Recent observations support the notion that telomerase expression is essential for the formation of human tumor cells [W-C. Hahn et al., Nature (Lond.), 400: 464–468, 1999]. The expression pattern of hTERT, the human telomerase catalytic subunit gene, is a rate-limiting determinant of the enzymatic activity of human telomerase. We have developed a real-time quantitative RT-PCR assay based on TaqMan fluorescence methodology to quantify the full range of hTERT mRNA copy numbers. We validated the method on a series of 134 unilateral invasive primary breast cancer patients with known long-term outcome. Three-quarters of the breast tumors (75.4%; 101 of 134) were hTERTpositive, i.e., contained detectable and quantifiable hTERT mRNA. hTERT-positive patients had significantly shorter relapse-free survival (P =0.017) after surgery compared with hTERT-negative patients. The prognostic significance of hTERT status persisted in Cox multivariate regression analysis. When we subdivided hTERT-positive patients (n = 101)into three equal groups (tumors showing small, intermediate, or high increase in hTERT mRNA content), we observed statistical(or a trend toward) links between high hTERT mRNA levels and Scarff-Bloom-Richardson histopathological grade III(P = 0.066), and negative estrogen(P = 0.002) and progesterone (P = 0.048) receptor status, and therefore with higher aggressiveness of breast tumors. High hTERT mRNA levels were also linked to MYC gene overexpression (P =0.007). These findings show that the quantitative evaluation of hTERT mRNA can have important prognostic significance in human breast cancer. In addition, our simple, rapid, and semiautomated assay method is suitable for routine hTERT mRNA detection and quantification and will be a powerful tool in large,randomized, prospective, cooperative group trials and in the hTERT-based therapy project.

Telomeres are structures located on the ends of eukaryotic chromosomes. In humans, they consist of several kilobases of a simple 5′-(TTAGGG)n-3′ repeat. Telomeres protect the ends of chromosomes against degradation by exonucleases and ligases and against end-to-end fusion, rearrangements, and the loss of terminal DNA segments that occurs when linear DNA replicates (1). Telomeres in human somatic cells gradually shorten with each successive cell division, through replication-dependent sequence loss at DNA termini; this results in chromosome instability, leading to cellular senescence (2). A possible cause of human telomere shortening is repression of telomerase, a specialized ribonucleoprotein that consists of multiple protein subunits and a structural RNA component that contains a template sequence for the telomeric repeat (3). Telomerase activity is inactivated or repressed in the majority of normal somatic tissues but is activated in germ cells and in most malignant tumors. Telomerase reactivation may thus be a major step in human carcinogenesis (4).

Considerable interest is being focused on the potential use of telomerase-based assays as diagnostic and prognostic methods and for the development of telomerase-based therapies (5). To date, most studies have used the TRAP3 to assay telomerase activity (6). In the TRAP assay, telomerase is extracted and allowed to synthesize extension products, which then serve as the templates for PCR amplification. However, the TRAP assay is time consuming, not accurate enough to quantify the full range of telomerase activity, and inappropriate to carry out retrospective studies of clinical outcome from standard archival material (7).

Recently, several components of human telomerase have been cloned,including the telomerase RNA component (hTERC; also termed hTR; Ref. 8) and the telomerase catalytic subunit (hTERT; also termed hTRT, hTCS1, or hEST2; Ref. 9). Telomerase activity correlates with the restricted expression pattern of the hTERT gene,whereas expression of the hTERC gene is widespread (9). Ectopic hTERT expression is sufficient to confer enzymatic activity to telomerase-negative cells (10), suggesting that hTERT mRNA may serve as a surrogate index for telomerase activity. Ectopic hTERTexpression in combination with two oncogenes (the SV40 large-T oncoprotein and an oncogenic allele of H-ras) results in direct tumorigenic conversion of normal human epithelial and fibroblast cells (11). Moreover, inhibition of hTERTresults in telomere loss and limits the growth of human cancer cell lines in vitro and their tumorigenic capacity in vivo(12).

We have developed a real-time quantitative RT-PCR assay based on TaqMan methodology to quantify hTERT mRNA in homogeneous total RNA solutions prepared from tumor samples (13). This recently developed method is based on use of the 5′-3′ exonuclease activity of Taq polymerase to cleave a dual-labeled probe annealed to a target sequence during the extension phase of PCR. This method of nucleic acid quantification in homogeneous solution may become a reference in terms of its performance, accuracy, sensitivity, wide dynamic range, and high throughput capacity, and it also eliminates the need for tedious post-PCR processing. Above all, this method is suited to the development of new target gene assays with a high level of interlaboratory standardization and yields statistical confidence values.

We used this technique to detect and to quantify hTERT gene expression in a series of 134 unilateral invasive primary breast tumor RNAs. We then compared hTERT gene expression with usual prognostic indicators and disease outcome.

In vitro studies suggest induction of telomerase activity by MYC overexpression (14, 15), and that both telomerase activity and Rb/CCND1/p16 pathway inactivation are necessary to immortalize human epithelial cells (16). In consequence, we also tested the possible link between hTERTexpression levels and altered mRNA expression of MYC, RB1,and CCND1 genes.

### Patients and Samples

We analyzed tissue from surgically removed primary breast tumors of 134 women followed at the Center René Huguenin from 1977 to 1989. The samples were examined histologically for the presence of tumor cells. A tumor sample was considered suitable for this study if the proportion of tumor cells was >60%. Immediately after surgery,the tumor samples were stored in liquid nitrogen until RNA extraction.

The patients (mean age, 58.3 years; range, 34–91 years) met the following criteria: primary unilateral nonmetastatic breast carcinoma on which complete clinical, histological, and biological data were available; and no radiotherapy or chemotherapy before surgery. The main prognostic factors are presented in Table 1. The median follow-up was 8.9 years(range, 0.3–15.9 years). Forty-eight patients relapsed (the distribution of first relapse events was as follows: 14 local and/or regional recurrences, 30 metastases, and 4 both).

To help validate the kinetic quantitative RT-PCR method, we also analyzed three breast tumor cell lines obtained from the American Tissue Type Culture Collection (SK-BR-3, BT-20, and MCF7). Ten specimens of adjacent normal breast tissue from breast cancers and normal breast tissue from 10 women undergoing cosmetic breast surgery were used to assess basal level of hTERT mRNA in normal breast tissue.

### Real-Time RT-PCR

#### Theoretical Basis.

Reactions are characterized by the point during cycling when amplification of the PCR product is first detected, rather than the amount of PCR product accumulated after a fixed number of cycles. The larger the starting quantity of the target molecule, the earlier a significant increase in fluorescence is observed. The parameter Ct (threshold cycle) is defined as the fractional cycle number at which the fluorescence generated by cleavage of the probe passes a fixed threshold above baseline. The hTERT target message in unknown samples is quantified by measuring Ct and by using a standard curve to determine the starting target message quantity.

The precise amount of total RNA added to each reaction mix (based on absorbance) and its quality (i.e., lack of extensive degradation) are both difficult to assess. We therefore also quantified transcripts of the RPLP0 gene (also known as 36B4) encoding human acidic ribosomal phosphoprotein P0 as the endogenous RNA control, and each sample was normalized on the basis of its RPLP0 content.

The relative target gene expression level was also normalized to a calibrator, or 1× sample, consisting of a breast tumor tissue sample that contained the smallest accurately quantifiable amount of hTERT mRNA. The calibrator thus indicates the limit of assay quantitation of the target which corresponds to a hTERTCt value of 35. Each sample normalized hTERT value is divided by the calibrator normalized hTERT value to give the final relative expression level.

Final results, expressed as N-fold differences in hTERT gene expression relative to the RPLP0 gene and the calibrator,termed NhTERT, were determined as follows:

$N_{hTERT}\ {=}\ \frac{hTERT_{sample}}{RPLP\mathit{0}_{\mathrm{sample}}}\left/\frac{\mathrm{hTERT}_{\mathrm{calibrator}}}{\mathrm{RPLP}\mathit{0}_{\mathrm{calibrator}}}\right.$

#### Primers, Probes, and PCR Consumables.

Primers and probes for the RPLP0 and hTERTgenes were chosen with the assistance of the computer programs Oligo 4.0 (National Biosciences, Plymouth, MN) and Primer Express(Perkin-Elmer Applied Biosystems, Foster City, CA). We conducted BLASTN searches against dbEST and nr (the non-redundant set of GenBank, EMBL,and DDBJ database sequences) to confirm the total gene specificity of the nucleotide sequences chosen for the primers and probes and the absence of DNA polymorphisms. The nucleotide sequences of the oligonucleotide hybridization probes and primers are shown in Table 2. Primers and probes are designated by the nucleotide position (relative to hTERT GenBank accession number AF015950 and RPLP0 GenBank accession number M17885)corresponding to the 5′ position, followed by the letter U for upper(sense strand) or L for lower (antisense strand). To avoid amplification of contaminating genomic DNA, one of the two primers was placed in a different exon. For example, the upper primer of hTERT (2673U) was placed in exon 10, whereas the probe(2711U) and the lower primer (2767 L) were placed in exon 11.

#### RNA Extraction.

Total RNA was extracted from breast specimens by using the acid-phenol-guanidinium method. The quality of the RNA samples was determined by electrophoresis through agarose gels and staining with ethidium bromide, and the 18S and 28S RNA bands were visualized under UV light.

#### cDNA Synthesis.

Reverse transcription of RNA was done in a final volume of 20 μl containing 1× RT buffer (500 μm each deoxynucleotide triphosphate, 3 mm MgCl2, 75 mm KCl, 50 mm Tris-HCl, pH 8.3), 10 units of RNasin RNase inhibitor (Promega Corp., Madison, WI), 10 mmDTT, 50 units of Superscript II RNase H reverse transcriptase (Life Technologies, Inc., Gaithersburg, MD), 1.5μ m random hexamers (Pharmacia, Uppsala, Sweden), and 1μg of total RNA. The samples were incubated at 20°C for 10 min and 42°C for 30 min, and reverse transcriptase was inactivated by heating at 99°C for 5 min and cooling at 5°C for 5 min.

#### Standard Curve Construction.

The relative kinetic method was applied using a standard curve constructed with 4-fold serial dilutions of cDNA obtained from the MCF7 breast cell line known to strongly express the hTERT gene (17); the cDNA was obtained by reverse transcription from 1 μg of total RNA and 5-fold dilution in 1× RT buffer. The standard curve used for PCR is composed of 5 points (equivalent to 100, 25,6.25, 1.6, and 0.4 ng of MCF7 total RNA).

#### PCR Amplification.

All PCR reactions were performed using a ABI Prism 7700 Sequence Detection System (Perkin-Elmer Applied Biosystems). For each PCR run, a master mix was prepared on ice with 1× TaqMan buffer, 5 mmMgCl2, 200 μm dATP, dCTP, dGTP, and 400 μm dUTP, 300 nm each primer, 150 nm probe, and 1.25 units of AmpliTaq Gold DNA polymerase(Perkin-Elmer Applied Biosystems). Ten μl of each appropriately diluted RT samples (standard curve points and patients’ samples) were added to 40 μl of the PCR master mix. The thermal cycling conditions comprised an initial denaturation step at 95°C for 10 min and 50 cycles at 95°C for 15 s and 65°C for 1 min.

Experiments were performed with duplicates for each data point. Each PCR run included the five points of the standard curve (4-fold serially diluted MCF7 cell line cDNAs), a no-template control, the calibrator cDNA, and 41 unknown patient cDNAs. The target gene mRNA copy value of the 41 patients was obtained in ∼2 h with this assay format.

#### Inclusion Criteria for hTERT Assay.

Breast tissue samples were considered eligible for study when the RPLP0 Ct value was ≤20, i.e., suggesting an appropriate starting amount and quality of total RNA. All samples with a coefficient of variation for RPLP0 and/or hTERTmRNA copy numbers >10% were also retested.

RFS was determined as the interval between diagnosis and detection of the first relapse (local and/or regional recurrences and/or metastases). Clinical, histological, and biological parameters were compared using the χ2 test. Differences between the two populations were judged significant at confidence levels >95%(P < 0.05). Survival distributions were estimated by the Kaplan-Meier method (18), and the significance of differences between survival rates was ascertained using the log-rank test. Multivariate analysis using Cox’s proportional hazards model (19) was used to assess the independent contribution of each variable to RFS.

### hTERT mRNA Detection in Normal Breast Tissues.

hTERT mRNA detection was assayed in 20 normal breast tissue RNAs. We observed no normal breast samples in which real-time PCR showed total absence of hTERT mRNA copies (hTERTCt, 50). Indeed, a very faint positive signal was observed in all normal breast samples, with high hTERTCt values. Because the hTERTCt values consistently fell between 37 and 45, values of 35 or less were considered to represent overexpression of the hTERT gene in tumor RNA samples, which were thus scored hTERT-positive.

### hTERT mRNA Levels in Breast Tumor Tissues.

Among the 134 breast tumor RNA samples tested, 33 (24.6%) were scored hTERT negative (hTERTCt, >35) and 101 (75.4%) were scored hTERT positive (hTERTCt, ≤35) with the possibility of NhTERT value determination, calculated as described in “Patients and Methods.” Among these 101 hTERT-positive tumors, major differences of NhTERT values were observed, ranging from 1.0 to 64.7. hTERT gene expression was also investigated in three breast tumor cell lines (SK-BR-3, BT-20, and MCF7), which were scored hTERT-positive with NhTERT values of 1.4 (BT-20), 3.6(SK-BR-3), and 8.2 (MCF7). Fig. 1 gives data on one hTERT-negative tumor (TERT91), on MCF7 cell line(NhTERT = 8.2) and on two hTERT-positive tumors with low (TERT22, NhTERT = 1.2) and high (TERT57, NhTERT = 64.7) hTERT mRNA levels.

The NhTERT value was based on the amount of the hTERT target message relative to the RPLP0endogenous control to normalize the amount and quality of total RNA;similar results were obtained by using a second endogenous RNA control,the gene TBP coding for the TATA box-binding protein (a component of the DNA-binding protein complex TFIID; data not shown).

### Correlation between Qualitative hTERT mRNA Status(hTERT negative/positive) and Clinical and Pathological Parameters.

We sought links between qualitative hTERT mRNA status(hTERT negative/positive) and standard clinical,pathological, and biological factors in breast cancer (Table 3). hTERT-positive status was not significantly associated (χ2 test) with menopausal status or standard prognostic factors such as macroscopic tumor size,histopathological grade, or lymph node or steroid receptor status. Nevertheless, patients with hTERT-positive tumors had a higher rate of relapse [42.6% (43 of 101) versus 15.2% (5 of 33)] than those with a hTERT-negative tumors(P = 0.004; Table 3). hTERT positivity was associated with reduced RFS after surgery (log-rank test, P = 0.017; Table 1; Fig. 2). The outcome for the 101 patients with hTERT-positive tumors was significantly worse than that of the 33 patients with a hTERT-negative tumors in term of RFS[7-year RFS, 66.7% (57.1–76.3) versus 84.8%(72.6–97.1); 10-year RFS, 57.3% (46.3–68.2) versus 84.8%(72.6–97.1)].

Using a Cox proportional hazards model, we also assessed the prognostic value for RFS of parameters that were significant in univariate analysis, i.e., lymph-node status and hTERTstatus (Table 1). The prognostic significance of these two parameters persisted in Cox multivariate regression analysis (Table 4). Adjusted relative risk of these two parameters, taking into account menopausal status, macroscopic tumor size, histological grade, and steroid receptor status, did not change their prognostic significance for RFS (data not shown).

### Correlation between Amount of hTERT mRNA and Clinical and Pathological Parameters.

To better analyze the hTERT mRNA level as a quantitative variable, patients with hTERT-positive tumors(n = 101) were subdivided into three equal groups (34,34, and 33 patients, respectively), with tumors with low (1.00–1.6),intermediate (1.6–3.5), and high (3.6–64.7) NhTERT values. We observed statistical links between high hTERT mRNA levels and negative estrogen(P = 0.002) and progesterone (P =0.048) receptor status (Table 5). A trend toward a link between high hTERT mRNA levels and SBR histopathological grade III was also observed (P =0.066). The outcomes of the patients in the three groups did not differ(Table 5 and log-rank test not shown).

### Relationship between hTERT mRNA Levels and MYC, RB1, and CCND1 Expression Status.

We tested the possible link between hTERT expression levels and altered mRNA expression of MYC, RB1, and CCND1 genes, tested previously for same tumor RNA series.4 We observed a statistical link between high hTERT mRNA levels and MYC overexpression (P = 0.007) but not between high hTERT mRNA expression and altered RB1 or CCND1 mRNA expression (Table 5).

Numerous published studies based on the TRAP assay have demonstrated that telomerase is activated in the vast majority of tumor types, including breast tumors, and also in certain normal tissues. Telomerase activity has been detected in 75–95% of breast tumor samples (20, 21, 22, 23, 24), and its up-regulation appears to be an early event in breast carcinogenesis (25). Studies that have correlated telomerase activity with clinical and pathological parameters have given conflicting results for traditional prognostic indicators, disease outcome, and telomerase activity (20, 21, 22, 23). This suggests that telomerase activity may be influenced by parameters other than clinical/pathological features alone, such as sensitivity, telomerase inhibitors, and tissue viability with regard to the different TRAP protocols used. Overall, however, the results suggest that telomerase reactivation may be an important step in the progression of normal epithelial tissue to breast cancer.

Recently, the gene encoding the catalytic subunit of human telomerase(hTERT) has been cloned (9). Several studies have demonstrated that hTERT expression is a rate-limiting determinant of the enzymatic activity of human telomerase and that up-regulation of hTERT expression may play a critical role in human carcinogenesis (11). In a recent report based on an RNA in situ hybridization assay, Kolquist et al.(26) showed that hTERT expression appeared early during breast tumorigenesis in vivo,beginning in normal epithelial cells with proliferative potential and increasing gradually during the neoplastic process.

In this study, we validated a recently developed RT-PCR method for the quantification of hTERT expression (13). The method is based on real-time analysis of PCR amplification and TaqMan methodology and has several advantages over other RT-PCR-based quantitative methods, as well as over TRAP assay. The real-time PCR method does not require post-PCR sample handling, thereby avoiding problems related to carryover; it has a high sample throughput and possesses a wide dynamic range, meaning that samples do not have to contain equal starting amounts of total RNA. This technique should,therefore, be suitable for analyzing small early-stage tumors, fine needle aspiration specimens, or formalin-fixed, paraffin-embedded tissues. Real-time RT-PCR-based hTERT mRNA assay has also specific technical advantages over the TRAP assay: (a)because standard archival material (formalin-fixed, paraffin-embedded tissues) can be used to quantify hTERT mRNA by real-time RT-PCR, and retrospective studies of clinical outcome can be carried out; (b) real-time PCR reaction has endogenous control(RPLP0 gene in this study) for each sample, whereas the controls for TRAP assay are from separate samples and reactions.

Finally, and above all, real-time PCR makes RNA quantitation much more precise and reproducible, being based on Ct values established in the early exponential phase of the PCR reaction (when none of the reagents is rate-limiting) rather than end point measurement of the amount of accumulated PCR product. Real-time PCR has high intraassay and interassay reproducibility and gives statistical confidence values.

We validated the method on 20 normal breast tissue RNAs and on a large series (n = 134) of unilateral invasive primary breast tumor RNAs. hTERT mRNA was detected in 100% of breast tumor RNAs but also in all normal breast RNAs. These results reflect the higher sensibility of RT-PCR methods compared with the TRAP methods used, in agreement with Snijders et al.(27). These latter authors also showed that the presence of hTERTmRNA itself was not indicative of telomerase activity, but that a certain threshold level of hTERT mRNA is required for real telomerase activity. In our series, all of the normal breast tissue RNAs (n = 20) and 33 (24.6%) of the human breast tumor RNAs showed very low levels of hTERT mRNA that were only detectable but not quantifiable by means of the real-time quantitative RT-PCR assay. An increase in hTERT mRNA levels compared with the normal breast tissues was observed clearly in 75.4% of breast tumors. This frequency of hTERT-positive breast tumors is in agreement with data reported by other teams using the TRAP assay (20, 21, 22, 23, 24). Because the real-time quantitative RT-PCR assay is accurate enough to quantify the full range of expression values, the hTERT-positive group was subdivided into three equal subgroups, with tumors of low, intermediate, and high hTERTmRNA copy numbers. These additional cut points allowed to better study the possible correlations between hTERT gene expression levels and the usual prognostic indicators and disease outcome.

Overall, the results of this study agree with those reported in the literature: (a) We confirm, by quantitative evaluation of hTERT gene expression with a real-time RT-PCR assay, the association between telomerase activity in breast tumors and poor outcome reported by several previous studies based on the TRAP method (22, 23); (b) we observed associations between high hTERT mRNA levels and SBR histopathological grade III and steroid receptor negativity, in agreement with Roos et al.(22), who showed that high hTERT mRNA levels are associated with aggressiveness of breast tumors. These results suggest that tumor cells might be continuously selected for incrementally higher levels of telomerase activity as they proliferate and acquire genetic changes associated with invasive cancer (26). In this regard, Hiyama et al.(20) showed that only tumors with high telomerase activity exhibited altered telomere lengths (33% of the breast tumors tested). This indicates that telomere alterations are linked to the multistep mutational events involved in tumor aggressiveness and occur a long time after reactivation of telomerase expression; (c) we observed a link between high hTERT expression levels and MYC gene overexpression. This in vivo study confirms the recently reported direct activation of hTERTtranscription by c-myc transcriptor factor (15). Conversely, no correlation was observed between high hTERTexpression levels and altered expression of the RB1 and/or CCND1 genes. This is in disagreement with data from Kiyono et al.(16), indicating that both telomerase activation and Rb/CCDN1/p16 pathway inactivation are required to immortalize primary epithelial cells.

In conclusion, this study points to a major role of the hTERT gene in breast tumorigenesis. In particular, we found evidence that hTERT mRNA status might serve as an exciting new prognostic tools in human breast cancer. These findings must now be confirmed in a larger series of breast cancer patients and in a large subpopulation of node-negative patients. Our rapid, highly sensitive,high-throughput RT-PCR-based hTERT mRNA assay should prove useful as a routine tool in hTERT-based clinical applications and therapeutic approaches to cancer.

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.

1

Supported by the Comité Régional de l’Essonne de la Ligue Nationale Contre le Cancer, the Association pour la Recherche sur le Cancer, and the Ministère de l’Enseignement Supérieur et de la Recherche. R. L. is a research director at the Institut National de la Santé et de la Recherche Médicale.

3

The abbreviations used are: TRAP, telomeric repeat amplification protocol; RFS, relapse-free survival; RT-PCR,reverse transcription-PCR; SBR, Scarff-Bloom-Richardson.

4

Unpublished data.

Fig. 1.

hTERT and RPLP0 mRNA amounts by real-time RT-PCR in the MCF7 cell line, three breast tumor samples, and the calibrator. MCF7 cell line (A11, D6, blue squares); tumor TERT57 (B6, B4, red squares);TERT22 (H4, G8, pink squares); TERT91 (A8, C1, black squares); CAL (C4, E3, green squares). The breast tumors are first divided into hTERT-positive samples (hTERTCt, <35; MCF7, TERT57, and TERT22) and hTERT-negative samples(hTERTCt, >35; TERT91). For the hTERT-positive samples,an NhTERT is next determined, as described in “Patients and Methods.” Briefly, the initial copy number of each sample is inferred from the Ct and by using the standard curve performed during the same experiment. The hTERT mRNA copy number is divided by the RPLP0 mRNA copy number to obtain a normalized hTERT/RPLP0 value, which is next divided by the normalized hTERT/RPLP0 value of the calibrator to obtain a final NhTERT value. Duplicates for each sample were performed, but the data for only one is shown here.

Fig. 1.

hTERT and RPLP0 mRNA amounts by real-time RT-PCR in the MCF7 cell line, three breast tumor samples, and the calibrator. MCF7 cell line (A11, D6, blue squares); tumor TERT57 (B6, B4, red squares);TERT22 (H4, G8, pink squares); TERT91 (A8, C1, black squares); CAL (C4, E3, green squares). The breast tumors are first divided into hTERT-positive samples (hTERTCt, <35; MCF7, TERT57, and TERT22) and hTERT-negative samples(hTERTCt, >35; TERT91). For the hTERT-positive samples,an NhTERT is next determined, as described in “Patients and Methods.” Briefly, the initial copy number of each sample is inferred from the Ct and by using the standard curve performed during the same experiment. The hTERT mRNA copy number is divided by the RPLP0 mRNA copy number to obtain a normalized hTERT/RPLP0 value, which is next divided by the normalized hTERT/RPLP0 value of the calibrator to obtain a final NhTERT value. Duplicates for each sample were performed, but the data for only one is shown here.

Close modal
Fig. 2.

RFS curves for patients with positive and negative hTERT tumors.

Fig. 2.

RFS curves for patients with positive and negative hTERT tumors.

Close modal
Table 1

Characteristics of the 134 patients and relation to RFS

No. of patientsRFS
No. of events (%)aP                  b
Age   NS
≤50 41 12 (29.3)
>50 93 36 (38.7)
Menopausal status   NS
Premenopausal 47 16 (34.0)
Postmenopausal 87 32 (36.8)
18 5 (27.8)
II 60 25 (41.7)
III 47 17 (36.2)
Lymph node status   0.022
Node-negative 50 10 (20.0)
Node-positive 84 38 (45.2)
ER status   NS
+ (≥10 fm/mg) 89 34 (38.2)
− (<10 fm/mg) 45 14 (31.1)
Macroscopic tumor sized   NS
≤30 mm 93 33 (35.5)
>30 mm 34 13 (38.2)
hTERT status   0.017
Negative 33 5 (15.2)
Positive 101 43 (42.6)
No. of patientsRFS
No. of events (%)aP                  b
Age   NS
≤50 41 12 (29.3)
>50 93 36 (38.7)
Menopausal status   NS
Premenopausal 47 16 (34.0)
Postmenopausal 87 32 (36.8)
18 5 (27.8)
II 60 25 (41.7)
III 47 17 (36.2)
Lymph node status   0.022
Node-negative 50 10 (20.0)
Node-positive 84 38 (45.2)
ER status   NS
+ (≥10 fm/mg) 89 34 (38.2)
− (<10 fm/mg) 45 14 (31.1)
Macroscopic tumor sized   NS
≤30 mm 93 33 (35.5)
>30 mm 34 13 (38.2)
hTERT status   0.017
Negative 33 5 (15.2)
Positive 101 43 (42.6)
a

Percentage of cases that had a relapse(local and/or regional recurrences, and/or metastases) in each subgroup of main clinical and pathological factors.

b

Log-rank test. NS, not significant.

c

SBR classification. Information available for 125 patients.

d

Information available for 127 patients.

Table 2

Oligonucleotide primer and probe sequences used

Gene and oligonucleotideLocationSequencePCR product size (pb)
hTERT
Upper primer 2673U 5′-TGACACCTCACCTCACCCAC-3′
Lower primer 2767L 5′-CACTGTCTTCCGCAAGTTCAC-3′ 95
Probe 2711U 5′-ACCCTGGTCCGAGGTGTCCCTGAG-3′
RPLP0
Upper primer 95U 5′-GGCGACCTGGAAGTCCAACT-3′
Lower primer 243L 5′-CCATCAGCACCACAGCCTTC-3′ 149
Probe 205L 5′-ATCTGCTGCATCTGCTTGGAGCCCA-3′
Gene and oligonucleotideLocationSequencePCR product size (pb)
hTERT
Upper primer 2673U 5′-TGACACCTCACCTCACCCAC-3′
Lower primer 2767L 5′-CACTGTCTTCCGCAAGTTCAC-3′ 95
Probe 2711U 5′-ACCCTGGTCCGAGGTGTCCCTGAG-3′
RPLP0
Upper primer 95U 5′-GGCGACCTGGAAGTCCAACT-3′
Lower primer 243L 5′-CCATCAGCACCACAGCCTTC-3′ 149
Probe 205L 5′-ATCTGCTGCATCTGCTTGGAGCCCA-3′
Table 3

Relationship between mRNA hTERTstatus (negative/positive) and the standard clinical pathological and biological factors

Total population (%)No. of patients (%)p                  a
hTERT negativehTERT positive
Total 134 (100.0) 33 (24.6) 101 (75.4)
Age    NS
≤50 41 (30.6) 11 (33.3) 30 (29.7)
>50 93 (69.4) 22 (66.7) 71 (70.3)
Menopausal status    NS
Premenopausal 47 (35.1) 12 (36.4) 35 (34.7)
Postmenopausal 87 (64.9) 21 (63.6) 66 (65.3)
18 (14.4) 5 (16.1) 13 (13.8)
II 60 (48.0) 19 (61.3) 41 (43.7)
III 47 (37.6) 7 (22.6) 40 (42.5)
Lymph node status    NS
Node-negative 50 (37.3) 13 (39.4) 37 (36.6)
Node-positive 84 (62.7) 20 (60.6) 64 (63.4)
ER status    NS
+ (≥10 fmol/mg) 89 (66.4) 22 (66.7) 67 (66.3)
−(<10 fmol/mg) 45 (33.6) 11 (33.3) 34 (33.7)
PR status    NS
+ (≥10 fmol/mg) 79 (59.0) 22 (66.7) 57 (56.4)
− (<10 fmol/mg) 55 (41.0) 11 (33.3) 44 (43.6)
Macroscopic tumor sizec    NS
≤30 mm 93 (73.2) 21 (70.0) 72 (74.8)
>30 mm 34 (26.8) 9 (30.0) 25 (25.8)
Relapses    0.004
48 (35.8) 5 (15.2) 43 (42.6)
− 86 (64.2) 28 (84.8) 58 (57.4)
Total population (%)No. of patients (%)p                  a
hTERT negativehTERT positive
Total 134 (100.0) 33 (24.6) 101 (75.4)
Age    NS
≤50 41 (30.6) 11 (33.3) 30 (29.7)
>50 93 (69.4) 22 (66.7) 71 (70.3)
Menopausal status    NS
Premenopausal 47 (35.1) 12 (36.4) 35 (34.7)
Postmenopausal 87 (64.9) 21 (63.6) 66 (65.3)
18 (14.4) 5 (16.1) 13 (13.8)
II 60 (48.0) 19 (61.3) 41 (43.7)
III 47 (37.6) 7 (22.6) 40 (42.5)
Lymph node status    NS
Node-negative 50 (37.3) 13 (39.4) 37 (36.6)
Node-positive 84 (62.7) 20 (60.6) 64 (63.4)
ER status    NS
+ (≥10 fmol/mg) 89 (66.4) 22 (66.7) 67 (66.3)
−(<10 fmol/mg) 45 (33.6) 11 (33.3) 34 (33.7)
PR status    NS
+ (≥10 fmol/mg) 79 (59.0) 22 (66.7) 57 (56.4)
− (<10 fmol/mg) 55 (41.0) 11 (33.3) 44 (43.6)
Macroscopic tumor sizec    NS
≤30 mm 93 (73.2) 21 (70.0) 72 (74.8)
>30 mm 34 (26.8) 9 (30.0) 25 (25.8)
Relapses    0.004
48 (35.8) 5 (15.2) 43 (42.6)
− 86 (64.2) 28 (84.8) 58 (57.4)
a

χ2 test. NS, not significant.

b

Scarff Bloom Richardson classification. Information available for 125 patients.

c

Information available for 127 patients.

Table 4

Multivariate analysis of RFS

RFS
Regression coefficientRelative risk (95% CI)aP
hTERT status (positive vs. negative) 1.07 2.92 (1.15–7.37) 0.024
Lymph node status (positive vs. negative) 0.79 2.21 (1.10–4.44) 0.027
RFS
Regression coefficientRelative risk (95% CI)aP
hTERT status (positive vs. negative) 1.07 2.92 (1.15–7.37) 0.024
Lymph node status (positive vs. negative) 0.79 2.21 (1.10–4.44) 0.027
a

95% confidence interval.

Table 5

Relationship between mRNA hTERT level and the standard clinical pathological and biological factors

N                  hTERTp                  a
LowIntermediateHigh
Total 34 (33.7)b 34 (33.7) 33 (32.6)
Age    NS
≤50 9 (26.5) 12 (35.3) 9 (27.3)
>50 25 (73.5) 22 (64.7) 24 (72.7)
Menopausal status    NS
Premenopausal 12 (35.3) 12 (35.3) 11 (33.3)
Postmenopausal 22 (64.7) 22 (64.7) 22 (66.7)
6 (18.2) 6 (20.0) 1 (3.2)
II 14 (42.4) 14 (46.7) 13 (41.9)
III 13 (39.4) 10 (33.3) 17 (54.8)
Lymph node status    NS
Node-negative 8 (23.5) 16 (47.0) 13 (39.4)
Node-positive 26 (76.5) 18 (53.0) 20 (60.6)
ERc status    0.002
+ (≥10 fmol/mg) 26 (76.5) 26 (76.5) 15 (45.5)
− (<10 fmol/mg) 8 (23.5) 8 (23.5) 18 (54.5)
PR status    0.048
+ (≥10 fmol/mg) 25 (73.5) 18 (53.0) 14 (42.4)
− (<10 fmol/mg) 9 (26.5) 16 (47.0) 19 (57.6)
Macroscopic tumor size    NS
≤30 mm 24 (70.6) 26 (83.9) 22 (68.7)
<30 mm 10 (29.4) 5 (16.1) 10 (31.3)
Relapses    NS
18 (52.9) 10 (29.4) 15 (45.5)
− 16 (47.1) 24 (70.6) 18 (54.5)
MYC mRNA    0.007
Normal 30 (88.2) 29 (85.3) 21 (63.6)
Overexpressed 4 (11.8) 5 (14.7) 12 (36.4)
RB1 mRNA    NS
Normal 24 (75.0) 27 (84.4) 22 (71.0)
Underexpressed 8 (25.0) 5 (15.6) 9 (29.0)
CCND1 mRNA    NS
Normal 28 (82.3) 23 (67.6) 28 (84.8)
Overexpressed 6 (17.7) 11 (32.4) 5 (15.2)
N                  hTERTp                  a
LowIntermediateHigh
Total 34 (33.7)b 34 (33.7) 33 (32.6)
Age    NS
≤50 9 (26.5) 12 (35.3) 9 (27.3)
>50 25 (73.5) 22 (64.7) 24 (72.7)
Menopausal status    NS
Premenopausal 12 (35.3) 12 (35.3) 11 (33.3)
Postmenopausal 22 (64.7) 22 (64.7) 22 (66.7)
6 (18.2) 6 (20.0) 1 (3.2)
II 14 (42.4) 14 (46.7) 13 (41.9)
III 13 (39.4) 10 (33.3) 17 (54.8)
Lymph node status    NS
Node-negative 8 (23.5) 16 (47.0) 13 (39.4)
Node-positive 26 (76.5) 18 (53.0) 20 (60.6)
ERc status    0.002
+ (≥10 fmol/mg) 26 (76.5) 26 (76.5) 15 (45.5)
− (<10 fmol/mg) 8 (23.5) 8 (23.5) 18 (54.5)
PR status    0.048
+ (≥10 fmol/mg) 25 (73.5) 18 (53.0) 14 (42.4)
− (<10 fmol/mg) 9 (26.5) 16 (47.0) 19 (57.6)
Macroscopic tumor size    NS
≤30 mm 24 (70.6) 26 (83.9) 22 (68.7)
<30 mm 10 (29.4) 5 (16.1) 10 (31.3)
Relapses    NS
18 (52.9) 10 (29.4) 15 (45.5)
− 16 (47.1) 24 (70.6) 18 (54.5)
MYC mRNA    0.007
Normal 30 (88.2) 29 (85.3) 21 (63.6)
Overexpressed 4 (11.8) 5 (14.7) 12 (36.4)
RB1 mRNA    NS
Normal 24 (75.0) 27 (84.4) 22 (71.0)
Underexpressed 8 (25.0) 5 (15.6) 9 (29.0)
CCND1 mRNA    NS
Normal 28 (82.3) 23 (67.6) 28 (84.8)
Overexpressed 6 (17.7) 11 (32.4) 5 (15.2)
a

χ2 test:high NhTERT value tumors versuslow/intermediate NhTERT value tumors. NS, not significant.

b

Number of patients (percentage).

c

ER, estrogen receptor; PR, progesterone receptor.

We thank the Center René Huguenin staff for assistance in specimen collection and patient care.

1
Blackburn E. H. Structure and function of telomeres.
Nature (Lond.)
,
350
:
569
-573,
1991
.
2
Sedivy J. M. Can ends justify the means? Telomeres and the mechanisms of replicative senescence and immortalization in mammalian cells.
,
95
:
9078
-9081,
1998
.
3
Morin G. B. The human telomere terminal transferase enzyme is a ribonucleoprotein that synthesizes TTAGGG repeats.
Cell
,
59
:
521
-529,
1989
.
4
Greider C. W. Telomerase activity, cell proliferation, and cancer.
,
95
:
90
-92,
1998
.
5
Dahse R., Fiedler W., Ernst G. Telomeres and telomerase: biological and clinical importance.
Clin. Chem.
,
43
:
708
-714,
1997
.
6
Kim N. W., Piatyszek M. A., Prowse K. R., Harley C. B., West M. D., Ho P. L., Coviello G. M., Wright W. E., Weinrich S. L., Shay J. W. Specific association of human telomerase activity with immortal cells and cancer.
Science (Washington DC)
,
266
:
2011
-2015,
1994
.
7
Lo Y. M. D. Quantitative assays for telomerase: means for studying the end.
Clin. Chem.
,
44
:
2399
-2400,
1998
.
8
Feng J., Funk W. D., Wang S. S., Weinrich S. L., Avilion A. A., Chiu C. P., Adams R. R., Chang E., Allsopp R. C., Yu J., Le S., West M. D., Harley C. B., Andrews W. H., Greider C. W., Villeponteau B. The RNA component of human telomerase.
Science (Washington DC)
,
269
:
1236
-1241,
1995
.
9
Nakamura T. M., Morin G. B., Chapman K. B., Weinrich S. L., Andrews W. H., Lingner J., Harley C. B., Cech T. R. Telomerase catalytic subunit homologs from fission yeast and human.
Science (Washington DC)
,
277
:
955
-959,
1997
.
10
Nakayama J., Tahara H., Tahara E., Saito M., Ito K., Nakamura H., Nakanishi T., Tahara E., Ide T., Ishikawa F. Telomerase activation by hTRT in human normal fibroblasts and hepatocellular carcinomas.
Nat. Genet.
,
18
:
65
-68,
1998
.
11
Hahn W. C., Counter C. M., Lundberg A. S., Beijersbergen R. L., Brooks M. V., Weinberg R. A. Creation of human tumour cells with defined genetic elements.
Nature (Lond.)
,
400
:
464
-468,
1999
.
12
Hahn W. C., Stewart S. A., Brooks M. W., York S. G., Eaton E., Kurachi A., Beijersbergen R. L., Knoll J. H. M., Meyerson M., Weinberg R. A. Inhibition of telomerase limits the growth of human cancer cells.
Nat. Med.
,
10
:
1164
-1170,
1999
.
13
Gibson U. E. M., Heid C. A., Williams P. M. A novel method for real time quantitative RT-PCR.
Genome Res.
,
6
:
995
-1001,
1996
.
14
Wang J., Xie L. Y., Allan S., Beach D., Hannon G. J. Myc activates telomerase.
Genes Dev.
,
12
:
1769
-1774,
1998
.
15
Wu K. J., Grandori C., Amacker M., Simon-Vermot N., Polack A., Lingner J., Dallas-Favera R. Direct activation of TERT transcription by c-MYC.
Nat. Genet.
,
21
:
220
-224,
1999
.
16
Kiyono T., Foster S. A., Koop J. I., McDougall J. K., Galloway D. A., Klingelhutz A. J. Both Rb/p16ink4a inactivation and telomerase activity are required to immortalize human epithelial cells.
Nature (Lond.)
,
396
:
84
-88,
1998
.
17
Villa R., Zaffaroni N., Folini M., Martelli G., De Palo G., Daidone M. G., Silvestrini R. Telomerase activity in benign and malignant breast lesions: a pilot prospective study on fine-needle aspirates.
J. Natl. Cancer Inst.
,
90
:
537
-539,
1998
.
18
Kaplan E. L., Meier P. Nonparametric estimation from incomplete observations.
J. Am. Stat. Assoc.
,
53
:
457
-481,
1958
.
19
Cox D. R. Regression models and life-tables.
J. R. Stat. Soc. B
,
34
:
187
-220,
1972
.
20
Hiyama E., Gollahon L., Kataoka T., Kuroi K., Yokoyama T., Gazdar A. F., Hiyama K., Piatyszek M. A., Shay J. W. Telomerase activity in human breast tumors.
J. Natl. Cancer Inst.
,
88
:
116
-122,
1996
.
21
Bednarek A. K., Sahin A., Brenner A. J., Johnston D. A., Aldaz C. M. Analysis of telomerase activity levels in breast cancer: positive detection at the in situ breast carcinoma stage.
Clin. Cancer Res.
,
3
:
11
-16,
1997
.
22
Roos G., Nilsson P., Cajander S., Nielsen N-H., Arnerlöv C., Landberg G. Telomerase activity in relation to p53 status and clinico-pathological parameters in breast cancer.
Int. J. Cancer
,
79
:
343
-348,
1998
.
23
Clark G. M., Osborne C. K., Levitt D., Wu F., Kim N. W. Telomerase activity and survival of patients with node-positive breast cancer.
J. Natl. Cancer Inst.
,
89
:
1874
-1880,
1997
.
24
Carey L. A., Hedican C. A., Henderson G. S., Umbricht C. B., Dome J. S., Varon D., Sukumar S. Careful histological confirmation and microdissection reveal telomerase activity in otherwise telomerase-negative breast cancers.
Clin. Cancer Res.
,
4
:
435
-440,
1998
.
25
Tsao J. I., Zhao Y., Lukas J., Yang X., Shad A., Press M., Shibata D. Telomerase activity in normal and neoplastic breast.
Clin. Cancer Res.
,
3
:
627
-631,
1997
.
26
Kolquist K. A., Ellisen L. W., Counter C. M., Meyerson M., Tan L. K., Weinberg R. A., Haber D. A., Gerald W. L. Expression of TERT in early premalignant lesions and a subset of cells in normal tissues.
Nat. Genet.
,
19
:
182
-186,
1998
.
27
Snijders P. J., van Duin M., Walboomers J. M., Steenbergen R. D., Risse E. K., Helmerhorst T. J., Verheijen R. H., Meijer C. J. Telomerase activity exclusively in cervical carcinomas and a subset of cervical intraepithelial neoplasia grade III lesions: strong association with elevated messenger RNA levels of its catalytic subunit and high-risk human papillomavirus DNA.
Cancer Res.
,
58
:
3812
-3818,
1998
.