Purpose: The purpose is to define molecular prognostic factors in patients with advanced breast cancer treated with high-dose chemotherapy (HDCT) and autologous stem cell transplantation (ASCT).

Experimental Design: Thirty-nine patients with breast cancer and extensive lymph node (level III) and/or systemic metastases from a prospective single-center study of sequential HDCT/ASCT were studied. Microsatellite analysis was performed after laser microdissection using 15 markers selected for sensitive detection of microsatellite instability (MSI) in breast cancer. Exons 5–9 of the P53 gene were directly sequenced. Expression of P53, HER-2/neu, and the mismatch repair proteins hMSH2 and hMLH1 was evaluated by immunohistochemistry.

Results: MSI of at least three markers was detected in 13 of 39 patients (33%) and was predominantly found at tetranucleotide markers. All MSI-positive tumors showed normal expression of hMSH2 and hMLH1. Complete sequence analysis of exons 5–9 of the P53gene was successful in 34 cases; 18% (n = 6) revealed a mutation. Overexpression of HER-2/neu and P53 was observed in 7 (22%) and 12 (46%) of 26 evaluated cases, respectively. The presence of MSI strongly correlated with shorter overall survival (OS; P = 0.0004) and progression-free survival (PFS; P = 0.02). None of the other investigated clinical or molecular factors correlated with OS in univariate analyses, with the exception of menopausal status and previous adjuvant chemotherapy. Testing various multivariate Cox regression models, MSI remained a highly significant, independent, and adverse risk factor for OS.

Conclusions: MSI is frequent in advanced breast cancer and could be an indicator of chemotherapy resistance and poor prognosis in breast cancer patients treated with HDCT/ASCT.

Throughout the 1990s, the focus in breast cancer research was on high-dose chemotherapy (HDCT) and dose-intensive regimens. But the treatment of breast cancer patients with extensive lymph node involvement or systemic metastases using HDCT and autologous stem cell transplantation (ASCT) is still subject of controversial scientific dispute. HDCT is based on the hypothesis that increased dosage will overcome drug resistance, eradicate metastatic disease, and increase cure rates. Continuous negative evidence for the benefit of HDCT/ASCT (1) contrasts thousands of HDCT/ASCT procedures performed in breast cancer patients. Over the course of the last decade, there has been tremendous interest in identifying more tolerable regimens that may also be more effective, with a focus on targeted therapies.

There are also no definitive prognostic makers for patient outcome in HDCT/ASCT. Identifying such markers could facilitate the definition of a subgroup of patients with advanced breast cancer that will most likely respond to this aggressive treatment. A plethora of molecular markers are currently evaluated in the treatment of breast cancer, but none of these markers are routinely used in clinical practice (2). Accepted prognostic factors for patients with metastatic breast cancer undergoing standard therapy regimens are hormone receptor status (estrogen receptor and progesterone receptor), menopausal status, localization of metastases, interval between initial diagnosis and appearance of metastases, and response to chemotherapy. On the basis of these clinical findings, Possinger et al.(3) have developed a prognostic score to classify patients with metastatic breast cancer into three different risk groups (high, intermediate, and low): metastatic pattern, receptor status, and disease-free survival were considered qualitative parameters. Combining these parameters provides a reliable indicator of tumor progression, but more precise prognostic factors are needed that reflect both the phenotype and the genetic alterations of the tumor. Standardized molecular analyses might help to select patients who would benefit from differential chemotherapeutic approaches. The cohort designated “chemoresistant” could then be offered alternative therapy modalities, which in turn could be derived from molecular characteristics of the tumor.

The aim of this study was to define molecular alterations that could predict treatment response and/or clinical outcome of breast cancer patients undergoing HDCT/ASCT.

Patients and Samples.

Thirty-nine patients with primary invasive carcinoma of the breast were studied as part of a prospective Phase II-trial of sequential HDCT/ASCT (Department of Hematology and Oncology, University of Regensburg, Regensburg, Germany). Informed consent was obtained from all patients, and the study was approved by the Institutional Review Board. Patients had either advanced local breast cancer with level III lymph node metastases or systemic metastases. After initial chemotherapy in standard dosage (vasoactive intestinal peptide-E; 500 mg/m3 etoposid, 4 g/m2 ifosfamid, 50 mg/m2 cisplatin, and 50 mg/m2 epirubicin) to test for chemosensitivity and to mobilize stem cells, patients received two cycles of HDCT (VIC; 1500 mg/m2 etoposid, 12 g/m2 ifosfamid, and 1500 mg/m2 carboplatin) followed by ASCT. Clinical follow-up was available for all patients, none of which had a family history of breast cancer. Clinical and histopathological data are summarized in Table 1. Sixteen patients (41%) had received standard adjuvant chemotherapy before enrollment into the HDCT/ASCT trial. Tissue samples of the primary tumor and corresponding additional histopathological data were obtained from 11 different pathology laboratories. In 14 cases, only archival H&E-stained tissue sections were available for microdissection, precluding additional immunohistochemical analyses. For the remaining cases, formalin-fixed, paraffin-embedded tissue (n = 25) and additional frozen tumor tissue (n = 4) were available. Nonneoplastic breast parenchyma distant from the tumor, skin, bone marrow, or gastric mucosa were used as a source for reference DNA in subsequent microsatellite analyses.

Microdissection.

Genomic DNA was isolated from 5-μm tissue sections in cases for which paraffin blocks were available. Pure tumor cell populations (>80% of tumor cells) were obtained using a PALM Robot-Microbeam (Wolfratshausen, Germany) laser microdissection device as described previously (6). H&E-stained archival slides were incubated in xylene overnight (cases for which paraffin blocks were not available). After removal of the coverslip, the slides were rehydrated in 96% ethylene but not exposed to 70% ethanol to preserve H&E staining. In 4 cases, frozen tissue was available for frozen sections and touch preparations from the partially thawed tissue surface as described previously (7). At least 200 tumor cells were isolated from each specimen to prevent preferential monoallelic amplification.

DNA Isolation.

For molecular analysis, normal and tumor DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s specifications. Elution with 2 × 100 μl of 70°C preheated water was performed to increase yield. Each elution step included a 5-min incubation of the QIAamp spin column with the preheated water at 70°C before centrifugation. PCR template concentration was additionally increased by reducing the elution volume to 50 μl (SpeedVac SC110; Savant, Farmingdale, NY) and by performing an improved primer extension preamplification PCR (whole genome amplification) as described previously (8).

Microsatellite Analysis.

Fifteen microsatellite markers on chromosomes 1, 2, 4, 5, 6, 11, 17, 18, 19, and 21 were selected for sensitive detection of microsatellite instability (MSI) in breast cancer according to literature review. Characteristics of microsatellite primers have been reported previously (6). PCR amplification reactions were performed using 50 ng of purified genomic DNA in a final volume of 20 μl in a MJ Research Thermocycler (PRC100; MJ Research, Watertown, MA; Ref. 9). PCR products were subsequently analyzed by 6.7% polyacrylamide/50% urea gel electrophoresis (1 h, 1500 V, 55°C) in a SequiGen sequencing gel chamber (Bio-Rad, Hercules, CA) followed by silver nitrate staining as described previously (10).

MSI was defined as the presence of novel bands after PCR amplification of tumor DNA that were not present in PCR products of the corresponding normal DNA. All gels were evaluated independently by three different investigators (P. J. W., W. D., and A. H). According to recommendations of the 1997 Bethesda Consensus Conference (11), MSI was diagnosed if at least 3 of 15 markers (≥20%) were unstable, samples with instability in only one or two markers were classified as MSI low. Microsatellite-stable samples (MSS) did not show instability in any of the investigated markers. Loss of heterozygosity was defined as a decrease in signal intensity of the tumor sample allele to at least 50% relative to the matched normal DNA allele. Chromosomal instability (CIN) was assumed if at least 40% (≥6 of 15 investigated markers) revealed loss of heterozygosity. All samples testing positive for MSI or loss of heterozygosity were reevaluated twice to exclude the possibility of false-positive results because of preferential monoallelic PCR amplification (8).

P53Mutations.

Exons 5–9 of the P53tumor suppressor gene were directly sequenced in 34 tumors; the methodology has been described in detail previously (12).

Immunohistochemistry.

Expression analyses of the mismatch repair proteins hMSH2 (n = 25) and hMLH1 (n = 26) were performed as described previously (13). Immunohistochemical studies for the expression of P53 (n = 26) and HER-2/neu (n = 26) used an avidin-biotin peroxidase method with a 3,3′-diaminobenzidine chromatogen. After antigen retrieval (microwave oven for 30 min at 250 W), immunohistochemistry was carried out in a NEXES immunostainer (Ventana, Tucson, AZ) following manufacturer’s instructions. The following antibodies were used: P53 (mouse monoclonal Bp53-12, Santa Cruz Biotechnology, Inc., Santa Cruz, CA; dilution 1:1000) and HER-2/neu (rabbit polyclonal A0485, DAKO, Glostrup, Denmark; dilution 1:400). One histopathologist performed a blinded immunohistochemical evaluation (A. H.), without knowledge of the molecular data. P53 positivity was defined as strong nuclear staining in at least 10% of the tumor cells. HER-2/neu expression was scored according to the DAKO HercepTest (14).

Possinger Score.

Metastatic pattern, hormone receptor status and disease-free survival before HDCT/ASCT were scored quantitatively as previously described by Possinger et al.(3), assigning each patient to one of three different risk groups: high, intermediate, or low.

Statistical Analyses.

Differences were considered statistically significant when Ps were <0.05. A statistical correlation between clinicopathological and molecular parameters was tested, using a two-sided Fisher’s exact test. A logistic regression was performed to study the correlation between age and categorical variables. Progression-free survival (PFS) and overall survival (OS) time were calculated according to the Kaplan-Meier survival curves (15). PFS and OS were measured from the start of HDCT and compared between women with or without any of the clinical, pathological, or molecular risk factors by two-sided log-rank statistics (16). A stepwise multivariable Cox regression model (17) was adjusted, testing the independent prognostic relevance of MSI. The limit for reverse selection procedures was P = 0.2. The proportionality assumption for all variables was assessed with log-negative-log survival distribution functions. The characteristics of all variables are shown in Table 2.

Molecular and Immunohistochemical Analyses.

A total of 39 patients with advanced breast cancer was studied using 15 microsatellite markers on 10 chromosomes. Instability in at least 3 of the 15 markers was found in 13 of 39 patients (33%). All MSI-positive tumors showed normal expression of the mismatch repair proteins hMSH2 and hMLH1. Nine patients (23%) were scored as chromosomal unstable (CIN), with at least 6 markers revealing loss of heterozygosity. The negative correlation of MSI and CIN reported for colorectal carcinoma (18) could not be detected in this series of breast cancers. Sequencing of exons 5–9 of the P53gene, a region that contains 90% of all mutations (19), revealed a P53missense mutation in 6 of 34 cases (18%) in which a complete sequence analysis of exons 5–9 could be performed. Expression of P53 and HER-2/neu was evaluated by immunohistochemistry. Overexpression of HER-2/neu was found in 7 of 27 (27%) and of P53 in 12 of 26 (46%) evaluated patients, respectively. As expected (20), P53 protein expression and mutation analysis correlated significantly (P = 0.001). All tumors with a missense mutation revealed nuclear P53 protein expression because of the prolonged half-life of mutated P53.

HDCT/ASCT.

Patients were followed for a median of 24.4 months. Of 39 patients, 4 (10%) achieved at least a partial remission, 22 patients (56%) were in complete remission after HDCT. Mean OS time was 40.1 months with a 95% confidence interval (CI) of 33.67–46.45. The median OS time could not be reached; at the qualifying date (March 1, 2000) 26 patients (67%) were still alive. No therapy-associated deaths were observed, but 2 patients progressed rapidly (Nos. 17 and 39). Mean and median PFS was 21.6 and 13.2 months, respectively. The 2-year survival rates and 95% CIs concerning PFS and OS were 0.23 (0.099–0.363) and 0.54 (0.382–0.695).

Possinger Score.

Applying the Possinger score for metastatic breast cancer (3), 12 patients (31%) were estimated to have a poor prognosis, another 12 patients to have an intermediate prognosis, and the remaining 15 patients (38%) were considered to have a favorable prognosis. However, PFS and OS were not significantly different between the three groups.

Descriptive and Univariate Analyses.

For descriptive data analysis, all relevant variables were correlated with the MSI status (Table 2), but no significant association was seen. Cases showing MSI (≥3 of 15 markers) were compared with microsatellite stable (MSS and MSI low) cases regarding PFS and OS by univariate log-rank statistics. MSI was highly associated with shorter PFS (P = 0.02) and OS (P = 0.0004; Figs. 1, A and B). Microsatellite stable cases (MSS and MSI low) had a median PFS of 28.4 months compared with 2.5 months in patients with MSI tumors. Median OS for MSI-positive cases was 22.3 months, whereas the median OS time for patients with MSS and low MSI could not be estimated because <50% of patients died within the time of follow-up. Initial tumor size (P = 0.008) and a history of adjuvant chemotherapy (P = 0.003) were the only other variables which correlated significantly with PFS. Menopausal status (P = 0.04) and previous standard adjuvant chemotherapy (P = 0.02) were associated with shorter OS. None of the other clinical, pathological, or molecular factors correlated significantly with OS (Table 2). Interestingly, younger patients showed a tendency to have microsatellite unstable tumors (P = 0.07; odds ratio = 1.11; 95% CI, 0.99–1.23).

Multivariate Analyses.

In a multivariate analysis, three different Cox regression models were developed for assessment of the OS rate. Characteristics of variables are shown in Table 3. Because of model assumptions (noninformative censoring, proportional hazards), only MSI, age at diagnosis, CIN, estrogen receptor status, and Possinger score were considered.

In the raw model, only MSI was correlated with poor outcome (P = 0.002). With reverse selection, the hazard ratio for death from breast cancer concerning MSI was 7.18 (95% CI, 2.07–24.88); accordingly, in cases with MSI, the probability of tumor-related death was seven times higher than that in stable cases (MSI low and MSS). Because of the assumption of proportional hazards, the probability of tumor-related death was consistently valid during the entire observation period. None of the other variables had a significant effect on OS.

Subsequently, the trichotomous variables age at diagnosis and Possinger score were dichotomized by the introduction of dummy variables (AGEDUM1, AGEDUM2, POSSDUM1, and POSSDUM2). Again, only MSI was significant in the global model. After reverse selection, AGEDUM1, CIN, and MSI (P = 0.0009; hazard ratio = 10.10; 95% CI, 2.57–39.68) remained in the model.

Finally, multiplicative terms of interaction (INTER1–6) were considered, representing interactions between MSI and dichotomous covariables. Because of the multitude of variables compared with the small number of cases, interpretation was difficult. The global model included POSSDUM2, INTER3, and INTER6 as significant variables. After reverse selection, a model containing MSI, AGEDUM1, POSSDUM2, INTER3, and INTER6 was found (P < 0.01).

Assuming different model constructs, the following conclusions can be drawn: (a) MSI was a prognostic factor for the OS probability of advanced breast cancer patients undergoing HDCT/ASCT; (b) other factors were relevant as well, particularly age at diagnosis (≤40 years) and Possinger score (score = 3 versus score = 1); and (c) potential interactions between the MSI status and OS were influenced by CIN and Possinger score (score = 3 versus score = 1).

This is the first study showing MSI of the primary tumor to be predictive of PFS and OS in patients with advanced breast cancer undergoing HDCT/ASCT. MSI, i.e., small deletions or insertions in short repetitive genomic sequences, has been investigated in numerous breast cancer studies (reviewed in Ref. 21), but large disparities in the percentage of microsatellite unstable tumors are reported. The range of MSI varied from 0% (22) to 100% (23), likely because of heterogeneous patient cohorts, suboptimal choices of MSI markers, and ambiguous diagnostic criteria for MSI. There are no well-defined criteria for the assessment of MSI in breast and other noncolorectal cancers, contrasting the rigorous diagnostic guidelines established in the hereditary nonpolyposis colorectal cancer model (11). We demonstrated MSI (defined as ≥3 of 15 markers unstable) in 33% (13 of 39) of cases, a frequency that is comparatively high. Possible explanations would include the use of laser-assisted tissue microdissection (6) and a selection of a sensitive MSI marker panel, but the highly selected patient cohort with advanced breast cancers remains a strong potential bias.

In colorectal cancer, MSI was shown to be independently predictive of a relatively favorable patient outcome (24), reduced likelihood of metastases (25), and better survival after adjuvant chemotherapy with fluorouracil-based regimens (26). However, Ribic et al.(27) have recently shown that adjuvant chemotherapy with fluorouracil benefited patients with colon tumors exhibiting CIN but not those with tumors exhibiting MSI. The data suggest that fluorouracil-based adjuvant therapy actually decreased the rate of survival among patients with tumors showing MSI. This is apparently an inconsistent finding in view of the fact that colon carcinomas with MSI were associated with an overall better survival than those with CIN. Patients with colorectal cancers that exhibit high-frequency MSI have longer survival than stage-matched patients with cancers revealing MSI (24, 26).

Several groups have reported a poorer prognosis for MSI-positive breast carcinomas (28, 29, 30, 31), but MSI has never been correlated with patient outcome in HDCT studies. Testing various multivariate Cox regression models, we could demonstrate that MSI is a highly significant, independent, and adverse risk factor for OS of breast cancer patients receiving HDCT/ASCT with a 7-fold increase in the relative risk of tumor-related death. MSI of the primary tumor is the most significant prognostic factor characterized in HDCT/ASCT studies thus far. On the analogy of Ribic et al.(27) for colon cancer, HDCT/ASCT was only beneficial for breast cancer patients exhibiting microsatellite stability or low instability (<3 of 15 markers). As expected for advanced tumor stages, lymph node status was of less prognostic importance for OS (P = 0.2) and was excluded from subsequent multivariate analyses. Event rates for tumor progression and tumor-related death were too small to meet the proportional hazards assumption regarding lymph node status.

There is growing evidence that MSI in breast cancer patients may represent a type of genetic instability different from that seen in colorectal cancer. All 13 MSI-positive breast tumors in our study showed normal expression of the mismatch repair proteins hMSH2 and hMLH1. Lack of MSI in breast cancers occurring in nonpolyposis colorectal cancer families has led to the exclusion of breast cancer as an integral tumor of the nonpolyposis colorectal cancer syndrome (32). MSI in breast cancer has typically been found in tri- and tetranucleotide repeats in this and other studies (30, 33, 34). In contrast, diagnosis of nonpolyposis colorectal cancer-associated replication errors is most easily determined by examination of a panel of simple mononucleotide (BAT25and BAT26) and dinucleotide (D5S346, D17S250, and D2S123) repeat sequences (11). A pattern of MSI similar to that found in the current study has been detected in non-small cell lung cancer, termed elevated MSI at selected tetranucleotide markers (35). This type of MSI was associated with P53mutations and displayed a phenotype inconsistent with defects in mismatch repair pathways. Similar to studies by Ahrendt et al.(35), our series of breast cancers revealed MSI predominantly in tetranucleotide repeats. Eighty-five percent of all MSI-positive tumors were unstable in at least one of the five tested tetranucleotide markers, but the mechanism causing these alterations still remains unclear.

In a randomized trial of HDCT in patients with operable breast cancer and extensive lymph node involvement with a median follow-up of 6.9 years, both the number of tumor-positive axillary lymph nodes after induction chemotherapy and the clinical T-stage before chemotherapy were significant factors for OS (36). Numerous novel molecular variables have been proposed as putative prognostic factors in breast cancer (2). However, only few studies investigated molecular alterations in the primary tumor in patients receiving HDCT/ASCT. In a cytogenetic analysis of 34 patients with lymph node-positive, high-risk breast cancer treated with HDCT/ASCT using comparative genomic hybridization, Climent et al.(37) showed that genomic loss of chromosome arm 18p predicted an adverse clinical outcome with shorter PFS.

Data on the importance of P53alterations as a predictor of poor outcome and chemoresistance in breast cancer are controversial (38), probably as a result of heterogeneity of studied patient cohorts and chemotherapeutic regimens, the different methods of assessing response (clinically versus histologically), and variations in the methods used to determine the P53status. In several studies, mutations in the P53gene correlated independently with poor patient outcome in breast cancer (20, 39, 40), likely reflecting an increased proliferative capacity of P53-mutant tumor cells and the greater resistance of such cells to the induction of apoptosis by a variety of chemotherapeutic agents (39). In a study of 149 high-risk primary breast cancer patients undergoing HDCT/ASCT, P53has been shown to be the strongest predictor of survival with a relative risk of 6.06 (41). However, in vitro results of an association of P53mutation and chemoresistance failed to translate into a significant correlation between immunohistochemically detected overexpression of P53 protein and clinical response to therapy in breast cancer patients (42, 43). Thus, the effect of P53mutations on the sensitivity of breast cancer to genotoxic drugs has been disputed. In high-grade, locally advanced breast cancers, inactivation of the P53pathway seemed to improve the response to HDCT (six cycles of 75 mg/m2 epirubicin and 1200 mg/m2 cyclophosphamide), whereas initial P53status and histological tumor response were strongly associated (P = 0.0001; Ref. 39).

HER-2/neu overexpression may represent yet another independent negative prognostic factor for patients with metastatic breast cancer who undergo HDCT/ASCT; two studies reported an association of Her-2/neu overexpression with generally shorter PFS and OS (44, 45). On the basis of these data, Nieto et al.(46) have proposed a prognostic model for relapse after HDCT in which HER-2/neu overexpression, number of tumor sites, and primary nodal ratio were independent predictors of long-term outcome (median follow-up 62 months) for stage IV oligometastatic breast cancer. In the present study, however, neither P53 nor HER-2/neu alterations correlated significantly with patient survival.

In summary, we demonstrated an independent negative correlation between MSI in the primary tumor and OS in advanced breast cancer patients treated with HDCT/ASCT. MSI may represent a potential molecular marker to predict differential patient’s responses to treatment and clinical outcomes. Larger prospective studies are currently conducted to validate our findings.

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.

Note: Data have been presented in part at the 92nd Annual Meeting of the American Association for Cancer Research, April 6–10, 2001, San Francisco, CA.

Requests for reprints: Arndt Hartmann, Institute of Pathology, University of Basel, Schönbeinstrasse 40, CH-4031, Basel, Switzerland. Phone: 41-61-265 2880; Fax: 41-61-265 3194; E-mail: [email protected]

Table 1

Clinicopathological characteristics at diagnosis

Patients (n = 39)
Median age at diagnosis (range) 41.0 years (25–56)  
Age at diagnosis (grouped)   
 <40 years 16 41.0% 
 40–49 years 17 43.6% 
 ≥50 years 15.4% 
Pathologic tumor sizea   
 ≤2 cm (pT1) 20.5% 
 >2 cm; ≤5 cm (pT2) 25 64.1% 
 >5 cm (pT3) 2.6% 
 Direct extension to chest   
 Wall or skin (pT4a–d) 12.8% 
Lymph nodes affecteda   
 0 (pN0) 20.5% 
 1–3 (pN1) 20.5% 
 4–9 (pN2) 20.5% 
 ≥10 (pN3) 11 28.2% 
 Unknown 10.3% 
Metastasesa   
 M0 21 53.8% 
 M1 12 30.8% 
 Unknown 15.4% 
Resection statusa   
 Complete (R0) 31 79.5% 
 Incomplete (R1) 15.4% 
 Unknown 5.1% 
Histologic grade   
 II 14 35.9% 
 III 25 64.1% 
Histopathological classificationb   
 Ductal 32 82.0% 
 Ducto-lobular 5.1% 
 Lobular 5.1% 
 Medullary 2.6% 
 Mucinous 2.6% 
 Tubular 2.6% 
Menopausal status   
 Premenopausal 20.5% 
 Postmenopausal 31 79.5% 
Estrogen receptor status   
 Negative 15 38.5% 
 Positive 24 61.5% 
Progesteron receptor status   
 Negative 18 46.2% 
 Positive 21 53.8% 
Patients (n = 39)
Median age at diagnosis (range) 41.0 years (25–56)  
Age at diagnosis (grouped)   
 <40 years 16 41.0% 
 40–49 years 17 43.6% 
 ≥50 years 15.4% 
Pathologic tumor sizea   
 ≤2 cm (pT1) 20.5% 
 >2 cm; ≤5 cm (pT2) 25 64.1% 
 >5 cm (pT3) 2.6% 
 Direct extension to chest   
 Wall or skin (pT4a–d) 12.8% 
Lymph nodes affecteda   
 0 (pN0) 20.5% 
 1–3 (pN1) 20.5% 
 4–9 (pN2) 20.5% 
 ≥10 (pN3) 11 28.2% 
 Unknown 10.3% 
Metastasesa   
 M0 21 53.8% 
 M1 12 30.8% 
 Unknown 15.4% 
Resection statusa   
 Complete (R0) 31 79.5% 
 Incomplete (R1) 15.4% 
 Unknown 5.1% 
Histologic grade   
 II 14 35.9% 
 III 25 64.1% 
Histopathological classificationb   
 Ductal 32 82.0% 
 Ducto-lobular 5.1% 
 Lobular 5.1% 
 Medullary 2.6% 
 Mucinous 2.6% 
 Tubular 2.6% 
Menopausal status   
 Premenopausal 20.5% 
 Postmenopausal 31 79.5% 
Estrogen receptor status   
 Negative 15 38.5% 
 Positive 24 61.5% 
Progesteron receptor status   
 Negative 18 46.2% 
 Positive 21 53.8% 
a

Staging according to the TNM Classification of Malignant Tumors (4).

b

Histopathological classification according to Rosen and Oberman (5).

Table 2

Clinicopathological characteristics in relation to microsatellite instability (MSI) and univariate analysis of prognostic factors regarding progression-free survival and overall survival in 39 metastatic breast cancer patients

NameVariableCharacteristicsMSS and MSI low (≤2/15)MSI (≥3/15)P                  aTumor progressionTumor-related Death
nEventsCensoredLog-rankbnEventsCensoredLog-rankb
AGEGR Age at diagnosis (grouped) 1 <40 years 0.07 16 12 0.39 16 10 0.20 
  2 40–49 years 12  17  17 10  
  3 ≥50 years    
Pathologic tumor sizec 1 pT1–pT3 21 13 0.15 34 24 10 0.008 34 13 21 0.08 
  2 pT4, pT4d    
Pathologic lymph node statusc 0 pN0 1.00 0.93 0.21 
  1 pN1, pN2, pN3 20  28 17 11  28 10 18  
Metastasesc 0 no metastases 14 1.00 21 10 11 0.12 21 16 0.34 
  1 metastases  12  12  
Resection statusc 0 complete 20 11 0.64 31 21 10 0.12 31 12 19 0.38 
  1 incomplete    
Grading 1 G2 1.00 14 0.47 14 10 0.73 
  2 G3 17  25 15 10  25  
MENOP Menopausal status 0 premenopausal 19 12 0.23 0.12 0.04 
  1 postmenopausal  31 21 10  31 13 18  
ER Estrogen receptor statusd 0 negative 11 0.73 15 10 0.78 15 0.91 
  1 positive 15  24 14 10  24 17  
PR Progesteron receptor statusd 0 negative 12 1.00 18 11 0.45 18 12 0.87 
  1 positive 14  21 13  21 14  
CIN Chromosomal instability 0 stable (LOHe <6/15 markers) 20 11 0.69 31 18 13 0.75 31 10 21 0.89 
  1 unstable (LOH ≥6/15 markers)    
HER2 HER-2/neu immunohistochemistryf 0 negative 11 1.00 19 10 0.67 19 13 0.74 
  1 positive    
P53IHC P53immunohistochemistryg 0 negative 0.45 14 0.27 14 10 0.86 
  1 positive  12  12  
P53 P53 mutation 0 no/silent P53-mutation 17 10 0.38 27 16 11 0.86 27 10 17 0.22 
  1 P53-mutation    
CT Adjuvant chemotherapy before HDCTh 0 no adjuvant CT 15 1.00 23 10 13 0.003 23 19 0.02 
  1 adjuvant CT 11  16 14  16  
POSS Possinger score 1 good prognosis 12 0.26 15 0.32 15 12 0.37 
  2 intermediate prognosis  12  12  
  3 poor prognosis  12 10  12  
NameVariableCharacteristicsMSS and MSI low (≤2/15)MSI (≥3/15)P                  aTumor progressionTumor-related Death
nEventsCensoredLog-rankbnEventsCensoredLog-rankb
AGEGR Age at diagnosis (grouped) 1 <40 years 0.07 16 12 0.39 16 10 0.20 
  2 40–49 years 12  17  17 10  
  3 ≥50 years    
Pathologic tumor sizec 1 pT1–pT3 21 13 0.15 34 24 10 0.008 34 13 21 0.08 
  2 pT4, pT4d    
Pathologic lymph node statusc 0 pN0 1.00 0.93 0.21 
  1 pN1, pN2, pN3 20  28 17 11  28 10 18  
Metastasesc 0 no metastases 14 1.00 21 10 11 0.12 21 16 0.34 
  1 metastases  12  12  
Resection statusc 0 complete 20 11 0.64 31 21 10 0.12 31 12 19 0.38 
  1 incomplete    
Grading 1 G2 1.00 14 0.47 14 10 0.73 
  2 G3 17  25 15 10  25  
MENOP Menopausal status 0 premenopausal 19 12 0.23 0.12 0.04 
  1 postmenopausal  31 21 10  31 13 18  
ER Estrogen receptor statusd 0 negative 11 0.73 15 10 0.78 15 0.91 
  1 positive 15  24 14 10  24 17  
PR Progesteron receptor statusd 0 negative 12 1.00 18 11 0.45 18 12 0.87 
  1 positive 14  21 13  21 14  
CIN Chromosomal instability 0 stable (LOHe <6/15 markers) 20 11 0.69 31 18 13 0.75 31 10 21 0.89 
  1 unstable (LOH ≥6/15 markers)    
HER2 HER-2/neu immunohistochemistryf 0 negative 11 1.00 19 10 0.67 19 13 0.74 
  1 positive    
P53IHC P53immunohistochemistryg 0 negative 0.45 14 0.27 14 10 0.86 
  1 positive  12  12  
P53 P53 mutation 0 no/silent P53-mutation 17 10 0.38 27 16 11 0.86 27 10 17 0.22 
  1 P53-mutation    
CT Adjuvant chemotherapy before HDCTh 0 no adjuvant CT 15 1.00 23 10 13 0.003 23 19 0.02 
  1 adjuvant CT 11  16 14  16  
POSS Possinger score 1 good prognosis 12 0.26 15 0.32 15 12 0.37 
  2 intermediate prognosis  12  12  
  3 poor prognosis  12 10  12  
a

Ps gives for differences between tumors of the two groups [MSI, microsatellite-stable sample (MSS), and MSI low] as determined by a two-sided Fisher’s exact test.

b

Bold type representing data with P < 0.05.

c

Staging according to the TNM Classification of Malignant Tumors (4).

d

1, positive (nuclear expression in >10% tumor cells); 0, negative.

e

LOH, loss of heterozygosity; CT, chemotherapy.

f

HER-2/neu scoring according to the DAKO HercepTest (14).

g

Negative, nuclear expression in ≤10% tumors cells; positive, nuclear expression in >10% of tumors cells.

h

Standard adjuvant chemotherapy before high-dose chemotherapy/autologous stem cell transplantation (n = 16).

Fig. 1.

Distribution of time to progression of disease (A) and time to death (B) among patients with microsatellite stable [microsatellite-stable sample (MSS) & microsatellite instability (MSI) low] and microsatellite unstable (MSI) advanced breast cancer treated with high-dose chemotherapy/autologous stem cell transplantation as estimated by the method of Kaplan and Meier (15).

Fig. 1.

Distribution of time to progression of disease (A) and time to death (B) among patients with microsatellite stable [microsatellite-stable sample (MSS) & microsatellite instability (MSI) low] and microsatellite unstable (MSI) advanced breast cancer treated with high-dose chemotherapy/autologous stem cell transplantation as estimated by the method of Kaplan and Meier (15).

Close modal
Table 3

Multivariate analysis of factors possibly influencing overall survival

NameVariableCharacteristicsGlobal PStepwise backward selection
Hazard ratio95% confidence intervalP
Raw model       
 MSI Microsatellite instability 0 stable (MSI <2/15 markers) 0.0061                  a 7.18 (2.07–24.88) 0.002 
  1 unstable (MSI ≥3/15 markers)     
 AGEGR Age at diagnosis (grouped) 1 <40 years 0.98 b   
  2 40–49 years     
  3 ≥50 years     
 CIN Chromosomal instability 0 stable (LOHc <6/15 markers) 0.32 –   
  1 unstable (LOH ≥6/15 markers)     
 ER Estrogen receptor status 0 negative 0.88 –   
  1 positive     
 POSS Possinger score 1 favorable prognosis 0.54 –   
  2 intermediate prognosis     
  3 poor prognosis     
Model with dichotomised dummy variables       
 MSI Microsatellite instability 0 stable (MSI <2/15 markers) 0.02 10.10 (2.57–39.68) 0.0009 
  1 unstable (MSI ≥3/15 markers)     
 AGEDUM1 Age at diagnosis (dummy variable 1) 0 AGEGR = 1 0.28 2.27 (0.71–7.24) 0.16 
  1 AGEGR = 2     
  0 AGEGR = 3     
 AGEDUM2 Age at diagnosis (dummy variable 2) 0 AGEGR = 1 1.00 –   
  0 AGEGR = 2     
  1 AGEGR = 3     
 CIN Chromosomal instability 0 stable (LOH <6/15 markers) 0.23 2.88 (0.73–11.35) 0.13 
  1 unstable (LOH ≥6/15 markers)     
 ER Estrogen receptor status 0 negative 0.63 –   
  1 positive     
 POSSDUM1 Possinger score (dummy variable 1) 0 POSS = 1 0.60 –   
  1 POSS = 2     
  0 POSS = 3     
 POSSDUM2 Possinger score (dummy variable 2) 0 POSS = 1 0.42 –   
  0 POSS = 2     
  1 POSS = 3     
Model with dichotomised dummy variables and interactions       
 MSI Microsatellite instability 0 stable (MSI <2/15 markers) 0.25 68.53 (5.48–856.76) 0.001 
  1 unstable (MSI ≥3/15 markers)     
 AGEDUM1 Age at diagnosis (dummy variable 1) 0 AGEGR = 1 0.10 30.18 (3.61–252.37) 0.002 
  1 AGEGR = 2     
  0 AGEGR = 3     
 AGEDUM2 Age at diagnosis (dummy variable 2) 0 AGEGR = 1 1.00 –   
  0 AGEGR = 2     
  1 AGEGR = 3     
 CIN Chromosomal instability 0 stable (LOH <6/15 markers) 0.53 –   
  1 unstable (LOH ≥6/15 markers)     
 ER Estrogen receptor status 0 negative 0.50 –   
  1 positive     
 POSSDUM1 Possinger score (dummy variable 1) 0 POSS = 1 1.00 –   
  1 POSS = 2     
  0 POSS = 3     
 POSSDUM2 Possinger score (dummy variable 2) 0 POSS = 1 0.02 35.99 (3.25–398.96) 0.004 
  0 POSS = 2     
  1 POSS = 3     
 INTER1 Interaction between AGEDUM 1 & MSI AGEDUM1 × MSI 0.49 –   
 INTER2 Interaction between AGEDUM 2 & MSI AGEDUM2 × MSI d –   
 INTER3 Interaction between CIN & MSI CIN × MSI 0.01 60.23 (5.17–701.64) 0.001 
 INTER4 Interaction between ER & MSI ER × MSI 0.97 –   
 INTER5 Interaction between POSSDUM1 & MSI POSSDUM1 × MSI 1.00 –   
 INTER6 Interaction between POSSDUM2 & MSI POSSDUM2 × MSI 0.01 0.004 (0.00–0.18) 0.004 
NameVariableCharacteristicsGlobal PStepwise backward selection
Hazard ratio95% confidence intervalP
Raw model       
 MSI Microsatellite instability 0 stable (MSI <2/15 markers) 0.0061                  a 7.18 (2.07–24.88) 0.002 
  1 unstable (MSI ≥3/15 markers)     
 AGEGR Age at diagnosis (grouped) 1 <40 years 0.98 b   
  2 40–49 years     
  3 ≥50 years     
 CIN Chromosomal instability 0 stable (LOHc <6/15 markers) 0.32 –   
  1 unstable (LOH ≥6/15 markers)     
 ER Estrogen receptor status 0 negative 0.88 –   
  1 positive     
 POSS Possinger score 1 favorable prognosis 0.54 –   
  2 intermediate prognosis     
  3 poor prognosis     
Model with dichotomised dummy variables       
 MSI Microsatellite instability 0 stable (MSI <2/15 markers) 0.02 10.10 (2.57–39.68) 0.0009 
  1 unstable (MSI ≥3/15 markers)     
 AGEDUM1 Age at diagnosis (dummy variable 1) 0 AGEGR = 1 0.28 2.27 (0.71–7.24) 0.16 
  1 AGEGR = 2     
  0 AGEGR = 3     
 AGEDUM2 Age at diagnosis (dummy variable 2) 0 AGEGR = 1 1.00 –   
  0 AGEGR = 2     
  1 AGEGR = 3     
 CIN Chromosomal instability 0 stable (LOH <6/15 markers) 0.23 2.88 (0.73–11.35) 0.13 
  1 unstable (LOH ≥6/15 markers)     
 ER Estrogen receptor status 0 negative 0.63 –   
  1 positive     
 POSSDUM1 Possinger score (dummy variable 1) 0 POSS = 1 0.60 –   
  1 POSS = 2     
  0 POSS = 3     
 POSSDUM2 Possinger score (dummy variable 2) 0 POSS = 1 0.42 –   
  0 POSS = 2     
  1 POSS = 3     
Model with dichotomised dummy variables and interactions       
 MSI Microsatellite instability 0 stable (MSI <2/15 markers) 0.25 68.53 (5.48–856.76) 0.001 
  1 unstable (MSI ≥3/15 markers)     
 AGEDUM1 Age at diagnosis (dummy variable 1) 0 AGEGR = 1 0.10 30.18 (3.61–252.37) 0.002 
  1 AGEGR = 2     
  0 AGEGR = 3     
 AGEDUM2 Age at diagnosis (dummy variable 2) 0 AGEGR = 1 1.00 –   
  0 AGEGR = 2     
  1 AGEGR = 3     
 CIN Chromosomal instability 0 stable (LOH <6/15 markers) 0.53 –   
  1 unstable (LOH ≥6/15 markers)     
 ER Estrogen receptor status 0 negative 0.50 –   
  1 positive     
 POSSDUM1 Possinger score (dummy variable 1) 0 POSS = 1 1.00 –   
  1 POSS = 2     
  0 POSS = 3     
 POSSDUM2 Possinger score (dummy variable 2) 0 POSS = 1 0.02 35.99 (3.25–398.96) 0.004 
  0 POSS = 2     
  1 POSS = 3     
 INTER1 Interaction between AGEDUM 1 & MSI AGEDUM1 × MSI 0.49 –   
 INTER2 Interaction between AGEDUM 2 & MSI AGEDUM2 × MSI d –   
 INTER3 Interaction between CIN & MSI CIN × MSI 0.01 60.23 (5.17–701.64) 0.001 
 INTER4 Interaction between ER & MSI ER × MSI 0.97 –   
 INTER5 Interaction between POSSDUM1 & MSI POSSDUM1 × MSI 1.00 –   
 INTER6 Interaction between POSSDUM2 & MSI POSSDUM2 × MSI 0.01 0.004 (0.00–0.18) 0.004 
a

Bold type representing data with P < 0.05.

b

Variable excluded from the model; limit for reverse selection procedures P = 0.2.

c

LOH, loss of heterozygosity.

d

Interpretation of the variable INTER2 not possible because of redundancy.

We thank Andrea Schneider, Monika Kerscher, and Rudolf Jung for excellent technical assistance, Holger Moch and Stefanie Meyer for carefully revising the manuscript, and Dr. Jürgen Löffler, medicomp mbH, Planegg, Germany, for excellent assistance with the complex statistical analyses.

1
Peters W. P., Rosner G., Vredenburgh J., Shpall E. J., Crump M., Marks L., Cirrincione C., Hurd D., Norton L. Updated results of a prospective, randomized comparison of two doses of combination alkylating agents (AA) as consolidation after CAF in high-risk primary breast cancer involving ten or more axillary lymph nodes (LN): CALGB 9082/SWOG 9114/NCIC Ma-13.
Proc. Am. Soc. Clin. Oncol.
,
20
:
21a
2001
.
2
Isaacs C., Stearns V., Hayes D. F. New prognostic factors for breast cancer recurrence.
Semin. Oncol.
,
28
:
53
-67,  
2001
.
3
Possinger K., Wagner H., Langecker P., Wilmanns W. Treatment toxicity reduction: breast cancer.
Cancer Treat. Rev.
,
14
:
263
-274,  
1987
.
4
Sobin L. H. Wittekind C. H. eds. .
TNM Classification of Malignant Tumors
, Wiley-Liss, Inc. New York, NY  
2002
.
5
Rosen P. P., Oberman H. A. Tumors of the mammary gland Rosai J. Sobin L. H. eds. .
Atlas of Tumor Pathology, fasc.
,
7
Armed Forces Institute of Pathology Washington, DC  
1992
.
6
Wild P., Knuechel R., Dietmaier W., Hofstaedter F., Hartmann A. Laser microdissection and microsatellite analyses of breast cancer reveal a high degree of tumor heterogeneity.
Pathobiology
,
68
:
180
-190,  
2000
.
7
Kovach J. S., McGovern R. M., Cassady J. D., Swanson S. K., Wold L. E., Vogelstein B., Sommer S. S. Direct sequencing from touch preparations of human carcinomas: analysis of p53 mutations in breast carcinomas.
J. Natl. Cancer Inst. (Bethesda)
,
83
:
1004
-1009,  
1991
.
8
Dietmaier W., Hartmann A., Wallinger S., Heinmoller E., Kerner T., Endl E., Jauch K. W., Hofstadter F., Ruschoff J. Multiple mutation analyses in single tumor cells with improved whole genome amplification.
Am. J. Pathol.
,
154
:
83
-95,  
1999
.
9
Hartmann A., Rosner U., Schlake G., Dietmaier W., Zaak D., Hofstaedter F., Knuechel R. Clonality and genetic divergence in multifocal low-grade superficial urothelial carcinoma as determined by chromosome 9 and p53 deletion analysis.
Lab. Investig.
,
80
:
709
-718,  
2000
.
10
Schlegel J., Bocker T., Zirngibl H., Hofstadter F., Ruschoff J. Detection of microsatellite instability in human colorectal carcinomas using a non-radioactive PCR-based screening technique.
Virchows Arch.
,
426
:
223
-227,  
1995
.
11
Boland C. R., Thibodeau S. N., Hamilton S. R., Sidransky D., Eshleman J. R., Burt R. W., Meltzer S. J., Rodriguez-Bigas M. A., Fodde R., Ranzani G. N., Srivastava S. A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer.
Cancer Res.
,
58
:
5248
-5257,  
1998
.
12
Hartmann A., Schlake G., Zaak D., Hungerhuber E., Hofstetter A., Hofstaedter F., Knuechel R. Occurrence of chromosome 9 and p53 alterations in multifocal dysplasia and carcinoma in situ of human urinary bladder.
Cancer Res.
,
62
:
809
-818,  
2002
.
13
Dietmaier W., Wallinger S., Bocker T., Kullmann F., Fishel R., Ruschoff J. Diagnostic microsatellite instability: definition and correlation with mismatch repair protein expression.
Cancer Res.
,
57
:
4749
-4756,  
1997
.
14
Graziano C. HER-2 breast assay, linked to Herceptin, wins FDA’s okay.
CAP Today
,
12
:
1, 14
-16,  
1998
.
15
Kaplan E. L., Meier P. Nonparametric estimation from incomplete observations.
J. Am. Stat. Assoc.
,
53
:
457
-481,  
1958
.
16
Peto R., Peto J. Regression models and life tables.
J. R. Stat. Soc. B
,
135
:
185
-188,  
1972
.
17
Cox D. R. Regression models and life tables.
J. R. Stat. Soc. B
,
34
:
187
-220,  
1972
.
18
Lengauer C., Kinzler K. W., Vogelstein B. Genetic instability in colorectal cancers.
Nature (Lond.)
,
386
:
623
-627,  
1997
.
19
Hartmann A., Blaszyk H., McGovern R. M., Schroeder J. J., Cunningham J., De Vries E. M., Kovach J. S., Sommer S. S. p53 gene mutations inside and outside of exons 5–8: the patterns differ in breast and other cancers.
Oncogene
,
10
:
681
-688,  
1995
.
20
Kovach J. S., Hartmann A., Blaszyk H., Cunningham J., Schaid D., Sommer S. S. Mutation detection by highly sensitive methods indicates that p53 gene mutations in breast cancer can have important prognostic value.
Proc. Natl. Acad. Sci. USA
,
93
:
1093
-1096,  
1996
.
21
Siah S. P., Quinn D. M., Bennett G. D., Casey G., Flower R. L., Suthers G., Rudzki Z. Microsatellite instability markers in breast cancer: a review and study showing MSI was not detected at ‘BAT 25′ and ‘BAT 26′ microsatellite markers in early-onset breast cancer.
Breast Cancer Res. Treat.
,
60
:
135
-142,  
2000
.
22
Anbazhagan R., Fujii H., Gabrielson E. Microsatellite instability is uncommon in breast cancer.
Clin. Cancer Res.
,
5
:
839
-844,  
1999
.
23
Risinger J. I., Barrett J. C., Watson P., Lynch H. T., Boyd J. Molecular genetic evidence of the occurrence of breast cancer as an integral tumor in patients with the hereditary nonpolyposis colorectal carcinoma syndrome.
Cancer (Phila.)
,
77
:
1836
-1843,  
1996
.
24
Lothe R. A., Peltomaki P., Meling G. I., Aaltonen L. A., Nystrom-Lahti M., Pylkkanen L., Heimdal K., Andersen T. I., Moller P., Rognum T. O. Genomic instability in colorectal cancer: relationship to clinicopathological variables and family history.
Cancer Res.
,
53
:
5849
-5852,  
1993
.
25
Muller A., Fishel R. Mismatch repair and the hereditary non-polyposis colorectal cancer syndrome (HNPCC).
Cancer Investig.
,
20
:
102
-109,  
2002
.
26
Gryfe R., Kim H., Hsieh E. T., Aronson M. D., Holowaty E. J., Bull S. B., Redston M., Gallinger S. Tumor microsatellite instability and clinical outcome in young patients with colorectal cancer.
N. Engl. J. Med.
,
342
:
69
-77,  
2000
.
27
Ribic C. M., Sargent D. J., Moore M. J., Thibodeau S. N., French A. J., Goldberg R. M., Hamilton S. R., Laurent-Puig P., Gryfe R., Shepherd L. E., Tu D., Redston M., Gallinger S. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer.
N. Engl. J. Med.
,
349
:
247
-257,  
2003
.
28
Zekri A. R., Bahnassi A. A., Bove B., Huang Y., Russo I. H., Rogatko A., Shaarawy S., Shawki O. A., Hamza M. R., Omer S., Khaled H. M., Russo J. Allelic instability as a predictor of survival in Egyptian breast cancer patients.
Int. J. Oncol.
,
15
:
757
-767,  
1999
.
29
De Marchis L., Contegiacomo A., D’Amico C., Palmirotta R., Pizzi C., Ottini L., Mastranzo P., Figliolini M., Petrella G., Amanti C., Battista P., Bianco A. R., Frati L., Cama A., Mariani-Costantini R. Microsatellite instability is correlated with lymph node-positive breast cancer.
Clin. Cancer Res.
,
3
:
241
-248,  
1997
.
30
Paulson T. G., Wright F. A., Parker B. A., Russack V., Wahl G. M. Microsatellite instability correlates with reduced survival and poor disease prognosis in breast cancer.
Cancer Res.
,
56
:
4021
-4026,  
1996
.
31
Tomita S., Deguchi S., Miyaguni T., Muto Y., Tamamoto T., Toda T. Analyses of microsatellite instability and the transforming growth factor-β receptor type II gene mutation in sporadic breast cancer and their correlation with clinicopathological features.
Breast Cancer Res. Treat.
,
53
:
33
-39,  
1999
.
32
Muller A., Edmonston T. B., Corao D. A., Rose D. G., Palazzo J. P., Becker H., Fry R. D., Rueschoff J., Fishel R. Exclusion of breast cancer as an integral tumor of hereditary nonpolyposis colorectal cancer.
Cancer Res.
,
62
:
1014
-1019,  
2002
.
33
Rosenberg C. L., de las M., Huang K., Cupples L. A., Faller D. V., Larson P. S. Detection of monoclonal microsatellite alterations in atypical breast hyperplasia.
J. Clin. Investig.
,
98
:
1095
-1100,  
1996
.
34
Aldaz C. M., Chen T., Sahin A., Cunningham J., Bondy M. Comparative allelotype of in situ and invasive human breast cancer: high frequency of microsatellite instability in lobular breast carcinomas.
Cancer Res.
,
55
:
3976
-3981,  
1995
.
35
Ahrendt S. A., Decker P. A., Doffek K., Wang B., Xu L., Demeure M. J., Jen J., Sidransky D. Microsatellite instability at selected tetranucleotide repeats is associated with p53 mutations in non-small cell lung cancer.
Cancer Res.
,
60
:
2488
-2491,  
2000
.
36
Schrama J. G., Faneyte I. F., Schornagel J. H., Baars J. W., Peterse J. L., van d. V., Dalesio O., van Tinteren H., Rutgers E. J., Richelt D. J., Rodenhuis S. Randomized trial of high-dose chemotherapy and hematopoietic progenitor-cell support in operable breast cancer with extensive lymph node involvement: final analysis with 7 years of follow-up.
Ann. Oncol.
,
13
:
689
-698,  
2002
.
37
Climent J., Martinez-Climent J. A., Blesa D., Garcia-Barchino M. J., Saez R., Sanchez-Izquierdo D., Azagra P., Lluch A., Garcia-Conde J. Genomic loss of 18p predicts an adverse clinical outcome in patients with high-risk breast cancer.
Clin. Cancer Res.
,
8
:
3863
-3869,  
2002
.
38
Bertheau P., Plassa F., Espie M., Turpin E., de Roquancourt A., Marty M., Lerebours F., Beuzard Y., Janin A., de The H. Effect of mutated TP53 on response of advanced breast cancers to high-dose chemotherapy.
Lancet
,
360
:
852
-854,  
2002
.
39
Bergh J., Norberg T., Sjogren S., Lindgren A., Holmberg L. Complete sequencing of the p53 gene provides prognostic information in breast cancer patients, particularly in relation to adjuvant systemic therapy and radiotherapy.
Nat. Med.
,
1
:
1029
-1034,  
1995
.
40
Berns E. M., Foekens J. A., Vossen R., Look M. P., Devilee P., Henzen-Logmans S. C., van S. I, van Putten W. L., Inganas M., Meijer-van Gelder M. E., Cornelisse C., Claassen C. J., Portengen H., Bakker B., Klijn J. G. Complete sequencing of TP53 predicts poor response to systemic therapy of advanced breast cancer.
Cancer Res.
,
60
:
2155
-2162,  
2000
.
41
Hensel M., Schneeweiss A., Sinn H. P., Egerer G., Solomayer E., Haas R., Bastert G., Ho A. D. P53 is the strongest predictor of survival in high-risk primary breast cancer patients undergoing high-dose chemotherapy with autologous blood stem cell support.
Int. J. Cancer
,
100
:
290
-296,  
2002
.
42
Hartmann A., Blaszyk H., Kovach J. S., Sommer S. S. The molecular epidemiology of p53 gene mutations in human breast cancer.
Trends Genet.
,
13
:
27
-33,  
1997
.
43
Makris A., Powles T. J., Dowsett M., Allred C. p53 protein overexpression and chemosensitivity in breast cancer.
Lancet
,
345
:
1181
-1182,  
1995
.
44
Kim Y. S., Konoplev S. N., Montemurro F., Hoy E., Smith T. L., Rondon G., Champlin R. E., Sahin A. A., Ueno N. T. HER-2/neu overexpression as a poor prognostic factor for patients with metastatic breast cancer undergoing high-dose chemotherapy with autologous stem cell transplantation.
Clin. Cancer Res.
,
7
:
4008
-4012,  
2001
.
45
Nieto Y., Cagnoni P. J., Nawaz S., Shpall E. J., Yerushalmi R., Cook B., Russell P., McDermit J., Murphy J., Bearman S. I., Jones R. B. Evaluation of the predictive value of Her-2/neu overexpression and p53 mutations in high-risk primary breast cancer patients treated with high-dose chemotherapy and autologous stem-cell transplantation.
J. Clin. Oncol.
,
18
:
2070
-2080,  
2000
.
46
Nieto Y., Nawaz S., Jones R. B., Shpall E. J., Cagnoni P. J., McSweeney P. A., Baron A., Razook C., Matthes S., Bearman S. I. Prognostic model for relapse after high-dose chemotherapy with autologous stem-cell transplantation for stage IV oligometastatic breast cancer.
J. Clin. Oncol.
,
20
:
707
-718,  
2002
.