MYC gene overexpression was identified recently as a downstream step at the end of the Wnt/APC/β-catenin pathway dysregulation observed in colorectal cancer (T-C. He et al., Science (Washington DC), 281: 1509–1512, 1998). It thus appears that an excess of c-myc protein is a primary cause of numerous cancers. In breast cancer, MYC has been studied mostly at the DNA level because of the poor quality of available antibodies against the protein product. The renewed interest in MYC calls for a sensitive and accurate method for analyzing MYC overexpression in breast tumors. We have developed a real-time quantitative reverse transcription-PCR assay based on TaqMan fluorescence methodology to quantify the MYC mRNA copy number. We validated the method on a large series of breast tumors. MYC gene overexpression was observed in 29 of 134 (22%) breast tumor RNAs, ranging from 3.2 to 19 times the level in normal breast tissues. These data imply that dysregulated MYC gene expression is potentially involved in the pathogenesis of breast cancer, especially by favoring local cell proliferation. We also found that MYC gene overexpression was rarely due to an increased MYC gene copy number in breast cancer. This new, simple, rapid, and semiautomated method will be useful for screening cancer patients for MYC overexpression and will prove a powerful tool in large, randomized, prospective, cooperative group trials and in the MYC-based therapy project.

The MYC proto-oncogene (also known as c-myc) appears to be involved in early embryogenesis, control of cell growth, cell differentiation, and programmed cell death (apoptosis; Ref. 1). It is also involved in many chromosomal abnormalities that play a role in tumorigenesis. These abnormalities include chromosomal translocations, activation by integration of RNA and DNA viruses, and gene amplification (2). Finally, MYC overexpression at the RNA and protein levels may contribute to the onset of some cancers, including prostate and colorectal cancers in which MYC gene alterations are rare. Recently, He et al.(3) showed that MYC is a late target in the dysregulated Wnt/APC/β-catenin pathway. Blockage of MYC expression with MYC antisense oligonucleotides has shown that MYC is crucial for cell proliferation (4). It thus appears that an excess of c-myc protein is a primary cause of cancer and not just a consequence (5).

MYC has been studied extensively in human breast cancer. One study showed an insertion of a LINE-1 mobile genetic element within the MYC gene in a breast carcinoma (6). Rare cases of MYC rearrangements in breast tumors have also been reported (7). Most studies show MYC gene amplification in breast cancer, with an incidence range from 4 to 52% (reviewed in Ref. 8). In the largest of these studies, MYC amplification was linked to Scarff-Bloom-Richardson histological grade III and steroid-receptor negativity, and sometimes with poor prognosis (9).

However, the mechanism by which MYC is activated in breast cancer is unclear. Indeed, the overall mean of the reported frequency of MYC amplification (∼15%) and the degree of MYC amplification (rarely more than five copies) are significantly lower than values observed for the ERBB2 and CCND1 genes, amplification of which is well documented in breast tumors (9, 10). Using a real-time PCR method, we observed extra copies of the MYC gene in only 10% of 108 breast tumor DNAs; the largest observed number of MYC gene copies was 4.6 (11). Visscher et al.(12) showed that MYC gene overrepresentation in breast carcinoma is mainly due to polysomy of chromosome 8 and/or genomic endoreduplication and not to specific MYC gene amplification.

Fewer data are available on MYC expression in breast cancer than on ERBB2 and CCND1, and almost all derive from immunochemistry-based studies at the protein level. The reported frequency of c-myc overexpression is as high as 100% in some studies and as low as 12% in others (reviewed in Ref. 8). This discordance among the different studies seems to be due to the quality of the antibodies used and to alterations of c-myc protein during procedures; in particular, the subcellular location of c-myc protein is affected by tumor fixation (13).

Pending the development of a reliable c-myc antibody, the best way to study MYC expression is at the RNA level. The Northern blotting method is not sensitive enough to detect low-level gene expression and not accurate enough to quantify the full range of expression. Northern blotting is also time-consuming, requires large amounts of RNA, and uses radioactive reagents, which means it cannot be used routinely in laboratories. An amplification step is therefore required to determine the MYC mRNA copy number in small amounts of tumor RNA (small early-stage tumors or cytopuncture specimens).

We have developed a real-time quantitative RT-PCR3 assay based on TaqMan methodology to quantify MYC mRNA levels in homogeneous total RNA solutions prepared from tumor samples (14). This recently developed method of nucleic acid quantification in homogeneous solution has the potential to become a reference in terms of performance, accuracy, sensitivity, wide dynamic range, and high throughput capacity and 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 measure MYC gene expression in a series of 134 unilateral invasive primary breast tumor RNAs. We also determined whether MYC gene overexpression correlated with DNA overrepresentation at the MYC locus, as determined previously using a real-time PCR method (11).

### Patients and Samples

We analyzed tissue from excised primary breast tumors of 134 women treated 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 following surgery, the tumor samples were stored in liquid nitrogen until RNA extraction.

The patients (mean age, 58.3 years; range, 34–91) 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 8.2 years (range, 1.0–15.9 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 six breast tumor cell lines obtained from the American Type Culture Collection (SK-BR-3, ZR-75–1, T-47D, BT-20, HBL-100, and MCF7). Specimens of adjacent normal breast tissue from 10 of the breast cancer patients and normal breast tissue from 10 women undergoing cosmetic breast surgery were used as sources of normal RNA. Total RNA from a pool of six normal human breast tissue samples was also purchased from Clontech (Palo Alto, CA).

### 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 is defined as the fractional cycle number at which the fluorescence generated by cleavage of the probe passes a fixed threshold above baseline. The MYC 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 (based on absorbance) and its quality (i.e., lack of extensive degradation) are both difficult to assess. We therefore also quantified transcripts of the gene coding for the TBP (a component of the DNA-binding protein complex TFIID) as the endogenous RNA control, and each sample was normalized on the basis of its TBP content. We selected the TBP gene as an endogenous control because the prevalence of its transcripts is similar to that of the MYC target gene, and because there are no known TBP retropseudogenes. (Retropseudogenes lead to coamplification of contaminating genomic DNA and thus interfere with RT-PCR, despite the use of primers in separate exons.) We therefore rejected the β-actin, GAPDH, and HPRT genes as endogenous controls because of the existence of corresponding retropseudogenes (15, 16); we also rejected the human 18S rRNA gene, which is intronless, has no poly(A) tail, and has a very high abundance of transcripts, and the β2-microglobulin gene, expression of which may be altered in some tumors (17).

For each experimental sample, the amount of the targets and endogenous reference is determined from the standard curve. Then, the target amount is divided by the endogenous reference amount to obtain a normalized target value.

The relative gene target expression level was also normalized to a normal breast tissue sample (calibrator), or 1× sample. Each of the normalized target values is divided by the calibrator normalized target value to generate the final relative expression levels.

Final results, expressed as N-fold differences in MYC gene expression relative to the TBP gene and the calibrator, termed RMYC, were determined as follows:

$RMYC\ {=}\ \frac{\frac{MYCSample}{TBPSample}}{\frac{MYCCalibrator}{TBPCalibrator}}$

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

Primers and probes for the TBP and MYC genes 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 nonredundant 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. To avoid amplification of contaminating genomic DNA, one of the two primers or the probe was placed at the junction between two exons or in a different exon. Primers were purchased from Scandinavian Gene Synthesis AB (Köping, Sweden), and probes were from Perkin-Elmer Applied Biosystems. The nucleotide sequences of the oligonucleotide hybridization probes and primers are available upon request.

#### 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.

#### Standard Curve Construction.

The relative kinetic method was applied using a standard curve. The latter was constructed with 4-fold serial dilutions of total RNA from normal human breast tissues (Clontech) in mouse total RNA (Clontech). The standard curve used for reverse transcription is composed of five points (1000, 250, 62.5, 15.6, and 3.9 ng of human normal breast total RNA). The series of diluted human total RNAs was aliquoted and stored at −80°C until use.

#### cDNA Synthesis.

Reverse transcription of RNA was done in a final volume of 20 μl containing 1× RT-PCR 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 mm DTT, 50 units of Superscript II RNase H reverse transcriptase (Life Technologies, Inc., Gaithersburg, MD), 1.5 μm random hexamers (Pharmacia, Uppsala, Sweden), and 1 μg of total RNA (standard curve point samples and patients’ samples). 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.

#### 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 mm MgCl2, 200 μm dATP, dCTP, and dGTP, 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 appropriate diluted reverse transcriptase sample (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, 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 human normal breast 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. All of the patients’ samples with a coefficient of variation for gene mRNA copy number data >10% were retested.

#### Statistical Analysis.

Relapse-free survival 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.

### Validation of the Standard Curve and Dynamic Range of Real-Time RT-PCR.

The standard curve was constructed with total RNA extracted from normal human breast tissues and serially diluted 4-fold in mouse total RNA (1000, 250, 62.5, 15.6, and 3.9 ng of human total RNA). It should be noted that the two primer pairs chosen to analyze the TBP and MYC genes do not amplify genomic mouse cDNA or human genomic DNA (data not shown). Fig. 1 shows the real-time RT-PCR standard curve for the MYC gene. The dynamic range was wide (at least three orders of magnitude) with samples containing as much as 50 ng or as little as 0.2 ng equivalent total cDNA. A strong linear relationship between the Ct and the log of the starting copy number was always demonstrated (R2 ≥ 0.99). The efficiency of the reaction (E), calculated by the formula: E = 101/m − 1, where m is the slope of the standard curve, ranged from 90 to 100% in the different assays.

### MYC mRNA Level in Normal Breast Tissues.

To determine the cutoff point for altered MYC gene expression at the RNA level in breast cancer tissue, the RMYC value, calculated as described in “Materials and Methods,” was determined for 20 normal breast tissue RNAs. Because this value consistently fell between 0.4 and 1.8 (mean ± SD, 1.05 ± 0.36), values of 3 (mean + 5 SD) or more were considered to represent MYC gene overexpression in tumor RNA samples.

### MYC mRNA Level in Tumor Breast Tissues.

Among the 134 breast tumor RNA samples tested, 29 (21.6%) showed MYC gene overexpression. Major differences in the amount of MYC mRNA were observed (RMYC from 3.2 to 19): 16 (11.9%) tumors gave an expression level three to five times, 10 (7.5%) tumors five to ten times, and 3 (2.2%) tumors more than 10 times relative to normal breast tissue RNA. The strongest expression was 19-fold in tumor MYC42, a tumor shown not to contain extra copies of the MYC gene in our previous breast tumor DNA series (11). Fig. 2 and Table 2 give data on tumors in which the MYC gene was expressed 19-fold (tumor MYC42), 5-fold (MYC98), and normally (MYC12). MYC gene expression was also investigated in six breast tumor cell lines (SK-BR-3, ZR-75-1, T-47D, BT-20, HBL-100, and MCF7). SK-BR-3 and BT20 showed MYC gene overexpression (RMYC = 7.6 and 4.7, respectively).

### Correlation between MYC mRNA Levels and Clinical and Pathological Parameters.

We sought links between quantitative MYC mRNA status and standard clinical, pathological, and biological factors in breast cancer (Table 3). Statistically significant links were found between MYC gene overexpression and Scarff-Bloom-Richardson histopathological grade III (P < 0.05), and large tumor size (P < 0.01).

Patients with tumors overexpressing MYC did not relapse more frequently (Table 3) and did not have significantly shorter relapse-free survival after surgery compared with patients whose tumors normally expressed MYC (log-rank test). We even observed a trend toward a link between MYC gene overexpression and good outcome (lower relapse rate; Table 3). To confirm this observation, we analyzed the MYC mRNA level as a continuous variable, with patients being subdivided into four groups with tumors with very low (RMYC from 0.1 to 0.9), low (1 to 1.9), intermediate (2 to 2.9), and high (3 to 19) MYC mRNA levels. The lower the MYC mRNA level, the poorer the outcome (Table 4).

### Relationship between MYC mRNA Levels and MYC Gene Copy Numbers.

Among the 134 tumors studied for MYC expression at the RNA level, 94 had been tested previously for MYC gene dose, using the same TaqMan technology (11). We observed no correlation between MYC gene expression status and the MYC gene copy number. Among the 10 tumors with extra copies of the MYC gene, there were only 4 cases in which real-time RT-PCR showed MYC gene overexpression, whereas a large number of MYC-overexpressing tumors (n = 16) did not show an increased MYC gene copy number (Table 5). With the same matched tumor RNA/DNA series, an overall link was observed for a real amplified gene in breast cancer (ERBB2 gene); all ERBB2-amplified tumors showed ERBB2 gene overexpression.4

Amplification of the MYC gene has been studied extensively in human breast cancer, but little is known about MYC expression status. We quantified MYC gene expression at the RNA level by means of real-time quantitative RT-PCR in a series of 134 unilateral invasive primary breast tumors. We did not observe a significant link between MYC gene overexpression and MYC gene overrepresentation, partly in keeping with previous data (19, 20). Our data suggest that MYC overexpression is not generally due to an increased MYC gene copy number. Interestingly, six tumors showed extra copies of the MYC gene without overexpressing MYC (Table 5). Visscher et al.(12) showed recently that, in most cases, MYC gene “amplification” in breast carcinomas is due to polysomy of chromosome 8 and/or genomic endoreduplication (i.e., DNA aneuploidy). Taken together, these data suggest that the MYC gene can be overexpressed irrespective of its genomic status, and that extra copies of the MYC gene in breast tumors (9, 10) are not due to selective pressures resulting from the dominant action of the MYC gene but simply correspond to the acquisition of additional copies of chromosome 8 or its long arm. Our results also suggest that these chromosome anomalies do not modify MYC gene expression. The link reported previously between MYC “amplification” and poor prognosis in breast cancer could be due to a relationship between MYC overrepresentation and DNA aneuploidy, as observed by Courjal et al.(10).

Potential mechanisms explaining MYC overexpression in breast tumors without MYC amplification include: (a) alterations of the BRCA1 gene, which could function as a negative regulator of MYC expression (21); (b) mutations of a putative breast tumor suppressor gene (MPB-1) encoding a MYC promoter-binding protein (22); and (c) mutations of one of the components of the Wnt/APC/β-catenin pathway, which activate MYC expression, as described recently in colorectal cancer (3).

A significant link was observed between MYC gene overexpression and large tumor size (macroscopic size, >30 mm) and histopathological grade III (Table 3), suggesting that MYC overexpression plays a role in breast tumor progression rather than initiation. These results are in agreement with a report from Pavelic et al.(23), who found a link between c-myc protein overexpression and Ki-67 activity, suggesting marked cell proliferation in MYC-overexpressing tumors. We observed no relationship with lymph node involvement, in contrast to the unique large breast cancer series of the mRNA MYC expression by Northern blot analysis (19). We also observed no link between MYC expression and steroid-receptor status, although it has been shown previously that mRNA MYC expression is up-regulated by estrogen in estrogen receptor-positive human breast cancer cell lines (24). Finally, MYC mRNA overexpression was not predictive of shorter relapse-free survival in our series. On the contrary, MYC mRNA overexpression in the primary tumor was associated with a better prognosis relative to primary tumors with no overexpression. This surprising finding, suggestive of minor involvement of the MYC gene in metastatic processes, must be confirmed in a large prospective series of breast cancer patients. However, it is in agreement with data from Smith and Goh (25), who also showed, in a large series of colorectal carcinomas, a more favorable prognosis among colorectal cancer patients whose tumors overexpressed MYC mRNA. It is possible that the apparently better prognosis of patients with MYC-overexpressing tumors is due either to higher rates of apoptosis or to higher levels of cell proliferation and, thus, greater chemosensitivity.

These latter findings also suggest than the determination of MYC expression status could be useful for predicting the response to cancer therapy. Leroy et al.(26) showed that, in an estrogen receptor-positive population, the tamoxifen-treated group had significantly lower MYC expression levels than the control group.

MYC is emerging as an outstanding therapeutic target (5). In particular, data published recently identify MYC as a major gene turned on by the Wnt/APC/β-catenin signal (3). In vitro studies using antisense DNA or RNA oligonucleotides targeting various sites of human MYC DNA or mRNA have shown that cell proliferation is inhibited (4, 27). This antiproliferative effect is associated with a significant reduction in the MYC expression level, confirming the role of the MYC gene in cell proliferation processes. Recent studies show that MYC can promote tumorigenesis by bypassing the normal p16/pRb pathway mediated by the positive action of MYC on cyclin E (28) or by inhibiting other direct targets, which may include p14ARF, p21WAF1, p27 KIP1, and p53 (8, 29, 30).

Optimal use of these new MYC-based clinical applications requires a simple, rapid, and standardized assay method. The study of MYC status at the RNA level presently seems to be the best choice. Indeed, fluorescence in situ hybridization would be inappropriate because MYC overexpression was independent of MYC gene overrepresentation in our breast tumor series. Immunohistochemical c-myc protein assays are rarely used, because of technical problems specific to this target. Previous data showed a high correlation between MYC mRNA copy number and c-myc protein abundance (13, 31). However, Northern blotting is not sufficiently sensitive and requires large amounts of RNA. In this study, we validated a recently developed RT-PCR method for the quantification of MYC gene expression (14). The method, based on real-time analysis of PCR amplification and TaqMan methodology, does not require post-PCR sample handling, thereby avoiding problems related to carry-over; it possesses a wide dynamic range and has a high sample throughput. Finally, and above all, real-time PCR makes RNA quantitation much more precise and reproducible, because it is 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 good intraassay and interassay reproducibility and yields statistical confidence values.

In conclusion, this study confirms the involvement of the MYC gene in breast tumorigenesis and points to a role in local cell proliferation rather than in metastatic processes. Additional studies are necessary to elucidate the genetic (or epigenetic) mechanism responsible for MYC gene overexpression and to examine the other components of the Wnt/APC/β-catenin/MYC pathway in breast cancer. We describe a rapid, highly sensitive, high-throughput RT-PCR assay to determine MYC status, which should prove useful as a routine tool in new MYC-based therapeutic approaches to breast 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 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: RT-PCR, reverse transcription-PCR; TBP, TATA box-binding protein.

4

I, Bièche, P. Onody, I. Lavrendeau, M. Olivi, D. Vidaud, R. Lidereau, and M. Vidaud. Real time RT-PCR assay for future management of ERBB2-based clinical applications. Clin. Chem., in press, 1999.

Fig. 1.

MYC standard curve by real-time RT-PCR. Top panel, amplification plots for reactions with the five points (A1, A3, A5, A7, and A10) of the MYC standard curve (4-fold serially diluted human normal breast cDNAs) and a no-template control (A11). Cycle number is plotted versus change in normalized reporter signal (ΔRn). For each reaction tube, the fluorescence signal of the reporter dye (FAM) is divided by the fluorescence signal of the passive reference dye (ROX), to obtain a ratio defined as the normalized reporter signal (Rn). ΔRn represents the normalized reporter signal (Rn) minus the baseline signal established in the first 15 PCR cycles. ΔRn increases during PCR as MYC PCR product copy number increases until the reaction reaches a plateau. Ct represents the fractional cycle number at which a significant increase in Rn above a baseline signal (horizontal black line) can first be detected. Two replicates for each standard curve point sample were performed, but the data for only one are shown here. Bottom panel, standard curve plotting log starting copy number versusCt. Black dots, data for standard curve point samples; red dots, data for unknown patients’ samples performed in duplicate. The standard curve shows three orders of linear dynamic range.

Fig. 1.

MYC standard curve by real-time RT-PCR. Top panel, amplification plots for reactions with the five points (A1, A3, A5, A7, and A10) of the MYC standard curve (4-fold serially diluted human normal breast cDNAs) and a no-template control (A11). Cycle number is plotted versus change in normalized reporter signal (ΔRn). For each reaction tube, the fluorescence signal of the reporter dye (FAM) is divided by the fluorescence signal of the passive reference dye (ROX), to obtain a ratio defined as the normalized reporter signal (Rn). ΔRn represents the normalized reporter signal (Rn) minus the baseline signal established in the first 15 PCR cycles. ΔRn increases during PCR as MYC PCR product copy number increases until the reaction reaches a plateau. Ct represents the fractional cycle number at which a significant increase in Rn above a baseline signal (horizontal black line) can first be detected. Two replicates for each standard curve point sample were performed, but the data for only one are shown here. Bottom panel, standard curve plotting log starting copy number versusCt. Black dots, data for standard curve point samples; red dots, data for unknown patients’ samples performed in duplicate. The standard curve shows three orders of linear dynamic range.

Close modal
Fig. 2.

MYC and TBP mRNA amounts by real-time RT-PCR in three breast tumor samples and the calibrator. Tumor MYC42 (B8 and B9, red squares), MYC98 (D12 and E3, green squares), and MYC12 (E11 and G11, black squares), and CAL (F5 and H3, blue squares). Given the Ct of each sample, the initial copy number is inferred from the standard curve performed during the same experiment. Duplicates for each sample were performed, but the data for only one is shown here. The results are shown in Table 2.

Fig. 2.

MYC and TBP mRNA amounts by real-time RT-PCR in three breast tumor samples and the calibrator. Tumor MYC42 (B8 and B9, red squares), MYC98 (D12 and E3, green squares), and MYC12 (E11 and G11, black squares), and CAL (F5 and H3, blue squares). Given the Ct of each sample, the initial copy number is inferred from the standard curve performed during the same experiment. Duplicates for each sample were performed, but the data for only one is shown here. The results are shown in Table 2.

Close modal
Table 1

Characteristics of the 134 patients and relation to relapse-free survival

No. of patients (%)Relapse-free survival
No. of eventsaP              b
Age   NS
H50 41 (30.6) 12
>50 93 (69.4) 36
Menopausal status   NS
Premenopausal 47 (35.1) 16
Postmenopausal 87 (64.9) 32
I 18 (14.4)
II 60 (48.0) 25
III 47 (37.6) 17
Lymph node status   <0.05
Node-negative 50 (37.3) 10
Node-positive 84 (62.7) 38
Estrogen receptor status   NS
+(≥10 fm/mg) 89 (66.4) 34
−(<10 fm/mg) 45 (33.6) 14
Progesterone receptor status   NS
+(≥10 fm/mg) 79 (59.0) 28
−(<10 fm/mg) 55 (41.0) 20
Macroscopic tumor sized   NS
H30 mm 93 (73.2) 33
>30 mm 34 (26.8) 13
No. of patients (%)Relapse-free survival
No. of eventsaP              b
Age   NS
H50 41 (30.6) 12
>50 93 (69.4) 36
Menopausal status   NS
Premenopausal 47 (35.1) 16
Postmenopausal 87 (64.9) 32
I 18 (14.4)
II 60 (48.0) 25
III 47 (37.6) 17
Lymph node status   <0.05
Node-negative 50 (37.3) 10
Node-positive 84 (62.7) 38
Estrogen receptor status   NS
+(≥10 fm/mg) 89 (66.4) 34
−(<10 fm/mg) 45 (33.6) 14
Progesterone receptor status   NS
+(≥10 fm/mg) 79 (59.0) 28
−(<10 fm/mg) 55 (41.0) 20
Macroscopic tumor sized   NS
H30 mm 93 (73.2) 33
>30 mm 34 (26.8) 13
a

First relapses (local and/or regional recurrences, and/or metastases).

b

Log-rank test. NS, not significant.

c

Scarff Bloom Richardson classification. Information is available for 125 patients.

d

Information is available for 127 patients.

Table 2

MYC mRNA level results

SampleMYCTBPMYC/TBP normalizedRMYCa
Copy no.MeanSDCopy no.MeanSD
MYC42 925583 916582 12730 1735 1744 13 525.6 19.1
907580   1753
MYC98 198902 204177 7459 1414 1390 35 146.9 5.3
209451   1365
MYC12 38177 37355 1163 1154 1176 31 31.8 1.2
36532   1198
CAL 29918 29816 144 1086 1083 27.5
29714   1079
SampleMYCTBPMYC/TBP normalizedRMYCa
Copy no.MeanSDCopy no.MeanSD
MYC42 925583 916582 12730 1735 1744 13 525.6 19.1
907580   1753
MYC98 198902 204177 7459 1414 1390 35 146.9 5.3
209451   1365
MYC12 38177 37355 1163 1154 1176 31 31.8 1.2
36532   1198
CAL 29918 29816 144 1086 1083 27.5
29714   1079
a

For each sample, the MYC mRNA copy number is divided by the TBP mRNA copy number to obtain a normalized MYC/TBP value, which is next divided by the normalized MYC/TBP value of the calibrator to obtain a final RMYC value.

Table 3

Relationship between mRNA MYC status and the standard clinical, pathological, and biological factors

Total population (%)Normal MYC mRNAOverexpressed MYC mRNAa
No. of patients (%)No. of patients (%)P              b
Total  134 (100.0) 105 (78.4) 29 (21.6)
Age    NS
H50 41 (30.6) 31 (29.5) 10 (34.5)
>50 93 (69.4) 74 (70.5) 19 (65.5)
Menopausal status    NS
Premenopausal 47 (35.1) 33 (31.4) 14 (48.3)
Postmenopausal 87 (64.9) 72 (68.6) 15 (51.7)
I 18 (14.4) 17 (17.2) 1 (3.8)
II 60 (48.0) 50 (50.5) 10 (38.5)
III 47 (37.6) 32 (32.3) 15 (57.7)
Lymph node status    NS
Node-negative 50 (37.3) 40 (38.1) 10 (34.5)
Node-positive 84 (62.7) 65 (61.9) 19 (65.5)
Estrogen receptor status    NS
+(≥10 fm/mg) 89 (66.4) 72 (68.6) 17 (58.6)
−(<10 fm/mg) 45 (33.6) 33 (31.4) 12 (41.4)
Progesterone receptor status    NS
+(≥10 fm/mg) 79 (59.0) 72 (59.1) 17 (58.6)
−(<10 fm/mg) 55 (41.0) 43 (40.9) 12 (41.4)
Macroscopic tumor sized    <0.01
H30 mm 93 (73.2) 81 (80.2) 12 (46.2)
>30 mm 34 (26.8) 20 (19.8) 14 (53.8)
Relapses    NS
+ 48 (35.8) 41 (39.1) 7 (24.1)
− 86 (64.2) 64 (60.9) 22 (75.9)
Total population (%)Normal MYC mRNAOverexpressed MYC mRNAa
No. of patients (%)No. of patients (%)P              b
Total  134 (100.0) 105 (78.4) 29 (21.6)
Age    NS
H50 41 (30.6) 31 (29.5) 10 (34.5)
>50 93 (69.4) 74 (70.5) 19 (65.5)
Menopausal status    NS
Premenopausal 47 (35.1) 33 (31.4) 14 (48.3)
Postmenopausal 87 (64.9) 72 (68.6) 15 (51.7)
I 18 (14.4) 17 (17.2) 1 (3.8)
II 60 (48.0) 50 (50.5) 10 (38.5)
III 47 (37.6) 32 (32.3) 15 (57.7)
Lymph node status    NS
Node-negative 50 (37.3) 40 (38.1) 10 (34.5)
Node-positive 84 (62.7) 65 (61.9) 19 (65.5)
Estrogen receptor status    NS
+(≥10 fm/mg) 89 (66.4) 72 (68.6) 17 (58.6)
−(<10 fm/mg) 45 (33.6) 33 (31.4) 12 (41.4)
Progesterone receptor status    NS
+(≥10 fm/mg) 79 (59.0) 72 (59.1) 17 (58.6)
−(<10 fm/mg) 55 (41.0) 43 (40.9) 12 (41.4)
Macroscopic tumor sized    <0.01
H30 mm 93 (73.2) 81 (80.2) 12 (46.2)
>30 mm 34 (26.8) 20 (19.8) 14 (53.8)
Relapses    NS
+ 48 (35.8) 41 (39.1) 7 (24.1)
− 86 (64.2) 64 (60.9) 22 (75.9)
a

RMYC value ≥3.

b

χ2 test. NS, not significant.

c

Scarff Bloom Richardson classification. Information is available for 125 patients.

d

Information is available for 127 patients.

Table 4

Relationship between RMYC value and subsequent relapse

No. of patientsRelapses
NoYes (%)aOdds ratio (95% confidence intervals)
RMYC value
0.1 to 0.9 35 19 16 (45.7) 1.76 (0.81–3.86)
1 to 1.9 47 28 19 (40.4) 1.36 (0.65–2.82)
2 to 2.9 23 17 6 (26.1) 0.58 (0.21–1.57)
3 to 19 29 22 7 (24.1) 0.50 (0.20–1.25)
Total population 134 86 48 (35.8)
No. of patientsRelapses
NoYes (%)aOdds ratio (95% confidence intervals)
RMYC value
0.1 to 0.9 35 19 16 (45.7) 1.76 (0.81–3.86)
1 to 1.9 47 28 19 (40.4) 1.36 (0.65–2.82)
2 to 2.9 23 17 6 (26.1) 0.58 (0.21–1.57)
3 to 19 29 22 7 (24.1) 0.50 (0.20–1.25)
Total population 134 86 48 (35.8)
a

Percentage of patients with relapses in each RMYC value group.

Table 5

Relationship between DNA MYC status and mRNA MYC status

Total population (%)mRNA MYC status
Normal (%)Overexpresseda (%)
DNA MYC status
Normal 84 (89.4) 68 (91.9) 16 (80.0)b
Amplified 10 (10.6) 6 (8.1) 4 (20.0)
Total population (%)mRNA MYC status
Normal (%)Overexpresseda (%)
DNA MYC status
Normal 84 (89.4) 68 (91.9) 16 (80.0)b
Amplified 10 (10.6) 6 (8.1) 4 (20.0)
a

RMYC value, ≥3.

b

Percentage of tumors with a normal MYC gene copy number among the MYC-overexpressing tumors.

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

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