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Gene expression patterns associated with p53 status in breast cancer

Gene expression patterns associated with p53 status in breast cancer Background: Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function). Methods: The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors. Results: Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data. Conclusion: In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes. Page 1 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 GTAATCTAC) was also prepared as a mismatch control. Background The tumor suppressor p53 is mutated in 30% of breast HEK-293T cells were transfected with 10 micrograms each of pSUPER.RETRO.puro vector, pVpack-GP (Stratagene), cancers [1], but rates of p53 mutation vary depending upon the subtype of breast cancer. For example, p53 and pVpack-Ampho (Stratagene) using Lipofectamine mutations are found more frequently in aggressive estro- Reagent and PLUS Reagent (Invitrogen). Supernatants gen receptor (ER)-negative breast cancers [1], and have containing replication-incompetent retrovirus were col- been shown to correlate with breast cancer subtype in lected 48 hours after transfection and applied to all four gene expression studies [2] and in a population-based cell lines. Stable populations of cell lines expressing p53- study [3]. Genetic abnormalities such as amplified HER2/ RNAi or mismatch-RNAi were selected for two weeks in 1 ERBB2 [1] and aneuploidy [4] are also frequently associ- µg per mL puromycin. ated with p53 mutation status. These correlations suggest intrinsic heterogeneity of p53 signaling across breast can- Western blots cer subtypes. Cells were treated for 24 h with 1 µM DOX, and cell free extracts, protein quantitation, and denaturation were as Gene expression studies can help to characterize breast described previously [5]. Forty µg of protein were electro- cancer heterogeneity. Previous in vitro studies of gene phoresed on a 4–20% Tris-HCl Criterion precast gel (Bio- expression have demonstrated that cell line models of Rad) and transferred to a Hybond-P membrane luminal breast cancers show a strong stress response fol- (Amersham Biosciences) by electroblotting. The blots lowing chemotherapeutic treatment, with notable were probed with antibodies against p53 (Santa Cruz; changes in p53-regulated genes such as p21 (Cip1). The D01) and β-actin (Abcam, AC-15) and then with anti- same magnitude of p53-regulated responses was not mouse IgG horseradish peroxidase linked whole antibody observed for cell line models of basal-like breast cancer from sheep (Amersham). Enhanced chemiluminescence [5]. Inherent differences in p53 signaling and function was used for detection (SuperSignal West Pico Chemilu- according to cell type of origin could account for the asso- minescent Substrate, Pierce). ciation between rates of p53 mutation and breast cancer subtype. In this study, we engineered isogenic cell line Microarray experiments Cell lines were grown, treated for 12, 24, or 36 h with pairs with and without p53 function using RNA interfer- ence (RNAi) and examined the stress responses of parent DOX at the IC50 concentration, and harvested using a and RNAi-transduced cell lines. Our aim was to assess previously described protocol [5]. Feeding control (sham) how variation in cell line backgrounds alters the effects of and reference mRNA samples were prepared as described p53 loss. We also aimed to identify a common response previously [5]. Cy3- and Cy5-labeled cDNAs were synthe- to p53 loss that is shared by most breast cancers. Thus, we sized from control or treated cell line mRNA, respectively, compared the lists of p53-responsive genes in vitro to gene according to a direct labeling protocol (Agilent Technolo- lists derived from in vivo breast tumor data to identify a set gies), and were hybridized to Human 1A oligonucleotide of common p53 responsive genes. The biological rele- arrays (Agilent Technologies). All microarray raw data vance of this common p53 signature was assessed by tables have been deposited in the Gene Expression Omni- using this gene list to predict outcomes on independent bus under the accession number of GSE3178 (submitter test data sets of breast cancer patients. C. Perou). Identification of p53-dependent DOX-response signature Methods Cells and culture conditions from microarray data Two hTERT immortalized Human Mammary Epithelial For all comparisons, in vitro and in vivo as described (HME) cell lines (ME16C and HME-CC) and two estab- below, genes that were significantly different in expres- lished breast cancer cell lines (MCF-7 and ZR-75-1) were sion were identified using a 2-class, unpaired Significance cultured as described previously [5]. A mitochondrial dye Analysis of Microarrays (SAM) [7]; for the SAM analysis, conversion (MTT) assay was used to measure cell line the data were first filtered to exclude genes that did not responses to 36 h of treatment with 0 – 10 µM doxoru- have mean signal intensity greater than twice the median bicin hydrochloride (DOX) [5]. background value for both the red and green channel in at least 70% of the experiments. The SAM delta values were Short hairpin RNAs (RNAi) against p53 were constructed adjusted to obtain the largest gene list that gave a false dis- using a 19-mer sequence (GACTCCAGTGGTAATCTAC) covery rate of less than 5%. Using the SAM-derived gene described previously [6], but using the pSU- lists, average linkage hierarchical cluster analysis was con- PER.RETRO.puro vector with stuffer (Oligo Engine, Seat- ducted using Pearson correlation in the Cluster program tle, WA). A version of this vector containing two and the data were visualized in Treeview [8,9]. EASE, the mismatches within the 19-mer sequence (GACTCCGGTT- Page 2 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Expression Analysis Systematic Explorer was used to iden- or Miller et al. tumor was classified according to the near- tify enriched biological themes in gene lists [10]. est centroid as determined by Spearman correlation. Each cell line was examined for p53 response in both Other statistical analyses untreated and DOX-treated states. To identify the gene Survival analyses were conducted using Sorlie et al. tumor expression effects of p53 loss in DOX-treated cells (p53- data (excluding duplicate samples from the same person, induced state), parent cell lines treated with DOX (n = 3 resulting in a total of 66 patients representing 31 disease- for each cell line) were compared to RNAi-transductants specific and 26 overall survival events for survival analy- treated with DOX (n = 3 for each cell line). To identify the ses), Change et al. tumor data [337 patients: 295 patients gene expression effects of p53 loss in the absence of DOX from [13] and 42 tumors published in an earlier paper treatment, sham-treated parent cell lines (wildtype p53) [16] from the same group, representing 126 disease spe- were compared to sham-treated RNAi-transductants (n = cific and 79 overall survival events] and Miller et al. [14] 3 for both treatment groups in each cell line). However, to data (236 patients, 52 disease specific events). For analy- derive a list of genes that were differentially expressed in ses of the Miller et al. dataset, patients that had survived at both in vitro and the in vivo data sets, the common p53 least ten years were censored to be consistent with previ- response across all four cell lines was the most relevant. ous analyses [14]. Kaplan Meier analyses were conducted Thus, we also performed an analysis comparing all RNAi- using WinStat for Microsoft Excel. transduced cell line experiments (n = 24) to all parent cell line experiments (n = 24). The resulting list represented Because the large data set of Chang et al. also included the common response to p53 loss across cell lines. data on other prognostic variables, Cox proportional haz- ards modeling was conducted (SAS version 9.1). The To identify the gene expression signature associated with reduced model that included ER status (positive vs. nega- p53 in vivo, we used primary breast tumor data [2,11,12] tive), tumor size (≤ 2 cm vs. > 2 cm), lymph node status that is publicly available from the Stanford Microarray (indicator coding with three categories: 0, 1–3, > 3 posi- Database and the Gene Expression Omnibus. DOX- tive nodes or metastatic), age (in decades), grade (indica- treated patients for which p53 status had been determined tor coding with three categories: 1, 2, 3), and treatment by sequence analysis [2] were included in our analysis (yes if treatment with chemo and/or hormonal therapy, (102 tumor samples, including 8 normal-like breast sam- no if no adjuvant therapy) was compared to a full model ples, one unclassified tumor, and 37 before and after that also included a binary variable indicating p53 classi- pairs, representing 69 patients in total). All tumor sub- fication (based on gene-expression). types described in Sorlie et al. [2] [classified using intrinsic analysis [12]] were included, except true normal breast To determine if p53 status differed according to tumor and normal-like breast tumor samples. This sample set subtype, a Fisher-Freeman-Halton (FFH) exact test was also included tumors collected before and after treatment conducted using SAS version 9.1 (Cary, NC). Analyses of with doxorubicin. The gene expression patterns of the p53 sequence-based mutation characteristics (e.g. missense/ mutant samples (n = 43) were compared to those of the in-frame vs. nonsense and frameshift, missense DNA p53 wildtype samples (n = 52). binding vs. non-DNA binding) in association with gene expression classification were also conducted using FFH Identification of p53 functional status in independent test exact tests. data sets A final 52 gene list was derived by identifying those genes Results that were differentially expressed in response to p53 loss Gene expression and phenotypic analysis of cell lines expressing p53 RNAi in both the in vitro and in vivo data sets. These genes were matched to publicly available array data [13,14], using To study the effects of p53 loss in vitro, an RNAi construct unique Unigene identifiers. Of the 52 genes, 48 and 50 specific for p53 [6] was stably expressed in MCF-7, ZR-75- were present on the Chang et al. data set and Miller et al. 1, ME16C and HME-CC cells. All four cell lines had data sets, respectively. Microarray platform/source sys- wildtype p53 sequence and expressed functional p53 tematic biases between the training and the test sets were (showed p53 induction in response to treatment with corrected using Distance Weighted Discrimination DOX, Figure 1) prior to transduction with the p53-RNAi (DWD) [15]. To classify tumors in the independent test retroviral construct. Expression of p53-RNAi substantially sets (Chang et al. or Miller et al.) as p53-functional or not, knocked down p53 protein levels in both treated and two centroids were created using the Sorlie et al. training untreated cells (Figure 1). set. The centroids were based on average gene expression in tumors in Figure 5A (mutant enriched) vs. that of The phenotypic effects of p53 knock-down varied by cell tumors in Figure 5B (wildtype enriched). Each Chang et al. line (Figure 2). MCF-7 cells became more resistant to Page 3 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 MCF-7 ZR-75-1 MM wt MM RNAi wt RNAi + + + + + DOX - - - - - p53 β−actin ME16C HME-CC MM MM wt RNAi wt RNAi + + + + + DOX + - - - - - p53 β−actin p Figure 1 53 protein expression is knocked down by RNAi expression p53 protein expression is knocked down by RNAi expression. The expression of p53 was examined by Western blot analysis of extracts from MCF-7, ZR-75-1, HME-CC, and ME16C cell line parents and the same cell lines stably transduced with p53-targeted RNAi vector or p53 mismatch (MM) RNAi vector. Treatment with doxorubicin (24 h, 1µM) induced p53 expres- sion in all cell lines and transductants, but induced levels were markedly lower in the p53-RNAi cells. DOX, while ZR-75-1, ME16C and HME-CC cells dis- treated samples (DOX-treated parent vs. DOX-treated played no change in DOX sensitivity. Consistent with the RNAi-expressing), three cell lines (HME-CC, MCF-7 and different responses in the DOX sensitivity assay, gene ZR-75-1) increased genes involved in mitosis after trans- expression signatures significantly associated with p53 duction with p53-RNAi. ME16C did not induce categories loss (in 2-class SAM analyses) were different for each cell of mitosis genes, but did suppress negative regulators of line and cell type (gene lists are given in Additional File 1). cell proliferation. Significant down-regulation of apop- As shown in Figure 3, MCF-7 and ZR-75-1 cells showed a totic genes was only seen in ZR-75-1 cells. stronger p53-dependent signature following treatment with DOX. The immortalized HMECs, conversely, showed The p53-response observed among DOX-treated cell lines stronger p53-dependent signatures in the absence of DOX differed from the p53-response in sham-treated cell lines. (i.e. parents vs. RNAi, both untreated). Analysis of SAM- For example, the luminal-like cell lines (MCF-7 and ZR- derived gene lists using gene ontology software (EASE) 75-1) that had the largest transcriptional response to showed enrichment for categories of genes with known DOX, showed a modest response to p53 loss in sham- relevance to p53 function. For example, among the DOX- treated samples (sham-treated parent versus sham-treated Page 4 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 ZR-75-1 MCF-7 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Doxorubicin (µM) Doxorubicin (µM) HME-CC ME16C 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Doxorubicin (µM) Doxorubicin (µM) Chemosensitivity Figure 2 is altered in the MCF-7 cell line following transduction with p53-RNAi Chemosensitivity is altered in the MCF-7 cell line following transduction with p53-RNAi. ME16C, HME-CC, and ZR-75-1 cell lines had similar chemosensitivity curves for wildtype (x), p53-targeted RNAi expressing cells (solid square) and p53 mismatch RNAi expressing cells (open square). Only the p53-RNAi MCF-7 cells showed significant chemoresistance. RNAi-expressing). Sham-treated MCF-7 cells showed no sham-treated combined from all parental lines vs. all p53- significant changes and ZR-75-1 cells showed few changes RNAi expressing lines) to analyze all four cell lines simul- in response to p53 loss. EASE analysis of the ZR-75-1 taneously. In addition to identifying a common response, changes did not identify categories with clear relevance to this analysis had a larger sample size and thus, had better p53 signaling. Only one down-regulated gene ontology power to detect a broader range of p53-regulated genes. category (extracellular region) was identified. Induced There were 696 genes which responded significantly to gene categories were transition metal homeostasis genes p53 loss in the cell lines (1). Included in this list were and genes with unknown roles in biological processes. many known direct p53 targets including MDM2, p21 However, among the basal-like cell line models, ME16C (Cip1), GADD45A, and ribonucleotide reductase M2. All significantly down-regulated anti-apoptosis genes and of these genes had lower expression in p53-RNAi lines, HME-CC significantly up-regulated mitosis/proliferation consistent with expectation. In total, 357 of the 696 signif- genes. The strong mitotic signature of sham-treated HME- icantly altered genes had lower expression in p53-RNAi CC cells showed overlap with the strong mitotic signature lines; EASE analysis indicated that apoptosis genes, cell observed in DOX-treated HME-CCs. Thus, p53 loss had death genes, and regulators of programmed cell death different effects across cell type and cell line. were significantly over-represented. Conversely, there were 339 genes (of 696 significantly altered genes) that Common patterns of expression shared by most of the were more highly expressed in RNAi lines, including genes four lines were identified using a 2-class SAM (DOX- and Page 5 of 13 (page number not for citation purposes) % Control % Control % Control % Control BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 -100 -200 MCF-7 ZR-75-1 HME-CC ME16C Numb treated (gray) cells Figure 3 er of genes with significantly different expression following p53 loss (by RNAi) in sham-treated (black) or doxorubicin- Number of genes with significantly different expression following p53 loss (by RNAi) in sham-treated (black) or doxorubicin-treated (gray) cells. Sham-treated cells represented feeding controls, treated with fresh media and harvested at the same time points as the doxorubicin treated cells. Positive y-axis indicates number of genes up-regulated and negative y- axis indicates number of genes down-regulated by p53 knockdown. Luminal cell lines had the largest transcriptional response to p53 loss following induction, while HMEC cell lines had a stronger signature for p53 loss in the uninduced state. involved in mitosis, cell cycle control, and regulation of topoisomerase IIA. EASE analysis confirmed that genes DNA repair. involved in mitosis and cell cycle progression were signif- icantly over-represented in the set of genes that had higher Gene expression signatures of primary tumors with wild- expression in p53 mutant tumors. type and mutant p53 Gene expression data for primary breast tumors with A cluster (Figure 4D) enriched for genes associated with known p53 mutation status is publicly available [2,12]. the luminal/ER+ tumor subtypes (N-acetyltransferase 1, Using this data, we found that the expression of 747 genes estrogen receptor 1, putative G-protein-coupled receptor, was significantly correlated with p53 status (Figure 4A). trefoil factor 3, GATA binding protein 3, and X-box bind- The hierarchical cluster of these genes across the primary ing protein 1) was also present in this gene set [2,11,12]. tumors contained two branches (Figure 4B), one enriched This cluster was more highly expressed in wildtype for wild-type tumors (left branch, 45 of 53 wildtype sam- tumors, likely due to a larger representation of luminal ples) and one enriched for mutant tumors (right branch, tumors in this branch. In fact, when the intrinsic subtype 34 of 42 mutant samples). A proliferation cluster/signa- of each of the patients in Figure 4 was determined by clus- ture was differentially expressed across the two branches tering all 95 tumor samples using the intrinsic list of Sorlie of the dendrogram (Figure 4C). This cluster had higher et al. [12], a statistically significant association between expression in p53 mutants, and included the cell cycle p53 status and tumor subtype was observed (p = 0.002), associated genes cyclin A2, CDC28 subunit 1B, CDC2, with 31% of luminal tumors and 80% of basal-like cyclin-dependent kinase inhibitor 3, polo-like kinase, and tumors having mutant p53. Because the frequencies of Page 6 of 13 (page number not for citation purposes) number of genes BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 >3 >1.5 1:1 >1.5 >3 relative to control expression elongin C W81684 eukaryotic translation initiation factor 2 R93621 prothymosin, alpha gene sequence 28 AA442991 triosephosphate isomerase 1 AA663983 nuclear transport factor 2 N75595 chromosome 10 open reading frame 7 AA448289 small nuclear ribonucleoprotein polypeptide C AA253448 HSPC163 protein AA053139 HSPC163 protein H98963 CNAP1 AA668256 high mobility group AT-hook 1 AA448261 phosphofructokinase, platelet AA608558 BTG family, member 3 N52496 gamma-glutamyl hydrolase AA455800 neuregulin 1 R72075 MCM4 AA485983 barren homolog Drosophila N54344 AA026682 v-myb2 AA456878 polymyositis/scleroderma autoantigen 1 AA458994 forkhead box M1 AA129552 CDC28 protein kinase regulatory subunit 1B AA459292 T54121 E2F transcription factor 1 H61303 cyclin-dependent kinase inhibitor 3 AA284072 ubiquitin-conjugating enzyme E2C AA430504 mitogen-activated protein kinase 13 AA157499 trophinin associated protein tastin H94949 karyopherin alpha 2 RAG cohort 1 AA676460 ubiquitin carrier protein AA464019 pituitary tumor-transforming 1 AA430032 CDC28 protein kinase regulatory subunit 2 AA292964 CDC28 protein kinase regulatory subunit 2 AA010065 CDC20 cell division cycle 20 homolog AA598776 chromosome 10 open reading frame 3 AA131908 cell division cycle 2, G1 to S and G2 to M AA598974 cyclin A2 AA608568 kinesin family member 23 AA452513 centromere protein F, 350/400ka mitosin AA701455 polo-like kinase Drosophila AA629262 thyroid hormone receptor interactor 13 AA630784 MAD2 mitotic arrest deficient-like 1 yeast AA481076 serine/threonine kinase 6 R11407 topoisomerase DNA II alpha 170kDa AA504348 replication factor C activator 1 4, 37kDa H54751 hematological and neurological expressed 1 AA459865 small nuclear ribonucleoprotein D1 polypeptide H16255 tubulin, alpha 1 testis specific AA180742 AA010188 PRO2000 protein H58234 kinesin family member C1 N69491 N-acetyltransferase 1 R91802 N-acetyltransferase 1 T67128 N-acylsphingosine amidohydrolase 1 AA664155 estrogen receptor 1 AA164585 hypothetical protein DKFZp434L142 N95180 methylcrotonoyl-Coenzyme A carboxylase 2 beta N31952 hydroxysteroid 17-beta dehydrogenase 4 AA487914 putative G protein-coupled receptor H50224 trefoil factor 3 intestinal N74131 fms-related tyrosine kinase 1 AA058828 GATA binding protein 3 R31441 GATA binding protein 3 H72474 X-box binding protein 1 W90128 T74639 estrogen receptor 1 AA291702 nuclear receptor binding protein W79308 hypothetical protein LOC255743 AA029948 RAB GTPase binding effector protein 1 AA428477 T55728 AA504327 hypothetical protein FLJ21062 AA028171 chromosome 14 open reading frame 45 AA460298 KIAA0876 protein AA431721 hypothetical protein FLJ10980 N45467 signal peptide, CUB domain, EGF-like 2 W74079 hypothetical protein FLJ14001 N63001 MKL/myocardin-like 2AA151572 Gene expression p Figure 4 attern of tumor samples for 747 genes correlated with p53 status in tumors Gene expression pattern of tumor samples for 747 genes correlated with p53 status in tumors. The fold change relative to the median expression value across all tumors is shown. Colored bars in A illustrate the location of clusters C and D. The dendrogram in B shows that experiments were divided into two primary branches, one enriched for mutant (red) and one enriched for wildtype (green) tumors. Tumor sample names are red for mutants and green for wildtypes based on sequence analysis. Clusters enriched for proliferation and cell cycle genes (C) and for luminal/estrogen responsive tumor mark- ers (D) are shown. Page 7 of 13 (page number not for citation purposes) BC708B-BE BC708A-AF BC706B-AF BC706A-BE BC-HBC4-T1 BC405B-AF BC405A-BE BC104B-AF BC104A-BE BC308B-BE BC116A-BE BC121B-BE BC1257-MET BC108B-AF BC503B-BE BC106B-BE BC106A-AF BC108A-BE BC124B-AF BC124A-BE BC31-0 BC118B-BE BC118A-AF BC201B-BE BC24 BC125B-AF BC125A-BE BC4-LN4 BC38 BC704B-AF BC702B-BE BC702A-AF BC608B-BE BC608A-AF BC40 BC210B-AF BC610A-BE BC807A-BE BC710B-AF BC710A-BE BC-HBC3 BC-HBC2 BC711B-BE BC711A-AF BC112B-BE BC112A-AF BC213B-BE BC114A-BE BC110B-BE BC110A-AF BC117A-BE BC808A-BE BC808A-AF BC111A-BE BC303B-BE BC303A-AF BC16 BC107B-BE BC107A-AF BC115A-AF BC206B-AF BC206A-BE BC111B-BE BC309A-BE BC709B-BE BC123B-BE BC123A-AF BC2 BC14 BC790 BC119B-AF BC119A-BE BC-HBC5 BC606B-AF BC205B-AF BC23 BC208B-AF BC208A-BE BC404B-BE BC404A-AF BC805B-AF BC805A-BE BC214B-BE BC214A-AF BC115B-BE BC305B-AF BC305A-BE BC120B-AF BC120A-BE BC713A-BE BC605B-BE BC121A-AF BC-A BC35-0 BC17 BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 >3 >1.5 1:1 >1.5 >3 relative to control expression Neogenin homolog 1 chicken AA454591 Formin binding protein 1 T73844 Tubulin, alpha 2 AA426374 Cyclin-dependent kinase inhibitor 1A p21, Cip1 N23941 Damage-specific DNA binding protein 2, 48kDa AA406449 Membrane targeting tandem C2 domain containing 1 AA075284 Anterior pharynx defective 1B-like N21153 Mitogen-activated protein kinase kinase 4 AA293050 Cyclin D1 PRAD1: parathyroid adenomatosis 1 AA487486 Aminopeptidase puromycin sensitive R24894 Ring finger protein 103 AA425020 Hypothetical protein FLJ10980 N45467 KIAA0040 gene product AA465478 GATA binding protein 3 H72474 RAS-like, estrogen-regulated, growth inhibitor AA037415 Solute carrier family 39 zinc transporter, member 6 H29315 Transcription elongation factor A SII-like 1 AA451969 HGS_RE408 N62855 Hypothetical protein DKFZp434L142 N95180 BTG family, member 2 H69582 Transcription factor RAM2 AA486418 Prolyl endopeptidase AA664056 Transporter 1, ATP-binding cassette, sub-family B MDR/TAP AA487429 Hypothetical protein 384D8_6 T83097 Cell division cycle 25C W95000 MCM3 minichromosome maintenance deficient 3 S. cerevisiae AA455786 ATPase family, AAA domain containing 2 H58234 Cell division cycle 25B AA448659 Chromosome 21 open reading frame 45 W72813 Gamma-glutamyl hydrolase conjugase AA455800 Tubulin, alpha 1 testis specific AA180742 Forkhead box M1 AA129552 CDC28 protein kinase regulatory subunit 1B AA459292 V-myb myeloblastosis viral oncogene homolog avian-like 2 AA456878 Cyclin-dependent kinase inhibitor 3 AA284072 Ubiquitin-conjugating enzyme E2C AA430504 Serine/threonine kinase 6 R11407 Pituitary tumor-transforming 1 AA430032 Cell division cycle 2, G1 to S and G2 to M AA598974 Kinesin family member 23 AA452513 Centromere protein F, 350/400ka mitosin AA701455 Cyclin A2 AA608568 Thyroid hormone receptor interactor 13 AA630784 MAD2 mitotic arrest deficient-like 1 yeast AA481076 Chromosome 10 open reading frame 3 AA131908 Replication factor C activator 1 4, 37kDa H54751 Topoisomerase DNA II alpha 170kDa AA504348 Kinesin family member C1 N69491 Nudix nucleoside diphosphate linked moiety X-type motif 1 AA443998 Antigen identified by monoclonal antibody Ki-67 AA425973 Polymerase DNA directed, delta 1, catalytic subunit 125kDa AA429661 Snf7 homologue associated with Ali x 3 AA099747 Cluster Figure 5 analysis of the tumor samples using 52 genes correlated with p53 status in tumors and cell lines Cluster analysis of the tumor samples using 52 genes correlated with p53 status in tumors and cell lines. The fold change relative to the median expression value across all tumors is shown. The red dendrogram branch (A) is enriched for p53 mutant tumors (sample names labeled red) while the green dendrogram branch (B) is enriched for p53 wild-type tumors (sample names labeled green). The dendrogram showing the gene clusters is shown in C and D. p53 status varied significantly by subtype, the list of p53- CDC2, cyclin A1). However, this list excluded ER and associated genes defined by SAM includes genes that were many of the luminal tumor-associated genes shown in associated with subtype. Some of these genes may have no Figure 4D. This list still contains a few p53-regulated causal association with p53 defects, and thus, refinement genes that are also ER associated (such as GATA3), how- of this list using our in vitro data was performed. ever their presence on this list cannot be viewed as an arti- fact of their association with ER status. Combined in vitro and in vivo analysis to identify p53- regulated genes Patterns of expression for these 52 genes are shown across The in vitro experiments that we conducted contained iso- the primary tumor data in Figure 5. Again, two dendro- genic pairs of cell lines that were representative of both gram branches were evident: one enriched for p53 luminal and basal-like tumors. The in vivo experiments mutants (Figure 5A) and the other enriched for p53- represented tumors derived from 69 different individuals, wildtypes (Figure 5B). Figure 5 also shows two main clus- also representing both luminal and basal-like tumors. By ters of genes, one of which (Figure 5C) was enriched for comparing the p53-associated gene lists from the tumors genes that are known to be p53-regulated including p21 to the cell lines, we refined our gene list and obtained a list (Cip1), BTG2, and damage-specific DNA binding protein of genes that were common to both data sets, representing 2. EASE analysis confirmed that this cluster, which had a stereotypic p53 signature that held across diverse genetic lower expression in mutant tumors, contained DNA dam- backgrounds. There were 52 genes that were identified in age response genes and negative regulators of cell prolifer- common between the in vivo (747 genes) and in vitro (696 ation. The second gene cluster (Figure 5D) was more genes) lists. This 52-gene list retained GATA binding pro- highly expressed in mutant tumors, and EASE analysis tein 3 and many of the proliferation cluster genes in Fig- confirmed that this cluster of genes was enriched for mito- ure 4C (ATPase Family AAA domain containing 2, sis and proliferation genes. gamma-glutamyl hydrolase, MYBL2, CDC28 subunit 1B, Page 8 of 13 (page number not for citation purposes) BC709B-BE BC206B-AF BC206A-BE BC114A-BE BC706B-AF BC706A-BE BC111A-BE BC121A-AF BC303B-BE BC303A-AF BC807A-BE BC107B-BE BC107A-AF BC35-0 BC16 BC-A BC305B-AF BC305A-BE BC14 BC605B-BE BC309A-BE BC713A-BE BC120B-AF BC120A-BE BC123B-BE BC123A-AF BC111B-BE BC2 BC115B-BE BC214B-BE BC214A-AF BC119B-AF BC119A-BE BC805B-AF BC805A-BE BC-HBC5 BC404B-BE BC404A-AF BC23 BC208B-AF BC208A-BE BC606B-AF BC205B-AF BC790 BC-HBC3 BC213B-BE BC708A-AF BC808A-BE BC808A-AF BC110B-BE BC104B-AF BC104A-BE BC117A-BE BC115A-AF BC125B-AF BC118B-BE BC118A-AF BC17 BC125A-BE BC4-LN4 BC38 BC31-0 BC405A-BE BC108B-AF BC503B-BE BC201B-BE BC108A-BE BC106B-BE BC106A-AF BC704B-AF BC608B-BE BC608A-AF BC405B-AF BC121B-BE BC210B-AF BC124B-AF BC124A-BE BC708B-BE BC702A-AF BC-HBC4-T1 BC-HBC2 BC710B-AF BC610A-BE BC116A-BE BC24 BC1257-MET BC40 BC112B-BE BC112A-AF BC710A-BE BC308B-BE BC711B-BE BC711A-AF BC110A-AF BC702B-BE BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Survival analyses using the 52-gene p53 signature downregulated and upregulated genes. This finding is Kaplan-Meier survival analysis yielded highly significant consistent with the previous literature showing that p53 survival differences between groups from Figure 5A transactivates genes such as p21 and GADD45 and tran- (mutant-like) and 5B (wildtype-like) using the Sorlie et al. srepresses genes such as topoisomerase IIA and CDC2. data. As shown in Figure 6, the 52-gene expression signa- Inactivation of p53 affects both transactivation and tran- ture (p = 0.001) significantly predicted overall survival srepression to alter cell growth. Inactivation of p53 is also (OS), while true mutation status on this set of samples likely to cause downstream, indirect effects. As more was not significant (p = 0.06). The expression signature (p research is conducted to identify pathway signatures -5 = 2.2 × 10 ) and true mutation status (p = 0.001) also sig- [17,18], evidence is growing that most, if not all, pathway nificantly predicted relapse-free survival (RFS). To further signatures include both direct and indirect targets. How- evaluate the prognostic value of this 52-gene signature, we ever, these signatures still appear to show pathway-spe- performed survival analyses using two independent breast cific activity and represent valuable assays for pathway tumor data sets [published by Chang et al. (2005) and activity [19]. So, while we cannot conclude that these are Miller et al. (2005)]. Kaplan-Meier analysis showed that exclusively direct targets of p53, genes in our signature do this signature significantly predicted OS (Figure 5C, p = represent a common response to p53 loss in the breast. -10 -7 7.2 × 10 ) and RFS (p = 2.6 × 10 ) for the Chang et al. dataset, and disease-specific survival (p = 0.007, RFS and This common p53-response list is biologically relevant, as OS data unavailable) for the Miller et al. dataset. We also shown by its ability to predict survival in patients across performed multivariate analysis, using the Chang et al. multiple true test data sets. Some of the genes in the com- dataset. Controlling for standard clinical predictors (ER, mon expression profile have been previously identified in grade, node status, size, age, and treatment), Cox propor- other signatures of prognostic relevance (e.g. prolifera- tional hazards ratios were estimated for both OS (RH, tion-associated genes) and are likely to be regulated by 95% CI: 2.4, 1.3 – 4.4) and RFS (RH, 95% CI: 1.9, 1.2 – multiple oncogenic pathways. Our aim was not to identify 3.0). Thus, independent of standard clinical predictors the a new prognostic signature that improves on previously p53 expression classifier significantly predicted both OS published signatures. Rather, we aimed to demonstrate (p = 0.006) and RFS (p = 0.006). that events that are downstream of functional p53 loss are clearly associated with prognostic outcome, and are there- In our training data set (Sorlie et al.), the gene expression fore biologically relevant. The predictive accuracy of p53- classifier had 82% agreement with sequence-based muta- dependent gene expression profiles [14] supports a role tion status. True mutation status data was not available for for p53 in breast cancer prognosis. Previous estimates of the Chang et al. data set, but our classifier had 82% agree- the relative hazard (RH) associated with p53 loss range ment with sequence-based mutation status in the Miller et from 1 (no effect) to 23 [20]. Our data suggests that this al. data set. We were able to examine the location and type variability may relate to limitations of the methods for of mutations and compare them to classifier results using characterizing p53 status. p53 mutation status is most the Miller et al. data. Of the 29 mutants incorrectly classi- commonly characterized by direct DNA sequencing or by fied as wildtype, 25 (86%) were either missense muta- immunohistochemistry (IHC). Sequencing analysis can- tions or in-frame insertions/deletions. This differs not distinguish sequence variants with and without func- significantly (p = 0.02) from the percentage of mutations tional consequences. A meta-analysis of p53 mutation that were missense or in-frame among correctly classified databases has demonstrated methodological biases asso- mutants (58%). Among the missense tumors, mutations ciated with sequence-based mutation status [21]. IHC in DNA binding domains of the p53 protein were also sig- analysis treats accumulation of p53 protein as indicative nificantly more frequent (p = 0.01) in tumors classified as of mutation; thus, IHC is biased toward identification of mutant (87%) than wildtype (45%). missense mutants and completely misses mutations that cause loss of p53 protein. With either IHC or sequence analysis, a narrow emphasis on p53 mutations can miss Discussion Identification of a p53-responsive signature in breast can- functional impairments in the p53 pathway (e.g. MDM2 cer is confounded by associations with important tumor amplification). These challenges could account for widely characteristics like ER status. The common p53 expression divergent estimates of p53's role in prognosis. signature shared by cell lines and tumors in this study addressed this confounding by conducting cell lines Our data analysis also showed that there was good agree- experiments with ER positive and ER negative cell lines, ment between mutation status and expression profiles. and using experimental data to refine the gene lists Using our 52 gene list, there was >80% agreement derived from observational studies in patients. The result- between p53 mutation status and p53 expression class in ing 52 gene, p53-associated list contained two biologi- both the Sorlie et al. and Miller et al. datasets. This high cally relevant gene clusters corresponding to level of agreement across data sets attests to the fact that Page 9 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Stanford, Gene Expression 0.8 Censored 0.6 p53 wildtype-like p53 mutant-like 0.4 -4 p=9.2X10 0.2 0 2040 6080 100 months Stanford, Mutation Status 0.8 Censored 0.6 p53 wildtype p53 mutant 0.4 p=0.06 0.2 0 2040 6080 100 months Chang et al., Gene Expression 0.8 Censored 0.6 p53 wildtype-like p53 mutant-like 0.4 -10 p=7.2X10 0.2 0 50 100 150 200 250 OS months Kaplan-Meier survival curves for p b Figure 6 ased p53 functional category or mutation atients wi stat th p53 wi us ldtype (solid line) or mutant (dashed line) tumors using gene expression- Kaplan-Meier survival curves for patients with p53 wildtype (solid line) or mutant (dashed line) tumors using gene expression-based p53 functional category or mutation status. Overall survival analysis comparing the two gene- expression based dendrogram groups (tumors in Figure 5A vs. tumors in Figure 5B) yielded a highly significant difference in sur- vival, which compares favorably with mutation status. Both analyses used 66 tumor samples and included 26 events. Survival analysis on a separate data set (Chang et al., 2005) including 337 tumors and 79 events (C) also yielded highly significant differ- ences based on gene expression classification. Page 10 of 13 (page number not for citation purposes) Probability Probability Probability BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 the signature is indicative of p53 across a wide range of A strength of our study was the use of cell line experiments cell backgrounds. If the signature were merely correlated to control a range of variables that influence p53- with proliferation, ER status, or another tumor character- response. The in vitro setting allowed for control of expres- istic, then poor concordance with p53 mutational status sion of p53 protein, breast cancer subtype, and p53- would be expected in cross validation. The samples where inducing events [26-29]. However, the in vitro approach is gene expression and mutation status disagree may repre- limited in that a small number of cell lines can be reason- sent true differences in the functional p53 pathway. For ably examined, representing only a handful of tumors. By example, the tumor BC606 was p53 wildtype by sequence combining the in vitro expression data with data from but clustered with p53 mutants using the 52 gene classi- human tumors assayed before and after DOX treatment, fier. This tumor overexpressed MDM2 mRNA (data not we examined a much wider range of individual responses shown), a key negative regulator of p53. Among the false to p53 loss than cell line experiments could reasonably negatives (sequence mutant but wildtype expression sig- examine, and performed a controlled experiment that nature), our analysis of the Miller et al. data showed that cannot be accomplished in humans. Previous studies have misclassified mutants had higher proportions of mutation characterized p53-responses in breast cancer using gene types that are likely to be less deleterious (missense muta- expression data from tumors and statistical models to try tions and in-frame insertion/deletions). to control the effects of breast cancer heterogeneity. For example, in Miller et al. [14], proliferation and ER status In addition to identifying a stereotyped signature associ- were treated as statistical confounders of the p53-gene ated with p53 loss, these results demonstrate that the rel- expression relation (based on p53 status and outcome ative importance of p53-regulated functions such as cell both having crude associations with grade and ER status). cycle control, DNA repair, and apoptosis are subject to sig- Thus, the final p53-mutant like gene expression profile nificant inter-individual variation. Each cell line displayed presented by Miller et al. [14] was derived using a statisti- a unique p53 response signature. However, similarities cal model that adjusted for these variables. Such adjust- according to cell type were also evident. Both of the ment assumes that grade and ER status are causally HMEC-derived cell lines showed a greater response to p53 upstream of p53 status. If grade and ER status are down- loss in the untreated state, while the MCF-7 and ZR-75-1 stream of p53 status, this approach will introduce a bias lines showed a stronger p53-regulated signature following toward exclusion of grade and ER-associated genes, even DOX treatment. These results extend previous observa- though those genes are influenced by p53 loss. In short, tions [5] suggesting a difference in p53 signaling pathways the validity of statistical adjustment depends upon having between luminal and basal-like breast cancers. These the correct model for the relation between breast cancer inherent differences in p53 signaling could lead to differ- subtype, ER status, proliferation and p53 biology. In the ent selection pressure for p53 loss in each cell type. Such presence of heterogeneity, experimental and biologically- differences could also explain the divergent rates of p53 based methods for assessing gene expression in relation to mutation by subtype that have been reported here and in p53 status are preferable to statistical methods. a population-based study [3]. Many of the genes associated with p53 loss in this analysis Our data reconfirmed the complex relation between were of prior interest in breast cancer. For example, chemosensitivity and p53 status [22]. Previous reports GATA3 is involved in growth control and maintenance of have demonstrated either heightened chemosensitivity of the differentiated state in breast epithelial cells and has p53 mutants [23,24] or heightened chemoresistance [25]. been hypothesized to play a role in tumorigenesis of ER- This paradox is reflected in our study where the four cell positive breast tumors [30]. p21 (Cip1), CDC2, and lines we studied varied widely in their DOX sensitivity fol- CDC25C are genes involved in p53-mediated regulation lowing p53-knockdown. Because p53 regulates many dif- of cell cycle arrest [31]. Pituitary tumor-transforming 1 is ferent pathways, including DNA repair, apoptosis, and a recently identified oncogene with p53-dependent and cell proliferation, and the balance of these various path- p53-independent functions [32]. Thus, as might be ways determines chemosensitivity, it is not surprising to expected, many of the direct and indirect targets of p53 find that both individuals and cell lines have responses to identified here are known p53- and cancer-associated chemotherapy that are difficult to predict. DOX also has genes. Further investigation of the specific p53 targets that many p53-independent toxicity mechanisms, so a diver- are regulated in common across breast cancers and inves- gence in sensitivity across lines may also reflect differences tigation of those that are differentially regulated across in how DOX toxicity is manifest across lines. These analy- breast cancer subtypes will add to our understanding of ses have demonstrated that breast cell lines have individ- the biology of breast cancer and breast cancer subtypes. ual, distinct responses to p53 loss. The genetic background of a given cell line, including cell type of ori- gin, plays a prominent role in mediating p53 signaling. Page 11 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Foundation. M.A.T. was supported by NIEHS Individual National Research Conclusion Service Award (NRSA) 5F32ES012374 and the UNC Lineberger Cancer In the presence of breast cancer heterogeneity, controlled Control Education Program (R25 CA57726). experiments in vitro combined with in vivo analyses, allowed for refinement of a p53-associated gene set. The References refined 52-gene list excluded genes that were associated 1. Borresen-Dale AL: TP53 and breast cancer. Hum Mutat 2003, with breast cancer subtype and not downstream of p53. 21:292-300. 2. 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Usary J, Llaca V, Karaca G, Presswala S, Karaca M, He X, Langerod A, Karesen R, Oh DS, Dressler LG, Lonning PE, Strausberg RL, Chanock S, Borresen-Dale AL, Perou CM: Mutation of GATA3 in human breast tumors. Oncogene 2004, 23:7669-7678. 31. Harris SL, Levine AJ: The p53 pathway: positive and negative feedback loops. Oncogene 2005, 24:2899-2908. 32. Hamid T, Kakar SS: PTTG and cancer. Histol Histopathol 2003, 18:245-251. Pre-publication history The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2407/6/276/pre pub Publish with Bio Med Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." 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Gene expression patterns associated with p53 status in breast cancer

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2006 Troester et al; licensee BioMed Central Ltd.
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Abstract

Background: Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function). Methods: The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors. Results: Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data. Conclusion: In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes. Page 1 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 GTAATCTAC) was also prepared as a mismatch control. Background The tumor suppressor p53 is mutated in 30% of breast HEK-293T cells were transfected with 10 micrograms each of pSUPER.RETRO.puro vector, pVpack-GP (Stratagene), cancers [1], but rates of p53 mutation vary depending upon the subtype of breast cancer. For example, p53 and pVpack-Ampho (Stratagene) using Lipofectamine mutations are found more frequently in aggressive estro- Reagent and PLUS Reagent (Invitrogen). Supernatants gen receptor (ER)-negative breast cancers [1], and have containing replication-incompetent retrovirus were col- been shown to correlate with breast cancer subtype in lected 48 hours after transfection and applied to all four gene expression studies [2] and in a population-based cell lines. Stable populations of cell lines expressing p53- study [3]. Genetic abnormalities such as amplified HER2/ RNAi or mismatch-RNAi were selected for two weeks in 1 ERBB2 [1] and aneuploidy [4] are also frequently associ- µg per mL puromycin. ated with p53 mutation status. These correlations suggest intrinsic heterogeneity of p53 signaling across breast can- Western blots cer subtypes. Cells were treated for 24 h with 1 µM DOX, and cell free extracts, protein quantitation, and denaturation were as Gene expression studies can help to characterize breast described previously [5]. Forty µg of protein were electro- cancer heterogeneity. Previous in vitro studies of gene phoresed on a 4–20% Tris-HCl Criterion precast gel (Bio- expression have demonstrated that cell line models of Rad) and transferred to a Hybond-P membrane luminal breast cancers show a strong stress response fol- (Amersham Biosciences) by electroblotting. The blots lowing chemotherapeutic treatment, with notable were probed with antibodies against p53 (Santa Cruz; changes in p53-regulated genes such as p21 (Cip1). The D01) and β-actin (Abcam, AC-15) and then with anti- same magnitude of p53-regulated responses was not mouse IgG horseradish peroxidase linked whole antibody observed for cell line models of basal-like breast cancer from sheep (Amersham). Enhanced chemiluminescence [5]. Inherent differences in p53 signaling and function was used for detection (SuperSignal West Pico Chemilu- according to cell type of origin could account for the asso- minescent Substrate, Pierce). ciation between rates of p53 mutation and breast cancer subtype. In this study, we engineered isogenic cell line Microarray experiments Cell lines were grown, treated for 12, 24, or 36 h with pairs with and without p53 function using RNA interfer- ence (RNAi) and examined the stress responses of parent DOX at the IC50 concentration, and harvested using a and RNAi-transduced cell lines. Our aim was to assess previously described protocol [5]. Feeding control (sham) how variation in cell line backgrounds alters the effects of and reference mRNA samples were prepared as described p53 loss. We also aimed to identify a common response previously [5]. Cy3- and Cy5-labeled cDNAs were synthe- to p53 loss that is shared by most breast cancers. Thus, we sized from control or treated cell line mRNA, respectively, compared the lists of p53-responsive genes in vitro to gene according to a direct labeling protocol (Agilent Technolo- lists derived from in vivo breast tumor data to identify a set gies), and were hybridized to Human 1A oligonucleotide of common p53 responsive genes. The biological rele- arrays (Agilent Technologies). All microarray raw data vance of this common p53 signature was assessed by tables have been deposited in the Gene Expression Omni- using this gene list to predict outcomes on independent bus under the accession number of GSE3178 (submitter test data sets of breast cancer patients. C. Perou). Identification of p53-dependent DOX-response signature Methods Cells and culture conditions from microarray data Two hTERT immortalized Human Mammary Epithelial For all comparisons, in vitro and in vivo as described (HME) cell lines (ME16C and HME-CC) and two estab- below, genes that were significantly different in expres- lished breast cancer cell lines (MCF-7 and ZR-75-1) were sion were identified using a 2-class, unpaired Significance cultured as described previously [5]. A mitochondrial dye Analysis of Microarrays (SAM) [7]; for the SAM analysis, conversion (MTT) assay was used to measure cell line the data were first filtered to exclude genes that did not responses to 36 h of treatment with 0 – 10 µM doxoru- have mean signal intensity greater than twice the median bicin hydrochloride (DOX) [5]. background value for both the red and green channel in at least 70% of the experiments. The SAM delta values were Short hairpin RNAs (RNAi) against p53 were constructed adjusted to obtain the largest gene list that gave a false dis- using a 19-mer sequence (GACTCCAGTGGTAATCTAC) covery rate of less than 5%. Using the SAM-derived gene described previously [6], but using the pSU- lists, average linkage hierarchical cluster analysis was con- PER.RETRO.puro vector with stuffer (Oligo Engine, Seat- ducted using Pearson correlation in the Cluster program tle, WA). A version of this vector containing two and the data were visualized in Treeview [8,9]. EASE, the mismatches within the 19-mer sequence (GACTCCGGTT- Page 2 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Expression Analysis Systematic Explorer was used to iden- or Miller et al. tumor was classified according to the near- tify enriched biological themes in gene lists [10]. est centroid as determined by Spearman correlation. Each cell line was examined for p53 response in both Other statistical analyses untreated and DOX-treated states. To identify the gene Survival analyses were conducted using Sorlie et al. tumor expression effects of p53 loss in DOX-treated cells (p53- data (excluding duplicate samples from the same person, induced state), parent cell lines treated with DOX (n = 3 resulting in a total of 66 patients representing 31 disease- for each cell line) were compared to RNAi-transductants specific and 26 overall survival events for survival analy- treated with DOX (n = 3 for each cell line). To identify the ses), Change et al. tumor data [337 patients: 295 patients gene expression effects of p53 loss in the absence of DOX from [13] and 42 tumors published in an earlier paper treatment, sham-treated parent cell lines (wildtype p53) [16] from the same group, representing 126 disease spe- were compared to sham-treated RNAi-transductants (n = cific and 79 overall survival events] and Miller et al. [14] 3 for both treatment groups in each cell line). However, to data (236 patients, 52 disease specific events). For analy- derive a list of genes that were differentially expressed in ses of the Miller et al. dataset, patients that had survived at both in vitro and the in vivo data sets, the common p53 least ten years were censored to be consistent with previ- response across all four cell lines was the most relevant. ous analyses [14]. Kaplan Meier analyses were conducted Thus, we also performed an analysis comparing all RNAi- using WinStat for Microsoft Excel. transduced cell line experiments (n = 24) to all parent cell line experiments (n = 24). The resulting list represented Because the large data set of Chang et al. also included the common response to p53 loss across cell lines. data on other prognostic variables, Cox proportional haz- ards modeling was conducted (SAS version 9.1). The To identify the gene expression signature associated with reduced model that included ER status (positive vs. nega- p53 in vivo, we used primary breast tumor data [2,11,12] tive), tumor size (≤ 2 cm vs. > 2 cm), lymph node status that is publicly available from the Stanford Microarray (indicator coding with three categories: 0, 1–3, > 3 posi- Database and the Gene Expression Omnibus. DOX- tive nodes or metastatic), age (in decades), grade (indica- treated patients for which p53 status had been determined tor coding with three categories: 1, 2, 3), and treatment by sequence analysis [2] were included in our analysis (yes if treatment with chemo and/or hormonal therapy, (102 tumor samples, including 8 normal-like breast sam- no if no adjuvant therapy) was compared to a full model ples, one unclassified tumor, and 37 before and after that also included a binary variable indicating p53 classi- pairs, representing 69 patients in total). All tumor sub- fication (based on gene-expression). types described in Sorlie et al. [2] [classified using intrinsic analysis [12]] were included, except true normal breast To determine if p53 status differed according to tumor and normal-like breast tumor samples. This sample set subtype, a Fisher-Freeman-Halton (FFH) exact test was also included tumors collected before and after treatment conducted using SAS version 9.1 (Cary, NC). Analyses of with doxorubicin. The gene expression patterns of the p53 sequence-based mutation characteristics (e.g. missense/ mutant samples (n = 43) were compared to those of the in-frame vs. nonsense and frameshift, missense DNA p53 wildtype samples (n = 52). binding vs. non-DNA binding) in association with gene expression classification were also conducted using FFH Identification of p53 functional status in independent test exact tests. data sets A final 52 gene list was derived by identifying those genes Results that were differentially expressed in response to p53 loss Gene expression and phenotypic analysis of cell lines expressing p53 RNAi in both the in vitro and in vivo data sets. These genes were matched to publicly available array data [13,14], using To study the effects of p53 loss in vitro, an RNAi construct unique Unigene identifiers. Of the 52 genes, 48 and 50 specific for p53 [6] was stably expressed in MCF-7, ZR-75- were present on the Chang et al. data set and Miller et al. 1, ME16C and HME-CC cells. All four cell lines had data sets, respectively. Microarray platform/source sys- wildtype p53 sequence and expressed functional p53 tematic biases between the training and the test sets were (showed p53 induction in response to treatment with corrected using Distance Weighted Discrimination DOX, Figure 1) prior to transduction with the p53-RNAi (DWD) [15]. To classify tumors in the independent test retroviral construct. Expression of p53-RNAi substantially sets (Chang et al. or Miller et al.) as p53-functional or not, knocked down p53 protein levels in both treated and two centroids were created using the Sorlie et al. training untreated cells (Figure 1). set. The centroids were based on average gene expression in tumors in Figure 5A (mutant enriched) vs. that of The phenotypic effects of p53 knock-down varied by cell tumors in Figure 5B (wildtype enriched). Each Chang et al. line (Figure 2). MCF-7 cells became more resistant to Page 3 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 MCF-7 ZR-75-1 MM wt MM RNAi wt RNAi + + + + + DOX - - - - - p53 β−actin ME16C HME-CC MM MM wt RNAi wt RNAi + + + + + DOX + - - - - - p53 β−actin p Figure 1 53 protein expression is knocked down by RNAi expression p53 protein expression is knocked down by RNAi expression. The expression of p53 was examined by Western blot analysis of extracts from MCF-7, ZR-75-1, HME-CC, and ME16C cell line parents and the same cell lines stably transduced with p53-targeted RNAi vector or p53 mismatch (MM) RNAi vector. Treatment with doxorubicin (24 h, 1µM) induced p53 expres- sion in all cell lines and transductants, but induced levels were markedly lower in the p53-RNAi cells. DOX, while ZR-75-1, ME16C and HME-CC cells dis- treated samples (DOX-treated parent vs. DOX-treated played no change in DOX sensitivity. Consistent with the RNAi-expressing), three cell lines (HME-CC, MCF-7 and different responses in the DOX sensitivity assay, gene ZR-75-1) increased genes involved in mitosis after trans- expression signatures significantly associated with p53 duction with p53-RNAi. ME16C did not induce categories loss (in 2-class SAM analyses) were different for each cell of mitosis genes, but did suppress negative regulators of line and cell type (gene lists are given in Additional File 1). cell proliferation. Significant down-regulation of apop- As shown in Figure 3, MCF-7 and ZR-75-1 cells showed a totic genes was only seen in ZR-75-1 cells. stronger p53-dependent signature following treatment with DOX. The immortalized HMECs, conversely, showed The p53-response observed among DOX-treated cell lines stronger p53-dependent signatures in the absence of DOX differed from the p53-response in sham-treated cell lines. (i.e. parents vs. RNAi, both untreated). Analysis of SAM- For example, the luminal-like cell lines (MCF-7 and ZR- derived gene lists using gene ontology software (EASE) 75-1) that had the largest transcriptional response to showed enrichment for categories of genes with known DOX, showed a modest response to p53 loss in sham- relevance to p53 function. For example, among the DOX- treated samples (sham-treated parent versus sham-treated Page 4 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 ZR-75-1 MCF-7 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Doxorubicin (µM) Doxorubicin (µM) HME-CC ME16C 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Doxorubicin (µM) Doxorubicin (µM) Chemosensitivity Figure 2 is altered in the MCF-7 cell line following transduction with p53-RNAi Chemosensitivity is altered in the MCF-7 cell line following transduction with p53-RNAi. ME16C, HME-CC, and ZR-75-1 cell lines had similar chemosensitivity curves for wildtype (x), p53-targeted RNAi expressing cells (solid square) and p53 mismatch RNAi expressing cells (open square). Only the p53-RNAi MCF-7 cells showed significant chemoresistance. RNAi-expressing). Sham-treated MCF-7 cells showed no sham-treated combined from all parental lines vs. all p53- significant changes and ZR-75-1 cells showed few changes RNAi expressing lines) to analyze all four cell lines simul- in response to p53 loss. EASE analysis of the ZR-75-1 taneously. In addition to identifying a common response, changes did not identify categories with clear relevance to this analysis had a larger sample size and thus, had better p53 signaling. Only one down-regulated gene ontology power to detect a broader range of p53-regulated genes. category (extracellular region) was identified. Induced There were 696 genes which responded significantly to gene categories were transition metal homeostasis genes p53 loss in the cell lines (1). Included in this list were and genes with unknown roles in biological processes. many known direct p53 targets including MDM2, p21 However, among the basal-like cell line models, ME16C (Cip1), GADD45A, and ribonucleotide reductase M2. All significantly down-regulated anti-apoptosis genes and of these genes had lower expression in p53-RNAi lines, HME-CC significantly up-regulated mitosis/proliferation consistent with expectation. In total, 357 of the 696 signif- genes. The strong mitotic signature of sham-treated HME- icantly altered genes had lower expression in p53-RNAi CC cells showed overlap with the strong mitotic signature lines; EASE analysis indicated that apoptosis genes, cell observed in DOX-treated HME-CCs. Thus, p53 loss had death genes, and regulators of programmed cell death different effects across cell type and cell line. were significantly over-represented. Conversely, there were 339 genes (of 696 significantly altered genes) that Common patterns of expression shared by most of the were more highly expressed in RNAi lines, including genes four lines were identified using a 2-class SAM (DOX- and Page 5 of 13 (page number not for citation purposes) % Control % Control % Control % Control BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 -100 -200 MCF-7 ZR-75-1 HME-CC ME16C Numb treated (gray) cells Figure 3 er of genes with significantly different expression following p53 loss (by RNAi) in sham-treated (black) or doxorubicin- Number of genes with significantly different expression following p53 loss (by RNAi) in sham-treated (black) or doxorubicin-treated (gray) cells. Sham-treated cells represented feeding controls, treated with fresh media and harvested at the same time points as the doxorubicin treated cells. Positive y-axis indicates number of genes up-regulated and negative y- axis indicates number of genes down-regulated by p53 knockdown. Luminal cell lines had the largest transcriptional response to p53 loss following induction, while HMEC cell lines had a stronger signature for p53 loss in the uninduced state. involved in mitosis, cell cycle control, and regulation of topoisomerase IIA. EASE analysis confirmed that genes DNA repair. involved in mitosis and cell cycle progression were signif- icantly over-represented in the set of genes that had higher Gene expression signatures of primary tumors with wild- expression in p53 mutant tumors. type and mutant p53 Gene expression data for primary breast tumors with A cluster (Figure 4D) enriched for genes associated with known p53 mutation status is publicly available [2,12]. the luminal/ER+ tumor subtypes (N-acetyltransferase 1, Using this data, we found that the expression of 747 genes estrogen receptor 1, putative G-protein-coupled receptor, was significantly correlated with p53 status (Figure 4A). trefoil factor 3, GATA binding protein 3, and X-box bind- The hierarchical cluster of these genes across the primary ing protein 1) was also present in this gene set [2,11,12]. tumors contained two branches (Figure 4B), one enriched This cluster was more highly expressed in wildtype for wild-type tumors (left branch, 45 of 53 wildtype sam- tumors, likely due to a larger representation of luminal ples) and one enriched for mutant tumors (right branch, tumors in this branch. In fact, when the intrinsic subtype 34 of 42 mutant samples). A proliferation cluster/signa- of each of the patients in Figure 4 was determined by clus- ture was differentially expressed across the two branches tering all 95 tumor samples using the intrinsic list of Sorlie of the dendrogram (Figure 4C). This cluster had higher et al. [12], a statistically significant association between expression in p53 mutants, and included the cell cycle p53 status and tumor subtype was observed (p = 0.002), associated genes cyclin A2, CDC28 subunit 1B, CDC2, with 31% of luminal tumors and 80% of basal-like cyclin-dependent kinase inhibitor 3, polo-like kinase, and tumors having mutant p53. Because the frequencies of Page 6 of 13 (page number not for citation purposes) number of genes BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 >3 >1.5 1:1 >1.5 >3 relative to control expression elongin C W81684 eukaryotic translation initiation factor 2 R93621 prothymosin, alpha gene sequence 28 AA442991 triosephosphate isomerase 1 AA663983 nuclear transport factor 2 N75595 chromosome 10 open reading frame 7 AA448289 small nuclear ribonucleoprotein polypeptide C AA253448 HSPC163 protein AA053139 HSPC163 protein H98963 CNAP1 AA668256 high mobility group AT-hook 1 AA448261 phosphofructokinase, platelet AA608558 BTG family, member 3 N52496 gamma-glutamyl hydrolase AA455800 neuregulin 1 R72075 MCM4 AA485983 barren homolog Drosophila N54344 AA026682 v-myb2 AA456878 polymyositis/scleroderma autoantigen 1 AA458994 forkhead box M1 AA129552 CDC28 protein kinase regulatory subunit 1B AA459292 T54121 E2F transcription factor 1 H61303 cyclin-dependent kinase inhibitor 3 AA284072 ubiquitin-conjugating enzyme E2C AA430504 mitogen-activated protein kinase 13 AA157499 trophinin associated protein tastin H94949 karyopherin alpha 2 RAG cohort 1 AA676460 ubiquitin carrier protein AA464019 pituitary tumor-transforming 1 AA430032 CDC28 protein kinase regulatory subunit 2 AA292964 CDC28 protein kinase regulatory subunit 2 AA010065 CDC20 cell division cycle 20 homolog AA598776 chromosome 10 open reading frame 3 AA131908 cell division cycle 2, G1 to S and G2 to M AA598974 cyclin A2 AA608568 kinesin family member 23 AA452513 centromere protein F, 350/400ka mitosin AA701455 polo-like kinase Drosophila AA629262 thyroid hormone receptor interactor 13 AA630784 MAD2 mitotic arrest deficient-like 1 yeast AA481076 serine/threonine kinase 6 R11407 topoisomerase DNA II alpha 170kDa AA504348 replication factor C activator 1 4, 37kDa H54751 hematological and neurological expressed 1 AA459865 small nuclear ribonucleoprotein D1 polypeptide H16255 tubulin, alpha 1 testis specific AA180742 AA010188 PRO2000 protein H58234 kinesin family member C1 N69491 N-acetyltransferase 1 R91802 N-acetyltransferase 1 T67128 N-acylsphingosine amidohydrolase 1 AA664155 estrogen receptor 1 AA164585 hypothetical protein DKFZp434L142 N95180 methylcrotonoyl-Coenzyme A carboxylase 2 beta N31952 hydroxysteroid 17-beta dehydrogenase 4 AA487914 putative G protein-coupled receptor H50224 trefoil factor 3 intestinal N74131 fms-related tyrosine kinase 1 AA058828 GATA binding protein 3 R31441 GATA binding protein 3 H72474 X-box binding protein 1 W90128 T74639 estrogen receptor 1 AA291702 nuclear receptor binding protein W79308 hypothetical protein LOC255743 AA029948 RAB GTPase binding effector protein 1 AA428477 T55728 AA504327 hypothetical protein FLJ21062 AA028171 chromosome 14 open reading frame 45 AA460298 KIAA0876 protein AA431721 hypothetical protein FLJ10980 N45467 signal peptide, CUB domain, EGF-like 2 W74079 hypothetical protein FLJ14001 N63001 MKL/myocardin-like 2AA151572 Gene expression p Figure 4 attern of tumor samples for 747 genes correlated with p53 status in tumors Gene expression pattern of tumor samples for 747 genes correlated with p53 status in tumors. The fold change relative to the median expression value across all tumors is shown. Colored bars in A illustrate the location of clusters C and D. The dendrogram in B shows that experiments were divided into two primary branches, one enriched for mutant (red) and one enriched for wildtype (green) tumors. Tumor sample names are red for mutants and green for wildtypes based on sequence analysis. Clusters enriched for proliferation and cell cycle genes (C) and for luminal/estrogen responsive tumor mark- ers (D) are shown. Page 7 of 13 (page number not for citation purposes) BC708B-BE BC708A-AF BC706B-AF BC706A-BE BC-HBC4-T1 BC405B-AF BC405A-BE BC104B-AF BC104A-BE BC308B-BE BC116A-BE BC121B-BE BC1257-MET BC108B-AF BC503B-BE BC106B-BE BC106A-AF BC108A-BE BC124B-AF BC124A-BE BC31-0 BC118B-BE BC118A-AF BC201B-BE BC24 BC125B-AF BC125A-BE BC4-LN4 BC38 BC704B-AF BC702B-BE BC702A-AF BC608B-BE BC608A-AF BC40 BC210B-AF BC610A-BE BC807A-BE BC710B-AF BC710A-BE BC-HBC3 BC-HBC2 BC711B-BE BC711A-AF BC112B-BE BC112A-AF BC213B-BE BC114A-BE BC110B-BE BC110A-AF BC117A-BE BC808A-BE BC808A-AF BC111A-BE BC303B-BE BC303A-AF BC16 BC107B-BE BC107A-AF BC115A-AF BC206B-AF BC206A-BE BC111B-BE BC309A-BE BC709B-BE BC123B-BE BC123A-AF BC2 BC14 BC790 BC119B-AF BC119A-BE BC-HBC5 BC606B-AF BC205B-AF BC23 BC208B-AF BC208A-BE BC404B-BE BC404A-AF BC805B-AF BC805A-BE BC214B-BE BC214A-AF BC115B-BE BC305B-AF BC305A-BE BC120B-AF BC120A-BE BC713A-BE BC605B-BE BC121A-AF BC-A BC35-0 BC17 BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 >3 >1.5 1:1 >1.5 >3 relative to control expression Neogenin homolog 1 chicken AA454591 Formin binding protein 1 T73844 Tubulin, alpha 2 AA426374 Cyclin-dependent kinase inhibitor 1A p21, Cip1 N23941 Damage-specific DNA binding protein 2, 48kDa AA406449 Membrane targeting tandem C2 domain containing 1 AA075284 Anterior pharynx defective 1B-like N21153 Mitogen-activated protein kinase kinase 4 AA293050 Cyclin D1 PRAD1: parathyroid adenomatosis 1 AA487486 Aminopeptidase puromycin sensitive R24894 Ring finger protein 103 AA425020 Hypothetical protein FLJ10980 N45467 KIAA0040 gene product AA465478 GATA binding protein 3 H72474 RAS-like, estrogen-regulated, growth inhibitor AA037415 Solute carrier family 39 zinc transporter, member 6 H29315 Transcription elongation factor A SII-like 1 AA451969 HGS_RE408 N62855 Hypothetical protein DKFZp434L142 N95180 BTG family, member 2 H69582 Transcription factor RAM2 AA486418 Prolyl endopeptidase AA664056 Transporter 1, ATP-binding cassette, sub-family B MDR/TAP AA487429 Hypothetical protein 384D8_6 T83097 Cell division cycle 25C W95000 MCM3 minichromosome maintenance deficient 3 S. cerevisiae AA455786 ATPase family, AAA domain containing 2 H58234 Cell division cycle 25B AA448659 Chromosome 21 open reading frame 45 W72813 Gamma-glutamyl hydrolase conjugase AA455800 Tubulin, alpha 1 testis specific AA180742 Forkhead box M1 AA129552 CDC28 protein kinase regulatory subunit 1B AA459292 V-myb myeloblastosis viral oncogene homolog avian-like 2 AA456878 Cyclin-dependent kinase inhibitor 3 AA284072 Ubiquitin-conjugating enzyme E2C AA430504 Serine/threonine kinase 6 R11407 Pituitary tumor-transforming 1 AA430032 Cell division cycle 2, G1 to S and G2 to M AA598974 Kinesin family member 23 AA452513 Centromere protein F, 350/400ka mitosin AA701455 Cyclin A2 AA608568 Thyroid hormone receptor interactor 13 AA630784 MAD2 mitotic arrest deficient-like 1 yeast AA481076 Chromosome 10 open reading frame 3 AA131908 Replication factor C activator 1 4, 37kDa H54751 Topoisomerase DNA II alpha 170kDa AA504348 Kinesin family member C1 N69491 Nudix nucleoside diphosphate linked moiety X-type motif 1 AA443998 Antigen identified by monoclonal antibody Ki-67 AA425973 Polymerase DNA directed, delta 1, catalytic subunit 125kDa AA429661 Snf7 homologue associated with Ali x 3 AA099747 Cluster Figure 5 analysis of the tumor samples using 52 genes correlated with p53 status in tumors and cell lines Cluster analysis of the tumor samples using 52 genes correlated with p53 status in tumors and cell lines. The fold change relative to the median expression value across all tumors is shown. The red dendrogram branch (A) is enriched for p53 mutant tumors (sample names labeled red) while the green dendrogram branch (B) is enriched for p53 wild-type tumors (sample names labeled green). The dendrogram showing the gene clusters is shown in C and D. p53 status varied significantly by subtype, the list of p53- CDC2, cyclin A1). However, this list excluded ER and associated genes defined by SAM includes genes that were many of the luminal tumor-associated genes shown in associated with subtype. Some of these genes may have no Figure 4D. This list still contains a few p53-regulated causal association with p53 defects, and thus, refinement genes that are also ER associated (such as GATA3), how- of this list using our in vitro data was performed. ever their presence on this list cannot be viewed as an arti- fact of their association with ER status. Combined in vitro and in vivo analysis to identify p53- regulated genes Patterns of expression for these 52 genes are shown across The in vitro experiments that we conducted contained iso- the primary tumor data in Figure 5. Again, two dendro- genic pairs of cell lines that were representative of both gram branches were evident: one enriched for p53 luminal and basal-like tumors. The in vivo experiments mutants (Figure 5A) and the other enriched for p53- represented tumors derived from 69 different individuals, wildtypes (Figure 5B). Figure 5 also shows two main clus- also representing both luminal and basal-like tumors. By ters of genes, one of which (Figure 5C) was enriched for comparing the p53-associated gene lists from the tumors genes that are known to be p53-regulated including p21 to the cell lines, we refined our gene list and obtained a list (Cip1), BTG2, and damage-specific DNA binding protein of genes that were common to both data sets, representing 2. EASE analysis confirmed that this cluster, which had a stereotypic p53 signature that held across diverse genetic lower expression in mutant tumors, contained DNA dam- backgrounds. There were 52 genes that were identified in age response genes and negative regulators of cell prolifer- common between the in vivo (747 genes) and in vitro (696 ation. The second gene cluster (Figure 5D) was more genes) lists. This 52-gene list retained GATA binding pro- highly expressed in mutant tumors, and EASE analysis tein 3 and many of the proliferation cluster genes in Fig- confirmed that this cluster of genes was enriched for mito- ure 4C (ATPase Family AAA domain containing 2, sis and proliferation genes. gamma-glutamyl hydrolase, MYBL2, CDC28 subunit 1B, Page 8 of 13 (page number not for citation purposes) BC709B-BE BC206B-AF BC206A-BE BC114A-BE BC706B-AF BC706A-BE BC111A-BE BC121A-AF BC303B-BE BC303A-AF BC807A-BE BC107B-BE BC107A-AF BC35-0 BC16 BC-A BC305B-AF BC305A-BE BC14 BC605B-BE BC309A-BE BC713A-BE BC120B-AF BC120A-BE BC123B-BE BC123A-AF BC111B-BE BC2 BC115B-BE BC214B-BE BC214A-AF BC119B-AF BC119A-BE BC805B-AF BC805A-BE BC-HBC5 BC404B-BE BC404A-AF BC23 BC208B-AF BC208A-BE BC606B-AF BC205B-AF BC790 BC-HBC3 BC213B-BE BC708A-AF BC808A-BE BC808A-AF BC110B-BE BC104B-AF BC104A-BE BC117A-BE BC115A-AF BC125B-AF BC118B-BE BC118A-AF BC17 BC125A-BE BC4-LN4 BC38 BC31-0 BC405A-BE BC108B-AF BC503B-BE BC201B-BE BC108A-BE BC106B-BE BC106A-AF BC704B-AF BC608B-BE BC608A-AF BC405B-AF BC121B-BE BC210B-AF BC124B-AF BC124A-BE BC708B-BE BC702A-AF BC-HBC4-T1 BC-HBC2 BC710B-AF BC610A-BE BC116A-BE BC24 BC1257-MET BC40 BC112B-BE BC112A-AF BC710A-BE BC308B-BE BC711B-BE BC711A-AF BC110A-AF BC702B-BE BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Survival analyses using the 52-gene p53 signature downregulated and upregulated genes. This finding is Kaplan-Meier survival analysis yielded highly significant consistent with the previous literature showing that p53 survival differences between groups from Figure 5A transactivates genes such as p21 and GADD45 and tran- (mutant-like) and 5B (wildtype-like) using the Sorlie et al. srepresses genes such as topoisomerase IIA and CDC2. data. As shown in Figure 6, the 52-gene expression signa- Inactivation of p53 affects both transactivation and tran- ture (p = 0.001) significantly predicted overall survival srepression to alter cell growth. Inactivation of p53 is also (OS), while true mutation status on this set of samples likely to cause downstream, indirect effects. As more was not significant (p = 0.06). The expression signature (p research is conducted to identify pathway signatures -5 = 2.2 × 10 ) and true mutation status (p = 0.001) also sig- [17,18], evidence is growing that most, if not all, pathway nificantly predicted relapse-free survival (RFS). To further signatures include both direct and indirect targets. How- evaluate the prognostic value of this 52-gene signature, we ever, these signatures still appear to show pathway-spe- performed survival analyses using two independent breast cific activity and represent valuable assays for pathway tumor data sets [published by Chang et al. (2005) and activity [19]. So, while we cannot conclude that these are Miller et al. (2005)]. Kaplan-Meier analysis showed that exclusively direct targets of p53, genes in our signature do this signature significantly predicted OS (Figure 5C, p = represent a common response to p53 loss in the breast. -10 -7 7.2 × 10 ) and RFS (p = 2.6 × 10 ) for the Chang et al. dataset, and disease-specific survival (p = 0.007, RFS and This common p53-response list is biologically relevant, as OS data unavailable) for the Miller et al. dataset. We also shown by its ability to predict survival in patients across performed multivariate analysis, using the Chang et al. multiple true test data sets. Some of the genes in the com- dataset. Controlling for standard clinical predictors (ER, mon expression profile have been previously identified in grade, node status, size, age, and treatment), Cox propor- other signatures of prognostic relevance (e.g. prolifera- tional hazards ratios were estimated for both OS (RH, tion-associated genes) and are likely to be regulated by 95% CI: 2.4, 1.3 – 4.4) and RFS (RH, 95% CI: 1.9, 1.2 – multiple oncogenic pathways. Our aim was not to identify 3.0). Thus, independent of standard clinical predictors the a new prognostic signature that improves on previously p53 expression classifier significantly predicted both OS published signatures. Rather, we aimed to demonstrate (p = 0.006) and RFS (p = 0.006). that events that are downstream of functional p53 loss are clearly associated with prognostic outcome, and are there- In our training data set (Sorlie et al.), the gene expression fore biologically relevant. The predictive accuracy of p53- classifier had 82% agreement with sequence-based muta- dependent gene expression profiles [14] supports a role tion status. True mutation status data was not available for for p53 in breast cancer prognosis. Previous estimates of the Chang et al. data set, but our classifier had 82% agree- the relative hazard (RH) associated with p53 loss range ment with sequence-based mutation status in the Miller et from 1 (no effect) to 23 [20]. Our data suggests that this al. data set. We were able to examine the location and type variability may relate to limitations of the methods for of mutations and compare them to classifier results using characterizing p53 status. p53 mutation status is most the Miller et al. data. Of the 29 mutants incorrectly classi- commonly characterized by direct DNA sequencing or by fied as wildtype, 25 (86%) were either missense muta- immunohistochemistry (IHC). Sequencing analysis can- tions or in-frame insertions/deletions. This differs not distinguish sequence variants with and without func- significantly (p = 0.02) from the percentage of mutations tional consequences. A meta-analysis of p53 mutation that were missense or in-frame among correctly classified databases has demonstrated methodological biases asso- mutants (58%). Among the missense tumors, mutations ciated with sequence-based mutation status [21]. IHC in DNA binding domains of the p53 protein were also sig- analysis treats accumulation of p53 protein as indicative nificantly more frequent (p = 0.01) in tumors classified as of mutation; thus, IHC is biased toward identification of mutant (87%) than wildtype (45%). missense mutants and completely misses mutations that cause loss of p53 protein. With either IHC or sequence analysis, a narrow emphasis on p53 mutations can miss Discussion Identification of a p53-responsive signature in breast can- functional impairments in the p53 pathway (e.g. MDM2 cer is confounded by associations with important tumor amplification). These challenges could account for widely characteristics like ER status. The common p53 expression divergent estimates of p53's role in prognosis. signature shared by cell lines and tumors in this study addressed this confounding by conducting cell lines Our data analysis also showed that there was good agree- experiments with ER positive and ER negative cell lines, ment between mutation status and expression profiles. and using experimental data to refine the gene lists Using our 52 gene list, there was >80% agreement derived from observational studies in patients. The result- between p53 mutation status and p53 expression class in ing 52 gene, p53-associated list contained two biologi- both the Sorlie et al. and Miller et al. datasets. This high cally relevant gene clusters corresponding to level of agreement across data sets attests to the fact that Page 9 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Stanford, Gene Expression 0.8 Censored 0.6 p53 wildtype-like p53 mutant-like 0.4 -4 p=9.2X10 0.2 0 2040 6080 100 months Stanford, Mutation Status 0.8 Censored 0.6 p53 wildtype p53 mutant 0.4 p=0.06 0.2 0 2040 6080 100 months Chang et al., Gene Expression 0.8 Censored 0.6 p53 wildtype-like p53 mutant-like 0.4 -10 p=7.2X10 0.2 0 50 100 150 200 250 OS months Kaplan-Meier survival curves for p b Figure 6 ased p53 functional category or mutation atients wi stat th p53 wi us ldtype (solid line) or mutant (dashed line) tumors using gene expression- Kaplan-Meier survival curves for patients with p53 wildtype (solid line) or mutant (dashed line) tumors using gene expression-based p53 functional category or mutation status. Overall survival analysis comparing the two gene- expression based dendrogram groups (tumors in Figure 5A vs. tumors in Figure 5B) yielded a highly significant difference in sur- vival, which compares favorably with mutation status. Both analyses used 66 tumor samples and included 26 events. Survival analysis on a separate data set (Chang et al., 2005) including 337 tumors and 79 events (C) also yielded highly significant differ- ences based on gene expression classification. Page 10 of 13 (page number not for citation purposes) Probability Probability Probability BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 the signature is indicative of p53 across a wide range of A strength of our study was the use of cell line experiments cell backgrounds. If the signature were merely correlated to control a range of variables that influence p53- with proliferation, ER status, or another tumor character- response. The in vitro setting allowed for control of expres- istic, then poor concordance with p53 mutational status sion of p53 protein, breast cancer subtype, and p53- would be expected in cross validation. The samples where inducing events [26-29]. However, the in vitro approach is gene expression and mutation status disagree may repre- limited in that a small number of cell lines can be reason- sent true differences in the functional p53 pathway. For ably examined, representing only a handful of tumors. By example, the tumor BC606 was p53 wildtype by sequence combining the in vitro expression data with data from but clustered with p53 mutants using the 52 gene classi- human tumors assayed before and after DOX treatment, fier. This tumor overexpressed MDM2 mRNA (data not we examined a much wider range of individual responses shown), a key negative regulator of p53. Among the false to p53 loss than cell line experiments could reasonably negatives (sequence mutant but wildtype expression sig- examine, and performed a controlled experiment that nature), our analysis of the Miller et al. data showed that cannot be accomplished in humans. Previous studies have misclassified mutants had higher proportions of mutation characterized p53-responses in breast cancer using gene types that are likely to be less deleterious (missense muta- expression data from tumors and statistical models to try tions and in-frame insertion/deletions). to control the effects of breast cancer heterogeneity. For example, in Miller et al. [14], proliferation and ER status In addition to identifying a stereotyped signature associ- were treated as statistical confounders of the p53-gene ated with p53 loss, these results demonstrate that the rel- expression relation (based on p53 status and outcome ative importance of p53-regulated functions such as cell both having crude associations with grade and ER status). cycle control, DNA repair, and apoptosis are subject to sig- Thus, the final p53-mutant like gene expression profile nificant inter-individual variation. Each cell line displayed presented by Miller et al. [14] was derived using a statisti- a unique p53 response signature. However, similarities cal model that adjusted for these variables. Such adjust- according to cell type were also evident. Both of the ment assumes that grade and ER status are causally HMEC-derived cell lines showed a greater response to p53 upstream of p53 status. If grade and ER status are down- loss in the untreated state, while the MCF-7 and ZR-75-1 stream of p53 status, this approach will introduce a bias lines showed a stronger p53-regulated signature following toward exclusion of grade and ER-associated genes, even DOX treatment. These results extend previous observa- though those genes are influenced by p53 loss. In short, tions [5] suggesting a difference in p53 signaling pathways the validity of statistical adjustment depends upon having between luminal and basal-like breast cancers. These the correct model for the relation between breast cancer inherent differences in p53 signaling could lead to differ- subtype, ER status, proliferation and p53 biology. In the ent selection pressure for p53 loss in each cell type. Such presence of heterogeneity, experimental and biologically- differences could also explain the divergent rates of p53 based methods for assessing gene expression in relation to mutation by subtype that have been reported here and in p53 status are preferable to statistical methods. a population-based study [3]. Many of the genes associated with p53 loss in this analysis Our data reconfirmed the complex relation between were of prior interest in breast cancer. For example, chemosensitivity and p53 status [22]. Previous reports GATA3 is involved in growth control and maintenance of have demonstrated either heightened chemosensitivity of the differentiated state in breast epithelial cells and has p53 mutants [23,24] or heightened chemoresistance [25]. been hypothesized to play a role in tumorigenesis of ER- This paradox is reflected in our study where the four cell positive breast tumors [30]. p21 (Cip1), CDC2, and lines we studied varied widely in their DOX sensitivity fol- CDC25C are genes involved in p53-mediated regulation lowing p53-knockdown. Because p53 regulates many dif- of cell cycle arrest [31]. Pituitary tumor-transforming 1 is ferent pathways, including DNA repair, apoptosis, and a recently identified oncogene with p53-dependent and cell proliferation, and the balance of these various path- p53-independent functions [32]. Thus, as might be ways determines chemosensitivity, it is not surprising to expected, many of the direct and indirect targets of p53 find that both individuals and cell lines have responses to identified here are known p53- and cancer-associated chemotherapy that are difficult to predict. DOX also has genes. Further investigation of the specific p53 targets that many p53-independent toxicity mechanisms, so a diver- are regulated in common across breast cancers and inves- gence in sensitivity across lines may also reflect differences tigation of those that are differentially regulated across in how DOX toxicity is manifest across lines. These analy- breast cancer subtypes will add to our understanding of ses have demonstrated that breast cell lines have individ- the biology of breast cancer and breast cancer subtypes. ual, distinct responses to p53 loss. The genetic background of a given cell line, including cell type of ori- gin, plays a prominent role in mediating p53 signaling. Page 11 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:276 http://www.biomedcentral.com/1471-2407/6/276 Foundation. M.A.T. was supported by NIEHS Individual National Research Conclusion Service Award (NRSA) 5F32ES012374 and the UNC Lineberger Cancer In the presence of breast cancer heterogeneity, controlled Control Education Program (R25 CA57726). experiments in vitro combined with in vivo analyses, allowed for refinement of a p53-associated gene set. The References refined 52-gene list excluded genes that were associated 1. Borresen-Dale AL: TP53 and breast cancer. Hum Mutat 2003, with breast cancer subtype and not downstream of p53. 21:292-300. 2. 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Usary J, Llaca V, Karaca G, Presswala S, Karaca M, He X, Langerod A, Karesen R, Oh DS, Dressler LG, Lonning PE, Strausberg RL, Chanock S, Borresen-Dale AL, Perou CM: Mutation of GATA3 in human breast tumors. Oncogene 2004, 23:7669-7678. 31. Harris SL, Levine AJ: The p53 pathway: positive and negative feedback loops. Oncogene 2005, 24:2899-2908. 32. Hamid T, Kakar SS: PTTG and cancer. Histol Histopathol 2003, 18:245-251. Pre-publication history The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2407/6/276/pre pub Publish with Bio Med Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." 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Journal

BMC Cancer – Springer Journals

Published: Dec 1, 2006

Keywords: cancer research; oncology; surgical oncology; health promotion and disease prevention; biomedicine, general; medicine/public health, general

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