Affiliations of authors: Department of Experimental Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy (JFR, LL, LDC, DC, SV, MGD, MG, MAP); Molecular Cancer Genetics Group, Fondazione Istituto FIRC di Oncologia Molecolare (IFOM), Milano, Italy (JFR, LL, LDC, MG, MAP)
Correspondence to: James F. Reid, Fondazione Istituto FIRC di Oncologia Molecolare (IFOM), Milano, Italy (e-mail: james.reid{at}ifom-ieo-campus.it); Manuela Gariboldi, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy (e-mail: manuela.gariboldi{at}istitutotumori.mi.it); Marco A. Pierotti, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy (e-mail: marco.pierotti{at}istitutotumori.mi.it).
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ABSTRACT |
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Recently, a tamoxifen-response predictive model consisting of only two genes has been described (14). By using microarray gene expression profiles of 60 tamoxifen-treated patients, HOXB13 and IL17BR were identified as the two genes whose expression ratio predicts clinical outcome. This finding was validated by use of real timequantitative polymerase chain reaction (RT-QPCR) on an independent set of 20 formalin-fixed, paraffin-embedded samples by correctly classifying the outcomes of 16 patients (P = .01). However, by considering the data from relapsed and disease-free patients separately, although the probability of obtaining such a correct classification by chance remained low for disease-free patients (nine of 10 correctly classified, P = .02; 95% confidence interval [CI] for the proportion of correctly classified samples = 0.55 to 0.99), this estimate increased drastically for relapsed patients (seven of 10 correctly classified, P = .34; 95% CI = 0.35 to 0.93). Although the proposed predictive model is very appealing from clinical and practical points of view because of its potential straightforward application in many laboratories, the results of the validation set (i.e., the statistically nonsignificant results for the relapsed patients) indicate that a larger validation set is required.
For this reason, we applied this two-gene predictive model for relapse to a dataset derived from a cohort of 58 patients with early-stage, estrogen receptorpositive primary breast cancer who were treated at the Istituto Nazionale Tumori between March 1, 1991, and December 31, 1997, with radical or conservative surgery plus radiotherapy followed by adjuvant monotherapy with tamoxifen (median treatment duration = 60 months, range = 2784 months). All patients signed an informed consent to donate any tissue leftover after diagnostic procedures to Istituto Nazionale Tumori. A tumor was classified as estrogen receptor positive if the ligand binding assay detected more than 10 fmol of estrogen bound per mg of total protein. Disease recurred with distant metastasis in 18 patients (16 patients as a first event and two as a second event after local-regional recurrence) of the 58 patients within a median time of 31 months (range = 1443 months) from surgery. Forty of the 58 patients were disease free after a median time of 93 months (range = 70125 months).
Clinical and pathobiologic details of these 58 patients are presented in supplemental Table 1 (Available at: http://jncicancerspectrum.oupjournals.org/jnci/content/vol97/issue12). Most patients were older than 50 years of age (93.1%) and had lymph nodepositive disease (77.5%; 53.5% had one to three positive lymph nodes and 24.0% had more than three positive lymph nodes). Their tumors were larger than 2 cm (62.1% of tumors), were progesterone receptor positive (79.3% of tumors; i.e., more than 25 fmol of progesterone bound per mg of total protein by ligand binding assay), and were HER-2/neu negative (77.6% of tumors). HER-2/neu status was immunohistochemically assessed with polyclonal antibody against p185HER2 protein (1:2000 dilution, DAKO, Milan, Italy) and defined as positive when strong membrane labeling was observed. A limitation of any validation study on independent cohorts can be related to having a different mixture of case patients than that of the original study. Compared with the previously described cohort (14), our cohort had a prevalence of tumors that were lymph node positive (77.5% vs. 47.2%), HER-2/neu positive (20.7% vs. 5.4%), and larger than 2 cm (62.1% vs. 47.2%).
RT-QPCR used TaqMan gene expression assays for the following genes: HOXB13 labeled with FAM-MGB (a 6-carboxyfluorescein fluorescent dye and a minor groove binding [MGB] molecule attached to the 3' end, which stabilizes the probe annealing; product Hs00197189), IL17BR labeled with FAM-MGB (product Hs00218889), and human GAPDH VIC-MGB (VIC is a proprietary fluorescent dye; product 4326317E), a housekeeping gene used for normalization (Applied Biosystems, Foster City, CA). Gene expression data were quantified as described by the manufacturer and log-transformed (Fig. 1, A, and raw data in supplemental Table 2; available at http://jncicancerspectrum.oupjournals.org/jnci/content/vol97/issue12).
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However, our analyses of this independent set of samples did not find any statistically significant association between the gene expression of HOXB13, IL17BR or their ratio and outcome after tamoxifen treatment (e.g., from univariate logistic regression, for HOXB13 odds ratio [OR] = 1.04, 95% confidence interval [CI] = 0.92 to 1.16, P = .54; for IL17BR, OR = 0.69, 95% CI = 0.40 to 1.20, P = .18; and for HOXB13/IL17BR, OR = 1.30, 95% CI = 0.88 to 1.93, P = .18). Results of the latter model, with the overlapping estimated probabilities of recurrence for disease-free patients and relapsed patients are shown in Fig. 1, B. Similar P values were obtained from t tests, MannWhitney tests, and AUC analyses (Table 1).
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In conclusion, in our cohort of patients we failed to validate the predictive model proposed by Ma et al. (14). Furthermore, building predictors for tamoxifen treatment by use of two independent microarray datasets did not provide promising results. These facts probably highlight the heterogeneous nature of the underlying disease and, hence, the need for microarray data sets from much larger and/or more homogeneous cohort of samples to build more reliable predictive models. For the time being, because of the relatively small sample sizes of microarray experiments, this challenging task may only be circumvented by thoughtful experiment design (21,22) and by providing public access to published microarray data (23), which will drive reproducible results and accelerate the design of appropriate meta-analytical techniques for integrating data from different studies. We believe that it is also crucial that microarray data be viewed as a valuable and rich source of additional information that can supplement information from clinical and pathobiologic markers toward the goal of developing efficient and subject-tailored treatment strategies.
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NOTES |
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M. Gariboldi and M. A. Pierotti contributed equally as senior co-authors.
This work was supported by grants CNR/MIUR "Progetto Strategico Oncologia" (02.00385.ST97 to M.A. Pierotti), AIRC (Associazione Italiana per la Ricerca sul Cancro): individual grants to M. Gariboldi and M. A. Pierotti, and Sixth Framework Programme from the European Community: "Combating Cancer", TRANSFOG integrated project (proposal number 503438).
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Manuscript received November 12, 2004; revised April 11, 2005; accepted April 13, 2005.
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