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Journal of Clinical Oncology, Vol 25, No 18 (June 20), 2007: pp. 2601-2606 © 2007 American Society of Clinical Oncology. DOI: 10.1200/JCO.2006.08.1661 Prognostic Factors for Survival in Adult Patients With Recurrent Glioma Enrolled Onto the New Approaches to Brain Tumor Therapy CNS Consortium Phase I and II Clinical Trials
From the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health and The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD; and the Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, NC Address reprint requests to Kathryn A. Carson, ScM, c/o The NABTT CNS Consortium, Cancer Research Building #2, 1550 Orleans St, Room 1M-16, Baltimore, MD 21231; e-mail: jfisher{at}jhmi.edu
Purpose: Prognostic factor analyses have proven useful in predicting outcome in patients with newly diagnosed malignant glioma. Similar analyses in patients with recurrent glioma could affect the design and conduct of clinical trials substantially. Patients and Methods: Between 1995 and 2002, 333 adults with recurrent gliomas were enrolled onto 10 phase I or II trials of systemic or local therapy. The studies had similar inclusion criteria and were conducted within the New Approaches to Brain Tumor Therapy CNS Consortium. Ninety-three percent of the patients have died. Cox proportional hazards (PH) regression and recursive partitioning analysis (RPA) were performed to identify prognostic factors.
Results: Factors associated with an increased risk of death were increased age, lower Karnofsky performance score (KPS), initial and on-study histologies of glioblastoma multiforme (GBM), corticosteroid use, shorter time from original diagnosis to recurrence, and tumor outside frontal lobe. The final PH model included initial histology of GBM (relative risk [RR] = 2.01), 10-year increase in age (RR = 1.23), KPS less than 80 (RR = 1.54), and corticosteroid use (RR = 1.49). RPA resulted in seven classes. Median survival time was poorest in non-GBM patients with KPS less than 80 or GBM patients, age Conclusion: Initial histology, age, KPS, and corticosteroid use are prognostic for survival in recurrent glioma patients. To allow comparisons across phase II trials, enrollment criteria may need to be restricted.
The prognosis for patients with recurrent glioma is poor and few therapies have been found to be efficacious.1-3 Novel therapies for this patient population are tested frequently in uncontrolled or historically controlled phase II trials with the assumption that the patients in these trials are homogeneous. However, the patients may differ on several characteristics, including age, performance status, initial or on-study histology, time from initial diagnosis to recurrence, location of tumor and whether it is resectable, number and type of prior therapies, and use of concomitant medications (eg, corticosteroids, anticonvulsants). Prognostic factor analyses have proven useful in predicting outcome in patients with newly diagnosed malignant glioma. Factors such as age, Karnofsky performance score (KPS), and extent of resection have been shown to be prognostic for survival in newly diagnosed patients in the Radiation Therapy Oncology Group recursive partitioning analysis (RPA).4 Given that these prognostic factors have more impact on survival than the currently available therapies, randomized prospective clinical trials in newly diagnosed high-grade gliomas are now stratified to account for these differences. Likewise, phase II studies in this patient population are analyzed using published RPA data so results can be compared fairly with other uncontrolled trials. If this were not done, studies enrolling patients with more favorable prognostic factors would appear superior even if the therapy were ineffective. Few studies have examined prognostic factors in patients with recurrent high-grade gliomas and the findings have not been consistent. Given that most novel agents and approaches are studied in patients with recurrent high-grade gliomas before they are administered to patients who have just been diagnosed, a similar RPA analysis in patients with recurrent disease would be extremely important in deciding which novel therapies should be pursued and which should be abandoned. The objective of our study was to determine whether baseline demographic and clinical characteristics are prognostic for survival in patients with recurrent glioma using RPA.
Between 1995 and 2002, 333 adult patients were enrolled onto 10 phase I or II clinical trials for the treatment of recurrent glioma conducted within the New Approaches to Brain Tumor Therapy (NABTT) CNS Consortium.5 These trials are described in Appendix Table A1 (online only). Six of the trials involved systemic chemotherapy treatment: 9-aminocamptothecin (9-AC),6 suramin,7 phenylbutyrate,8 aprinocarsen,9 irinotecan,10,11 and oral procarbazine.12 The other four were trials of local chemotherapy or brachytherapy: carmustine wafer (Gliadel wafer, Guilford Pharmaceuticals; Baltimore, MD),13 the alternative temporary brachytherapy technique, GliaSite (CYTYC, Palo Alto, CA) Radiation Therapy System (RTS),14 Onyx-015 (Onyx Pharmaceuticals, Redmond, CA),15 and carmustine wafer and intravenous O6-benzylguanine.16 The trials were approved by the Cancer Therapy Evaluation Program at the National Cancer Institute and the institutional review boards at each NABTT CNS Consortium participating site. The sites that participated in at least one of these trials were the University of Alabama (Birmingham, AL), Brown University (Providence, RI), the Cleveland Clinic (Cleveland, OH), Columbia University (New York, NY), Emory University (Atlanta, GA), Henry Ford Hospital (Detroit, MI), the Johns Hopkins University (Baltimore, MD), Massachusetts General Hospital (Boston, MA), Moffitt Cancer Center (Tampa, FL), Northwestern University (Chicago, IL), the University of Pennsylvania (Philadelphia, PA), the University of Texas at San Antonio (San Antonio, TX), Wake Forest University (Winston-Salem, NC), and Washington University (St. Louis, MO). None of the treatments in the phase II trials were found to be efficacious.
The studies had similar inclusion criteria. Eligible patients were at least 18 years of age, had histologically proven malignant glioma (anaplastic astrocytoma (AA), anaplastic oligodendroglioma, or glioblastoma multiforme [GBM]), had progression or recurrence, and had a KPS
Prognostic Factors Twelve patients were enrolled onto two of the studies and are included in the analysis twice. However, the demographic, clinical characteristics, and survival data are specific to the respective study, and therefore these are treated as independent observations. Although this may not be ideal, excluding the patients’ data from either or both of the studies also could introduce a bias.
Survival
Statistical Considerations The comparability of survival times for each protocol was assessed in two ways. First, the hazard rate, calculated as the number of deaths divided by the total patient-years of follow-up, and 95% CI were computed for each protocol. Then Cox proportional hazards (PH) regression analysis19 was used to compare survival from each protocol with the combined data from the other protocols. Univariate PH regression analysis was performed to identify possible prognostic factors associated with an increased risk of death. Factors identified as being possible predictors in the univariate analysis, defined as P < .20, were then included in a backward stepwise multivariate analysis. The least significant factor was dropped from the model, and the model was refit. This process was repeated until only significant factors (P < .05) remained. Because some factors had missing data, a forward analysis was also performed to confirm the final model. RPA was performed based on the log-likelihood ratios from the PH regressions.20 Data were partitioned on the factor with the greatest log likelihood, then univariable PH regressions were refit on the partitioned data. This process continued until either no more factors were significant at the P < .05 level or the remaining sample size was less than 20. Kaplan-Meier survival estimates21 were obtained for each of the RPA classes and tested for inequality with the Wilcoxon test. Amalgamation of classes with similar survival times was then performed and the curves were plotted. CIs were calculated using standard methods. Analyses were performed using SAS version 9 (SAS Institute, Cary, NC). All reported P values are two sided.
Demographic and clinical characteristics of the patients by trial are presented in Table 1 and for all patients combined in Table 2. The hazard rate and 95% CI for each protocol are shown in Figure 1. Only one protocol was significantly different from the others for survival on PH regression analysis. The phase II study of aprinocarsen exhibited an increased risk when compared with the combined data from the other trials (risk ratio [RR] = 1.62, 95% CI, 1.02 to 2.56). Grossman et al9 propose that the integrity of the blood-brain barrier could be a possible reason for this increased risk of death. However, there were only 21 patients in this trial and differences in known prognostic factors, such as KPS and histology, cannot be ruled out as the cause. Therefore, we included these patients in our primary analysis reported here. We then excluded these patients, repeated the analysis, and obtained nearly identical results.
The results of the univariate PH regression analysis are listed in Table 3. The factors that were at least marginally associated with an increased risk of death (P < .20) included increased age, lower KPS, initial and on-study histologies of GBM, corticosteroid use, shorter time from initial diagnosis, and tumor located outside of the frontal lobe. These factors were included in the multivariate PH regression analysis. The final multivariate regression model included initial histology of GBM versus other (RR = 2.01; 95% CI, 1.50 to 2.70; P < .0001), decade increase in age (RR = 1.23; 95% CI, 1.10 to 1.37; P = .0002), KPS of 60 to 70 versus 80 to 100 (RR = 1.54; 95% CI, 1.18 to 2.00; P = .001), and corticosteroid use (RR = 1.49; 95% CI, 1.13 to 1.96; P = .005).
The RPA, displayed in Figure 2, resulted in seven terminal nodes (or classes). The first partition was initial histology. For the non-GBM patients, the next split was KPS, dichotomized at less than 80 and  80. For the partition with poorer KPS, no additional variables were prognostic for survival. For the better KPS group, an additional partition of tumor location was prognostic. For the GBM patients, age dichotomized as younger than 50 and 50 years was the next split. KPS, dichotomized as less than 90 and 90 was the final partition for patients younger than age 50 years. For patients who were 50 years of age, corticosteroid use was the final partition.
Median survival time for these seven RPA classes ranged from 3.8 to 25.7 months. The survival times for class 3 compared with class 7 and for class 5 compared with class 6 were not significant on the Wilcoxon test for inequalities, so these classes were combined and the resulting Kaplan-Meier survival curves were plotted (Fig 3). The median overall survival for all patients was 7.0 months (95% CI, 6.2 to 8.0). Median survival was poorest in non-GBM patients with KPS less than 80 or GBM patients, age 50 years, and taking corticosteroids (4.4 months; 95% CI, 3.6 to 5.4), and was best in patients with initial histology other than GBM with KPS 80 and tumor confined to the frontal lobe (25.7 months; 95% CI, 18.7 to 52.5 months).
The RPA analysis selected different prognostic factors for the GBM and non-GBM strata. To validate this finding, we stratified the patients by initial histology and ran separate PH models on each stratum. For the non-GBM stratum, the final multivariate PH model included KPS of 60 to 70 versus 80 to 100 (RR = 3.38; 95% CI, 1.94 to 5.86; P < .0001), age 60 versus younger than 60 (RR = 2.45; 95% CI, 1.16 to 5.17; P = .02) and tumor outside frontal lobe versus confined to frontal lobe (RR = 2.04; 95% CI, 1.09 to 3.82; P = .03). This model contains all of the factors that were significant in the RPA and the additional prognostic factor of age dichotomized at 60 years. For the GBM stratum, the final PH model included age 50 versus younger than 50 years (RR = 1.51; 95% CI, 1.13 to 2.03; P = .006), 10 point increase in KPS (RR = 1.13; 95% CI, 1.01 to 1.27; P = .04) and corticosteroid use (RR = 1.42; 95% CI, 1.01 to 1.99; P = .04). The factors in this model were the same as in the RPA, although the cut points were different for age and KPS. Tumor grade was available for 94 of the 98 patients who had initial histology other than GBM; 17 (18%) were low-grade and 77 (82%) were high-grade gliomas. All 17 of the patients with low-grade glioma had died and 47% of them had on-study histology of GBM. The percentage of patients with low-grade glioma at initial histology in RPA class 1 was 26%, 16% in RPA class 2, and 18% in RPA class 3. Although a large number of factors were tested in the RPA analysis, the significance level was set at P < .05. If a more stringent requirement of P < .01 were used, most of the partitions would remain. Only the partition of tumor location (P = .03) in the non-GBM stratum and the partition of KPS (P = .03) for the GBM, age younger than 50 years stratum would not have been made. The comparison of the RPA nodes would reflect these same results: RPA classes 1 and 2 would not be significantly different (P = .03), and the RPA classes 2 and 4 would not be significantly different (P = .02).
Our data suggest that patients with recurrent gliomas entering clinical trials have widely variable outcomes based on baseline demographic and clinical characteristics. The most significant of these prognostic factors are initial histology, KPS, age, and corticosteroid use. The differences seen in the median survival time between our seven RPA classes are larger than the treatment effects being evaluated in many studies. RPA classes 1 and 2, for which the initial histology was other than GBM and KPS was 80, consisted of 20% of the patients in our database. Their median survival time was greater than 17 months. Compared with the 7.0-month median survival time for our entire cohort of patients, this survival difference is quite large. If the analysis were restricted to those patients whose initial diagnosis was GBM, the median survival times for the nodes varies from 4.9 to 10.4 months. Many phase II studies in recurrent glioma restrict enrollment to GBM or GBM and AA, but they do not restrict enrollment to those who had an initial diagnosis of high-grade glioma. Our results suggest that this may be inappropriate if the goal is to have a relatively homogeneous population that could be compared with other similarly constructed trials. Although a few of the studies in the present analysis allowed on-study diagnoses of anaplastic oligodendroglioma, there were relatively few patients with this diagnosis. Excluding these patients from the analysis did not reduce the median survival time for RPA nodes 1 and 2 by more than 0.3 months. The finding of age, KPS, and extent of resection being prognostic for survival in recurrent glioma is not unexpected. They have been shown to be powerful prognostic factors for survival in newly diagnosed patients using RPA.4 Other studies suggest that corticosteroid dose was prognostic for survival in newly diagnosed gliomas.22-24 The study by Hohwieler Schloss et al22 included patients with all histologic grades and found that the 15 patients with less corticosteroid dependency had a median survival time of 29 months compared with 5 months for the 29 patients with greater corticosteroid dependency. Nevertheless, very few studies have directly addressed prognostic factors in recurrent gliomas. Wong et al25 performed RPA in recurrent glioma, using histology at recurrence as a prognostic factor and limiting the sample to those with recurrent GBM or AA. Similar to our study, histology was the first split in their RPA, and for the AA subgroup, the next split was KPS. However, they had no additional significant splits in their analysis. Perhaps that is due to the younger age of their patients (median 45 years compared with median 50 years in the present study), and that they were testing age dichotomized at 40 years. A second study found no association of tumor grade, and only KPS was prognostic for survival in recurrent glioma.26 Our study focused on prognostic factors related to survival rather than on progression-free survival (PFS). There are several reasons for this. First, the NABTT CNS Consortium studies included in this analysis had formal end points that were either response rate or survival. Second, the survival end point is unequivocal in contrast to the frequently used 6-month PFS. This measure uses bimonthly MRIs, which are a measure of blood-brain barrier dysfunction rather than true tumor size. Furthermore, it is subjective, susceptible to manipulations of glucocorticoid dose or therapies (which may increase or decrease blood-brain barrier dysfunction), and has an artificial 6-month cutoff even if in retrospect that patient had been experiencing slow progression. Finally, given the huge effect that prognostic factors have on survival in patients with recurrent gliomas as shown in this study, it is highly likely that the same results would apply to PFS as Wong et al25 reported in their study. There are a few limitations to our study. First, the resulting algorithm has not been validated in a different data set. We plan to validate the algorithm when sufficient patients have been treated on other phase I and II NABTT recurrent glioma trials. Second, no data were collected on subsequent therapies that patients received after going off the study included here. However, this is frequently the case in many trials, and thus contributes to the generalizability of the results. Data from both phase I and II studies were included. However, none of the phase II studies were found to be efficacious, and we first compared the survival times for each study. Lastly, there is some variation in the patient eligibility criteria between studies. Phase II trials play a critical role in the assessment of novel therapeutic approaches in patients with high-grade gliomas. Given that it would be far too costly and inefficient to conduct large, randomized, prospective trials of all novel treatment concepts, estimated response rates, 6-month PFS, and overall survival from phase II trials are compared to decide which therapies should be studied further. From the data presented in this article, it is clear that patients with recurrent gliomas have strikingly different prognoses depending on their initial histology, age, KPS, and corticosteroid use. The RPA data from this study will permit investigators to design studies with more homogeneous patient populations or to adjust outcome data retrospectively, thereby improving investigators’ abilities to compare outcomes across phase II studies appropriately.
The author(s) indicated no potential conflicts of interest.
Conception and design: Kathryn A. Carson, Stuart A. Grossman, Joy D. Fisher, Edward G. Shaw Administrative support: Joy D. Fisher Provision of study materials or patients: Stuart A. Grossman, Joy D. Fisher, Edward G. Shaw Collection and assembly of data: Kathryn A. Carson, Stuart A. Grossman, Joy D. Fisher, Edward G. Shaw Data analysis and interpretation: Kathryn A. Carson, Stuart A. Grossman, Joy D. Fisher, Edward G. Shaw Manuscript writing: Kathryn A. Carson, Stuart A. Grossman, Joy D. Fisher, Edward G. Shaw Final approval of manuscript: Kathryn A. Carson, Stuart A. Grossman, Joy D. Fisher, Edward G. Shaw
Supported by NABTT Grant No. CA62475. Presented in part at the 41st Annual Meeting of the American Society of Clinical Oncology, May 13-17, 2005, Orlando, FL. Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.
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Grossman SA, Fisher JD, Piantadosi S, et al: The New Approaches to Brain Tumor Therapy (NABTT) CNS Consortium: Organization, objectives, and activities. Cancer Control 5:107-114, 1998[Medline] 6. Hochberg F, Grossman SA, Mikkelsen T, et al: Lack of efficacy of 9-aminocamptothecin in adults with newly diagnosed glioblastoma multiforme and recurrent high-grade astrocytoma: NABTT CNS consortium. Neuro-oncol 2:29-33, 2000[Abstract] 7. Grossman SA, Phuphanich S, Lesser G, et al: Toxicity, efficacy, and pharmacology of suramin in adults with recurrent high-grade gliomas. J Clin Oncol 19:3260-3266, 2001 8. Phuphanich S, Baker SD, Grossman SA, et al: Oral sodium phenylbutyrate in patients with recurrent malignant gliomas: A dose escalation and pharmacologic study. Neuro-oncol 7:177-182, 2005[Abstract] 9. Grossman SA, Alavi JB, Supko JG, et al: Efficacy and toxicity of the antisense oligonucleotide aprinocarsen directed against protein kinase C-alpha delivered as a 21-day continuous intravenous infusion in patients with recurrent high-grade astrocytomas. Neuro-oncol 7:32-40, 2005[Abstract] 10. Gilbert MR, Supko JG, Batchelor T, et al: Phase I clinical and pharmacokinetic study of irinotecan in adults with recurrent malignant glioma. Clin Cancer Res 9:2940-2949, 2003 11. Batchelor TT, Gilbert MR, Supko JG, et al: Phase 2 study of weekly irinotecan in adults with recurrent malignant glioma: Final report of NABTT 97-11. Neuro-oncol 6:21-27, 2004[Abstract] 12. Grossman SA, Carson KA, Batchelor TT, et al: The effect of enzyme-inducing antiseizure drugs on the pharmacokinetics and tolerability of procarbazine hydrochloride. Clin Cancer Res 12:5174-5181, 2006 13. 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Hohwieler Schloss M, Freidberg SR, Heatley GJ, et al: Glucocorticoid dependency as a prognostic factor in radiotherapy for cerebral gliomas. Acta Oncol 28:51-55, 1989[Medline] 23. Odrazka K, Petera J, Kohlova T, et al: Prognostic impact of hemoglobin level prior to radiotherapy on survival in patients with glioblastoma. Strahlenther Onkol 179:615-619, 2003[CrossRef][Medline] 24. Gundersen S, Lote K, Hannisdal E: Prognostic factors for glioblastoma multiforme: Development of a prognostic index. Acta Oncol 35:123-127, 1996 (suppl 8) 25. Wong ET, Hess KR, Gleason MJ, et al: Outcomes and prognostic factors in recurrent glioma patients enrolled onto phase II clinical trials. J Clin Oncol 17:2572-2578, 1999 26. Eagan RT, Scott M: Evaluation of prognostic factors in chemotherapy of recurrent brain tumors. J Clin Oncol 1:38-44, 1983[Abstract] Submitted July 12, 2006; accepted April 4, 2007. This article has been cited by other articles:
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Copyright © 2007 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
