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Clinical Cancer Research 13, 5355, September 15, 2007. doi: 10.1158/1078-0432.CCR-07-0249
© 2007 American Association for Cancer Research
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Imaging, Diagnosis, Prognosis

Comparison of Gene Sets for Expression Profiling: Prediction of Metastasis from Low-Malignant Breast Cancer

Mads Thomassen1,4, Qihua Tan1,4,5, Freyja Eiriksdottir1,4, Martin Bak2, Søren Cold3 and Torben A. Kruse1,4

Authors' Affiliations: 1 Department of Biochemistry, Pharmacology, and Genetics, 2 Department of Pathology, and 3 Department of Oncology, Odense University Hospital and 4 Human Microarray Centre and 5 Institute of Public Health, University of Southern Denmark, Odense, Denmark

Requests for reprints: Mads Thomassen, Department of Biochemistry, Pharmacology, and Genetics, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark. Phone: 456-5411911; Fax: 456-5411911; E-mail: mads.thomassen{at}ouh.regionsyddanmark.dk.


   Abstract
Top
 Abstract
Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Purpose: In the low-risk group of breast cancer patients, a subgroup experiences metastatic recurrence of the disease. The aim of this study was to examine the performance of gene sets, developed mainly from high-risk tumors, in a group of low-malignant tumors.

Experimental Design: Twenty-six tumors from low-risk patients and 34 low-malignant T2 tumors from patients with slightly higher risk have been examined by genome-wide gene expression analysis. Nine prognostic gene sets were tested in this data set.

Results: A 32-gene profile (HUMAC32) that accurately predicts metastasis has previously been developed from this data set. In the present study, six of the eight other gene sets have prognostic power in the low-malignant patient group, whereas two have no prognostic value. Despite a relatively small overlap between gene sets, there is high concordance of classification of samples. This, together with analysis of functional gene groups, indicates that the same pathways may be represented by several of the gene sets. However, the results suggest that low-risk patients may be classified more accurately with gene signatures developed especially for this patient group.

Conclusion: Several gene sets, mainly developed in high-risk cancers, predict metastasis from low-malignant cancer.

Breast cancer patients are classified into groups with high- or low-risk of recurrence using a combination of clinical and pathologic criteria. The high-risk group includes the majority of patients, and they are offered adjuvant treatment; whereas the low-risk patients are not offered treatment besides surgery, according to treatment guidelines and risk criteria from the Danish Breast Cancer Cooperative Group. However, a considerable overtreatment occurs in the high-risk group, and recurrence still occurs in the low-risk group.

Recently, promising results for improvement of risk assessment, mainly in the high-risk group, have been obtained by gene expression profiling of breast tumors. Different strategies and platforms have been used to accomplish this. Genome-wide gene expression analysis with long oligonucleotides or Affymetrix chips has been used by several groups. A Dutch group used a Rosetta chip with 60-mer oligonucleotides and developed a 70-gene profile, which could predict development of metastasis within 5 years among lymph node–negative patients with higher accuracy than the classic clinical-pathologic methods (1). The 70-gene signature has been validated with similar results by the same group on a cohort of patients, including patients with lymph node–positive disease (2, 3). Another group, also from the Netherlands, did a similar study with the Affymetrix platform and found a 76-gene profile for prediction of distant metastasis within 5 years in patients who had no adjuvant treatment and no lymph node involvement of the disease (4, 5). Furthermore, by use of Affymetrix chips, a Swedish group has developed a 64-gene signature classifying patients into three risk groups (6). These studies have mainly addressed the overtreatment in the high-risk group, and it has been possible to classify a considerable group of the nonmetastatic tumors correctly although having a high sensitivity.

A different approach has been used by Ramaswamy et al. who compared the expression profiles from primary tumors and nonmatched metastases from several different tissues and identified a 17-gene profile characteristic for metastasis but also present in a subset of primary tumors, suggesting prognostic value of the 17 genes. The prognostic value of this signature was confirmed in the data set from van't Veer et al. (7). Chang et al. (8) hypothesized that features of normal wound healing might play an important role in cancer. They identified a core serum response (CSR) profile and validated the prognostic performance of this on the data provided by van de Vijver et al. (8, 9). Sotiriou et al. aimed at a more precise measure for histologic grade and developed a 97-gene profile capable of separation of a considerable fraction of grade 2 tumors as grade 1 or grade 3 like. This profile had also shown prognostic power in several data sets (10).

Besides the genome-wide approaches, focused expression analysis with real-time PCR on candidate genes has also been used. A 21-gene profile was developed for paraffin-embedded tissue and could predict development of metastasis within a large cohort of tamoxifen-treated patients (11). In another study, the expression of only three genes served as an independent prognostic marker (12).

There is a need for these prognostic signatures to be tested on independent data sets before they come into clinical use. However, a general problem is that different platforms are used and these can be difficult to compare.

Besides the overtreatment in the high-risk group, another important issue is to identify women who would benefit from a treatment they are not offered today. In the low-risk group of patients, ~10% of patients experience recurrence and a significant proportion of these patients would probably benefit from adjuvant treatment. The above-mentioned studies hardly include any low-risk patients with metastatic outcome who did not receive adjuvant therapy. Patients receiving adjuvant treatment are less informative because treatment response will bias outcome classification. We have previously developed a 32-gene profile (HUMAC32) that accurately predicts development of metastasis in this group of patients (13). In this study, we compare prediction of metastasis in the low-malignant group by the HUMAC32 profile and the above-mentioned prognostic gene sets mainly developed in higher risk cancers. The study is designed with pairs of metastasizing and nonmetastasizing tumors matched according to classic prognostic markers, demonstrating the independent information from the classifiers. We have developed classification algorithms with the gene sets on our data, reducing the effect of different platforms.


   Materials and Methods
Top
 Abstract
 Materials and Methods
Results
 Discussion
 Conclusion
 References
 
Patient's samples. For a large cohort of breast cancer patients treated in the county of Funen from 1982 to 1999, tumor biopsies have routinely been collected. Because a very small fraction of low-risk patients develop metastasis, a nested case-control design was applied. Thirteen patients who fulfilled the Danish Breast Cancer Cooperative Group low-risk criteria and developed metastasis and 13 matched patients for whom no metastasis were detected at the end of follow up were selected (the 26 tumors are called low-malignant T1 or low-risk tumors). The Danish Breast Cancer Cooperative Group low-risk criteria are essentially the same as the criteria defined at the 8th St. Gallen Meeting in 2003 (ref. 14)6: node negative, T ≤ 20 mm, grade = 1 if ductal carcinoma (not otherwise specified), receptor positive, and age ≥ 35. In addition, a group of 17 low-malignant T2 tumors [node negative, 20 mm < T ≤ 50 mm, grade = 1 if ductal carcinoma (not otherwise specified), receptor positive, and age ≥ 35] from patients who developed metastasis and 17 matched patients who did not develop metastasis were included. This group did not fulfill the Danish Breast Cancer Cooperative Group low-risk criteria because the tumor size was 20 to 50 mm, but satisfied all other criteria (this group is called low-malignant T2 tumors). The tumors were matched pair-wise according to tumor type as well as year of surgery, tumor size, and age as far as possible. None of the patients had received adjuvant systemic therapy. The average follow up for nonmetastasizing patients was 12.3 years. The study was approved by the regional ethical committee of Southern Denmark.

Gene-expression analysis. RNA was purified from tumor biopsies with Trizol followed by further purification and DNase treatment on RNeasy micro columns (Qiagen). For gene expression analysis, a 29K oligonucleotide chip with duplicate measurement of each gene was used as previously described (15). The sequence of the 70 original oligonucleotides reported by van't Veer (1) was downloaded from Rosetta Inpharmatics website,7 and identical oligonucleotides were spotted on the chips. The same approach could not be used for the other gene sets because of different length of targets. Labeled aRNA was prepared from 500 ng RNA using the Ambion Amino Allyl MessageAmpTM aRNA kit as previously described (15).

Data analysis. Identification of spot locations and quantification was done using arrayWoRx software (Applied Precision). Raw intensity data were normalized using the variance stabilization normalization procedure (16), implemented in the R package vsn. The prediction of outcome and development of a 32-gene classifier (HUMAC32) is described elsewhere (13). Briefly, classification was done by leaving one matched pair of tumors out and selecting genes with nearest shrunken centroids method in R package pamr (17). The selected genes were submitted to support vector machines (SVM; R package e1071) to build a hyper plane to separate the training set (58 samples) with maximal margin and to use to classify new samples in the testing set (two samples; ref. 18). Cross-validation was done for all 30 pairs, a scheme called 30 classifier scheme. The optimal classifier (HUMAC32) was developed by applying nearest shrunken centroids method to the entire data set. All mentioned R packages are implemented in the R-based Bioconductor package.8

Genes from previously reported prognostic gene sets were annotated to the 29K chip by gene bank accession numbers, Unigene ID, or gene symbol. The different gene sets were submitted to SVM and classification done by leaving one matched pair of tumors out at a time and cross-validation as described above, except that the gene set was fixed. The output from SVM is probability of poor outcome for each tumor. A probability cutoff of 0.5 was applied to all classifications to obtain comparable results. Furthermore, mean values of probability of poor outcome were plotted for the different classifiers to compare the separation of samples.

Concordance between two given classifiers were calculated as the fraction of samples classified as identical. The functional analyses of gene sets were generated through the use of expression analysis systematic explorer (19). The program uses Fishers exact test to calculate the probability of randomly selecting the number of genes present in a gene set with a certain function from the gene list represented on the used chip. The P value is subsequently corrected for multiple testing by the Bonferroni method. The overlap between gene sets was investigated with Microsoft Access using the annotation from the 29K chip.


   Results
Top
 Abstract
 Materials and Methods
 Results
Discussion
 Conclusion
 References
 
A 30-classifier scheme constituted by 30 slightly different gene sets and algorithms was developed. With this 30-classifier scheme, 24 metastases and 23 nonmetastases were classified correctly of the 60 samples corresponding to 78% accuracy (Fig. 1A ).


Figure 1
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  		Fig. 1. Testing of nine prognostic reporter sets. Probability of poor outcome is plotted versus sample number. A, HUMAC32; B, 70-gene set; C, 76-gene set; D, 64-gene set; E, 97-gene set; F, CSR; G, 17-gene set; H, 21-gene set; I, 3-gene set. The metastasizing tumors are presented left to the dashed line, and tumors from patients in whom no metastases were detected right to this line. Samples above the horizontal line (P = 0.5) are classified as having poor prognosis. Asterisks, low-risk tumors; triangles, T2 tumors. Panel A is reprinted with permission (13).

 
The objective in the present study was to evaluate the prognostic value of a series of other previously reported gene sets identified with different approaches. Eight gene sets, however, all of them not fully represented on the 29K chip, were tested with the same SVM method, leave-one-pair-out-cross-validation. All the genes from the 70-gene signature were evaluated by measuring the gene expression with the 70 original oligonucleotide sequences reported by van't Veer (1), resulting in 75% accuracy (Fig. 1B). The 76-gene profile consisted of 60 genes used for receptor-positive tumors and 16 genes for receptor-negative tumors (4). To classify the present receptor-positive tumors, 46 of the 60 genes were identified in the 29K oligonucleotide set. Classification resulted in 75% accuracy (Fig. 1C). The 64 genes, reported by Pawitan et al. (6), were represented by 50 oligonucleotides, and 68% accuracy was shown (Fig. 1D). Part of this data set was also used by Sortiriou et al. (10) for development of a 97-gene grade predictor. Ninety-one of these genes were represented on the 29K chip, and the resulting accuracy was 73% (Fig. 1E). The serum response gene set was represented as 252 genes, minimally required for prognostic evaluation of data sets obtained with Stanford and Rosetta chips, resulting in 70% accuracy in the present data (Fig. 1F; ref. 9). Seventeen gene sets reported by Ramaswamy et al. (7), of which one was not represented on the chip, resulted in poor separation of samples and only 45% accuracy (Fig. 1G).

Finally, two gene sets, developed for reverse transcription-PCR (RT-PCR) analysis of candidate genes, were tested. A 21-gene set, of which 5 house keeping genes were omitted and one gene was not represented on the chip, resulted in clear sample separation and 73% accuracy (Fig. 1H). The three-gene set had low prognostic power in the present data set (48% accuracy; Fig. 1I). Likewise an intrinsic gene set from a Norwegian study, done with a cDNA chip from Stanford University (20), was tested and resulted in accuracy comparable with a random distribution of the samples (data not shown).

To examine concordance of classification of different gene sets, the classification results from Fig. 1 are summarized in Table 1 . Furthermore, instead of evaluating the performance of the classifiers with the somewhat arbitrary probability cutoff limit of 0.5, inspection of mean probability of poor outcome may be more informative (Fig. 2 ).


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  		Table 1. Concordance of gene sets

 

Figure 2
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  		Fig. 2. Mean probability of poor outcome. Mean probability of poor outcome for low-risk T1 tumors and low-malignant T2 tumors for the metastasis and nonmetastasis group, respectively. The x axis is set to a probability of 0.5 corresponding to a random distribution of the samples.

 
It is often found that the overlap between gene sets, identified by different groups for prediction of cancer outcome, is minor. The overlap of the nine gene sets is shown in Table 2 . Instead of using the 30 different gene sets from the 30 classifier scheme, the optimal gene set from the present data set, HUMAC32, was included in this analysis. One gene, coding for the FLJ23468 protein (also known as MLF1P), was present in five gene sets (64-gene, CSR, 76-gene, 97-gene, and HUMAC32). CCNE2 and PRC1 were present in four gene sets and 11 genes (ANKT, H2AFZ, HEC, DC13, MKI67, MYBL2, CDC2, MELK, IMAGE:4826434, ASPM, and BM039) were present in three gene sets. The performance of these 14 genes overlapping at least three classifiers was tested in the present data set resulting in 70% accuracy (data not shown).


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  		Table 2. Overlap between gene sets

 
To determine the overlap in biological function of the gene sets, a comparison analysis was done by functional annotation of genes (Table 3 ). This analysis was done for the seven gene sets obtained with genome wide chip analysis.


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  		Table 3. Gene ontology annotation of gene sets

 

   Discussion
Top
 Abstract
 Materials and Methods
 Results
 Discussion
Conclusion
 References
 
We have analyzed a low-malignant group of breast cancer patients of which approximately one half would not receive adjuvant therapy today. Very few of these low-risk patients have been examined in previous reports. This group of patients is particularly difficult to examine because a very low proportion experiences recurrence. The follow-up time needs to be very long to observe the often late on-setting metastasis, and the tumors are small causing low occurrence in tumor banks and low RNA yield. Among the recurrences are a high proportion of contralateral cancers, which are currently difficult to determine as metastases or new cancers. In this study, distant and regional metastases, which are also the most severe form of recurrence, are used as end point. We have previously developed a prognostic 30-classifier scheme accurately predicting metastasis in this patient group (Fig. 1A; ref. 13).

In the present study, eight other prognostic gene sets are examined. We have not been able to use the original algorithms because the data have not been available or the platforms have been so different that this would not be meaningful. For this reason, the SVM procedure with leave-one-pair-out cross-validation seems reasonable for testing these sets. The 30-classifier scheme had higher accuracy and better separation of the samples than the other tested gene sets (Figs. 1 and 2). This may be explained by lower power for the other classifiers, developed for higher risk cancers, in the present cohort of lower risk tumors. This is supported by inspection of the two different risk groups in the present tumor set: T1 and T2 tumors. With the 30-classifier scheme, 31% of the misclassified tumors are T1 tumors (4 of 13), whereas it is 43% (6 of 14) for 76-gene set, 50% (10 of 20) for 64-gene set, 50% (9 of 18) for CSR, and 40% (6 of 15) for the 70-gene set, respectively (Fig. 1). The 21-gene set had comparable performance with the 30-classifier scheme (27%, 4 of 15); however, this gene set was also developed on lymph node–negative and estrogen receptor–positive tumors. A crucial effect of receptor status on gene expression profile has been shown (4). When inspecting separation of samples, in terms of probability of poor outcome, the same tendency that T1 tumors are separated better than T2 tumors with 21-gene and HUMAC32 gene sets compared with the other gene sets is also observed (Fig. 2). The pair-wise matching of samples according to currently used clinical and pathologic prognostic markers may also explain the lower performance of the classifiers developed in cohorts. Cohort classifiers are trained to track the classic prognostic markers because these are strongly biased in outcome groups, whereas the present design enable examination of prognostic value independent from these markers. Several other factors like different platforms, incomplete gene representation on the present chip, different diagnostic procedures, and different sampling procedures may explain the higher accuracy of the 30-classifier scheme. However, this would most likely not change the misclassification rate and separation of samples between T1 and T2 tumors in the present data. The lack of prognostic power in the 17-gene profile reported by Ramaswamy may be explained by the fact that this profile was developed on cancers originating from six different tissues possibly reflecting other metastatic mechanisms. The low performance of the three-gene and 17-gene set, measured by low accuracy of classification, was supported by low concordance to the other gene set corresponding to a random distribution of the samples (Table 1).

Taking the low overlap between the gene sets in consideration (Table 2), the relatively high agreement of classification between the gene sets (Table 1) may indicate that the same underlying biological pathways are represented in the gene sets. Indeed there are several overlaps in the functions of the classifier genes, with cell cycle and cell proliferation being the predominant gene ontology categories (Table 3). This is supported by Fan et al. (21), who recently reported high concordance of an intrinsic gene set with four other signatures. Unlike that study, we have done comparisons of all gene sets mutually. Furthermore, we have developed classification algorithms with SVM instead of adopting classification methods from original studies, reducing the bias of different used platforms.

The MLF1P gene present in five of the seven gene sets deduced from genome-wide expression profiling might be a potent prognostic marker. High expression of this gene has been shown in glioblastoma cell lines compared with normal brain tissue in rat (22), and it binds to MLF1, a negative regulator of cell cycle functioning upstream p53 (23). However, genes overlapping in several classifiers may not be adequate classifier genes, because 14 genes overlapping in three or more classifiers had lower performance than HUMAC32 in the present data. This may indicate that HUMAC32 contain additional genes specific for metastasizing from low-malignant cancer.

The clinical relevance of the current study is to prevent metastasis among low-risk patients. Furthermore, the results show a better classification of the patients with low-malignant T2 tumors compared with the classic methods indicating a potential to reduce the considerable overtreatment in this group. Although only 26 low-risk tumors were included in this study, it is actually the largest of its kind. The cohort study by van de Vijver et al. (2) only included 22 patients of whom four developed distant metastasis. In the study by Wang et al. (4), 14 low-risk patients, including three who developed metastasis, were included in the testing sample set. The low-risk patients used for development and validation of the 21-gene profile were treated with tamoxifen, biasing the prognostic performance of the signature (11). The present study shows considerable prognostic power of 76-gene and 70-gene classifiers among low-risk patients, thereby validating their performance in this patient group.

The pair-wise matching of the present tumors corrects for several factors which could bias the results. This include diagnostic procedures that have changed over time e.g., implementation of more sensitive techniques for detection of lymph node metastasis and receptor status. The sampling methods that might have changed slightly and the storage time at –80°C may have effect on the expression profiles, but these biases are also minimized with the sample matching. Bias from technical variation during purification and microarray procedure of the samples has also been minimized by performing simultaneous processing of the matched pairs.


   Conclusion
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
References
 
In the low-risk group of breast cancer patients, a subgroup experiences metastatic recurrence of the disease. Furthermore, a considerable overtreatment occurs among patients with low-malignant T2 tumors. We have developed a 30-classifier scheme that accurately predicts metastasis in these groups. Six other gene sets derived with different strategies were shown to have prognostic power in the groups, whereas two had none. This, together with functional analysis, indicates that the same pathways may be represented in the gene sets. However, there are indications suggesting that the low-malignant group may be classified more precisely with classifiers, like HUMAC32, developed especially for this group. Further studies with larger cohorts of patients are required to address this question.


   Footnotes
 
Grant support: Danish Research Agency Danish Biotechnology Instrumentation Centre (Human Microarray Centre), Regional Institute of Health Sciences Research at University of Southern Denmark (T.A. Kruse), Clinical Institute at University of Southern Denmark and eight private foundations (M. Thomassen): Raimond and Dagmar Ringgård Bohns Fond, Dagmar Marshalls Fond, AP Møllers Fond til Lgevidenskabens Fremme, Fabrikant Einar Willumsens Mindelegat, Kurt Boennelyckes Fond, Købmand Svend Hansens Fond, Else Poulsens Mindelegat, and Bankdirektør Hans Stener og Hustru Agnes Steners Legat.

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 http://www.dbcg.dk Back

7 http://rii.com Back

8 http://www.bioconductor.org Back

Received 1/31/07; revised 5/ 9/07; accepted 6/12/07.


   References
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 

  1. van 't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530–6.[CrossRef][Medline]
  2. van de Vijver, He YD, van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999–2009.[Abstract/Free Full Text]
  3. Buyse M, Loi S, van't VL, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 2006;98:1183–92.[Abstract/Free Full Text]
  4. Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005;365:671–9.[Medline]
  5. Foekens JA, Atkins D, Zhang Y, et al. Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 2006;24:1665–71.[Abstract/Free Full Text]
  6. Pawitan Y, Bjohle J, Amler L, et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res 2005;7:R953–64.[CrossRef][Medline]
  7. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet 2003;33:49–54.[CrossRef][Medline]
  8. Chang HY, Sneddon JB, Alizadeh AA, et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol 2004;2:E7.[CrossRef][Medline]
  9. Chang HY, Nuyten DS, Sneddon JB, et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A 2005;102:3738–43.[Abstract/Free Full Text]
  10. Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006;98:262–72.[Abstract/Free Full Text]
  11. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817–26.[Abstract/Free Full Text]
  12. Bieche I, Tozlu S, Girault I, Lidereau R. Identification of a three-gene expression signature of poor-prognosis breast carcinoma. Mol Cancer 2004;3:37.[CrossRef][Medline]
  13. Thomassen M, Tan Q, Eiriksdottir F, et al. Prediction of metastasis from low-malignant breast cancer by gene expression profiling. Int J Cancer 2006;120:1070–5.[CrossRef]
  14. Goldhirsch A, Wood WC, Gelber RD, et al. Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer. J Clin Oncol 2003;21:3357–65.[Abstract/Free Full Text]
  15. Thomassen M, Skov V, Eiriksdottir F, et al. Spotting and validation of a genome wide oligonucleotide chip with duplicate measurement of each gene. Biochem Biophys Res Commun 2006;344:1111–20.[CrossRef][Medline]
  16. Huber W, von HA, Sultmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002;18 Suppl 1:S96–104.[Abstract]
  17. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 2002;99:6567–72.[Abstract/Free Full Text]
  18. Brown MP, Grundy WN, Lin D, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 2000;97:262–7.[Abstract/Free Full Text]
  19. Hosack DA, Dennis G, Jr., Sherman BT, Lane HC, Lempicki RA. Identifying biological themes within lists of genes with EASE. Genome Biol 2003;4:R70.[CrossRef][Medline]
  20. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003;100:8418–23.[Abstract/Free Full Text]
  21. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006;355:560–9.[Abstract/Free Full Text]
  22. Hanissian SH, Teng B, Akbar U, et al. Regulation of myeloid leukemia factor-1 interacting protein (MLF1IP) expression in glioblastoma. Brain Res 2005;1047:56–64.[CrossRef][Medline]
  23. Yoneda-Kato N, Tomoda K, Umehara M, Arata Y, Kato JY. Myeloid leukemia factor 1 regulates p53 by suppressing COP1 via COP9 signalosome subunit 3. EMBO J 2005;24:1739–49.[CrossRef][Medline]



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