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Cancer Epidemiology Biomarkers & Prevention Vol. 15, 80-85, January 2006
© 2006 American Association for Cancer Research

p53 Mutation Analysis in Breast Tumors by a DNA Microarray Method

Meredith Tennis1, Shiva Krishnan1, Matthew Bonner3, Christine B. Ambrosone2, John E. Vena4, Kirsten Moysich2, Helen Swede2, Susan McCann2, Per Hall5, Peter G. Shields1 and Jo L. Freudenheim3

1 Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia; 2 Roswell Park Cancer Institute and 3 Department of Social and Preventive Medicine, University of Buffalo, Buffalo, New York; 4 Department of Epidemiology and Biostatistics, School of Public Health, University of South Carolina, Columbia, South Carolina; and 5 Department of Epidemiology, Karolinska Insitutet, Stockholm, Sweden

Requests for reprints: Peter G. Shields, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3800 Reservoir Road Northwest, LL (s) Level, Room 150, Box 571465, Washington, DC 20057-1465. Phone: 202-687-0003; Fax: 202-687-0004. E-mail: pgs2{at}georgetown.edu.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
The p53 gene acts as a regulator of cell growth and DNA repair in normal cells; inactivation of the gene seems to lead to cancer. It is the most commonly mutated gene in human cancers, and a high-throughput sequencing method is needed for cancer etiology studies using large sample sets. In our population-based case-control study of breast cancer, the p53 gene was amplified by PCR for 392 subjects from seven hospitals in Western New York using the Affymetrix GeneChip technology. One hundred thirty-eight (35%) of the breast tumors had p53 mutations, of which 88% were located in exons 5 to 8. New hotspots were identified at codons 179, 195, 196, 213, 217, 249, 254, 278, 281, and 298, and previously reported hotspots were found at codons 175, 248, and 273. Manual sequencing for exons 5 to 9 of the p53 gene was done for 139 tumors to validate the Affymetrix assay. The two methods had 100% concordance for mutations detectable by the Affymetrix assay. We also successfully assayed paraffin-embedded breast and lung tumors from as early as 1958 and employed a nested PCR strategy to improve weak PCR amplification. To have statistical power, the investigation of gene environment interactions and cancer requires a large number of tumor analyses, which are frequently only available from archived tissue from multiple sources. We have shown the utility of the Affymetrix GeneChip method under these challenging conditions and provided new data for the mutational spectra of breast cancer in a population-based study. (Cancer Epidemiol Biomarkers Prev 2006;15(1):80–5)


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Loss of tumor suppressor function is a frequent feature of human cancers through various mechanisms, including gene deletions, insertions, and point mutations. The p53 tumor suppressor gene is the most commonly mutated gene in cancer. Almost 50% of all human cancers and 30% of breast cancers contain a p53 mutation (1, 2). It has been suggested that the p53 mutational spectra could be a marker of mutagen exposure and gene environment effects, useful in clarifying exposure-disease relationships (3-5). There are several examples of particular carcinogens associated with higher frequencies of particular p53 mutations (6-11). Several p53 mutation hotspots have been identified in breast cancer, predominantly at codons 175, 248, and 273, where the highest percentage of base changes are G-T transversions (12). All three of these p53 hotspots have been examined in breast cancer and were found to cause dominant-negative effects when mutated (13).

New technologies have significantly increased the throughput for p53 sequencing, particularly the Affymetrix GeneChip system (a technology that is now owned by Roche Diagnostics, Basel, Switzerland). This method can be used in a clinical setting, where a large number of samples must be analyzed in a short period of time for prognostic or therapeutic uses of p53 and for large epidemiologic studies. Four previously conducted smaller studies compared microarray sequencing methods to conventional sequencing methods in ovarian, lung, bladder, and breast cancer. Three showed that chip sequencing had high sensitivity and specificity; the breast cancer study used formalin-fixed, paraffin-embedded tumors and suggested that a combination of methods yields better results (14-17). Large-scale studies of p53 mutations in breast cancer, however, have not been conducted. Additionally, epidemiologic studies often rely on stored tumor samples. In this study, we analyzed paraffin-embedded breast tumors from 420 subjects for p53 status by microarray sequencing and compared the results with conventional manual sequencing methods. Additional analyses were done on archived, paraffin-embedded tumors identified through the Swedish Cancer Registry dating back to 1958, and a nested PCR strategy was employed, both to show the versatility of the method and to improve results from low-quality samples.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Study Subjects and Tissue Collection
We obtained archived paraffin-embedded tumor blocks for 420 breast cancer cases diagnosed between 1986 and 1991 at five hospitals in the Western New York area. These blocks were fixed with either formalin, ethanol, or Lilly's medium. These cases were women who had participated in the Western New York Diet Study, a case control study that has been previously described (18-20). Separately, 15 lung and breast tumors diagnosed between 1958 and 1968 and an additional 23 lung and breast tumors were identified using the Swedish Cancer Registry and collected in Stockholm, Sweden. Formalin fixed, paraffin-embedded tumors were cut into 20- or 50-µm sections, placed onto slides, and shipped in temperature-controlled containers.

DNA Extraction
Extractions were done in PCR-free lab areas. The tumor tissue was separated from normal tissue on the slide by microdissection with a fine needle under a microscope, limiting the amount of surrounding stromal and normal cells included in the extraction. A separate H&E-stained slide was used as a guide for microdissection. Microdissected tissues were placed in a solution of 1x TE (pH 7.5), 0.1% SDS, and proteinase K at 55°C for 2 days. Two extractions were then done on each sample with 25:24:1 phenol/chloroform/isoamyl alcohol and precipitated with glycogen, 10 mol/L ammonium acetate, and 100% ethanol, as previously described (21). The concentration of DNA was measured using a GeneQuant II RNA/DNA spectrophotometer (Amersham-Pharmacia, Piscataway, NJ). For tumors preserved in Lilly's fixative (n = 40), the microdissected samples were first treated with 0.01% ML-201 (reagent gift of Dr. Aizen Marrogi, Walter Reed Army Hospital, Washington, DC) and Chelex-100 (Bio-Rad, Hercules, CA) for 10 minutes in a 95°C water bath. After cooling to room temperature, proteinase K was added, the samples were incubated at 65°C overnight, and the protocol was continued as with other samples.

Affymetrix p53 GeneChip Assay
Exons 2 to 11 of the p53 coding region were amplified by a single 10-exon multiplex PCR and then sequenced using the Affymetrix p53 GeneChip system (Affymetrix, Santa Clara, CA). The PCR reagent mix included Perkin-Elmer PCR buffer II (Perkin-Elmer, Wellesley, MA), 25 mmol/L magnesium chloride, 100 mmol/L deoxynucleotide triphosphates, p53 primers (Affymetrix), and Amplitaq Gold (Perkin-Elmer) in a final reaction volume of 100 µL. Each reaction contained 250 ng of template DNA. Amplification conditions were as follows: initial denaturation at 95°C for 10 minutes, 35 cycles of 95°C for 30 seconds, 60°C for 30 seconds, and 72°C for 45 seconds and final extension at 72°C for 10 minutes. PCR products were resolved on a 4% NuSieve agarose gel (Cambrex, East Rutherford, NJ) and visualized with ethidium bromide to ensure PCR amplification quality. PCR product (45 µL) was fragmented with the GeneChip Fragmentation Reagent (Affymetrix) and calf alkaline phosphatase at 25°C for 15 minutes followed by enzyme inactivation for 10 minutes at 95°C. Fragmented DNA was then labeled at the 3' end with Fluorescein N6 Dideoxy ATP (Enzobio, Farmingdale, NY), terminal deoxynucleotidyl transferase buffer, and terminal transferase (Promega, Madison, WI) for 45 minutes at 37°C followed by heat inactivation at 95°C for 5 minutes. The labeled product was then hybridized to the p53 GeneChip Array (Affymetrix) in a mix of 6x saline-sodium phosphate-EDTA, 0.5% Triton X-100, 2 mg/mL acetylated bovine serum albumin, and 2 nmol/L control oligonucleotide (Affymetrix) for 30 minutes at 45°C. The array was then washed twice with a solution of 3x saline-sodium phosphate-EDTA and 0.005% Triton X-100 at 35°C. Hybridization and washing were done in the Affymetrix GeneChip Fluidics Station 400 (Affymetrix).

The array was scanned by the Agilent G2500A Gene Array Scanner (Affymetrix), and the light intensity at each position on the chip was measured. Affymetrix Reference DNA was used as a wild-type template for sample comparison. Each site containing a single base deletion or a substitution was given a numerical score according to an algorithm within the Affymetrix software, with higher scores indicating higher light intensity and greater likelihood of a true mutation (15). We previously validated the GeneChip score assignment by performing the p53 GeneChip assay on 100 lung tumors that had been analyzed for p53 mutations by single-strand conformation polymorphism analysis (22). We determined that scores of ≥15 assigned by the GeneChip algorithm were valid mutations.6 Any samples with exon 4 data missing after the DNA was subject to GeneChip analysis were individually amplified and manually sequenced to generate complete data for those samples. Exon 4 PCR products were generated using the specific primer sequences from the Affymetrix primer set and the same PCR conditions as for the multiplex reaction. The p53 GeneChip is not designed to identify insertions or deletions of >1 bp. We tested five known multibase deletions and five insertions with the GeneChip array to determine what the software would report in these instances. The system reported a misread at these sites, indicated by "N," which is indistinguishable from samples with insufficient DNA or other technical problems.

Sample Amplification by Nested p53 PCR
For the nested PCR strategy, tumor DNA was amplified with the Affymetrix multiplex PCR, hybridized to the p53 GeneChip, and analyzed. These samples were then amplified with a nested PCR, first with primers designed to amplify a region outside the original Affymetrix sequence (Qiagen, Valencia, CA; see Table 1 for sequences), then with the original Affymetrix primers. The nested PCR reagent mix contained the same reagents as the original Affymetrix PCR mix, with the exception of 1 µL of PCR product as the template and the inner primers. Amplification conditions, visualization of the PCR products, and analysis with the Affymetrix p53 GeneChip were as described above.


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  		Table 1. Sequences for the primers used in the first step of the p53 nested PCR protocol

 
Dideoxy p53 Sequencing
p53 exons were individually amplified (in a separate reaction from the amplification for GeneChip analysis) and manually sequenced using the Amplicycle sequencing kit (Perkin-Elmer). The PCR product and primer were heated to 95°C, chilled, and mixed in microcentrifuge tubes with dye terminators and the sequencing cocktail containing 3 mol/L dilution buffer, 0.5 mol/L DTT, pyrophosphatase, sequenase enzyme, and S35-labeled dCTP. The reaction was incubated at 42°C for 5 minutes. Each dye terminator sequencing reaction product was electrophoresed on a 7% polyacrylamide gel using a S2000 gel apparatus and exposed to XAR5 film (Kodak, Rochester, NY) overnight at –80°C. The autoradiograph was reviewed by two readers, and samples with disagreement were repeated.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Affymetrix p53 GeneChip Mutation Analysis
We used a single 10-exon multiplex PCR as part of the Affymetrix p53 GeneChip system to successfully amplify 392 of 420 samples (93%) from formalin-fixed, paraffin-embedded breast tumors in the Western New York Diet Study (Fig. 1). The study set included 40 tumors fixed in Lilly's medium that were initially extracted using the standard phenol chloroform protocol, but the amplification success rate in the multiplex PCR was only 7%. After extraction using Chelex-100, the amplification success rate for these samples increased to 80% (Fig. 2).


Figure 1
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  		Figure 1. p53 multiplex PCR from Western New York Diet Study samples. Exons 2 to 11 are amplified with a single PCR reaction and visualized by electrophoresis on an agarose gel. Lane 1, ladder; lanes 2-4 and lanes 6-8, samples; lane 5, water.

 

Figure 2
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  		Figure 2. p53 multiplex PCR results from DNA extracted from paraffin-embedded, Lilly's medium–fixed samples with and without mercury. Lanes 1-6, samples; lane 7, positive control; lane 8, water; and lane 9, ladder.

 
p53 mutations were detected in 138 of 392 tumors (35.3%) in the Western New York Diet Study set, and 121 (87.7%) of the mutations occurred in exons 5 to 8. Of the 17 mutations (12.3%) in other regions, 11 were in exon 9, 3 in exon 4, 2 in exon 10, and 1 in exon 3 (Fig. 3). We detected three single base deletions in exons 6, 7, and 8 with the p53 GeneChip assay. p53 mutations can be categorized into several subgroups, which are useful for analysis of the mutational spectrum: individual exons, hotspot codons (175, 248, and 273), evolutionarily conserved regions, regions affecting protein to DNA binding, regions affecting protein stability by denaturing, and zinc binding regions, and L2 and L3 loops affecting the tertiary protein conformation (23). Eleven percent of p53 mutations in our study were located in known hotspots, 12.9% in codons affecting protein stability, 9.3% in codons affecting DNA binding, 26.6% in the L2 or L3 loops, and 38.1% in evolutionarily conserved regions (Table 2). Seventy-two percent of mutations were missense mutations, 22% were silent, 4% were nonsense mutations, and 2% were frameshift mutations caused by deletions.


Figure 3
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  		Figure 3. Distribution of p53 mutations in the Western New York Diet Study by exon location.

 

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  		Table 2. p53 mutations in breast cancer in the Western New York Diet Study

 
Affymetrix p53 GeneChip Validation by Manual Sequencing
We selected 139 tumors for manual sequencing, all with one or more exons presenting a score above 9 by the Affymetrix p53 GeneChip. (A new amplification product was generated to avoid duplicating any Taq polymerase errors or artifacts resulting from formalin fixation that may have lead to a mutation detected by the GeneChip.) In these samples, the Affymetrix p53 GeneChip identified 78 samples as negative for a p53 mutation and 61 samples as positive for a p53 mutation. By manual sequencing, 77 samples were negative and 56 were positive for a p53 mutation. Six samples that were positive for a mutation by the p53 GeneChip had high background in manual sequencing that rendered the results less reliable. Manual sequencing detected one single base insertion, which was not detected by the Affymetrix p53 GeneChip due to limitations of the technology. Disregarding the six samples that could not be read by manual sequencing, results were 98% concordant for mutation detection between the Affymetrix p53 GeneChip and manual sequencing for all mutation types. Results were 100% concordant for mutations detectable by the Affymetrix GeneChip assay.

Affymetrix p53 GeneChip Reproducibility and Quality Control
One hundred eleven samples were randomly chosen to be repeated in the Affymetrix p53 GeneChip assay and had concordant results. Although the actual scores changed somewhat, all samples that were identified as positive for a p53 mutation in the first analysis had fluorescence scores above 15 on the second analysis, and no samples identified as wild type in the first analysis had scores above 15 in the second analysis (Table 3).


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  		Table 3. Sampling of duplicate analysis Affymetrix p53 GeneChip scores from 27 samples with mutations in the Western New York Diet Study

 
For 13 tumors, we analyzed more than one slide from different parts of the tumor, for a total of 44 internally blinded duplicates. We found 77% concordance for these repeated measures. Eight tumors were wild type in all blocks, and two tumors had the same mutation identified in all blocks. The mutations in blocks from the same tumor were always the same, the observed discrepancies were the presence or absence of a mutation and not the type or location of a mutation. Three tumors had a positive mutation in some blocks and wild type in others. For example, in four blocks from one tumor, two were wild type and two were positive for a C-T transition at codon 278. The discordance was likely due to a low tumor to normal tissue ratio in these samples, making microdissection difficult. Some samples required a second extraction to perform quality control protocols. Twelve of the 14 (86%) samples requiring a second extraction were concordant with results from the first extraction.

The 15 samples collected from the Swedish Cancer Registry between 1956 and 1968 were analyzed using the Affymetrix p53 GeneChip assay. These samples were formalin-fixed, paraffin-embedded tissues archived for several decades, and we were able to obtain complete results from the p53 GeneChip assay. Three of nine lung tumor samples and two of six breast tumor samples were positive for p53 mutations. Mutations occurred at codons 179, 195, 248, 249, and 273 with fluorescence scores ranging from 18 to 31. Manual sequencing of the Swedish samples confirmed that the p53 GeneChip assay was 100% accurate in identifying mutations.

Affymetrix p53 GeneChip Using Nested PCR
A nested PCR method was used to improve our ability to use samples with weak amplification by a single PCR. Twenty-three lung and breast tumors with complete results after amplification with Affymetrix primers and analysis by p53 GeneChip were selected from the Swedish Cancer Registry samples. These samples were repeated using a nested PCR strategy, where the first PCR was designed to amplify an area larger than the Affymetrix primers, and the second PCR was done using the Affymetrix primers. Eleven of the 23 samples (48%) were positive for p53 mutations (with scores above 15) using the Affymetrix primers in a single PCR (data not shown). After nested PCR and analysis by p53 GeneChip, all 11 original mutations were confirmed. Six samples that were wild type by the first analysis had intensity scores above 15 after the nested PCR, all of which would be manually sequenced to eliminate false positives. Six samples were wild type by the Affymetrix primer analysis and had no scores above 15 by the nested PCR analysis, confirming wild-type status.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Our results show that the Affymetrix p53 GeneChip assay is a fast, accurate, and sensitive method of identifying p53 mutations and thus is suitable for mutation screening in large population-based studies. We confirmed our p53 GeneChip results from formalin-fixed, paraffin-embedded breast tumors with manual sequencing and repeat GeneChip assays. We have shown that the assay can be used for paraffin-embedded tissues dating back to 1956 and that with careful DNA extraction and PCR, exon 4 can often be amplified from paraffin-embedded tissues. Mutations identified with the GeneChip assay should be confirmed with another method using a new PCR product to eliminate false positives from polymerase errors or fixation artifacts, although we did not find such artifacts. The Affymetrix GeneChip is designed to oversample the more commonly reported p53 mutations by employing multiple binding sites. Thus, there is a risk of bias for detecting these mutations. However, the sensitivity of the GeneChip, as confirmed by manual sequencing, indicates that it is sufficient to identify other mutations. In addition, there are a sufficient number of breast cancer p53 mutation studies to allow for such a strategy to be employed for the GeneChip without significant concerns about biasing epidemiology results. A cutoff score of 15 was found sufficient to identify mutations in the p53 gene. It should be noted, though, that we continue to manually sequence all exons with a mutation score of >12, to avoid laboratory error.

Using a nested PCR strategy, we reproduced 100% of mutations identified by a single PCR. These data suggest that the additional PCR does not compromise mutation detection. Although additional mutations detected by nested PCR indicate that there may be an increase in the false positive rate, all samples with scores above 15 in our studies are manually sequenced to eliminate false positives. Data from our nested PCR show that the use of this technology could be further extended to samples that have weak amplification due to low quality or quantity of DNA. The blinded duplicates and repeat extractions included for quality control indicate that the assay is reproducible. Variability in these controls was likely due to microdissection techniques from small tumor samples with adjacent normal tissue contamination or a lack of homogeneity of the tumor.

Studies that use manual sequencing to identify p53 mutations typically examine only exons 5 to 9 because of the significant labor and material requirements for sequencing 10 exons. The p53 GeneChip performs sequence analysis on exons 2 to 11 of the p53 gene and checks the flanking intronic sequences for splice junction analysis. Labor and time are considerably reduced when using the p53 GeneChip assay compared with manual sequencing, which is of great importance for large studies.

The IARC maintains a database of published p53 mutations, listing the reference, the frequency of mutations, and other information (http://www-p53.iarc.fr/index.html; R10 July 2005). We compared our results to the IARC p53 mutation database and found a slightly different spectrum (Fig. 4). GC:AT transitions make up about 50% of all breast mutations from studies in the United States in the IARC database, whereas our study found only 32%. We found a higher percentage of GC:TA transversions (29% versus 9%) and a higher percentage of AT:TA transitions (13% versus 3%). Differences in mutation type between our study and the IARC database may be attributable to population differences or may suggest that the etiology of tumors in this study differs, to some extent, from the etiology of tumors in other studies. The p53 GeneChip assay detected three single base deletions (2.2%), which is slightly lower than the IARC database frequency of single base pair deletions in breast cancer (4.7%). This difference may be due to the choice of cutoff score for mutations reported by the GeneChip, as other researchers have suggested lower cutoff score is necessary to detect deletions (15). However, additional sequencing of exons from 139 samples with scores of ≥9 resulted in no additional deletions being detected. The number of single base pair deletions missed in this study by using the cutoff score of 15 would be <5% of all mutations in these samples, according to the rates reported in the IARC database. The GeneChip system is limited by the inability to detect insertions and multibase deletions, which make up 10% of breast p53 mutations in the IARC USA database. Another mutation analysis method may be considered in conjunction with the p53 GeneChip to detect these mutations. However, compared with prior conventional sequencing methods that focus only on exons 5 to 9 because of labor considerations, the GeneChip detects the additional 7% of p53 mutations in breast cancer that occur in exons 2 to 4 and 10 to 11.


Figure 4
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  		Figure 4. Comparison of p53 mutation types identified in the IARC database for breast cancer and the Western New York (WNY) Diet Study. A, all p53 mutations in breast cancer from the IARC database. B, p53 mutations in breast cancer in the United States from the IARC database. C, p53 mutations in breast cancer from the Western New York Diet Study.

 
Eleven percent of p53 mutations in this study were at known hotspot codons 175, 248, and 273, and new hotspot mutations (codons where >2% of study mutations were located) were identified at codons 179, 195, 196, 213, 217, 225, 229, 249, 254, 278, 281, and 298. Together, these hotspot mutations comprised 49% of mutations in this study set. In the IARC database, known hotspot mutations at codons 175, 248, and 273 comprise 16% of all p53 mutations in breast tumors in the United States (see Fig. 5 for IARC USA data.). Previous studies have found slightly higher rates of known hotspot mutations than those observed in our study: Powell et al. (23), 20%; Conway et al. (24), 19%; Hartmann et al. (4), 17%. Others have had rates similar to or lower than ours: Cooper et al. (16), 12%; Chen et al. (25), 0%. The IARC database for p53 mutations in breast cancer in the United States contains 64% missense mutations, 12% silent, 12% frameshift mutations, and 7% nonsense mutations. Our study found a higher rate of missense mutations and silent mutations and lower rate of nonsense mutations and frameshift mutations. Differences may be due to study populations.


Figure 5
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  		Figure 5. Comparison of hotspot codons with >2% p53 mutation rate in the IARC breast cancer U.S. database and the Western New York (WNY) Diet Study.

 
In conclusion, we have shown the use of the Affymetrix p53 GeneChip assay for analyzing p53 mutations in archived tissues. Although previous studies have compared DNA array sequencing to manual sequencing, none have examined a large population of breast tumors, and few have presented data on analysis of p53 exon 4 in formalin-fixed, paraffin-embedded tissues. The use of archived tissues for population studies is becoming increasingly attractive and practical, with technologies developing to meet the challenges of using paraffin-embedded tissues.


   Footnotes
 
Grant support: NIH grant R01CA92040. DOD grant DAMD-03-01-0300 and ROI CA92705-2.

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

6 Unpublished data. Back

Received 6/20/05; revised 10/13/05; accepted 11/ 9/05.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Powell B, Soong R, Iacopetta B, et al. Prognostic significance of mutations to different structural and functional regions of the p53 gene in breast cancer. Clin Cancer Res 2000;6:443–51.[Abstract/Free Full Text]
  2. Borresen AL, Andersen TI, Eyfjord JE, et al. TP53 mutations and breast cancer prognosis: particularly poor survival rates for cases with mutations in the zinc-binding domains. Genes Chromosomes Cancer 1995;14:71–5.[Medline]
  3. Dale LC, Ebbert JO, Schroeder DR, et al. Bupropion for the treatment of nicotine dependence in spit tobacco users: a pilot study. Nicotine Tob Res 2002;4:267–74.[CrossRef][Medline]
  4. Hartmann A, Blaszyk H, Kovach JS, et al. The molecular epidemiology of p53 gene mutations in human breast cancer. Trends Genet 1997;13:27–33.[CrossRef][Medline]
  5. Greenblatt MS, Bennett WP, Hollstein M, et al. Mutations in the p53 tumor suppressor gene: clues to cancer etiology and molecular pathogenesis. Cancer Res 1994;54:4855–78.[Free Full Text]
  6. Brash DE, Rudolph JA, Simon JA, et al. A role for sunlight in skin cancer: UV-induced p53 mutations in squamous cell carcinoma. Proc Natl Acad Sci U S A 1991;88:10124–8.[Abstract/Free Full Text]
  7. Ziegler A, Jonason AS, Leffell DJ, et al. Sunburn and p53 in the onset of skin cancer. Nature 1994;372:773–6.[CrossRef][Medline]
  8. Nakazawa H, English D, Randell PL, et al. UV and skin cancer: specific p53 gene mutation in normal skin as a biologically relevant exposure measurement. Proc Natl Acad Sci U S A 1994;91:360–4.[Abstract/Free Full Text]
  9. Hsu IC, Metcalf RA, Sun T, et al. Mutational hotspot in the p53 gene in human hepatocellular carcinomas. Nature 1991;350:427–8.[CrossRef][Medline]
  10. Bressac B, Kew M, Wands J, et al. Selective G to T mutations of p53 gene in hepatocellular carcinoma from southern Africa. Nature 1991;350:429–31.[CrossRef][Medline]
  11. Brennan JA, Boyle JO, Koch WM, et al. Association between cigarette smoking and mutation of the p53 gene in squamous-cell carcinoma of the head and neck. N Engl J Med 1995;332:712–7.[Abstract/Free Full Text]
  12. Saitoh S, Cunningham J, De Vries EM, et al. p53 gene mutations in breast cancers in midwestern US women: null as well as missense-type mutations are associated with poor prognosis. Oncogene 1994;9:2869–75.[Medline]
  13. Marutani M, Tonoki H, Tada M, et al. Dominant-negative mutations of the tumor suppressor p53 relating to early onset of glioblastoma multiforme. Cancer Res 1999;59:4765–9.[Abstract/Free Full Text]
  14. Wen W, Bernstein L, Lescallett J, et al. Comparison of TP53 mutations identified by oligonucleotide microarray and conventional DNA sequence analysis. Cancer Res 2000;60:2716–22.[Abstract/Free Full Text]
  15. Ahrendt SA, Halachmi S, Chow JT, et al. Rapid p53 sequence analysis in primary lung cancer using an oligonucleotide probe array. Proc Natl Acad Sci U S A 1999;96:7382–7.[Abstract/Free Full Text]
  16. Cooper M, Li SQ, Bhardwaj T, et al. Evaluation of oligonucleotide arrays for sequencing of the p53 gene in DNA from formalin-fixed, paraffin-embedded breast cancer specimens. Clin Chem 2004;50:500–8.[Abstract/Free Full Text]
  17. Wikman FP, Lu ML, Thykjaer T, et al. Evaluation of the performance of a p53 sequencing microarray chip using 140 previously sequenced bladder tumor samples. Clin Chem 2000;46:1555–61.[Abstract/Free Full Text]
  18. Graham S, Hellmann R, Marshall J, et al. Nutritional epidemiology of postmenopausal breast cancer in western New York [see comments]. Am J Epidemiol 1991;134:552–66.[Abstract/Free Full Text]
  19. Freudenheim JL, Marshall JR, Vena JE, et al. Premenopausal breast cancer risk and intake of vegetables, fruits, and related nutrients. J Natl Cancer Inst 1996;88:340–8.[Abstract/Free Full Text]
  20. Ambrosone CB, Freudenheim JL, Sinha R, et al. Breast cancer risk, meat consumption and N-acetyltransferase (NAT2) genetic polymorphisms. Int J Cancer 1998;75:825–30.[CrossRef][Medline]
  21. Sepp R, Szabo I, Uda H, et al. Rapid techniques for DNA extraction from routinely processed archival tissue for use in PCR. J Clin Pathol 1994;47:318–23.[Abstract/Free Full Text]
  22. Vahakangas KH, Bennett WP, Castren K, et al. p53 and K-ras mutations in lung cancers from former and never-smoking women. Cancer Res 2001;61:4350–6.[Abstract/Free Full Text]
  23. Powell B, Soong R, Iacopetta B, et al. Prognostic significance of mutations to different structural and functional regions of the p53 gene in breast cancer. Clin Cancer Res 2000;6:443–51.[Abstract/Free Full Text]
  24. Conway K, Edmiston SN, Cui L, et al. Prevalence and spectrum of p53 mutations associated with smoking in breast cancer. Cancer Res 2002;62:1987–95.[Abstract/Free Full Text]
  25. Chen FM, Hou MF, Wang JY, et al. High frequency of G/C transversion on p53 gene alterations in breast cancers from Taiwan. Cancer Lett 2004;207:59–67.[Medline]



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J Peltonen, J A Welsh, and K H Vahakangas
Is there a role for PCR-SSCP among the methods for missense mutation detection of TP53 gene?
Human and Experimental Toxicology, January 1, 2007; 26(1): 9 - 18.
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