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Long-Term Survival in Metastatic Transitional-Cell Carcinoma and Prognostic Factors Predicting Outcome of Therapy

From the Genitourinary Oncology Service, Division of Solid Tumor Oncology, Department of Medicine, Division of Urology, Department of Surgery, and Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, and Department of Medicine, Cornell University Medical College, New York, NY.

Address reprint requests to Dean F. Bajorin, MD, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10021.

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION REFERENCES

 
PURPOSE: The variation in reported survival of patients with metastatic transitional-cell carcinoma (TCC) treated with systemic chemotherapy may be a consequence of pretreatment patient characteristics. We hypothesized that a prognostic factor–based model of survival among patients treated with methotrexate, vinblastine, doxorubicin, and cisplatin chemotherapy could account for such differences and help guide clinical trial design and interpretation.

PATIENTS AND METHODS: A database of 203 patients with unresectable or metastatic TCC was retrospectively subjected to a multivariate regression analysis to determine which patient characteristics had independent prognostic significance for survival. Patients were assigned to three risk categories depending on the number of unfavorable characteristics. Patient selection in phase II studies was addressed by developing a table of expected median survival for patient cohorts that had varying proportions of patients from the three risk categories.

RESULTS: Two factors had independent prognosis: Karnofsky performance status (KPS) less than 80% and visceral (lung, liver, or bone) metastasis. Median survival times for patients who had zero, one, or two risk factors were 33, 13.4, and 9.3 months, respectively (P = .0001). The median survival time of patient cohorts could vary from 9 to 26 months simply by altering the proportion of patients from different risk categories.

CONCLUSION: The presence of baseline KPS less than 80% or visceral metastasis has an impact on survival. Reporting the proportion of patients with zero, one, and two risk factors will facilitate understanding of the relevance of the median survival in phase II trials. Phase III trials should stratify patients according to the number of risk factors to avoid imbalance in treatment arms.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION REFERENCES

 
THE ACTIVITY OF THE four-drug regimen of methotrexate, vinblastine, doxorubicin, and cisplatin (M-VAC) in transitional-cell carcinoma (TCC) of the urothelial tract is well documented. The initial trial with this combination at Memorial Sloan-Kettering Cancer Center (MSKCC) reported a 72% major response proportion, including 36% of patients rendered disease-free by chemotherapy alone or chemotherapy in combination with postchemotherapy surgery.¹ In two subsequent phase III trials, an improvement in both the response proportion and survival was demonstrated for M-VAC relative to single-agent cisplatin and the three-drug regimen of cisplatin, doxorubicin, and cyclophosphamide.^2,3 On the basis of these data, M-VAC represents one standard of care for patients with unresectable or metastatic TCC. However, the median survival of patients treated with M-VAC approximates 1 year, and long-term survival occurs in only a small proportion of patients.⁴

Recent efforts to improve the outcome of patients with metastatic TCC have focused on the identification of novel drugs with single-agent activity and on their incorporation into platinum-based combination regimens. Paclitaxel, docetaxel, ifosfamide, and gemcitabine are among the most active of the newly identified agents for TCC.^5-9 A large number of phase I and II trials have evaluated these agents in two- and three-drug combination regimens.^10-18 Despite similarities in some of these regimens, there is marked variation in outcome. The response proportions observed with these combinations vary considerably, and median survival times range from 8 to 18 months.^10,11,18 In contrast, median survival for the M-VAC combination is consistently observed at 11 to 13 months.^1-3 Conceivably, the reported efficacy of these new regimens is affected by the pretreatment prognostic features of the treated populations. Because the prevalence of prognostic features may profoundly affect trial outcome, a thorough understanding of these data in a trial population is essential.

Pretreatment prognostic features have an impact on individual patient outcome. Similarly, the prevalence of prognostic features within a treatment population has an impact on trial results. Several analyses have demonstrated that pretreatment prognostic features affect the outcome of patients treated with M-VAC chemotherapy for metastatic TCC.^2,4,19 The prognostic factors found to be most predictive of poor response and survival are bone or liver metastases (determined either by radiographic abnormality or by an elevated baseline alkaline phosphatase level) and poor performance status. The intergroup study by Loehrer et al² reported a median survival of 18.2 months for patients with the most favorable combination of prognostic features, compared with only 4.4 months for patients with the least favorable combination. The impact of prognostic features on survival was further emphasized in a long-term follow-up report from the intergroup trial in which none of the patients with liver or bone metastasis and only one patient with Karnofsky performance status (KPS) less than 80% survived for 6 years.⁴

It is conceivable that the distribution of prognostic factors within a trial population may affect the response and survival characteristics of a phase II cohort of patients. A therapy that has a certain inherent efficacy could result in a more favorable outcome when applied to a cohort of patients with more favorable prognostic features and less efficacy in patients with unfavorable prognostic characteristics. Despite the data showing that pretreatment characteristics affect individual patient outcome, no methodology exists that standardizes these prognostic factors or estimates their impact on the reported survival of new combination chemotherapy in clinical trials. Therefore, modeling survival based on the prevalence of pretreatment prognostic features in a population of patients treated with standard therapy might facilitate comparison of the efficacy of differing regimens.

We hypothesized that a prognostic factor–based model of survival among patients treated with M-VAC could be used to assess the efficacy of novel treatment regimens relative to that observed with M-VAC therapy. Development of this model required several steps. First, to determine which patient characteristics had independent prognostic significance, a Cox proportional hazards model was fit using data from a population of 203 patients with unresectable or metastatic TCC treated on five consecutive trials of M-VAC chemotherapy. Second, the choice of the variables and the parameter estimates were validated internally by generating data using a bootstrap resampling technique. Finally, a table with expected median survival for cohorts of patients who had a varying prevalence of prognostic features was tabulated.

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Patient Population
Two hundred twenty-nine patients were treated on five trials of M-VAC chemotherapy at our institution between 1983 and 1994 (Table 1). One hundred thirty-three patients were treated in our original trial examining the efficacy of standard-dose M-VAC chemotherapy.¹ Twenty-seven patients were treated in a trial examining the utility of recombinant human granulocyte colony-stimulating factor (rhG-CSF) in combination with standard-dose M-VAC.²⁰ Twenty-nine patients were enrolled onto a phase II trial testing methotrexate 1,000 mg/m² and leucovorin rescue with standard-dose VAC.²¹ Twenty-three patients were enrolled in a phase I/II trial examining the feasibility of dose-intense M-VAC using rhG-CSF support.²² Finally, 17 patients in a phase II randomized trial comparing dose-intense M-VAC plus rhG-CSF with gallium nitrate plus fluorouracil received M-VAC as initial therapy.²³ Of these 229 patients, 26 were excluded from the current analysis: three had urothelial tumors of non-TCC histologic origin, four were treated in an adjuvant setting, and 19 had clinical tumor-node-metastasis stages T3bN0 and were treated in the neoadjuvant setting. The remaining 203 patients, with unresectable or metastatic TCC, form the basis of this report.

Inclusion criteria for the five studies were similar. Histologic confirmation of TCC was performed at MSKCC in all cases. Patients with known brain metastasis were excluded from all studies. Adequate renal, hepatic, and cardiac function was required in all cases. In the first study, low KPS was not an exclusion criteria, whereas patients who had KPS less than 60% were excluded from the other four trials. All trials were approved by the institutional review board at MSKCC, and informed consent was given by all patients. Twelve of the 203 patients in our database had received prior systemic treatment. Agents included methotrexate (12 patients), vinblastine (six patients), and cisplatin (three patients). All pretreated patients in our database were enrolled onto the first of the five trials, which was the only one that allowed pretreatment.

Patients received a median of four cycles (range, 1 to 12 cycles) of M-VAC. Response was assessed by re-evaluation of known sites of disease by physical examination, cystoscopy, and radiography after every two cycles of therapy or as clinically indicated. After achieving a maximum response to chemotherapy, selected patients were referred for postchemotherapy surgery for resection of residual carcinoma and assessment of pathologic response.

Survival Analysis
Survival was measured from time of initiation of M-VAC chemotherapy until death or last follow-up. Eighteen clinical features were examined in univariate analysis for their association with survival. The variables considered were alkaline phosphatase, lactate dehydrogenase (LDH), AST, albumin, bilirubin, creatinine, hemoglobin, WBCs, platelets, age at the start of M-VAC therapy, sex, KPS (< 80% v 80%), primary tumor site (bladder v nonbladder), history of resection of the primary tumor site (yes v no), presence or absence of liver, lung or bone metastasis, any site of visceral metastasis (lung, liver, or bone), and presence or absence of lymph node metastasis. Pretreatment laboratory parameters and age were evaluated as both continuous and categorical variables (normal v abnormal). When necessary, log transformations of continuous variables were used to reduce the skewness of their distributions.

Survival distributions were estimated using the Kaplan-Meier method.²⁴ The relationship between survival and each of the variables was analyzed, using the log-rank test for categorical variables and a score test based on the Cox proportional hazards model for continuous variables. This model equates the relative risk of survival with the exponential of a constant times the covariate of interest:

where x is the value of the covariate, t is the time to death or last follow-up, beta is the regression parameter associated with the covariate, h(t,x) is the hazard function for an individual at time t with covariate value x, and h_o(t) is the baseline hazard corresponding to a covariate value 0 (or the average covariate value if x is continuous). This model generalizes to more than one covariate by considering a linear combination of covariates on the right side of the equation. A multivariate model based on a subset of all potential predictors was selected using standard subset selection techniques, such as stepwise selection. A positive estimate for the regression coefficient implied that the risk of death increased with higher values of the variable.

Validation of the Survival Model by Bootstrap Technique
Variable selection and the corresponding parameter estimation of the model were internally validated through a two-step nonparametric bootstrapping process.²⁵ In the bootstrap procedure, the original data set of size N becomes a parent population from which samples of size N are randomly drawn with replacement. In the first step of validation, 300 bootstrap samples were created, a model was fit as in the original modeling, and the percentage of samples for which each variable was included in the model was calculated. Percent inclusion was used to determine the prognostic importance of a variable, as it is expected that an important prognostic variable will be included in the model for a majority of the bootstrap samples. A model was formulated that included variables with a high percentage of inclusion. The variables chosen were then compared with those obtained from the original stepwise modeling, as a confirmation. In the second step, 300 bootstrap samples were again created. For each of the samples, the model with the final variables chosen at the first step was refit, and the regression parameters and risk ratios were estimated. The closeness of these estimates to those obtained in the final Cox model again provided confirmation.

	RESULTS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Patient Characteristics
The distribution of pretreatment clinical characteristics for the 203 patients is shown in Table 2 . The median age at initiation of therapy was 63 years (range, 23 to 83 years), and 80% of patients were male. KPS was available for 199 patients; the median KPS for the entire cohort was 80%, and 41% of patients had a KPS of less than 80%. The primary tumor site was the bladder in 79% of patients, the renal pelvis in 10%, and the ureter or urethra each in 5%. Visceral metastases were relatively prevalent in the cohort, with 49% of patients having lung, liver, or bone disease.

Response to Chemotherapy and Survival Distribution
One hundred ninety-four patients were assessable for response to chemotherapy. Responses included complete responses in 46 patients, partial responses in 84 patients, and no responses or progression in 64 patients. Among the 203 patients, there were six treatment-related deaths.

The median survival time for the entire cohort of 203 patients was 14.8 months (95% confidence interval, 12.1 to 16.2 months) at a median follow-up of 40 months (Fig 1). The 5-year survival rate was 17%, and at the time of this analysis, 19 (9%) of the 203 patients were alive.

View larger version (15K): [in this window] [in a new window]   Fig 1. Survival for 203 patients in the M-VAC database. The dotted lines indicates the 95% confidence interval for survival.

Univariate Survival Analysis
Eighteen variables were subjected to univariate analysis for prognostic significance (Table 3). The following factors were associated with an adverse prognosis: high LDH level, WBC count, alkaline phosphatase level, and platelet count; low hemoglobin level, albumin level, and KPS; and previous surgery to remove the primary tumor, the presence of bone, lung or liver metastasis, and any visceral metastasis. Alkaline phosphatase had univariate significance when considered as a continuous variable but not when considered as normal versus abnormal. The protocol on which a patient was treated, the primary site of the TCC, sex, and age of patient did not have an impact on survival.

Only three of 100 patients with visceral metastasis were alive at the time of the analysis, including two of 53 patients with lung metastasis, one of 52 with bone metastasis, and zero of 26 with liver metastasis (Table 3). Among patients without visceral metastasis, 16 of 103 remained alive. Only two of 81 patients with KPS less than 80% were alive at the time of this analysis, compared with 17 of 118 patients with a baseline KPS of 80%.

Multivariate Survival Analysis
Nine variables with univariate significance were entered into the multivariate regression analysis using the Cox proportional hazards model. These variables were selected because of their statistical significance in univariate analysis and their perceived clinical relevance. These characteristics included KPS (< 80% v 80%), hemoglobin, LDH, alkaline phosphatase, albumin, lung metastasis, liver metastasis, bone metastasis, and any visceral metastasis. All four biochemical parameters were considered in their continuous form. The modeling was done on 199 patients because data for KPS were not available for four patients. Three characteristics were determined to have independent significance for survival in the Cox model: any visceral metastasis, KPS as a discrete variable (< 80% v 80%), and hemoglobin (Table 4). KPS 80%, presence of visceral metastasis, and abnormal hemoglobin were associated with hazard ratios of 1.93, 1.99, and 1.12, respectively (Table 4). KPS and visceral metastasis imparted similar hazard ratios, whereas hemoglobin had a lower hazard ratio and also seemed to reflect clinical information similar to KPS. Therefore, a two-variable model based on KPS and visceral metastasis was chosen.

Internal Validation
Confirmation of the results of the multivariate analysis was undertaken using the bootstrap sampling technique (Table 5). KPS (96%), visceral metastases (79%), and hemoglobin (68%) most frequently emerged as having independent prognostic significance, thereby confirming the Cox model. Parameter estimates at the validation stage were close to the original model and hence are not shown here.

Risk Groups
The model demonstrated marked differences in survival based upon the number of risk factors present in individual patients (Table 6 and Fig 2). The percentage of patients in the M-VAC database belonging to zero-risk, one-risk, and two-risk categories was 32%, 45%, and 23%, respectively. Patients with no risk factors (KPS 80% and no visceral metastasis) had a median survival time of 33 months and a 33% likelihood of 5-year survival. The survival impact of KPS less than 80% and of visceral metastasis was nearly identical. Patients with one risk factor (either KPS < 80% or presence of visceral metastases) had a median survival time of approximately 13 months and an 11% likelihood of 5-year survival. Patients who had both KPS less than 80% and visceral metastases had a median survival time of approximately 9 months and no chance of 5-year survival. There was a significant difference in survival profiles of the three risk groups (P < .0001).

View larger version (17K): [in this window] [in a new window]   Fig 2. Survival for all patients grouped according to number of risk factors present at baseline. Poor risk factors include KPS < 80% and presence of visceral metastasis.

Expected Median Survival Table Interpretation
A clear association between the prevalence of risk factors and survival emerged. Cohorts with higher proportions of patients from the no-risk category had a greater expected median survival than those with a higher proportion in the two-risk category. For example, a cohort with 70% of patients from the zero-risk, 20% from the one-risk, and 10% from the two-risk categories has an expected median survival time of 20.1 months (Table 7). Conversely, the expected median survival time would be 10.9 months for a cohort with 20% of patients from the zero-risk, 20% from the one-risk, and 60% from the two-risk categories. In the M-VAC database, the two-risk category has only 45 patients; hence, to allow for the scenario in which we sample 90% of patients from that category, a maximum sample size of only 50 could be allowed. As a consequence, the confidence intervals are quite wide.

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION REFERENCES

 
This analysis establishes performance status and visceral metastatic sites of disease as independent prognostic factors for predicting survival in patients with metastatic TCC. The impact of low performance status has previously been reported in analyses of phase II studies from our center as well as from the intergroup randomized trial of M-VAC versus cisplatin.^2,19 The presence of visceral disease has also demonstrated prognostic significance in previous analyses, although the specific measure of visceral disease has varied. The presence of lung, liver, or bone metastasis was of independent significance in the current analysis and in the initial intergroup report.² In a long-term follow-up of the intergroup study patients, only liver and bone metastasis retained prognostic significance.⁴ A previous analysis from our institution did not specifically analyze the prognostic impact of visceral metastasis but did determine that elevated alkaline phosphatase, an indirect measure of visceral metastasis to bone or liver, was prognostically important.¹⁹ Thus, lower KPS and the presence of visceral metastases are patient characteristics consistently demonstrated to have independent prognostic significance for survival in patients with metastatic TCC.

A simple model of the impact of prognostic factors on survival was developed which emphasizes the profound survival impact of poor performance status and visceral sites of metastasis. Patients who had no risk factors fared best, with a median survival time of 33.0 months, compared with 13.4 months for patients with one risk factor and 9.3 months for those with two risk factors. Because performance status and baseline visceral metastasis have independent prognostic significance, reporting these patient characteristics in studies examining novel chemotherapy regimens would enhance trial interpretation. For example, a trial of a new regimen reporting a high response proportion and enhanced survival may simply reflect the inclusion of a high proportion of patients with zero or one risk factor. An imbalance of prognostic factors may also affect the outcome of phase III trials. For these reasons, we propose that the proportion of patients with zero, one, and two risk factors should be included in phase II trials and that phase III trials should be stratified accordingly.

The availability of new agents with activity in TCC, such as paclitaxel and gemcitabine, has led to a large number of novel regimens.^10-18 Results obtained in trials of these combinations vary substantially in the reported response rates and survival, making trial interpretation difficult. For example, median survival times for taxane-containing regimens range from 8.5 months to 18 months,^10,11,18 and the differences may not be related to the chemotherapy regimens. Therefore, choosing a regimen for phase III study based solely on enhanced survival may be problematic if the phase II study enlisted a large proportion of good-risk patients.

We sought to address this issue by developing a nomogram of expected median survival times of M-VAC–treated patients based on hypothetical proportions of patients in each risk category for a typical phase II trial. The goal of this exercise was to produce a table enabling informal comparison of survival data from phase II trials of novel combinations in the context of known prognostic factors. The table was developed using bootstrap resampling technique and Kaplan-Meier methodology.

It facilitates comparison of the relative efficacy of M-VAC in phase II studies at our center with novel treatmentregimens in phase II study, on the basis of the prevalence of prognostic factors in the treated populations. Median survivals for cohorts of patients who have a predefined prevalence of risk factors treated with M-VAC are provided. By knowing the distribution of risk factors in a clinical trial, the reader can assess the relative efficacy of a new agent or regimen.

This approach facilitates phase II study interpretation and phase III trial design. It reduces the likelihood of a false interpretation of a regimen studied in a poor-risk population showing a relatively lower median survival compared with another study with more favorable results based on inclusion of patients with more favorable risk factors. The model makes it convenient to select chemotherapy regimens for phase III study. Promising regimens can be chosen for a randomized trial if the reported median survival of the phase II trial population is longer than that expected for conventional therapy for a patient population with similar risk factors.

Although the survival model has been internally validated, its predictive accuracy still needs to be corroborated with an external data set of similarly treated patients. Moreover, median survivals are predicted based on treatment with conventional M-VAC in the 1980s and early 1990s. It is possible that improvements in therapy, including the introduction of new, active agents used in the second- and third-line setting, may prolong survival in contemporary patients.

In summary, we report a cumulative 17% 5-year survival rate from M-VAC studies in our center. Pretreatment prognostic factors significantly affect survival. Patients with a poor KPS and evidence of visceral disease did not experience long-term survival, in contrast to a 33% likelihood of 5-year survival in patients with none of these poor risk features. Lastly, these factors can be used to predict survival and aid in the interpretation and conduct of phase II and III trials.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION REFERENCES

 
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Submitted March 1, 1999; accepted June 10, 1999.

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