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Survivin is an independent prognostic marker for risk stratification of breast cancer patients.

Improvement of current breast cancer diagnostics and therapy would be invaluable. With an annual incidence of --200 000 new cases and 70 000 deaths each year in the European Community, breast cancer is the leading malignancy in women and a leading cause of death. The basic understanding of breast cancer initiation and progression is still incomplete. In addition, there is a need to develop improved methods to stratify breast cancer patients into different risk groups more accurately than can be achieved with current clinicopathologic classification methods. Hence, low-risk patients can be spared unnecessary treatment, avoiding side effects and reducing the cost of treatment. Moreover, high-risk patients could be rapidly identified and offered treatment modalities customized (more aggressive) to individual patients. Newly designed biological therapies aimed at specific tumor cell-associated target molecules could also be devised.

Survivin [baculoviral inhibitor of apoptosis (IAP) [4] repeat-containing protein-5 (Birc-5)] is a member of the IAP gene family, which has been implicated in both inhibition of apoptosis and mitosis regulation [for a review, see Ref. (1)]. Survivin is one of the most uniformly up-regulated genes in tumor tissues compared with healthy tissues (2). Uncontrolled growth of cancerous cells requires antiapoptotic strategies to extend an otherwise limited lifespan and to counter the customary apoptotic triggers. In addition, cells that are unresponsive to apoptotic triggers will also be more resistant to radiation and chemotherapy. Indeed, high survivin expression in the primary tumor is almost invariably associated with poor patient prognosis in many cancer types (3-14).

The association of survivin with prognosis in breast cancer patients is, however, ambiguous; previous studies have reported it to be either irrelevant (15) or associated with poor (16) or with good prognosis (17). However, qualitative reverse transcription-PCR (RT-PCR) or immunohistochemistry using antibodies with different sensitivities toward the known survivin variants were used in these studies. Measuring survivin mRNA by real-time fluorescence RT-PCR has the advantages of being more quantitative then classical RT-PCR and, in general, more specific and sensitive then antibody-based assays. Because survivin concentrations are largely controlled at the level of gene transcription (1,18), quantitative RT-PCR should yield representative data on survivin protein concentrations. In the present study, we measured survivin mRNA by quantitative TagMan RT-PCR in 275 breast cancer tissues and correlated the copy numbers with established clinicopathologic factors, relapse-free survival (RFS), and overall survival (OS).

Patients and Methods

PATIENTS

The current study was approved by the institutional ethics committee. A series of 275 patients with unilateral, resectable breast cancer who underwent surgery of their primary tumor between November 1987 and December 1997 were selected on the basis of availability of frozen tissue in the tumor bank of the Department of Chemical Endocrinology of the University Medical Center Nijmegen. This bank contains frozen tumor tissue of patients with breast cancer from five different hospitals of the Comprehensive Cancer Center East in the Netherlands. Our hospital laboratory measured the concentrations of estrogen receptor (ER) and progesterone receptor (PgR) in all samples from these hospitals. The clinical data were collected retrospectively. Patients had no previous diagnosis of carcinoma, no distant metastases at time of diagnosis, and no evidence of disease within 1 month after primary surgery. Patients receiving neoadjuvant therapy or with only carcinoma in situ were excluded. The median age was 60 years (range, 30-88 years). Patients underwent modified radical mastectomy (n = 201) or a breast-conserving lumpectomy (n = 74). Two hundred patients had undergone postoperative radiotherapy to the regional lymph nodes or to the breast after an incomplete resection or breast-conserving surgery. Lymph node involvement was found in 144 patients. Subsequent systemic adjuvant therapy was given based on established clinicopathologic criteria at that time. Patients treated with adjuvant endocrine therapy (n = 90) received 40 mg of tamoxifen twice daily for at least 2 years. In total, 30 patients received adjuvant chemotherapy. Adjuvant chemotherapy consisted of the classic cyclophosphamide-methotrexate-5-fluorouracil (CMF) schedule: six 4-week cycles of 100 mg/[m.sup.2] cyclophosphamide orally each day on days 1-14; 40 mg/[m.sup.2] methotrexate intravenously on days 1 and 8; and 600 mg/[m.sup.2] 5-fluorouracil on days 1 and 8. Some patients received adjuvant chemotherapy with five cycles of 5-fluorouracil, epirubicin/Adriamycin, and cyclophosphamide (FEC/ FAC). Seventeen patients received both endocrine and chemotherapy. The median follow-up time of surviving patients was 83 months (range, 2-169 months). Patients were seen (history, physical examination, routine laboratory investigations) once every 3 months during the first 2 years, once every 6 months for 5 years, and once a year thereafter. Once a year, x-ray mammography was performed, and, in later years, in case of doubt, breast magnetic resonance imaging was performed as well. During follow-up, 102 patients had a recurrence (22 locoregional, 78 distant metastases, 2 both), and 81 patients died (61 of confirmed breast cancer-related causes, 2 as a result of other malignancies, l8 of unknown causes). Thirteen patients died without previous recurrence and were censored at time of death for RFS analysis. Contralateral breast cancer and second malignancies were not considered as recurrent disease.

TISSUE PROCESSING

After primary surgery, a representative part of the tumor was macroscopically selected by a pathologist, frozen in liquid nitrogen, and sent to our department for routine determination of ER and PgR status by ligand-binding assay according to the dextran-charcoal method. Aliquots of tissue were pulverized with a microdismembrator (Braun) and kept in liquid nitrogen until RNA isolation. The samples were coded, and clinical information was unavailable to the technicians performing the mRNA quantification. Total RNA was isolated from 20 mg of tissue powder by use of the RNeasy mini reagent set (Qiagen) with on-column DNase-I treatment. The quality of the RNA was checked by examining ribosomal RNA bands after agarose gel electrophoresis and by amplifying hypoxanthine phosphoribosyltransferase (HPRT) as a control (see below). RNA concentrations were determined spectrophotometrically based on absorbance at 260 nm (Genequant; Amersham). No association of RNA degradation or concentration with length of storage was found. The selected part of the tumor tissue was considered representative for the whole tumor because mammacarcinoma is a heterogeneous tumor in which both malignant cells and "healthy' tissue (components) interact. Additionally, in our experience only limited amounts of RNA can be extracted from healthy breast tissue because this contains mostly fat and fibrogenous materials. Measuring survivin inRNA and correcting for HPRT concentration thus partly compensates for "dilution' by healthy tissue.

RT-PCR

Purified total RNA (1.0 [micro]g) was denatured for 10 min at 70[degrees]C and immediately cooled on ice. Reverse transcription was performed with the Reverse Transcription System (Promega Benelux BV) according to the manufacturer's protocol. After annealing of random hexamers for 10 min at 20[degrees]C, cDNA synthesis was performed for 60 min at 42[degrees]C, followed by an enzyme inactivation step for 5 min at 95[degrees]C. Quantitative PCR was performed as reported previously (12), with both survivin and HPRT mRNA concentrations expressed in absolute copy numbers. Four plates were run for each amplicon in the present study. Survivin and HPRT mRNA copy numbers were quantified by constructing plasmids containing either of the amplicons. A triplicate 5-log-range calibration curve containing 10 to [10.sup.6] copies of either survivin or HPRT was included in each real-time PCR assay plate. The characteristics of the survivin calibration curves were as follows [mean (SD) of four curves]: slope = -3.464 (0.089); y-intercept = 38.323 (0.426); and correlation coefficient = 0.996 (0.004). For HPRT this amounted to the following: slope = -3.468 (0.057); y-intercept = 37.006 (0.231); and correlation coefficient = 0.998 (0.001).

STATISTICAL ANALYSES

Statistical analyses were carried out with SPSS 10.0.5 software (SPSS Benelux BV). The normality of the distribution was tested by the method of Kolmogorov-Smirnov. Differences in expression in samples from patients categorized by clinicopathologic characteristics, used as grouping variables, were assessed with Student's t-test or ANOVA after normalization by log-transformation where appropriate. Nonparametric correlations ([r.sub.s]) were established using Spearman rank correlation testing. RFS time (defined as the time from surgery until diagnosis of recurrent disease) and OS time (defined as the time between date of surgery and death by any cause) were used as follow-up endpoints. The Cox proportional hazards model was used to assess the prognostic value of survivin expression as a log-transformed continuous factor and in addition to other clinicopathologic factors. Kaplan-Meier survival curves were generated after we established an optimum cutoff value in the total group of patients to visually inspect the proportionality assumption. Equality of survival distributions was tested by log-rank testing. Two-sided P values <0.05 were considered to be statistically significant. Cases with >96 months of follow-up were censored at 96 months because of the rapidly decreasing number of patients still surviving after that length of time, although data on some patients were available for up to 169 months after primary surgery. After a certain period of observation, patients were frequently redirected to their general practitioners for checkups and mammography and ceased to belong to the outpatients collective of our breast cancer clinic. Further inclusion of the small remaining groups in statistical analyses would be noninformative.

Results

DISTRIBUTION AND ASSOCIATION WITH CLINICOPATHOLOGY

In the total patient group, survivin mRNA concentrations in the tumor ranged from 10 to 14 000 (median, 220) copy numbers after normalization using HPRT (copies/copies). The survivin mRNA concentrations exhibited a gaussian distribution after log-transformation (P = 0.200, Kolmogorov-Smirnov; Fig. 1). We next investigated the correlation of survivin mRNA concentrations in the primary tumor with other clinicopathologic factors (age, menopausal status, nodal category, tumor histology, tumor size, histology grade, ER and PgR status) and with type of surgery and adjuvant therapy (Table 1). Patients <50 years of age had significantly higher survivin mRNA concentrations (P = 0.021). We found a negative correlation between continuous survivin concentrations and age ([r.sub.s] = -0.173; P = 0.004). Survivin mRNA concentrations were higher in ductal than in lobular tumors (P = 0.015), and grade III tumors had significantly higher survivin mRNA concentrations than did grade I or grade II tumors (P = 0.008). Most pronounced was the association of high survivin concentrations with ER- and PgR-negative tumors (P <0.001 for both). This was not the result of a relationship between survivin and absolute ER concentrations; we found no correlation within the ER-positive group ([r.sub.s] = -0.028; P = 0.710). Thus, survivin mRNA concentrations were highest in tumors that exhibited a generally worse prognosis according to present established clinicopathologic factors.

ASSOCIATION WITH PROGNOSIS

Whether survivin is indeed associated with a poor prognosis, as suggested by its association with other patient and tumor characteristics, was subsequently investigated in univariate survival analyses. Survivin concentrations were normalized by log-transformation and entered in univariate Cox regression analysis for RFS and OS. For RFS, survivin concentrations were significantly associated with poor prognosis with a hazard ratio (HR) of 1.99 [95% confidence interval (CI), 1.31-3.02; P = 0.001; Table 2]. Because survivin is entered as a log-transformed continuous factor, this HR stands for the increase in risk for every 10-fold increase in survivin concentration. For O5, a significant contribution of survivin to poor prognosis was found with a HR of 2.76 (95% CI, 1.67-4.55; P <0.001; Table 3).

[FIGURE 1 OMITTED]

DICHOTOMIZATION INTO RISK GROUPS

To allow analysis of survivin expression as a categorized variable, and for visualization in Kaplan-Meier survival curves to classify tumors as high vs low risk for relapse, tumor survivin concentrations were dichotomized by an optimal cutoff value. We found the most significant difference in RFS (P <0.0001, log-rank; Fig. 2A) after dichotomizing at a survivin/HPRT ratio of 420. When we used this ratio to dichotomize results, tumors from 207 (75.3%) patients were considered having low and 68 (24.7%) as having high concentrations of survivin. The patients with high survivin concentrations had a HR of 2.34 (95% CI, 1.54-3.56; P <0.001). For optimal differentiation of patients for their OS time, 206 (75.7%) patients had low and 66 (24.3%) had high survivin concentrations in their tumors (P = 0.0004, log-rank; Fig. 2B). For O5, patients with high survivin concentrations had a HR of 2.39 (95% CI, 1.45-3.94; P = 0.001).

MULTIVARIATE REGRESSION ANALYSIS

The independent relationship of survivin with RFS and OS was studied with Cox multivariate regression analysis. We thus could establish whether the prognostic value of survivin as found in univariate analyses was attributable to its relationship with other clinicopathologic factors or whether survivin itself contributes independently to prognosis. A multivariate analysis was performed including age, menopausal status, nodal category, tumor size, tumor grade, and ER/PgR status. For RFS, age (P = 0.027), nodal category (P <0.001), and survivin concentration (HR = 1.78; 95% CI,1.18-2.68; P = 0.006) contributed significantly to the model (Table 2). For O5, only nodal category (P <0.001) and survivin concentration (HR = 3.05; 95% CI, 1.83-5.10; P <0.001) were significant (Table 3).

Discussion

In the present study we show that survivin mRNA, as measured by quantitative RT-PCR in the primary tumor, has strong and independent prognostic value in human breast cancer. Our finding that survivin expression is higher in ductal rather than in lobular breast cancers concurs with earlier findings (19, 20), as do the positive relationship with grade and the inverse relationship with ER and PgR status (19). Remarkably, considering this inverse relationship with ER and PgR status, in vitro studies have shown that survivin is up-regulated by estrogens (21). In accordance with this, within the ER-positive group of tumors described here, survivin concentrations were indeed higher in tumors from premenopausal than those from postmenopausal women (P = 0.048; data not shown), which would be in line with an estrogen dependency of survivin expression in ER-positive tumors. The fact that survivin concentrations are higher in ER/PgR-negative cells might be related to a difference in the cellular origin of ER-negative tumors (22, 23) rather than that estrogen-mediated suppression of survivin can be deduced.

Our results concur with studies in other tumor types, which have shown that survivin relates to poor disease outcome in a variety of tumors, i.e., neuroblastoma (3), colorectal cancer (4), non-small-cell lung cancers (5), B-cell lymphoma (6), T-cell leukemia (7), hepatocellular carcinoma (8), esophageal carcinoma (9), rectal cancer (10), glioma (11), bladder cancer (12), soft tissue sarcoma (13), and astrocytic tumors (14). Results obtained in previous studies into the association of survivin with prognosis of breast cancer patients were inconclusive (15) or contradictory (16,17). The prediction of a poor prognosis by survivin mRNA concentrations, as we show here, is to be anticipated from the function of survivin as an inhibitor of effector caspases, thus inhibiting both intrinsic and extrinsic apoptosis pathways (1). Cells that are unresponsive to apoptotic triggers will also be more resistant to cytotoxic treatments, as are cells that overexpress survivin (24). As such, the recent report on survivin as playing a pivotal role in vascular endothelial growth factor (VEGF)-mediated chemoresistance in endothelial cells (25) is of importance because we recently found VEGF to be an excellent predictor of poor disease outcome in breast cancer patients treated with either radiotherapy (26) or endocrine therapy (27). Possibly, the prediction of poor prognosis by survivin we report here is attributable to its association with (VEGF-related) therapy resistance. Indeed, the fact that absolute, log-transformed survivin concentrations are more strongly related to OS than to RFS would suggest an association with success of therapy given after relapse. It should be noted that the stronger prognostic value of survivin for OS than for RFS was not appreciable after the patient group was dichotomized on the basis of an optimal cutoff value for survivin mRNA. Survivin might also function as a mitosis regulator, and the overexpression of survivin in tumor cells irrespective of cell cycle progression might relate to independent proliferation irrespective of therapy (1). Whether survivin is related to disease progression (i.e., prognostic) or treatment resistance (predictive) should be addressed in larger patient groups to allow for proper subgroup analyses.

Measuring survivin mRNA by real-time RT-PCR has the advantages of being more quantitative than classical RT-PCR. Because survivin concentrations are largely controlled at the level of gene transcription (1,18), quantitative RT-PCR should yield representative data on survivin protein concentrations. One previous study also used RT-PCR to detect survivin mRNA in breast cancer tissues, but in contrast to our findings, they failed to find a correlation with disease outcome (15). Importantly, a qualitative RT-PCR was used in that study, in a more limited number (n = 106) of tissues than we report on here. We show in the present study that the quantity of survivin mRNA is related to prognosis. Furthermore, the same group reported that nuclear survivin protein, as measured by immunohistochemistry in 293 samples, was associated with a favorable disease outcome in breast cancer (17). This could not be replicated in the 106 samples used for the RT-PCR study (15). These results are at variance with another immunohistochemical study that reported that survivin protein concentrations were correlated with inhibition of apoptosis and, indirectly, with poor prognosis (16). Of importance, different antibodies have been used in the immunohistochemical analyses with differing sensitivities toward the splice variants of survivin (16,17). Only the antibody used by Kennedy et al. (17) was capable of detecting the survivin [DELTA]Ex3 splice variant. It could be speculated that nucleus-localized survivin is represented by the survivin [DELTA]Ex3 splice variant. This splice variant lacks the nuclear export signal and, because of a frame shift induced by exon 3 skipping, possesses a nuclear localization signal (1). Indeed, similar to in breast cancer (17), nucleus-localized survivin is associated with favorable prognosis in gastric cancer (28) and osteosarcoma (29). However, nuclear staining for survivin can also be related to poor prognosis in esophageal squamous cell carcinoma (30) and Mantle cell lymphoma (31). Thus, the prognostic value of nucleus-localized survivin is not necessarily different from that of cytosolic survivin.

In conclusion, survivin mRNA, as measured by quantitative RT-PCR in the primary tumor, has strong and independent prognostic value in human breast cancer. Survivin mRNA concentrations in the tumor can be used to classify breast cancer patients into different risk groups. Thus, low-risk patients can be spared unnecessary treatment and high-risk patients can be offered more aggressive treatment modalities. These results also support survivin as a promising target for therapy in breast cancer, e.g., by vaccination or administration of antisense oligonucleotides or mutant survivin adenoviruses (1).

[FIGURE 2 OMITTED]

We thank Hanneke van Beek, Bimmer Claessen, and Esther van der Zee for assistance in analysis of the data; we also thank all of the contributors, especially the surgeons and internists, of the UMC Nijmegen and of the community hospitals in the region, Ziekenhuiscentrum Apeldoorn (Apeldoorn), Rijnstate Ziekenhuis (Arnhem), Maasziekenhuis (Boxmeer), Deventer Ziekenhuis (Deventer), Gelderse Vallei (Ede), and Canisius-Wilhelmina Ziekenhuis (Nijmegen), for their assistance in collecting the patients' clinical follow-up data. Doorlene van Tienoven and Anneke Geurts of the Department of Chemical Endocrinology of the University Medical Center Nijmegen are acknowledged for their excellent work with collecting and archiving the breast tumor samples.

References

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(2.) Velculescu VE, Madden SL, Zhang L, Lash AE, Yu J, Rago C, et al. Analysis of human transcriptomes. Nat Genet 1999;23:387-8.

(3.) Adida C, Berrebi D, Peuchmaur M, Reyes-Mugica M, Altieri DC. Anti-apoptosis gene, survivin, and prognosis of neuroblastoma. Lancet 1998;351:882-3.

(4.) Kawasaki H, Altieri DC, Lu CD, Toyoda M, Tenjo T, Tanigawa N. Inhibition of apoptosis by survivin predicts shorter survival rates in colorectal cancer. Cancer Res 1998;58:5071-4.

(5.) Monzo M, Rosell R, Felip E, Astudillo J, Sanchez JJ, Maestre J, et al. A novel anti-apoptosis gene: Re-expression of survivin messenger RNA as a prognosis marker in non-small-cell lung cancers. J Clin Oncol 1999;17:2100-4.

(6.) Adida C, Haioun C, Gaulard P, Lepage E, Morel P, Briere J, et al. Prognostic significance of survivin expression in diffuse large B-cell lymphomas. Blood 2000;96:1921-5.

(7.) Nakayama K, Kamihira S. Survivin an important determinant for prognosis in adult T-cell leukemia: a novel biomarker in practical hemato-oncology. Leuk Lymphoma 2002;43:2249-55.

(8.) Ikeguchi M, Hirooka Y, Kaibara N. Quantitative analysis of apoptosis-related gene expression in hepatocellular carcinoma. Cancer 2002;95:1938-45.

(9.) Ikeguchi M, Kaibara N. Survivin messenger RNA expression is a good prognostic biomarker for oesophageal carcinoma. Br J Cancer 2002;87:883-7.

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(11.) Chakravarti A, Noll E, Black PM, Finkelstein DF, Finkelstein DM, Dyson NJ, et al. Quantitatively determined survivin expression levels are of prognostic value in human gliomas. J Clin Oncol 2002;20:1063-8.

(12.) Schultz IJ, Kiemeney LA, Witjes JA, Schalken JA, Willems JL, Swinkels DW, et al. Survivin mRNA expression is elevated in malignant urothelial cell carcinomas and predicts time to recurrence. Anticancer Res 2003;23:3327-31.

(13.) Kappler M, Kotzsch M, Bartel F, Fussel S, Lautenschlager C, Schmidt U, et al. Elevated expression level of survivin protein in soft-tissue sarcomas is a strong independent predictor of survival. Clin Cancer Res 2003;9:1098-104.

(14.) Kajiwara Y, Yamasaki F, Hama S, Yahara K, Yoshioka H, Sugiyama K, et al. Expression of survivin in astrocytic tumors: correlation with malignant grade and prognosis. Cancer 2003;97:1077-83.

(15.) O'Driscoll L, Linehan R, Kennedy SM, Cronin D, Purcell R, Glynn S, et al. Lack of prognostic significance of survivin, survivin-deltaEx3, survivin-2B, galectin-3, bag-1, bax-a and MRP-1 mRNAs in breast cancer. Cancer Lett 2003;201:225-36.

(16.) Tanaka K, Iwamoto S, Gon G, Nohara T, Iwamoto M, Tanigawa N. Expression of survivin and its relationship to loss of apoptosis in breast carcinomas. Clin Cancer Res 2000;6:127-34.

(17.) Kennedy SM, O'Driscoll L, Purcell R, Fitz-Simons N, McDermott EW, Hill AD, et al. Prognostic importance of survivin in breast cancer. Br J Cancer 2003;88:1077-83.

(18.) Bao R, Connolly DC, Murphy M, Green J, Weinstein JK, Pisarcik DA, et al. Activation of cancer-specific gene expression by the survivin promoter. J Natl Cancer Inst 2002;94:522-8.

(19.) Ryan B, O'Shea C, O'Donovan N, Kennedy S, Crown J, McDermott E, et al. Survivin mRNA and protein in breast cancer. Br J Cancer 2002;86(Suppl 1):S87.

(20.) Korkola JE, DeVries S, Fridlyand J, Hwang ES, Estep AL, Chen YY, et al. Differentiation of lobular versus ductal breast carcinomas by expression microarray analysis. Cancer Res 2003;63:7167-75.

(21.) Frasor J, Danes JM, Komm B, Chang KC, Lyttle CR, Katzenellenbogen BS. Profiling of estrogen up- and down-regulated gene expression in human breast cancer cells: insights into gene networks and pathways underlying estrogenic control of proliferation and cell phenotype. Endocrinology 2003;144:4562-74.

(22.) Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature 2000;406:747-52.

(23.) Gruvberger S, Ringner M, Chen Y, Panavally S, Saal LH, Borg A, et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res 2001; 61:5979-84.

(24.) Zaffaroni N, Daidone MG. Survivin expression and resistance to anticancer treatments: perspectives for new therapeutic interventions. Drug Resist Updat 2002;5:65-72.

(25.) Tran J, Master Z, Yu JL, Rak J, Dumont DJ, Kerbel RS. A role for survivin in chemoresistance of endothelial cells mediated by VEGF. Proc Natl Acad Sci U S A 2002;99:4349-54.

(26.) Manders P, Sweep CGJ, Tjan-Heijnen VCG, Geurts-Moespot J, Van Tienoven TH, Foekens JA, et al. Vascular endothelial growth factor independently predicts the efficacy of postoperative radiotherapy in node-negative breast cancer patients. Clin Cancer Res 2003; 9:6363-70.

(27.) Manders P, Beex LVAM, Tjan-Heijnen VCG, Span PN, Sweep CGJ. Vascular endothelial growth factor is associated with the efficacy of endocrine therapy in patients with advanced breast carcinoma. Cancer 2003;98:2125-32.

(28.) Okada E, Murai Y, Matsui K, Isizawa S, Cheng C, Masuda M, Takano Y. Survivin expression in tumor cell nuclei is predictive of a favorable prognosis in gastric cancer patients. Cancer Lett 2001;163:109-16.

(29.) Trieb K, Lehner R, Stulnig T, Sulzbacher I, Shroyer KR. Survivin expression in human osteosarcoma is a marker for survival. Eur J Surg Oncol 2003;29:379-82.

(30.) Grabowski P, Kuhnel T, Muhr-Wilkenshoff F, Heine B, Stein H, Hopfner M, et al. Prognostic value of nuclear survivin expression in oesophageal squamous cell carcinoma. Br J Cancer 2003;88: 115-9.

(31.) Martinez A, Bellosillo B, Bosch F, Ferrer A, Marce S, Villamor N, et al. Nuclear survivin expression in Mantle cell lymphoma is associated with cell proliferation and survival. Am J Pathol 2004;164: 501-10.

PAUL N. SPAN, [1] * FRED C.G.J. SWEEP, [1] ERWIN T.G. WIEGERINCK, [2] VIVIANNE C.G. TJAN-HEIJNEN [3] PEGGY MANDERS, [1,3] LOUK V.A.M. BEEX [3] and TAOUES B. DE KOK [2]

Departments of [1] Chemical Endocrinology, [2] Clinical Chemistry, and [3] Medical Oncology, University Medical Center Nijmegen, Nijmegen, The Netherlands.

[4] Nonstandard abbreviations: IAP, inhibitor of apoptosis; Birc-5 baculoviral IAP repeat-containing protein-5; RT-PCR, reverse transcription-PCR; RFS, relapse-free survival; OS, overall survival; ER, estrogen receptor; PgR, progesterone receptor; HPRT, hypoxanthine phosphoribosyltransferase; HR, hazard ratio; CI, confidence interval; and VEGF, vascular endothelial growth factor.

* Address correspondence to this author at: 530 Department of Chemical Endocrinology, University Medical Center Nijmegen, PO Box 9101, 6500 HB Nijmegen, The Netherlands. Fax 31-24-3541484; e-mail p.span@ace.umcn.nl.

Received June 24, 2004; accepted August 6, 2004.

Previously published online at DOI: 10.1373/clinchem.2004.039149
Table 1. Associations of clinicopathologic factors with
survivin expression.

 Mean (SD) (b)
 n survivin/
 Variable (total = 275) (a) HPRT ratio P

Age, years 0.021 (c)
 <50 64 295 (64)
 [greater than or
 equal to] 50 211 191 (16)
Menopausal status 0.052 (c)
 Premenopausal 70 275 (58)
 Postmenopausal 205 195 (16)
Node category 0.682 (d)
 Negative 112 209 (19)
 1-3 nodes 85 209 (37)
 4-9 nodes 49 257 (28)
 [greater than or 10 182 (26)
 equal to] 10 nodes
Tumor histology 0.015 (d)
 Ductal 167 245 (28)
 Lobular 31 135 (37)
 Other 31 151 (16)
Tumor size 0.469 (d)
 pT1 69 214 (8)
 pT2 151 219 (28)
 pT3/4 51 178 (44)
Histologic grade 0.008 (d)
 I 11 234 (5)
 II 83 182 (15)
 III 94 295 (19)
 Unknown 87 174 (40)
ER, fmol/mg of protein 0.001 (c)
 <10 96 331 (28)
 [greater than or 175 166 (15)
 equal to] 10
PgR, fmol/mg of protein 0.001 (c)
 <10 114 316 (23)
 [greater than or 158 158 (18)
 equal to] 10
Surgery 0.249 (c)
 Breast-saving
 procedure 74 240 (16)
 Mastectomy 201 204 (28)
Adjuvant radiotherapy 0.032 (c)
 None 74 166 (18)
 Any 200 234 (26)
Adjuvant systemic
therapy 0.023 (c)
 None 137 224 (19)
 Endocrine 90 209 (26)
 Chemo 30 282 (21)
 Both 17 100 (64)

(a) Because of missing values, numbers do not always add up to 275.

(b) Mean (SD) of log-transformed values.

(c) P for Student's t-test.

(d) P for ANOVA.

Table 2. Prognostic value of clinicopathologic factors and survivin
expression for RFS in univariate and multivariate Cox regression
analysis.

 Univariate analysis

Variable HR (95% CI) P

Age, years 0.006
 <50 1.00
 [greater than or equal to] 50 0.55 (0.35-0.84)
Node category <0.001
 Negative 1.00
 1-3 nodes 1.69 (1.00-2.84)
 4-9 nodes 3.41 (1.97-5.89)
 [greater than or equal to] 10 nodes 5.90 (2.66-13.1)
Menopausal status 0.029
 Premenopausal 1.00
 Postmenopausal 0.62 (0.40-0.95)
Tumor size 0.012
 pT1 1.00
 pT2 2.04 (1.15-3.60)
 pT3 + pT4 2.42 (1.25-4.70)
Histologic grade 0.046
 I 1.00
 II 1.50 (0.35-6.35)
 III 2.85 (0.69-11.8)
 Unknown 1.88 (0.45-7.88)
ER, fmol/mg of protein 0.171
 <10 1.00
 [greater than or equal to] 10 0.75 (0.49-1.13)
PgR, fmol/mg of protein 0.380
 <10 1.00
 [greater than or equal to] 10 0.83 (0.55-1.25)
Survivin expression 0.001
 Log-transformed 1.99 (1.31-3.02)

 Multivariate analysis

Variable HR (95% CI) P

Age, years 0.027
 <50 1.00
 [greater than or equal to] 50 0.60 (0.38-0.94)
Node category <0.001
 Negative 1.00
 1-3 nodes 1.71 (1.01-2.91)
 4-9 nodes 3.33 (1.90-5.85)
 [greater than or equal to] 10 nodes 7.36 (3.25-16.7)
Menopausal status
 Premenopausal
 Postmenopausal
Tumor size
 pT1
 pT2
 pT3 + pT4
Histologic grade
 I
 II
 III
 Unknown
ER, fmol/mg of protein
 <10
 [greater than or equal to] 10
PgR, fmol/mg of protein
 <10
 [greater than or equal to] 10
Survivin expression 0.006
 Log-transformed 1.78 (1.18-2.68)

Table 3. Prognostic value of clinicopathologic factors and survivin
expression for OS in univariate and multivariate Cox
regression analysis.

 Univariate analysis

Variable HR (95% CI) P

Age, years 0.262
 <50 1.00
 [greater than or equal to] 50 0.73 (0.43-1.25)
Node category <0.001
Negative 1.00
 1-3 nodes 1.43 (0.76-2.68)
 4-9 nodes 3.23 (1.69-6.19)
 [greater than or equal to] 10 nodes 5.67 (2.25-14.3)
Menopausal status 0.25
 Premenopausal 1.00
 Postmenopausal 0.74 (0.43-1.24)
Tumor size 0.126
 pT1 1.00
 pT2 1.64 (0.87-3.08)
 pT3 + pT4 2.08 (0.99-4.38)
Histologic grade 0.599
 I 1.00
 II 1.36 (0.32-5.82)
 III 1.79 (0.42-7.52)
 Unknown 1.24 (0.29-5.37)
ER, fmol/mg of protein 0.478
 <10 1.00
 [greater than or equal to] 10 0.83 (0.50-1.38)
PgR, fmol/mg of protein 0.458
 <10 1.00
 [greater than or equal to] 10 0.83 (0.51-1.36)
Survivin expression <0.001
 Log-transformed 2.76 (1.67-4.55)

 Multivariate analysis

Variable HR (95% CI) P

Age, years
 <50
 [greater than or equal to] 50
Node category <0.001
Negative 1.00
 1-3 nodes 1.44 (0.77-2.70)
 4-9 nodes 2.97 (1.53-5.78)
 [greater than or equal to] 10 nodes 7.29 (2.86-18.6)
Menopausal status
 Premenopausal
 Postmenopausal
Tumor size
 pT1
 pT2
 pT3 + pT4
Histologic grade
 I
 II
 III
 Unknown
ER, fmol/mg of protein
 <10
 [greater than or equal to] 10
PgR, fmol/mg of protein
 <10
 [greater than or equal to] 10
Survivin expression <0.001
 Log-transformed 3.05 (1.83-5.10)
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Title Annotation:Molecular Diagnostics and Genetics
Author:Span, Paul N.; Sweep, Fred C.G.J.; Wiegerinck, Erwin T.G.; Tjan-Heijnen, Vivianne C.G.; Manders, Peg
Publication:Clinical Chemistry
Date:Nov 1, 2004
Words:5136
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