Journal of Clinical Oncology, Vol 17, Issue 8
(August), 1999: 2530
© 1999 American Society for Clinical Oncology
Survival and Prognostic Stratification of 670 Patients With Advanced Renal Cell Carcinoma
Robert J. Motzer,
Madhu Mazumdar,
Jennifer Bacik,
William Berg,
Alison Amsterdam,
Joseph Ferrara
From the Genitourinary Oncology Service, Division of Solid Tumor Oncology, Department of Biostatistics and Epidemiology, Memorial Sloan-Kettering Cancer Center; and Department of Medicine, Cornell University Medical College, New York, NY.
Address reprint requests to Robert J. Motzer, MD, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10021
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ABSTRACT
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PURPOSE: To identify prognostic factors and a model predictive for survival in patients with metastatic renal-cell carcinoma (RCC).
PATIENTS AND METHODS: The relationship between pretreatment clinical features and survival was studied in 670 patients with advanced RCC treated in 24 Memorial Sloan-Kettering Cancer Center clinical trials between 1975 and 1996. Clinical features were first examined univariately. A stepwise modeling approach based on Cox proportional hazards regression was then used to form a multivariate model. The predictive performance of the model was internally validated through a two-step nonparametric bootstrapping process.
RESULTS: The median survival time was 10 months (95% confidence interval [CI], 9 to 11 months). Fifty-seven of 670 patients remain alive, and the median follow-up time for survivors was 33 months. Pretreatment features associated with a shorter survival in the multivariate analysis were low Karnofsky performance status (<80%), high serum lactate dehydrogenase (> 1.5 times upper limit of normal), low hemoglobin (< lower limit of normal), high "corrected" serum calcium (> 10 mg/dL), and absence of prior nephrectomy. These were used as risk factors to categorize patients into three different groups. The median time to death in the 25% of patients with zero risk factors (favorable-risk) was 20 months. Fifty-three percent of the patients had one or two risk factors (intermediate-risk), and the median survival time in this group was 10 months. Patients with three or more risk factors (poor-risk), who comprised 22% of the patients, had a median survival time of 4 months.
CONCLUSIONS: Five prognostic factors for predicting survival were identified and used to categorize patients with metastatic RCC into three risk groups, for which the median survival times were separated by 6 months or more. These risk categories can be used in clinical trial design and interpretation and in patient management. The low long-term survival rate emphasizes the priority of clinical investigation to identify more effective therapy.
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INTRODUCTION
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RENAL CELL CARCINOMA (RCC) is the most common tumor arising in the kidney, affecting approximately 30,000 individuals each year in the United States.1,2 The outlook for patients with distant metastases is poor, with a 5-year survival rate of less than 10% for patients presenting with stage IV disease.1,2 This reflects the lack of effective systemic therapy for patients with metastases. RCC is resistant to chemotherapy and hormonal therapy because no agent consistently achieves a response in more than 10% of patients.3 Immunotherapy, ie, interleukin-2 and interferon alpha, achieves responses in 10% to 20% of patients.1 However, the low response rate, toxicity associated with high-dose regimens,4 and few long-term survivors after treatment with interferon-alpha or interleukin-2 provide the rationale for clinical trials as a priority for management of patients with this disease.
Determining prognostic factors of survival for patients with advanced RCC would be valuable in directing therapy and interpreting results of clinical trials. Clinical trials in RCC frequently use biologic agents where responses may be delayed for 3 months or more after the institution of therapy,5 and prospective assessment of patient survival is necessary to determine appropriate eligibility. Response proportions to interferon-alpha, interleukin-2, or combination programs vary considerably among phase II trials,6 implying patient selection is an important factor in achieving a favorable treatment outcome. Clinical trials that include survival as an end point must account for prognostic factors to assure that treatment groups are comparable so that the proper interpretation of trial outcome can be ascertained. Also, an assessment of patient survival benefits both patient and physician in clinical management.
Published analyses of prognostic factors performed in a multivariate analysis have been limited in both the number of series and the number of patients studied.7-12 To define pretreatment features predictive of survival, we performed a retrospective study on 670 patients with advanced RCC treated in successive clinical trials at the Memorial Sloan-Kettering Cancer Center (MSKCC). The results were examined by multivariate analysis, and a model was developed to stratify patients according to risk.
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PATIENTS AND METHODS
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Patients
All patients were treated on MSKCC Institutional Review Board–approved clinical trials conducted between September 1975 and July 1996. The patients were identified through registration on 24 consecutive MSKCC clinical trials; the specific eligibility and treatment programs have previously been described (Table 1).13-33 Eligibility details are described in individual reports but all included histologic confirmation of RCC, stage IV disease with presence of measurable lesions, adequate Karnofsky performance status, lack of severe comorbid conditions, and adequate hematologic, renal, and hepatic function.
Patients entered onto more than one clinical trial were evaluated for this study at the time of entry on their first MSKCC trial. Routine studies at the time of clinical trial entry included the following: detailed history and physical examination, complete blood count, prothrombin and partial thromboplastin times, creatinine, total bilirubin, alkaline phosphatase, AST lactate dehydrogenase, blood urea nitrogen, calcium, total protein, albumin, and imaging studies to assess measurable disease. The majority of patients had a computerized tomography scan of the abdomen and chest to assess extent of disease. Response to treatment, time to progression after systemic therapy, and survival and current status were recorded.
Survival Analysis
The end point of interest was survival time, defined as the time from treatment initiation to the death date or last follow-up date. Clinical features examined included number and sites of metastases (lung, mediastinum, bone, liver, and retroperitoneum), Karnofsky performance status, prior treatment (radiation, chemotherapy, and immunotherapy), prior nephrectomy, the time interval from diagnosis to the start of treatment, and selected baseline biochemical features. The biochemical features were based on a previous analysis and consisted of hemoglobin, serum albumin, alkaline phosphatase, lactate dehydrogenase, and total calcium concentrations.34 To separate out the effects of protein binding and assess free calcium, an adjustment formula was used: "Corrected" calcium = total calcium - 0.707 [albumin-3.4].35 The "corrected" calcium value was used in the survival analyses.
Survival distributions were estimated using the Kaplan-Meier method.36 The relationship between survival and each of the variables was analyzed using the log-rank test37 for categorical variables and a score test based on Cox proportional hazards regression model38 for continuous variables. Bivariate relationships among the variables were explored to better understand how the variables interacted and how these interactions related to survival. There were few missing values for any of the variables (no more than 2%), and in all analyses, case deletion was used to handle the missing values. When necessary, a logarithmic transformation was used to reduce skewness.
Two types of exploratory plots were used to display the functional relationship between continuous covariates (eg, lactate dehydrogenase and hemoglobin) and patient survival. The first was the running median survival time plot,39 which divided the covariate values into overlapping intervals, calculated the Kaplan-Meier–based median survival time for corresponding patients, and plotted these median survival times against the midpoint of the intervals. The second was the predictive failure time plot,40 which plotted the predicted median survival time based on a Cox regression model against each of the observed covariate values. These two plots are more descriptive of the relationship between a continuous covariate and survival time than a Kaplan-Meier plot. They allow the risk of death to vary according to the value of the covariate instead of assuming that all individuals in one group are at an equivalent risk of death.
Multivariate Model
Using a significant relationship with survival as criteria for including a variable in the stepwise modeling procedure, seven variables were retained and entered into a multivariate model. Because this retrospective study included patients in clinical trials from 1975 through 1996, whose treatment included both cytotoxic therapy and immunotherapy, a stratified Cox proportional hazards model41 was used to account for differences in the year of treatment and the type of therapy. This model states that the hazard or risk of death at time t for a patient in strata j with variables x = (x1j, x2j, . . . , xpj) is
where  0j(t) is the baseline hazard function for strata j and ß1, ß2, . . . , ßp are the regression coefficients. According to this model, when the regression coefficient is positive, then the risk of death increases with higher values of the variable. When the regression coefficient is negative, the risk of death decreases with higher values of the variable. Using a stepwise modeling algorithm with a .15 significance level for entering and removing explanatory variables, the independent risk factors were determined and the model was formed.
Because it was desired to dichotomize the continuous variables chosen in the modeling for ease of clinical use, a minimum P value approach as well as the above exploratory plots were used to perform a cut point analysis.42 In the minimum P value approach, selected values of the prognostic factor are examined as candidates for the cut point. The value is chosen that best separates patient outcomes according to a maximum  2 statistic and minimum P value or a maximum relative risk. The P value is adjusted to account for the problem of multiple testing. The running median survival time plot and predicted failure time plot were used to restrict a region for the cut point search. Laboratory information about biologic cut points coupled with the information from the statistical techniques guided the decision about which cut point to use for each of the variables. It was verified that the relationship between survival and the prognostic factor remained significant when the variable was dichotomized.
The categorical counterparts of the risk factors determined in the model were used to assign each patient to one of three risk groups: those with zero risk factors (favorable-risk), those with one or two (intermediate-risk), and those with three or more (poor-risk). Survival curves for each of these groups were estimated, and the groups were compared using the log-rank test.
Validation of Model by Bootstrap Technique
The predictive performance of the model was internally validated through a two-step nonparametric bootstrapping process.43 In the bootstrap procedure, the original set of data of size N becomes a parent population from which samples of size N are randomly drawn with replacement. In the first step of internal validation, the bootstrapping technique was used for variable selection. Two hundred bootstrap samples were created, and a stepwise procedure was applied to each sample using the same significance level for entering and removing a variable as in the original model. From this analysis, the percentage of samples for which each variable was included in the model from the 200 samples was calculated. Percent inclusion was used to determine the prognostic importance of a variable because it was expected that a prognostically important variable would be included in the model for a majority of the bootstrap samples. A model was formulated that included all variables whose percent inclusion was greater than or equal to 65%.44 The models obtained from the stepwise modeling algorithm and the bootstrapping technique were compared.
In the second internal validation step, the bootstrap was used for parameter estimation. Three hundred bootstrap samples were created, and, for each of the samples, the model with the five final variables was refit and the regression parameters and risk ratios were estimated. The sample mean and SD of the 300 risk ratios for each parameter were computed and used to formulate confidence intervals about the risk ratio. These estimates were compared with those quantities obtained in the final Cox model.
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RESULTS
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Patient Characteristics and Treatment
The median age of the patient group was 58 years; 67% were male (Table 2). Sixty-five percent had undergone a prior nephrectomy, 61% had two or more sites of metastases, 22% had received prior radiation therapy, and 18% had received prior immunotherapy or cytotoxic chemotherapy. Thirty-seven percent of patients had an interval from diagnosis to treatment of 1 year or more. Six hundred eight patients (91%) were treated at MSKCC, whereas 62 (9%) were treated at an outside hospital on an MSKCC trial. Treatment consisted of immunotherapy in 396 patients (59%) and chemotherapy (or hormonal therapy) in 274 patients (41%) (Table 1). With regard to immunotherapy, 294 patients were treated with interferon alpha, 68 patients with interleukin-2a, and 34 patients with a combination program. The overall response rate for the 670 patients was 12.5%, which included 10 complete responses and 41 partial responses.
Survival Distribution
The median overall survival time was 10 months (95% confidence interval [CI], 9 to 11 months) (Fig 1). Fifty-seven (8%) of the 670 patients remained alive and the median follow-up time for the survivors was 33 months (range, 0.9 to 187 months). The percentage of patients surviving at 1 year was 42%; the 2- and 3-year survival percentages were 20% and 11%, respectively.
Univariate Survival Analysis
Factors considered in the univariate analyses included number and site of metastases, prior therapy, Karnofsky performance status, and baseline biochemical parameters (Tables 3 and 4). Factors associated with an adverse prognosis included presence of hepatic metastasis, two or more sites of metastases, a Karnofsky performance status less than 80, prior radiation or chemotherapy, lack of prior nephrectomy, and a time interval from disease diagnosis to treatment of less than 1 year. The median survival time according to Karnofsky performance status was 2.7 months for 60%, 6.1 months for 70%, 10.6 months for 80%, and 14.4 months for 90% (P < .0001).
The first two columns of Table 4 list parameter estimates and P values for testing the association of each biochemical parameter (in its continuous form) with survival. The negative regression coefficients on Karnofsky performance status, serum albumin, and hemoglobin concentrations indicate that, as the values of these three covariates increased, the risk of death decreased. The positive regression coefficients on the other variables indicate that the risk of death increased as the value of the covariate increased. The biochemical parameters found to be significant for an adverse prognosis included low serum albumin, elevated serum alkaline phosphatase, low hemoglobin, an elevated serum lactate dehydrogenase level, and a high corrected serum calcium level. For lactate dehydrogenase, a logarithmic transformation was used to reduce skewness.
The effect on survival of the treatment year and program was evaluated (Table 5). Patients were classified according to treatment with immunotherapy, ie, interferon alpha and/or interleukin-2a, versus chemotherapy (cytotoxics or hormonal therapy) and according to when they received treatment (1975 to 1980, 1981 to 1990, 1991 to 1996). Survival was greater for patients treated with immunotherapy (P < .0001) and for patients treated in more recent years (P < .0001). To account for these effects and to develop a prognostic model based on pretreatment features, type and year of treatment were included as strata in the multivariate survival analysis.
Multivariate Survival Analysis
The seven variables included in the multivariate analysis were hemoglobin, serum lactate dehydrogenase, corrected calcium, prior nephrectomy, Karnofsky performance status, hepatic metastases, and the interval from diagnosis to treatment. Using a .15 significance level for entering and removing explanatory variables, it was determined that hemoglobin, lactate dehydrogenase, corrected calcium, nephrectomy, and Karnofsky performance status were independent risk factors predicting survival.
Cut Point Analysis
For hemoglobin and corrected calcium, the cut point suggested by the minimum P value approach matched the upper/lower limit of normal. For these variables, the running median survival time plot and predicted failure time plot were continuously increasing or decreasing (monotonic) and did not indicate any obvious cut point. For lactate dehydrogenase, these two plots suggested that its relationship to survival was essentially monotonically decreasing, but that a cut point search could be restricted to values between 100 and 500 U/L (Figs 2A and 2B). The graph from the minimum P value approach showed a peak at 319 U/L with a maximum 2 value of 153.8 and a minimum P value of < .0001 (Fig 3). The adjusted P value remained significant; therefore, serum lactate dehydrogenase was categorized at the value of 300 U/L (1.5 times upper limit of the normal value).
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Fig 2. (A) Running median survival time plot and (B) predicted failure time plot for serum lactate dehydrogenase concentration.
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The last three columns of Table 4 list the cut points chosen for each of the continuous variables along with the results of the univariate survival analysis for the dichotomized versions of the variables. The magnitudes of the 2 and the corresponding risk ratio illustrate the magnitude of the covariate's effect on survival. For example, the risk ratio for lactate dehydrogenase was 3.3. This indicates that a patient with a lactate dehydrogenase value greater than 300 U/L was 3.3 times more likely to die than a patient with a value less than 300 U/L. All dichotomized biochemical parameters were significant at the .0001 level.
Risk Groups
Low Karnofsky performance status (< 80%), high lactate dehydrogenase (> 1.5 times upper limit of normal), low serum hemoglobin (< lower limit of normal), high corrected serum calcium (> 10 mg/dL), and absence of nephrectomy were risk factors. Using the dichotomized versions of these variables, a Cox model was fit (Table 6). Each patient was then assigned to one of three risk groups: those with no risk factors (favorable-risk), those with one or two risk factors (intermediate-risk), and those with three or more risk factors (poor-risk).
The median time to death in 25% of patients deemed favorable-risk was 20 months, and the 1-, 2-, and 3-year survival rates were 71%, 45%, and 31%, respectively. Fifty-three percent of the patients were in the intermediate-risk group. The median survival time for this group was 10 months, with 1-, 2-, and 3-year survival rates of 42%, 17%, and 7%, respectively. In contrast, the poor-risk group, which comprised 22% of the patients, had a median survival time of 4 months, with 1-, 2-, and 3-year survival rates of 12%, 3%, and 0%. Only one poor-risk patient remained alive at the time of last follow-up. There was a significant difference in the survival profiles of the three risk groups (P < .0001) (Fig 4).
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Fig 4. Survival stratified according to risk group (n = 656; 14 patients missing one or more of the five risk factors were excluded). Vertical lines indicate last follow-up.
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Type and year of treatment were included as strata in the multivariate survival analysis. When patients were stratified according to risk, the median survival time was greater in each of the three risk groups for patients treated in more recent years versus those treated earlier. The median survival time was also greater for patients treated with immunotherapy versus those treated with chemotherapy. For patients treated with immunotherapy (interferon and/or interleukin-2), the median survival times for favorable-risk, intermediate-risk, and poor-risk patients were 26 months, 12 months, and 6 months, respectively.
Bootstrap Validation
In the bootstrap procedure, the original set of data of size N becomes a parent population from which samples of size N are randomly drawn with replacement. The bootstrap samples are then treated as if they come from the true distribution of advanced RCC patients, and inferences about the risk ratios for each covariate are based on the empirical distribution of the risk ratios. For the first step of validation, five of the seven variables had a percent inclusion greater than 65% (Table 7). These were hemoglobin, lactate dehydrogenase, corrected calcium, prior nephrectomy, and Karnofsky performance status. The results of this model selection technique confirmed the variables chosen in the original modeling procedure.
In the second step of validation, a risk ratio with a 95% confidence interval was estimated for each covariate in the final model. Risk ratios (Table 8) were similar to those obtained in the original multivariate model (Table 6). For example, the risk ratio for Karnofsky performance status from the bootstrap procedure was 1.53 (1.20 to 1.85), whereas in the original model it was 1.50 (1.24 to 1.81). The results of these two steps provide evidence of the predictive ability of the final model.
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DISCUSSION
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This study resulted in a model based on five pretreatment clinical features that predicted survival for patients with advanced RCC. Risk factors associated with a shorter survival period were low Karnofsky performance status (< 80%), high lactate dehydrogenase (> 1.5 times upper limit of normal), low serum hemoglobin (< lower limit of normal), high corrected serum calcium (> 10 mg/dL), and absence of prior nephrectomy. These risk factors were used to stratify patients into three different groups. Three-year survival percentages for the favorable-risk (no risk factors), intermediate-risk (one or two risk factors), and poor-risk (three or more risk factors) groups were 31%, 7%, and 0%, respectively.
Validation was performed by the bootstrap method.43 Repeated sampling of the original data with replacement allowed independent samples of RCC patients to be generated from which the predictive accuracy of the model was assessed. In addition, we have applied the prognostic model to an external data set taken from a trial by Eastern Cooperative Oncology Group.32 The external group was composed of 175 patients treated on a randomized trial of interferon-alpha with or without 13-cis-retinoic acid. In this group, the median survival times of favorable-, intermediate-, and poor-risk patients were 29, 14, and 4 months, respectively.
There are few reports of prognostic factors studied by multivariate analysis in patients with metastatic RCC.7-11,45-50 The prognostic factors vary among the studies but consistently include performance status, nephrectomy, and a measure of extent of disease. A summary of multivariate analyses resulting in criteria for risk stratification is listed in Table 9.7-12
A study by Elson et al7 contained a number of patients similar to the retrospective study in this article. The population was composed of 610 patients treated with chemotherapy on phase II trials between 1975 and 1984.7 The lack of patients treated with immunotherapy in this analysis7 is seen by some as a present-day limitation.9 The model stratified patients into five categories with a difference in median survival time of as little as 1.3 months between groups and included subjective criteria of "weight loss in previous 6 months" as a component. Today the patient population is different from that of Elson et al's7 study, reflecting improvement in imaging techniques and selection factors used to choose patients with RCC exclusively for phase II trials of cytotoxic agents. The median survival time for all patients treated in that series was 5.6 months,7 compared with 10 months in the present series. Also, the median survival time in the most favorable risk group was 12.8 months, which comprised 18% of the entire group,7 compared with a median survival time of 20 months for favorable-risk patients who participated in the study reported in this article.
The model reported in this article categorized patients into three distinct groups with median survival times of 20, 10, and 4 months. The criteria was based on history of nephrectomy, performance status obtained at physical examination, and assessment of hemoglobin, lactate dehydrogenase, calcium, and albumin (to assess corrected calcium) performed as routine blood tests. Nephrectomy was not performed for the purpose of cytoreduction before the start of systemic therapy. The patient population was selected by fulfilling individual protocol eligibility criteria. For example, patients with brain metastases were excluded. However, the experience was comprehensive, represented a 21-year effort at our center, and included patients treated on clinical trials with cytotoxic, hormonal, and immunotherapies.
In conclusion, five prognostic factors for predicting survival were identified in patients with stage IV RCC selected for clinical trials and used to categorize patients into three risk groups, for which the median survival times were separated by 6 months or more. The 2-year survival rates for patients meeting favorable-, intermediate-, and poor-risk criteria were 45%, 17%, and 3%, respectively. These risk categories can be used in clinical trial design and interpretation, as well as in clinical management. The low percentage of patients achieving long-term survival emphasizes the priority of clinical investigation to identify more effective therapy.
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ACKNOWLEDGMENTS
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Supported in part by National Institutes of Health grants no. CM-57732 and CA-05826
We thank Martin Fleisher, PhD, and Carol Pearce for their review of the manuscript.
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Submitted January 13, 1999;
accepted April 12, 1999.
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TOP
ABSTRACT
INTRODUCTION
