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A Predictive Model for Relapse in High-Risk Primary Breast Cancer Patients Treated with High-Dose Chemotherapy and Autologous Stem-Cell Transplant

Bone Marrow Transplant Program [Y. N., P. J. C., E. J. S., S. I. B., R. B. J.] and Department of Biostatistics [X. X., J. M.], University of Colorado, Denver, Colorado 80262, and Bone Marrow Transplant Program, Duke University, Durham, North Carolina [J. V., N. J. C.]

	ABSTRACT

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High-dose chemotherapy (HDCT) is currently under evaluation for high-risk primary breast cancer (HRPBC), defined by extensive axillary nodal involvement or inflammatory breast carcinoma. Phase II studies of HDCT for HRPBC show that 30–40% of patients eventually relapse. We retrospectively reviewed 176 patients enrolled in clinical trials of HDCT for HRPBC at the University of Colorado and analyzed 23 potential predictive variables for relapse. All of the patients received the same regimen, with cyclophosphamide, cisplatin, and BCNU. Nine patients who experienced a toxic death were excluded from this analysis. The resulting predictive model was subsequently tested in an independent patient set treated at Duke University with the same HDCT regimen. Nodal ratio (number of involved nodes:number of sampled nodes), tumor size, grade, stage, estrogen receptor, progesterone receptor, and clinical inflammatory breast carcinoma correlated with risk of relapse. Nodal ratio, tumor size, and the combined estrogen receptor/progesterone receptor status were independent predictors. A scoring system using those three variables determines the risk of relapse, with a sensitivity and specificity of 60 and 90%, respectively, and a positive and negative predictive value of 65 and 88%, respectively. The differences in relapse-free survival and overall survival between high- and low-score patients were highly significant (P < 0.000001). This model was subsequently validated in the Duke patient set. This model can identify two subgroups of HRPBC patients with low (12%) and high (65%) risk for recurrence after HDCT. Future research that tests new therapies will focus on those patients with a high score.

	INTRODUCTION

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Although conventional chemotherapy improves the outcome of primary breast cancer (1) , most patients with HRPBC² FS still relapse and die of their disease (2, 3, 4) . Stem-cell support allows substantial dose increments of the antineoplastic drugs in an attempt to maximally capitalize on their dose-response effect and to overcome tumor cell resistance (5 , 6) . Phase II trials of HDCT for HRPBC have reported 57–71% RFS rates at 2–5 years (7, 8, 9, 10, 11, 12) .

The important question of the relative value of HDCT compared with conventional chemotherapy in HRPBC is presently under evaluation in randomized Phase III trials. Preliminary analyses of an Intergroup study, comparing HDCT using cyclophosphamide, cisplatin, and BCNU (STAMP-I or CCB) with intermediate doses of the same drugs in patients with 10 or more involved nodes, shows a higher relapse rate in the control arm with nonoverlapping confidence intervals, a higher toxic death rate in the HDCT arm, and no significant differences in RFS and OS between both arms at a follow-up of 37 months (13) . In the 5-year analysis of a South African trial that randomized HRPBC patients to two upfront cycles of high-dose cyclophosphamide, mitoxantrone, and etoposide, or to six cycles of cyclophosphamide, Adriamycin, and 5-fluorouracil, RFS and OS were significantly superior in the high-dose arm (14) . Preliminary analysis of a Scandinavian trial, comparing high-dose cyclophosphamide, thiotepa, and carboplatin (STAMP-V), with a dose-intensified combination of cyclophosphamide, epirubicin, and 5-fluorouracil, shows no difference in outcome at median follow-up of 24 months (15) .

While definitive results of most randomized trials are pending, it is important to identify subgroups of HRPBC patients who may not benefit from HDCT as presently delivered. Very little is known about predictive factors in this setting, in contrast to patients treated with conventional chemotherapy. In the analysis by Somlo et al. (16) of HRPBC patients treated with two different HDCT combinations, PR negativity was the only independent predictor of relapse.

In this study, we analyzed potential predictive variables in HRPBC patients enrolled in clinical trials of HDCT, using STAMP-I. The resulting predictive model was validated in an independent patient set.

	PATIENTS AND METHODS

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Patient Population.
We reviewed 176 patients treated with HDCT between 1991 and 1996 at the University of Colorado (Table 1) , enrolled in the following Institutional Review Board-approved clinical trials: 10 positive axillary nodes (n = 84), 4–9 positive nodes (n = 70), and IBC (n = 22). Nine patients who experienced a toxic death (3.8% of the total patient accrual) were not analyzed. Follow-up was estimated from the first day of HDCT. Median follow-up for all of the patients analyzed and for the alive patients is 41 (range, 7–84 months) and 45 (range, 12–84) months, respectively.

Variables Analyzed.
Because male gender (n = 1) and synchronous bilateral tumors (n = 1) were uncommon in our series, neither variable was analyzed (Table 1) . Tumor size was the pathological measurement of the invasive component in the surgical specimen. Histological grade was determined using the Elston and Ellis system (17) . Ploidy was considered a categorical variable: diploid (DNA index: 0.9–1.1), aneuploid/tetraploid (1.1–2.1) and hypertetraploid (>2.1; Ref. 18 ). S-phase fraction was interpreted as follows: (a) diploid tumors: <2%, low; 2–4%, medium; 4%, high;and (b) aneuploid tumors: <5%, low; 5–11%, medium; 11%, high (19) . Clinical IBC was defined by skin induration, erythema, warmth, and erysipeloid margins. Pathological IBC was defined by dermal lymphatic involvement. Extensive intraductal component was defined as >25% of the total tumor. Multifocality refers to presence of cells beyond the primary focus. We defined nodal ratio as the quotient of the number of positive nodes divided by the number of sampled nodes.

Patient Samples.
Sample A consists of those patients from the University of Colorado who underwent upfront surgery (n = 154).%All of the patients with IBC from the University of Colorado (n = 22) received preoperative chemotherapy. Their data were not used to develop the scoring system, which was derived from sample A.

Sample B (n = 225) includes all of the non-IBC patients from Duke University (Table 2) treated with upfront surgery, enrolled onto clinical trials for HRPBC with the same HDCT regimen, and surviving transplant. Their median follow-up was 46 (4–127) months.

The prognostic model, derived from sample A, was tested in samples B and C, similarly staged and treated. In all of the samples, patients with a toxic death were excluded.

Statistical Analysis.
Time of RFS and OS were estimated from the start of HDCT using the Kaplan-Meier product-limit method (20) . For time-to-event models, univariate analyses used the two-sided log-rank test (21) , and multivariate analyses used the proportional-hazards regression method (22) . Logistic regression estimated the probability of relapse without regard to time-to-event.

Cutoff levels for the tumor size were based on the current Tumor-Node-Metastasis staging system (23) . Cutoffs for the number of positive nodes were based on the inclusion criteria of the trials. To determine whether there was a potential prognostic threshold number of positive nodes, this variable was analyzed as both trichotomous (4–9, 10–20 and >20) and dichotomous (4–20 and >20). The cutoff for the nodal ratio was established at 0.8, because it segregates two groups, each comprising 20% of patients, with high predictive capacity. Variables used in the final model were also significant as continuous variables.

To predict who would relapse, a step-up procedure was used to obtain a multiple logistic model, using relapse (yes/no) as the outcome variable. Two predictive systems were then developed, resulting from the sum of the products of the independent factors and their estimated coefficients in the logistic model: the first one calculates the probability of relapse for a given patient, and the second one is a scoring system that allocates patients to a prognostic group, based on a cutoff score.

Statistical analyses used the SAS 6.12 package (SAS Institute Inc., Cary, NC).

	RESULTS

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Time-to-Event Univariate Analysis.
The following factors were significantly associated with RFS and OS: ER, PR, nodal ratio, tumor size, stage, grade and clinical IBC (Table 3) . The prognostic value of nodal ratio, both as a continuous (P = 0.0006) or dichotomous variable (P = 0.001), contrasted with the lack of significance of the number of positive nodes, either as a continuous (P = 0.09) or categorical variable (P = 0.48 if trichotomous, and P = 0.1 if dichotomous). Patients with nodal ratio <0.8 had a lower number of positive nodes (median 8, range, 4 to 29) than those with ratio 0.8 (median 20, 5 to 43; P < 0.000001). The median numbers of sampled nodes in patients with nodal ratio <0.8 and 0.8 (19, range 9–46, and 22, range 5–46, respectively), did not differ significantly (P = 0.1).

Time-to-Event Multivariate Analysis.
Nodal ratio (P = 0.001), tumor size (P = 0.005), and ER/PR (P = 0.01) were independent predictors of RFS (Figs. 1 2 3) . When ER and PR were analyzed separately, neither one had independent value (P = 0.11 and P = 0.34, respectively) in contrast to the combined ER/PR status (Table 4) .

View larger version (15K): [in this window] [in a new window] [Download PPT slide]   Fig. 1. RFS according to the nodal ratio (P = 0.0002). Patients who died of treatment-related toxicity were excluded (this is valid for all of the of the survival curves presented).

View larger version (17K): [in this window] [in a new window] [Download PPT slide]   Fig. 2. RFS according to the pathological tumor size (P = 0.01).

View larger version (16K): [in this window] [in a new window] [Download PPT slide]   Fig. 3. RFS according to the ER/PR status (P < 0.005). ER/PR is considered positive if either ER or PR are positive, and negative if both are negative.

In this formula, tumor size is entered in cm, and ER/PR status is assigned "0" if negative (both ER- and PR-negative) and "1" if positive (ER- and/or PR-positive). The probability of relapse can be calculated for each patient, but it seems more useful to classify patients into groups based on cutoff values. Thus, a simpler scoring system was subsequently derived from the previous formula:

A cutoff score of 2.41 confers the following properties to this model: (a) sensitivity = 0.6, specificity = 0.88, positive predictive value = 0.65, and negative predictive value = 0.86. We can then use scores of 2.41 or <2.41 to allocate patients to the high- or low-risk category, respectively. For example, the score is 2.55 for a patient with a 4.5-cm tumor, involving 8 of 13 nodes, and who is ER/PR negative, which assigns her a high risk. Dividing patients into two categories based on this risk score, we find that differences in RFS (P < 0.000001) and OS (P < 0.00005) are highly significant between high- and low-score patients (Figs. 4 and 5) . This provides verification that the prospective allocation of patients in this fashion may have clinical value.

View larger version (15K): [in this window] [in a new window] [Download PPT slide]   Fig. 4. RFS of low-score (n = 123) and high-score patients (n = 31; P < 0.000001) in the series from the University of Colorado (sample A).

View larger version (15K): [in this window] [in a new window] [Download PPT slide]   Fig. 5. OS of low-score (n = 123) and high-score patients (n = 31; P < 0.000001) in the series from the University of Colorado (sample A).

View larger version (15K): [in this window] [in a new window] [Download PPT slide]   Fig. 6. RFS of low-score (n = 171) and high-score patients (n = 54; P < 0.000001) from Duke University (sample B).

Testing of the Model in IBC Patients Treated with Preoperative Chemotherapy.
Low-score patients of sample C had a better RFS (P = 0.00005) and OS (P < 0.001) than those with a high score (Fig. 7) . This score and model have the following properties in this setting: 85% sensitivity, 80% specificity, 77% positive predictive value, 87% negative predictive value, 82% accuracy, and 5.73 risk ratio.

View larger version (14K): [in this window] [in a new window] [Download PPT slide]   Fig. 7. Application of the model to all of the IBC patients in the Colorado and Duke series combined (sample C): RFS of low-score (n = 20) and high-score patients (n = 21; P = 0.00005).

View larger version (19K): [in this window] [in a new window] [Download PPT slide]   Fig. 8. Comparison of the RFS curves of low-score and high-score patients with upfront mastectomy (sample A+B) or after preoperative chemotherapy (sample C). I, low-score sample C (n = 20); II, low-score sample A+B (n = 294); (P between I and II = 0.5); III, high-score sample C (n = 21); IV, high-score sample A+B (n = 85; P between III and IV = 0.01).

	DISCUSSION

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Independently of their result, it is possible that important subgroups of HRPBC may experience major benefit from HDCT, whereas others may require different approaches. The present study evaluated 23 variables and determined that axillary nodal ratio, ER/PR status, and tumor size had independent predictive value. The resulting predictive model was validated when applied to a different HRPBC patient set treated similarly at Duke University. Importantly, the model seems to have sufficient sensitivity and specificity to allow prospective assignment of patients to the high-risk category to test new treatments in this group.

The prognostic significance of tumor size and hormone receptors in primary breast cancer is well established (24) . Of note, nodal ratio emerged in our analysis as a potent predictive indicator, whereas the number of involved nodes, the most powerful prognostic factor after conventional therapy (24) , lacked significance. Fisher et al. (25 , 26) reported that in early trials of the NSABP, the number of involved nodes, but not of sampled nodes, predicted relapse, which implied that nodal ratio had less predictive value than the number of involved nodes. The overall median number of sampled nodes in those NSABP studies was 15 (range, 0–81), possibly not different from our analysis. Possible explanations for our opposite observation include: (a) major differences between treatment given in those NSABP trials (none for most patients, single agent thiotepa or fluorouracil, or oophorectomy) and HDCT; (b) lack of inclusion of patients with 0–3 positive nodes in our HDCT trials; (c) smaller sample size in our analysis than in the NSABP trials, and (d) differences in other less obvious patient-related variables.

Patients with operable (27) or locally advanced disease (28) can be downstaged by preoperative chemotherapy. Moreover, tumor response correlates with outcome (29, 30, 31, 32) . In contrast, preoperative chemotherapy does not substantially modify tumor grade (33 , 34) or DNA ploidy (35) . To avoid a confounding effect from preoperative chemotherapy, the model was developed using pathological data only from patients submitted to upfront surgery. However, it also seems applicable to patients receiving preoperative chemotherapy. Of note, patients with a high score after preoperative chemotherapy had significantly worse RFS than those with a high score after upfront surgery. This observation may reflect the chemoresistance of tumors still presenting high-risk pathological features after preoperative chemotherapy.

It is important to recognize several caveats to our results. This model includes prognostic features that are pathological surrogates of the biology of HRPBC. The prognostic value of molecular alterations of Her-2/neu or p53 in this setting is under investigation. Our patients had negative histological exams of bone marrow. Marrow involvement detected with more sensitive techniques, such as PCR (36, 37, 38) or immunocytochemistry (39, 40, 41) , correlates with relapse and may have independent value.

The purpose of this study was not to show results of HDCT in HRPBC, much less to advocate for its use over conventional-dose chemotherapy in this patient population. We intended to develop a model that could prospectively identify two categories of patients with low- and high-risk of relapse after HDCT. Very large prospective randomized trials would be required to demonstrate any further improvement with new therapies in the former category. This model will allow investigators to focus on the high-score group for purposes of testing alternative treatment approaches. It would be useful to test this model in other groups including: (a) HRPBC treated with other HDCT regimens; (b) HRPBC treated with conventional chemotherapy; and (c) patients with 0–3 positive nodes treated with conventional chemotherapy. This evaluation may strengthen its validity and also allow identification of additional patients with high-risk features for whom new treatments are needed.

	ACKNOWLEDGMENTS

 
We are indebted to Linda Cox for her assistance.

	FOOTNOTES

 
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 To whom requests for reprints should be addressed, at University of Colorado Bone Marrow Transplant Program, B 190, 4200 East Ninth Avenue, Denver, CO 80262. Fax: (303) 372-9003; E-mail: yxnieto{at}entente.uhcolorado.edu

² The abbreviations used are: HRPBC, high-risk primary breast cancer; RFS, relapse-free survival; OS, overall survival; HDCT, high-dose chemotherapy; IBC, inflammatory breast carcinoma; PR, progesterone receptor; ER, estrogen receptor; NSABP, National Surgical Adjuvant Breast and Bowel Project .

Received 6/23/99; revised 8/25/99; accepted 8/27/99.

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