184760 CBF2 CCAAT-box-binding transcription factor 0.16 Ϫ0.15 82 SE 859359 Hs.50649 PIG3 Pig3 ϭ p53-inducible gene 0.26 Ϫ0.15

2788

Table 3 Continued Region Clone ID UniGene ID Gene Description p53 Effect Rx effect 83 N 796110 Hs.252317 ESTs Ϫ0.06 0.13 84 N 298268 Hs.77054 BTG1 BTG1 ϭ B-cell translocation gene 1 ϭ antiproliferative Ϫ0.03 0.13 85 S 323074 Hs.92260 HMG2L1 High-mobility group protein 2-like 1 Ϫ0.09 Ϫ0.19 86 SE 488178 Hs.9634 C10 ϭ predicted protein adjacent to SHP-1 on 12p13 0.12 Ϫ0.17 87 S 250667 Hs.28726 RAB9 RAB9, member RAS oncogene family 0.05 Ϫ0.12 88 S 755752 Hs.6151 KIAA0235 KIAA0235 protein 0.01 Ϫ0.13 89 SE 814054 Hs.158282 KIAA0040 KIAA0040 gene product 0.27 Ϫ0.13 90 SE 910018 Hs.77439 PRKAR2B cAMP-dependent protein kinase type II-␤ regulatory chain 0.31 Ϫ0.11 91 NW 359021 Hs.25601 CHD3 CHD3 ϭ nuclear protein Ϫ0.1 0.18 92 NW 724397 Hs.87450 CTSW C1 peptidase expressed in natural killer and cytotoxic T cells Ϫ0.28 0.18 93 NW 612224 Hs.54929 PHKG1 Phosphorylase kinase ␥ subunit Ϫ0.23 0.15 94 NW 344432 Hs.81874 MGST2 Microsomal glutathione S-transferase 2 Ϫ0.2 0.18 95 NW 756092 Hs.198253 HLA-DQA1 MHC class II ϭ DQ␣ Ϫ0.4 0.19 96 W 265267 Hs.8997 HSPA1A HSP70 Ϫ0.42 0.07 97 W 265267 Hs.8997 HSPA1A HSP70 Ϫ0.42 0.05 98 SW 950092 Hs.167460 SFRS3 Splicing factor, arginine/serine-rich 3 Ϫ0.17 Ϫ0.11 99 S 630013 Hs.78934 MSH2 MSH2 ϭ DNA mismatch repair Ϫ0.05 Ϫ0.12 Ϫ Ϫ 100 W 884719 Hs.180414 HSPA10 Heat shock Mr 70,000 protein 10 0.23 0.05 Ϫ Ϫ 101 W 50615 Hs.80288 HSPA1L Heat shock Mr 70,000 protein 1 0.31 0.05 Ϫ 102 W 50615 Hs.80288 HSPA1L Heat shock Mr 70,000 protein 1 0.29 0.07 103 N 511623 Hs.199067 ERBB3 V-erb-b2 avian erythroblastic leukemia viral oncogene Ϫ0.07 0.2 104 N 181998 Hs.77810 NFATC4 NFAT3 ϭ NFATc4 Ϫ0.06 0.2 105 NW 182264 Hs.73800 SELP P-selectin Ϫ0.19 0.18 106 N 511600 Hs.195175 CFLAR FLICE-like inhibitory protein long form Ϫ0.08 0.22 107 NW 525540 Hs.31210 BCL3 BCL-3 Ϫ0.11 0.22 108 N 510002 Hs.181390 CSNK1G2 Casein kinase I ␥ 2 Ϫ0.02 0.19 109 SW 46286 Hs.6685 TRIP8 Thyroid hormone receptor interactor 8 Ϫ0.14 Ϫ0.16 110 SW 796284 Hs.96063 IRS1 IRS-1 ϭ insulin receptor substrate-1 Ϫ0.15 Ϫ0.13 111 S 796284 Hs.96063 IRS1 IRS-1 ϭ insulin receptor substrate-1 Ϫ0.09 Ϫ0.14 112 S 788136 Hs.188 PDE4B Phosphodiesterase 4B Ϫ0.09 Ϫ0.13 113 S 429368 Hs.89583 HOX11 HOX-11 homeobox protein Ϫ0.05 Ϫ0.12 114 SW 502486 Hs.146847 TANK TRAF family member-associated NFKB activator Ϫ0.24 Ϫ0.12 115 SW 252953 Hs.29882 GS3786 Predicted osteoblast protein Ϫ0.42 Ϫ0.11 116 W 42739 Hs.227777 PTP4A1 Protein tyrosine phosphatase Ϫ0.37 Ϫ0.04 117 SW 502396 Hs.4055 C21ORF50 Chromosome 21 open reading frame 50 Ϫ0.25 Ϫ0.16 118 SW 52339 Hs.288497 ESTs Ϫ0.22 Ϫ0.15 119 SW 207358 Hs.169902 SLC2A1 Glucose transporter Ϫ0.28 Ϫ0.17 120 SW 267186 Hs.34348 Homo sapiens mRNA Ϫ0.27 Ϫ0.12 121 SW 108658 Hs.4084 KIAA1025 KIAA1025 protein Ϫ0.22 Ϫ0.14 122 SW 784772 Hs.788 AKAP12 A kinase (PRKA) anchor protein (gravin) 12 Ϫ0.16 Ϫ0.21 123 SW 343061 Hs.788 AKAP12 Gravin Ϫ0.17 Ϫ0.22 124 SW 811740 Hs.271986 ITGA2 Integrin, ␣ 2 Ϫ0.11 Ϫ0.19 125 NW 795877 Hs.3838 SNK Serum-inducible kinase Ϫ0.17 0.15 126 W 241489 Hs.2551 ADRB2 ␤-2 Adrenergic receptor Ϫ0.43 0.04 127 SW 51532 Hs.75249 ARL6IP ADP-ribosylation factor-like 6 interacting protein Ϫ0.21 Ϫ0.13 128 SW 138861 Hs.21201 DKFZP566 B084 Nectin 3 Ϫ0.53 Ϫ0.12 129 W 327094 Hs.111460 Similar to calcium/calmodulin-dependent protein kinase Ϫ0.53 Ϫ0.09 130 W 327094 Hs.111460 Similar to calcium/calmodulin-dependent protein kinase Ϫ0.48 Ϫ0.08 131 S 811771 Hs.154879 DGSI DiGeorge syndrome critical region gene DGSI Ϫ0.09 Ϫ0.22 132 S 739625 Hs.227489 KIAA0973 KIAA0973 protein Ϫ0.09 Ϫ0.2 133 SW 377708 Hs.188 PDE4B 3Ј5Ј-cyclic AMP phosphodiesterase Ϫ0.14 Ϫ0.2 134 SW 626502 Hs.11538 ARPC1B Actin-related protein Ϫ0.1 Ϫ0.27 135 SW 220372 Hs.40968 HS3ST1 Heparan sulfate sulfotransferase Ϫ0.29 Ϫ0.27 136 SE 376370 Hs.738 EGR1 EGR-1 ϭ Early growth response protein 1 ϭ zinc finger protein 0.16 Ϫ0.12 137 SW 781047 Hs.98658 BUB1 Putative mitotic checkpoint protein Ϫ0.3 Ϫ0.13 138 SW 129865 Hs.250822 STK6 Serine/threonine kinase 6 Ϫ0.28 Ϫ0.15 139 SW 209066 Hs.250822 STK6 Aurora/IPL1-related kinase Ϫ0.29 Ϫ0.13 140 SW 744047 Hs.77597 PLK Polo (Drosophila)-like kinase Ϫ0.25 Ϫ0.19 141 S 627173 Hs.272458 PPP3CA Protein phosphatase 3 Ϫ0.02 Ϫ0.16 142 S 612404 Hs.182366 TRAP1 Tumor necrosis factor type 1 receptor-associated protein Ϫ0.05 Ϫ0.15 143 SW 898138 Hs.811 UBE2B Ubiquitin-conjugating enzyme E2B Ϫ0.16 Ϫ0.16 144 SW 431296 Hs.272458 PPP3CA Protein phosphatase 3 Ϫ0.2 Ϫ0.15 145 SW 563130 Hs.23960 CCNB1 Cyclin B1 Ϫ0.22 Ϫ0.14 146 SW 809515 Hs.77597 PLK Polo serine/threonine kinase Ϫ0.18 Ϫ0.21 147 SW 128947 Hs.72550 HMMR Hyaluronan-mediated motility receptor Ϫ0.15 Ϫ0.22 148 SW 774409 Hs.76753 ENG Endoglin Ϫ0.22 Ϫ0.19 149 S 795352 Hs.740 PTK2 FAK ϭ focal adhesion kinase 0.02 Ϫ0.17 150 S 687009 Hs.199263 SPAK DCHT ϭ similar to rat pancreatic serine threonine kinase 0.03 Ϫ0.16 151 S 114048 Hs.89548 EPOR Erythropoietin receptor 0 Ϫ0.17 152 S 376515 Hs.105737 H. sapiens cDNA FLJ10416 fis Ϫ0.02 Ϫ0.14 153 S 503119 Hs.166994 FAT hFat ϭ homologue of Drosophila FAT gene 0.04 Ϫ0.22 154 S 504248 Hs.75350 VCL Vinculin Ϫ0.04 Ϫ0.13 155 SW 310493 Hs.268012 FACL3 Fatty-acid-coenzyme A ligase Ϫ0.18 Ϫ0.12 156 SW 767994 Hs.183105 G2SNA Nuclear autoantigen Ϫ0.19 Ϫ0.11 157 S 298128 Hs.12802 DDEF2 Development and differentiation enhancing factor 2 Ϫ0.1 Ϫ0.12 158 SW 26568 Hs.74088 EGR3 T cell transcription factor Ϫ0.11 Ϫ0.15 159 SW 825583 Hs.74111 RALY RNA-binding protein (autoantigenic) Ϫ0.13 Ϫ0.12 160 S 469229 Hs.70500 KIAA0370 KIAA0370 protein Ϫ0.06 Ϫ0.13 161 SW 299360 Hs.118962 FUBP1 FUSE binding protein1 ϭ myc transcription factor Ϫ0.12 Ϫ0.13 162 E 502761 Hs.82285 GART Phosphoribosylglycinamide formyltransferase 0.13 Ϫ0.07 163 S 768316 Hs.27007 CHC1L Chromosome condensation 1-like 0 Ϫ0.15 164 S 320392 Hs.184340 MBLL C3H-type zinc finger protein Ϫ0.05 Ϫ0.18 165 S 773254 Hs.180610 SFPQ Splicing factor proline/glutamine rich Ϫ0.01 Ϫ0.18 166 S 278053 Hs.109694 KIAA1451 KIAA1451 protein 0.03 Ϫ0.16 Ϫ 167 SE 320509 Hs.31086 H. sapiens mRNA for cytochrome b5 0.17 0.19 a EST, expressed sequence tag; MAP, mitogen-activated protein. 2789

Table 4 Comparison of microarray and real-time PCR expression ratio (treated versus KIAA0838 (spot 60), a chimeric clone that includes sequences for both untreated) of selected genes after 1.5-h treatment with LD topotecan glutaminase C and PLAB. The similar expression patterns of PLAB Expression ratio (treated/untreated) (two separate spots; spots 59 and 62), KIAA0838 (spot 60), and Spot Gene no. Clone ID HCT-116 (p53Ϫ/Ϫ) HCT-116 (p53ϩ/ϩ) MADH6/SMAD6 (spot 61) transcripts are shown quantitatively as CFLAR 106 511600 1.65 (1.6)a 1.36 (1.3) functions of time and drug concentration in Fig. 6. Another gene in the BCL3 107 525540 1.86 (2.0) 1.26 (1.6) NE region is the proapoptotic SIVA gene (spot 63). The time course CSNK1G2 108 510002 1.77 (1.8) 1.25 (1.5) and dose response of SIVA showed behavior parallel to that of the a Number in parentheses ϭ real-time RT-PCR detection. TGF-␤ pathway genes (Fig. 6). The opposite of the NE region is the Southwest (SW) region, which be up-regulated by topotecan treatment. As expected, CDKN1A/ contains transcripts expressed at lower levels in p53ϩ/ϩ cells and p21Waf1/Cip1 (spot 58) fell into this category, as did other known p53 down-regulated by drug treatment. This region contains ENG/CD105 target genes [PPM1D/WIP1 (spot 13), TNFRSF6/Fas (spot 14), and (endoglin; spot 148), a TGF-␤ type III receptor (15, 16). The expres- ATF3 (spots 53 and 54); see Fig. 5 and Table 3]. Other genes not sion behavior of this transcript is inverse to that of PLAB and SIVA, previously linked to p53 transcriptional activity appear in the NE as shown in Fig. 6. Another transcript in this region is HS3ST1 (spot region. Among them are a set of genes related to the TGF-␤ growth 135 in Table 3). It codes for a heparan sulfate glucosamine sulfotrans- regulatory pathway. As noted earlier, these genes [PLAB (2 spots; ferase, which may be involved in transmission of growth factors from spots 59 and 62) and MADH6/SMAD6 (spot 61)] and the proapoptotic the extracellular matrix to the cell surface. SIVA gene (spot 63) belong to the ␤ cluster. Also in the NE region is The East (E) and West (W) regions contain p53-dependent

Fig. 5. GEM. Genes were mapped according to the effects of p53 status (horizontal axis; “longitude”) and treatment (vertical axis; “latitude”) on their expression levels with reference to untreated p53 cells. The origin point (0, 0) corresponds, therefore, to a gene whose expression is unaffected by p53 status or treatment. Each axis shows log10 of the contribution of each effect to the observed overall response (expression difference). For example, a gene located at X ϭϩ0.3, Y ϭϩ0.3 is expressed at ϳ2-fold higher level in p53ϩ/ϩ cells than in p53Ϫ/Ϫ cells and an additional ϳ2-fold higher level in response to treatment with topotecan. This result is about a 4-fold increase when treated p53ϩ/ϩ cells are compared with untreated p53Ϫ/Ϫ cells. Calculation of the treatment effects is described fully in “Materials and Methods.” The GEM is divided into eight regions containing genes with roughly the same qualitative behavior in terms of p53 status and treatment response. For convenience, regions are labeled according to compass directions (N, NE, E, SE, S, SW, W, and NW). The majority of the 6720 genes on the array were found in the central region but, for clarity, are not shown. Clusters ␣, ␤, and ␥ from Figs. 3 and 4 are shown as red circles, green triangles, and blue squares, respectively. 2790

Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2003 American Association for Cancer Research. PHARMACOGENOMIC OF p53 AND TOPOTECAN IN COLON CANCER transcripts that are not substantially responsive to drug treatment. In the E region, the S100A4/mst1 transcript (spot 56) has the most intensely p53-dependent expression (Ͼ25-fold). S1004A4/mst1 is 2ϩ a Mr 11,000 protein that belongs to the S100 family of Ca - binding proteins, different members of which have diverse cellular functions (17). Its involvement in tumor metastasis has generated recent interest (18). Differential expression of S100A4 was confirmed by immunoblots (Fig. 7), which demonstrated a strong signal in the p53ϩ/ϩ cells and no detectable signal in p53Ϫ/Ϫ cells. This observation suggests a close association between S100A4 expression and p53 function. The W region, which corresponds to drug-independent transcripts expressed more in the p53Ϫ/Ϫ cells than in the p53ϩ/ϩ cells, contains the Mr 70,000 HSPs [HSPA1A (spots 96 and 97), HSPA10 (spot 100), and HSPA1L (spots 101 and 102)] already mentioned above in connection with cluster ␥ in Figs. 3 and 4. Consistent with this observation, Kannan et al. (6) have reported down-regulation of HSP70 in cells that overexpress a temperature-sensitive p53. In that Fig. 7. Western blot of S100A4 protein in whole cell lysates from topotecan-treated study, the down-regulation might have been associated with a non- cells. HCT-116 cells were treated with HD (1 ␮M) topotecan for 1.5, 3, and 6 h. Cell physiological level of expression of p53; however, our results were lysates were Western blotted with antibodies directed against S100A4 (Dako). obtained with physiological expression of wild-type p53. The North (N) and South (S) regions include transcripts that were enhanced or suppressed, respectively, by topotecan exposure, inde- Our finding that cFLIP expression can be induced by topotecan pendent of p53 status. The N region includes the FOS/JUN genes independent of p53 status (Fig. 5; see also Fig. 3, spot 106; and Table (spots 47 and 49–51), consistent with the role of activator protein 1 in 3) is novel (21). Casein kinase I was recently shown to inhibit stress responses (19). The N region (Fig. 5) also includes CFLAR/ apoptosis by preventing the activation of Bid and therefore inhibiting cFLIP (spot 106) and casein kinase I (CSNK1G2; spot 108), both of mitochondrion-dependent apoptosis (type II apoptosis; Ref. 21). which can modulate apoptosis. cFLIP (CFLAR), a potent inhibitor of Among the transcripts in the S and SW regions (i.e., down-regu- the death receptor pathway (type I apoptosis), binds to Fas-associated lated by topotecan treatment, independent or negatively dependent on death domain and blocks activation of FLICE (caspase 8; Ref. 20). p53 status) are ARPC1B (spot 134; a gene related to actin), DGS1 (spot 131; DiGeorge syndrome critical region gene), and GRAVIN (two spots; spots 122 and 123 that clustered next to each other). GRAVIN was first identified as an autoantigen in some myasthenia gravis patients. It functions as a scaffold protein for protein kinases A and C and interacts with actin (22). Perhaps future research will indicate why these S and SW region transcripts were down-regulated, but, at present, we cannot discern a pattern.

DISCUSSION p53, a key tumor suppressor gene, is mutated in the majority of human cancers (23). Successful outcome of chemotherapy and radio- therapy, in many cases, depends on functional p53 (1, 2, 24, 25). Therefore, elucidation of the function, regulation, and molecular interactions of p53 is of great importance for cancer therapy. Because p53 is a transcription factor with an expanding repertoire of genes that are known to be directly or indirectly under its control, global analysis of gene expression profiles represents the best approach for studying the p53 response (6, 25, 26). In previous studies, patterns of gene expression were observed following overexpression of wild-type p53. For example, the Vo- gelstein group used serial analysis of gene expression (SAGE) in a colorectal carcinoma cell line to analyze inducible overexpression of wild-type or mutant p53 (25). Of the 9,954 transcripts identified, 34 (0.34%) were markedly increased in p53-overexpressing cells. Many of those 34 genes had not previously been shown to be regulated by p53. The Levine group (26) used microarrays to study the response of p53-regulated genes in a human colon carcinoma EB-1 cell line stably transfected with a construct that included a zinc-inducible promoter. Using cluster analysis, they found that response patterns depended on whether the genes were induced by ␥-radiation, UV-radiation, or the Fig. 6. Time- and dose-dependent alterations of gene expression in p53ϩ/ϩ and p53Ϫ/Ϫ cells. Expression profiles of PLAB, Siva, Smad6, and Endoglin after treatment inducible p53 gene. Recently, Wang et al. (27) showed that 1,501 of with LD (blue circles) and HD (red circles) topotecan. 33,615 genes (4.5%) that contained the p53 consensus binding se- 2791

Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2003 American Association for Cancer Research. PHARMACOGENOMIC OF p53 AND TOPOTECAN IN COLON CANCER quence responded positively to p53 expression. Although this study TNF receptor family (26, 27) and induces apoptosis after phospho- used different criteria to define genes with different experiential rylation by the Abl-related gene (ARG) tyrosine kinase in response to levels, this number is close to our finding here that 167 of 6,500 genes oxidative stress (28). Thus, SIVA could be a proapoptotic p53 effec- (2.5%) respond consistently and significantly to both p53 expression tor. To the best of our knowledge, the p53 dependence of SIVA and drug treatment. expression has not been reported previously. In the present study, we compared drug-induced transcriptional The metastasis-associated protein (S100A4/mst1) belongs to the responses in wild-type HCT-116 human colon carcinoma cells and an S100 family of small calcium-binding proteins (for review, see Ref. isogenic p53-deleted line (8). Comparison of isogenic cell lines, in 29) recently reported to cooperate with p53 to induce apoptosis (30, combination with pharmacologically induced stress, allowed us to 31). Calcium binding induces conformational changes in the S100 study not simply the effects of drug treatment or the effect of p53 but protein structure, allowing interaction with target proteins including the combination of both treatment and p53 (see Fig. 1) in a physio- p53 (30, 31), the protein tyrosine phosphatase Liprin bl (32), non- logically driven system. muscle myosin heavy chain, and another S100 family member, The need to analyze both genotypic/phenotypic and pharmacolog- S100A1 (see the references in Ref. 32). Binding of S100A4 to the ical variables simultaneously prompted us to develop new types of COOH-terminal domain of p53 inhibits p53 phosphorylation by pro- experimental design and data analysis. To encompass a wide range of tein kinase C (but not by CKII) and modulates p53 transcriptional potential responses in both the p53 wild-type and knockout cells, it activity in a gene-specific manner (32). S100A4 stimulates p53- seemed important to study different doses and treatment times. Hence, mediated Bax transcriptional activation and suppresses transcription the design included comparisons among 12 conditions of drug con- of the antiapoptotic p21Waf1/Cip1 gene (31). These gene-specific tran- centration, time, and p53 status. Included were standard, direct pair- scriptional effects provide an explanation for the observed coopera- wise comparisons (i.e., on a single slide) of treated and untreated tion between wild-type p53 and S100A4 in the induction of an samples for each cell type. The design also included direct compari- apoptotic response (31). Thus, S100A4 might contribute to the apop- sons of the p53 wild-type and knockout states in baseline and treated totic response to topotecan in wild-type HCT-116 cells, once p53 states. Overall, this cross-referenced network design (Fig. 1) provided levels increase. Our finding that both protein and mRNA levels of built-in redundancy. It also enabled us to obtain quite robust estimates S100A4 were markedly higher in wild-type HCT-116 cells than in of the relative expression levels by multiple linear least-squares re- HCT-116 p53 knockout cells was unexpected, considering that Grigo- gression calculations for the entire network of experimental condi- rian et al. (31) observed an inverse relationship between p53 wild-type tions. The experimental design and approach to analysis represent status and S100A4 expression in 26 tumor-derived cell lines, not advances over other studies that use a single “reference” sample in including HCT-116 cells. one channel of a two-label microarray hybridization experiment. Among the genes that were down-regulated by p53, we identified Interpretation focused on 167 genes whose expression levels re- the antiapoptotic protein chaperone HSP70 genes. These genes sponded most clearly in a consistent and significant way (see Fig. 3 [HSP1A (spots 96 and 97), HSP10 (spot 100), and HSP1L (spots 101 and Table 3). and 102)] fell in the ␥ cluster (Figs. 3 and 4) and in the W quadrant A novel two-dimensional visualization map, the GEM, identified of the GEM (Fig. 5). HSP70 has recently been shown to suppress the components of observed gene expression differences attributable apoptosis by blocking the apoptosome (33). Two genes with possible to each of the two major factors in the design: p53 status; and antiapoptotic activity fell in the p53-independent, drug-responsive treatment with the topoisomerase I-targeted drug topotecan. For sim- region (N region): cFLAR/cFLIP (spot 106; Ref. 20); and CSKNK1G2 plicity of description, we can identify eight possible generic outcomes (spot 108; Ref.21). In conclusion, these experiments have added, at (“territories” in the GEM), each representing a different response least incrementally, to our understanding of how p53 activation can pattern, as indicated in Fig. 5. If a set of genes from a CIM cluster precipitate cell death in response to pharmacological stress. Perhaps as (Figs. 3 and 4) falls largely within a single territory in the GEM, we important, investigators elsewhere who approach these carefully gen- may be led to the hypothesis that some of those genes operate in a erated data sets with different questions and different perspectives will common pathway. Also genes up-regulated in one quadrant might be able to observe relationships that we cannot now observe. More operate coherently with genes down-regulated in the complementary generally, the cross-referenced network experimental design and quadrant. methods of analysis introduced here can be applied widely to the Two examples of coherent gene responses are the TGF-␤ and simultaneous study of genotypic/phenotypic and pharmacological Jun/Fos clusters. Four genes in the TGF-␤ pathway appear in the NE variables. quadrant of the GEM (Fig. 5) and in the ␤ cluster (Figs. 3, 4, and 6): PLAB (spots 59 and 62); KIAA0888 (spot 60); and MAD6 (spot 61). ACKNOWLEDGMENTS The up-regulation of these genes was p53 dependent. This induction of the TGF-␤ pathway provides an example of the indirect effects of We thank the Advanced Technology Center of the National Cancer Institute p53 on gene transcription by activation of other transcription factors. for providing the Human Oncochips. HCT-116 and an isogenic p53 knockout cell line were kindly provided by Dr. Bert Vogelstein (Johns Hopkins Medical Also up-regulated was the Jun/Fos pathway. Three Jun B spots (spots Institute, Baltimore, MD). 49, 50, and 55) and the FosB gene (spot 51) are in the N quadrant of the GEM (Fig. 5) and cluster next to each other in the CIM (spots REFERENCES 49–51 and 55 in Fig. 3). The Jun/Fos up-regulation was dependent on topotecan treatment but independent of p53 status. 1. Levine, A. J. p53, the cellular gatekeeper for growth and division. Cell, 88: 323–331, 1997. Using the GEM analysis, we find that p53 regulates (directly or 2. Vogelstein, B., Lane, D., and Levine, A. J. Surfing the p53 network. Nature (Lond.), indirectly) a large number of genes that can induce apoptosis or cell 408: 307–310, 2000. cycle arrest. Some of the p53-dependent responses, to our knowledge, 3. Ko, L. J., and Prives, C. p53: puzzle and paradigm. Genes Dev., 10: 1054–1072, 1996. have not been reported before. Among the genes that exhibited p53 4. Colman, M. S., Afshari, C. A., and Barrett, J. C. Regulation of p53 stability and dependence in the positive direction, we identified two recently de- activity in response to genotoxic stress. Mutat. Res., 462: 179–188, 2000. 5. Zambetti, G., Bargonetti, J., Walker, J., Prives, C., and Levine, A. Wild-type p53 scribed proapoptotic genes (SIVA and S100A4) and several genes from mediates positive regulation of gene expression through a specific DNA sequence the TGF-␤ pathways. SIVA (spot 63) binds to CD27, a member of the element. Genes Dev., 6: 1143–1152, 1992. 2792

6. Kannan, K., Amariglio, N., Rechavi, G., Jakob-Hirsch, J., Kela, I., Kaminski, N., and induces caspase-8-independent cell death in DLD-1 cells. Oncogene, 20: 571– Getz, G., Domany, E., and Givol, D. DNA microarrays identification of primary and 580, 2001. secondary target genes regulated by p53. Oncogene, 20: 2225–2234, 2001. 21. Desagher, S., Osen-Sand, A., Montessuit, S., Magnenat, E., Vilbois, F., Hochmann, 7. Ginsberg, D., Mechta, F., Yaniv, M., and Oren, M. Wild-type p53 can down modulate A., et al. Phosphorylation of bid by casein kinase I and II regulates its cleavage by the activity of various promoters. Proc. Natl. Acad. Sci. USA, 8: 9979–9983, 1991. caspase 8. Mol. Cell, 8: 601–611, 2001. 8. Bunz, F., Hwang, P. M., Torrance, C., Waldman, T., Zhang, Y., Dillehay, L., 22. Lin, X., and Gelman, I. H. Calmodulin and cyclin D anchoring sites on the Src- Williams, J., Lengauer, C., Kinzler, K. W., and Vogelstein, B. Disruption of p53 in suppressed C kinase substrate, SSeCKS. Biochim. Biophys. Res. Commun., 290: human cancer cells alters the responses to therapeutic agents. J. Clin. Investig., 104: 1368–1375, 2002. 263–269, 1999. 23. Hollstein, M., Sidransky, D., Vogelstein, B., and Harris, C. p53 mutation in human 9. Kollmannsberger, C., Mross, K., Jakob, A., Kanz, L., and Bokemeyer, C. Topotecan, cancers. Science (Wash. DC), 253: 49–53, 1991. a novel topoisomerase I inhibitor: pharmacology and clinical experience. Oncology 24. Lowe, S. W., Bodis, S., McClatchey, A., et al. p53 status and the efficacy of cancer (Basel), 56: 1–12, 1999. therapy. Science (Wash. DC), 266: 807–810, 1994. 10. Pommier, Y., Pourquier, P., Fan, Y., and Strumberg, D. Mechanism of action of 25. Yu, J., Zhang, L., Hwang, P. M., Rago, C., Kinzler, K. W., and Vogelstein, B. eukaryotic DNA topoisomerase I and drugs targeted to the enzyme. Biochem. Identification and classification of p53-regulated genes. Proc. Natl. Acad. Sci. USA, Biophys. Acta, 1400: 83–105, 1998. 95: 14517–14522, 1999. 11. Carlisle, A. J., Prabhu, V. V., Elkahloun, A., Hudson, J., Trent, J. M., Linehan, W. M., 26. Zhao, R., Gish, K., Murphy, M., Yin, Y., Notterman, D., Hoffman, W. H., Tom, E., et al. Development of a prostate cDNA microarray and statistical gene expression Mack, D. H., and Levine, A. J. Analysis of p53-regulated gene expression patterns analysis package. Mol. Carcinog., 28: 12–22, 2000. using oligonucleotide arrays. Genes Dev., 14: 981–993, 2000. 12. Weinstein, J. N., Myers, T. G., O’Connor, P. M., Friend, S. H., Fornace, A. J., Kohn, 27. Wang, L., Wu, Q., Qiu, P., Mirza, A., McGuirk, M., Kirschmeier, P., et al. Analyses K. W., et al. An information-intensive approach to the molecular pharmacology of of p53 target genes in the human genome by bioinformatic and microarray ap- cancer. Science (Wash. DC), 275: 343–349, 1997. proaches. J. Biol. Chem., 276: 43604–43610, 2001. 13. Scherf, U., Ross, D. T., Waltham, M., Smith, L. H., Lee, J. K., Tanabe, L., et al. A 28. Cao, C., Ren, X., Kharbanda, S., Koleske, A., Prasad, K. V., and Kufe, D. The ARG gene expression database for the molecular pharmacology of cancer. Nat. Genet., 24: tyrosine kinase interacts with Siva-1 in the apoptotic response to oxidative stress. 236–244, 2000. J. Biol. Chem., 276: 11465–11468, 2001. 14. Shao, R. G., Cao, C. X., Shimizu, T., O’Connor, P. M., Kohn, K. W., and Pommier, 29. Donato, R. S100: a multigenic family of calcium-modulated proteins of the EF-hand Y. Abrogation of an S-phase checkpoint and potentiation of camptothecin cytotox- type with intracellular and extracellular functional roles. Int. J. Biochem. Cell Biol., icity by 7-hydroxystaurosporine (UCN-01) in human cancer cell lines, possibly by 33: 637–668, 2001. p53 function. Cancer Res., 57: 4029–4035, 1997. 30. Scotto, C., Deloulme, J. C., Rousseau, D., Chambaz, E., and Baudier, J. Calcium and 15. Lastres, P., Letamendia, A., Zhang, H., Rius, C., et al. Endoglin modulates cellular S100 B regulation of p53-dependent cell growth arrest and apoptosis. Mol. Cell. Biol., responses to TGF-␤1. J. Cell Biol., 133: 1109–1121, 1996. 18: 4272–4281, 1998. 16. Li, C., Hampson, I. N., Hampson, L., Kumar, P., et al. CD 105 antagonizes the 31. Grigorian, M., Andresen, S., Tulchinsky, E., Kriajevska, M., Carlberg, C., Kruse, C., inhibitory signaling of transforming growth factor ␤ 1 on human vascular endothelial Cohen, M., et al. Tumor suppressor p53 protein in a new target for the metastasis- cells. FASEB J., 14: 55–64, 2000. associated Mts1/S100A4 protein: functional consequences of their interaction. J. Biol. 17. Sherbet, G. V., and Lakshmi, M. S. S100A4 (MTS1) calcium binding protein in Chem., 276: 22699–22708, 2001. cancer growth, invasion and metastasis. Anticancer Res., 18: 2415–2422, 1998. 32. Kriajevska, M., Fischer-Larsen, M., Moertz, E., Vorm, O., Tulchinsky, E., Grigorian, 18. Kriajevska, M., Tarabykina, S., Bronstein, I., Maitland, N., et al. Metastasis-associ- M., Ambartsumian, N., and Lukanidin, E. Liprin ␤ 1, a member of the family of LAR ated Mts1 (S100A4) protein modulates protein kinase C phosphorylation of the heavy transmembrane tyrosine phosphatase-interacting proteins, is a new target for the chain of nonmuscle myosin. J. Biol. Chem., 273: 9852–9856, 1998. metastasis-associated protein S100A4 (Mts1). J. Biol. Chem., 277: 5229–5235, 2002. 19. Wisdom, R. AP-1: One switch for many signals. Exp. Cell Res., 253: 180–185, 1999. 33. Ravagnan, L., Gurbuxani, S., Susin, S. A., Maisse, C., Daugas, E., Zamzami, N., Mak, 20. Bartke, T., Siegmund, D., Peters, N., Reichwein, M., Henkler, F. Scheurich,. P, and T., Jaattela, M., Penninger, J. M., Garrido, C., and Kroemer, G. Heat-shock protein 70 Wajant, H. p53 upregulates cFLIP inhibits transcription of NF-␬B-regulated genes antagonizes apoptosis-inducing factor. Nat. Cell Biol., 3: 839–843, 2001.

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