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Progression-Free Survival Is a Surrogate for Survival in Advanced Colorectal Cancer

Marc Buyse, Tomasz Burzykowski, Kevin Carroll, Stefan Michiels, Daniel J. Sargent, Langdon L. Miller, Gary L. Elfring, Jean-Pierre Pignon, Pascal Piedbois

From the International Drug Development Institute, Louvain-la-Neuve; Center for Statistics, Hasselt University, Diepenbeek, Belgium; Oncology Therapy Area, AstraZeneca Research and Development, Macclesfield, United Kingdom; Biostatistics and Epidemiology Unit, Institut Gustave Roussy, Villejuif; Oncology Therapy Area, AstraZeneca, Rueil Malmaison, France; Division of Biostatistics, Mayo Clinic, Rochester, MN; and PTC Therapeutics, South Plainfield, NJ

Address reprint requests to Marc Buyse, ScD, IDDI, 30 avenue Provinciale, 1340 Louvain-la-Neuve, Belgium; e-mail: marc.buyse{at}iddi.com

	ABSTRACT

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Purpose The traditional end point for assessing efficacy of first-line chemotherapies for advanced cancer is overall survival (OS), but this end point requires prolonged follow-up and is potentially confounded by the effects of second-line therapies. We investigated whether progression-free survival (PFS) could be considered a valid surrogate for OS in advanced colorectal cancer.

Patients and Methods Individual patient data were available from 10 historical trials comparing fluouracil (FU) + leucovorin with either FU alone (1,744 patients) or with raltitrexed (1,345 patients) and from three validation trials comparing FU + leucovorin with or without irinotecan or oxaliplatin (1,263 patients). Correlation coefficients were estimated in historical trials between the end points of PFS and OS, and between the treatment effects on these end points. Treatment effects on OS were predicted in validation trials, and compared with the observed effects.

Results In historical trials, 1,760 patients (57%) had progressed or died at 6 months, and 1,622 (52%) had died at 12 months. The rank correlation coefficient between PFS and OS was equal to 0.82 (95% CI, 0.82 to 0.83). The correlation coefficient between treatment effects on PFS and on OS ranged from 0.99 (95% CI, 0.94 to 1.04) when all trials were considered to 0.74 (95% CI, 0.44 to 1.04) after exclusion of one highly influential trial. In the validation trials, the observed OS hazard ratios were within the 95% prediction intervals. A hazard ratio of 0.77 or lower in terms of PFS would predict a benefit in terms of OS.

Conclusion PFS is an acceptable surrogate for OS in advanced colorectal cancer.

	INTRODUCTION

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Approximately 50% of patients diagnosed with colorectal carcinoma have metastatic or nonresectable disease at time of diagnosis or will develop metastases or a locoregional recurrence after their initial diagnosis. Substantial progress has been made during the last 20 years in the use of fluorouracil (FU) to treat advanced colorectal cancer, with a doubling of tumor response through modulation of FU by leucovorin or methotrexate, or through continuous intravenous infusion of FU instead of a bolus injection.¹ These therapeutic approaches were shown to yield a modest but statistically significant impact on overall survival (OS).² More recently, the chemotherapeutic agents irinotecan and oxaliplatin have become available after randomized trials showed they increased response rates, progression-free survival (PFS), and OS.^3-6

Most patients with metastatic colorectal cancer still die as a result of their disease. The ultimate goal of chemotherapy is to cure the disease, or failing that, to improve patient symptoms, quality of life, and OS. It seems justified, therefore, to use OS to assess the efficacy of chemotherapies for advanced colorectal cancer. However, patient death can be observed only after prolonged follow-up, and with the increasing number of active compounds available in this disease, any effect of first-line therapies on OS may be confounded or diminished by the effects of subsequent therapies. It is therefore of interest to investigate whether PFS could replace OS as the primary end point in randomized trials for the treatment of patients with advanced colorectal cancer.

In this article, we quantify the relationship between PFS and OS in a set of historical trials, and we investigate whether the results observed in these trials could have been used to predict the effects of irinotecan and oxaliplatin in a set of more recently conducted validation trials that played a pivotal role in the development of these newer drugs.

	PATIENTS AND METHODS

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Trials
Individual patient data were available on 10 historical trials^2,7-9 and three validation trials^3-5 that all had a FU + leucovorin treatment group (Table 1). Historical trials consisted of all trials comparing FU + leucovorin with FU alone (seven trials, 1,744 patients), and all trials comparing FU + leucovorin with raltitrexed (three trials, 1,345 patients). They accrued patients between 1981 and 1990 (median follow-up, 30.4 months). Validation trials (1,263 patients in total) consisted of all trials comparing FU + leucovorin with the same plus irinotecan (two trials, 843 patients) or with the same plus oxaliplatin (one trial, 420 patients). They accrued patients between 1995 and 1998 (median follow-up, 22.0 months). A meta-analysis of trials comparing FU + leucovorin with FU was previously reported.² The other trials were those carried out for the registration of the new drugs raltitrexed,^7-9 irinotecan,^3,4 and oxaliplatin.⁵

Survival Analyses
PFS and OS analyses were based on all randomly assigned patients using the intention-to-treat approach. PFS was defined as the time from random assignment to progressive disease (as assessed in each individual trial) or death from any cause. OS was defined as the time from random assignment to death from any cause. The distributions of PFS and OS were estimated using the Kaplan-Meier method. Estimation procedures and hypothesis tests were stratified for trial. The effect of treatment on PFS and on OS was quantified through hazard ratios (HRs), respectively HR_PFS and HR_OS, estimated through a proportional hazards model with treatment as the only factor.¹⁰

Surrogacy Criteria
A correlation approach was used to assess the validity of PFS as a surrogate for OS, which is appropriate when multiple randomized trials are available in which both end points are measured.¹¹ The method comprised estimation of , the rank correlation coefficient between PFS and OS, and of R, the correlation coefficient between the treatment effects on PFS and on OS, expressed respectively as log HR_PFS and log HR_OS. For small treatment effects, log HR 1 –HR; hence, log HR is an approximate estimate of the risk reduction. PFS would be claimed an acceptable surrogate end point for OS if (a) were close to 1, indicating a strong correlation between PFS and OS, and (b) R were close to 1, indicating a strong correlation between treatment effects on PFS and on OS.¹²

Correlation Coefficients
The rank correlation coefficient between PFS and OS was estimated through a Hougaard bivariate copula distribution of these end points over the entire time range,¹³ or using the Kaplan-Meier estimates of PFS at 6 months and OS at 12 months. The correlation coefficient R between treatment effects on PFS and OS was estimated though a linear regression model, using all events over the entire time range, or up until 6 months for PFS and 12 months for OS.

Validation Strategy
Correlations between treatment effects were estimated in the historical trials, and used to predict the treatment effects on OS in the validation trials, based on the treatment effects on PFS actually observed in the validation trials. The observed treatment effect on OS (and its 95% CI) was compared with the predicted effect (and its 95% prediction interval). The linear regression model between treatment effects was used to compute the surrogate threshold effect, the minimum treatment effect on PFS required to predict a nonzero treatment effect on OS in a future trial.¹²

	RESULTS

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Correlation Between End Points
In historical trials, similar numbers of events were observed for PFS at 6 months (1,760 events) as for OS at 12 months (1,622 events). The degree of association between Kaplan-Meier estimates of 6-month PFS and 12-month OS was weak, with a rank correlation coefficient equal to only 0.32 (95% CI, –0.14 to 0.67; Fig 1). There was no evidence that the correlation between PFS and OS differed between treatments (Fig 1). In contrast, PFS and OS over the entire time range were reasonably well correlated, with a rank correlation coefficient equal to 0.82 (95% CI, 0.82 to 0.83).

View larger version (11K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. Correlation between 6-month progression-free survival and 12-month overall survival in different treatment groups of historical trials. Symbol size is proportional to the number of patients. FU, fluouracil; LV, leucovorin.

View larger version (16K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. Progression-free survival (PFS) and overall survival curves (OS). FU, fluouracil; LV, leucovorin.

View larger version (26K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 3. Forest plots of hazard ratios (ratio of hazard in FU + LV group to hazard in experimental group) for progression-free survival (PFS; yellow lines) and overall survival (OS; blue lines). Symbol size is proportional to the number of patients. NCCTG, North Central Cancer Treatment Group; EORTC, European Organisation for Research and Treatment of Cancer; SWOG, Southwest Oncology Group; SAKK, Swiss Group for Clinical Cancer Research; HECOG, Hellenic Cooperative Oncology Group; TCCSG, Tomudex Colorectal Cancer Study Group; EU, European Union.

View larger version (18K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 4. Correlation between treatment effects on progression-free and on overall survival in historical trials (circles), in irinotecan trials (squares), and in oxaliplatin trial (diamond). A logarithmic scale is used for both axes. Symbol size is proportional to the number of patients. HR, hazard ratio; EU, European Union.

Surrogate Threshold Effect
The surrogate threshold effect, as shown on Figure 4, corresponds to PFS HRs of 0.86 (or 1.16 if new treatment was worse). Thus, in order to predict a nonzero treatment effect on OS in a future trial, a hazard ratio of at most 0.86 (or at least 1.16) would need to be ascertained. After exclusion of the Crema trial, the surrogate threshold effect corresponded to hazard ratios of 0.77 (or 1.30).

Predicted Effects on OS
In validation trials, the observed treatment effect on OS was compared with the predicted treatment effect on OS, on the basis of the observed treatment effect on PFS. Table 2 compares predicted with observed treatment effects, and also shows the proportion of patients receiving second-line therapy after experiencing failure of their randomized first-line treatment: any second-line therapy, a second-line regimen containing the same new drug as their first-line therapy (irinotecan or oxaliplatin), or a second-line therapy with crossover to the other new drug (oxaliplatin or irinotecan).

	DISCUSSION

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Reviews of the literature suggest a tight correlation between PFS and OS in advanced colorectal cancer,¹⁸ but this observation alone does not make PFS a good surrogate for survival.¹⁹ Although there is no consensus regarding the theoretical conditions required for a surrogate end point to be valid, recent work suggests that surrogacy can be assessed through the correlation between the end points and the treatment effects on these end points in a series of trials.^11-12 In resectable colorectal cancer, this approach was used successfully to show that 3-year DFS was an excellent surrogate for 5-year OS.²⁰ In metastatic colorectal cancer, this approach was also used to investigate the relationship between tumor response and OS. Although patients who achieved a response had a significantly prolonged OS, treatment effects on response were poorly correlated with treatment effects on OS, making tumor response an unacceptable surrogate in this disease.^1,21 The analyses presented here show that, in historical trials comparing FU + leucovorin with single-agent FU or with raltitrexed, PFS was an acceptable surrogate for OS. Indeed, the two end points were well correlated ( = 0.82), and so were the effects of treatment on the two end points (R = 0.99), although the latter finding was heavily influenced by one trial that had a much larger treatment effect than all others. When this trial was excluded, the treatment effects were still correlated, but less impressively so (R = 0.74). This observation is in line with previously reported analyses of another data set, wherein the lack of treatment effect on either end point made it impossible to validate PFS as a surrogate for OS.¹³ Hence for the validation to be effective, a range of treatment effects is desirable on both the surrogate and the true end points.

When our analyses were censored at 6 months for PFS and at 12 months for OS, the effects of treatment on the two end points remained highly correlated (R = 0.94), but the correlation between the Kaplan-Meier estimates of 6-month PFS and 12-month OS was much lower ( = 0.32; Fig 1). This finding indicates that using summary statistics for time-related end points (such as medians or Kaplan-Meier estimates at a given time point) is insensitive and potentially misleading. This approach should not be used to validate potential surrogate end points when complete individual patient data are available.²² In practice, however, the estimate of a surrogate end point at a single time point will often be used to predict the true end point at a later time point.

In the present article, we extended the validation methodology to investigate the predictive value of PFS as a surrogate end point for OS. We calculated the surrogate threshold effect and showed that if a treatment achieved an HR for PFS of 0.86 or less, it would be expected to ultimately achieve a benefit in terms of OS (Fig 4). After exclusion of the Crema trial, the surrogate threshold effect corresponded to HRs of 0.77, suggesting the need for much larger but still achievable treatment effects on PFS. HRs in the range of 0.7 to 0.8 for PFS are realistic and have, in fact, been achieved by several treatments recently approved for the treatment of advanced colorectal cancer.^3-6,16-17 Similar conclusions were reached independently in a meta-analysis of a large number of published trials.²³ In practice, the threshold effect depends on the size of the future trial, because the estimation error of the PFS HR must be accounted for in the construction of the prediction limits for the OS HR.

The trials used in our analyses were conducted over a long period of time. The correlation between PFS and OS could largely be explained by a trend for both PFS and OS to improve over time, the former as a result of more effective first-line treatments, and the latter as a result of more effective second-line treatments. However, our claim of surrogacy is also based on a high correlation between the effects of treatment on PFS and OS, and there is no plausible way in which the correlation between treatment effects could simply be a result of time trends.

To validate results from historical trials in independent, more recent trials, we also investigated whether treatment effects on OS were reliably predicted by the treatment effects on PFS in three validation trials testing the new drugs irinotecan and oxaliplatin. In these trials, the prediction intervals of the predicted effect were narrower than the CI of the observed effect (Table 2), which underscores the potential gain arising from the use of surrogate end points for which more events are available than for the true end point. Such a gain would be even more pronounced for trials with less mature OS data, although those trials would also have less mature PFS data, and therefore more uncertainty in the estimated treatment effect on PFS. The predicted effects agreed extremely well with the observed effects in trials testing irinotecan, but less well in the trial testing oxaliplatin, in which the predicted effect overestimated the observed effect (Table 2). However, all of the observed effects fell within the prediction limits (Fig 4), and these differences could be a chance finding. They could also be a result of the effect of second-line treatments. In a recently reported US Intergroup trial testing oxaliplatin added to FU + leucovorin versus irinotecan added to FU + leucovorin (not included in our analyses), a much higher proportion of patients were crossed over to the other drug on disease progression in the first-line oxaliplatin arm (60%) than in the first-line irinotecan arm (24%). The observed OS benefit of first-line oxaliplatin compared with first-line irinotecan was larger (HR = 0.57) than would have been predicted from the benefit on PFS (predicted HR = 0.79), possibly because of the larger number of crossovers.⁶ Second-line use of new agents is likely to produce lesser antitumor effect than first-line use,²⁴ but recent observations strongly suggest that use of effective second-line therapies may extend the time between first-line disease progression and death.^25,26 Thus, the ultimate OS benefits of improvements in first-line PFS may be reduced as greater numbers of effective second-line therapies are introduced. Our analyses indicate that PFS would have been an acceptable surrogate for OS in developing the drugs considered here. Similar analyses should be repeated with data from randomized trials testing newer drugs in patients receiving effective second- or even third-line therapies.

PFS offers a direct measure of new drug activity that is not obscured by subsequent therapies. Unlike response rate, PFS also has the intrinsic advantage of assessing the time of tumor control. As more active drugs enter the clinic, PFS will become an even more desirable end point than OS as the primary efficacy end point for trials in colorectal cancer.^27,28 Its use can reduce sample size, shorten accrual time, and speed time until first analysis, besides serving as an appropriate indicator of clinical benefit. Of course, increasing reliance on assessment of PFS raises the challenge of ensuring that ascertainment of tumor progression in clinical trials is reliable and unbiased.²⁹

	AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

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Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a "U" are those for which no compensation was received; those relationships marked with a "C" were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment or Leadership Position: Kevin Carroll, AstraZeneca (C); Pascal Piedbois, AstraZeneca (C) Consultant or Advisory Role: None Stock Ownership: None Honoraria: None Research Funding: None Expert Testimony: None Other Remuneration: None

	AUTHOR CONTRIBUTIONS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
Conception and design: Marc Buyse, Tomasz Burzykowski, Pascal Piedbois

Financial support: Marc Buyse, Tomasz Burzykowski

Administrative support: Marc Buyse, Tomasz Burzykowski

Collection and assembly of data: Marc Buyse, Tomasz Burzykowski, Kevin Carroll, Stefan Michiels, Jean-Pierre Pignon, Pascal Piedbois

Data analysis and interpretation: Marc Buyse, Tomasz Burzykowski, Kevin Carroll, Stefan Michiels, Daniel J. Sargent, Langdon L. Miller, Gary L. Elfring, Jean-Pierre Pignon, Pascal Piedbois

Manuscript writing: Marc Buyse, Tomasz Burzykowski, Kevin Carroll, Stefan Michiels, Daniel J. Sargent, Langdon L. Miller, Gary L. Elfring, Jean-Pierre Pignon, Pascal Piedbois

Final approval of manuscript: Marc Buyse, Tomasz Burzykowski, Kevin Carroll, Stefan Michiels, Daniel J. Sargent, Langdon L. Miller, Gary L. Elfring, Jean-Pierre Pignon, Pascal Piedbois

	ACKNOWLEDGMENTS

 
The authors are grateful to the Meta-Analysis Group in Cancer and to the principal investigators of the validation trials for permission to reanalyze individual patient data: D. Cunningham, G. Cocconi, R. Pazdur, L. Saltz, J.Y. Douillard, and A. de Gramont. The datasets analyzed were provided by the Biostatistics and Epidemiology Unit of Institut Gustave-Roussy, Villejuif, France; AstraZeneca, Macclesfield, United Kingdom; Sanofi-aventis, Swiftwater, PA (Dr R. Bigelow); and Pfizer, New York, NY (Dr L. Cisar). All statistical analyses were performed at the Center for Statistics, Hasselt University, and the International Drug Development Institute (IDDI, Brussels, Belgium, with statistical support from E. Quinaux). T. Burzykowski gratefully acknowledges financial support from the IAP research Network P6/03 of the Belgian Government (Belgian Science Policy).

	NOTES

 
Supported by the IAP Research Network P6/03 of the Belgian Government (Belgian Science Policy; T.B.).

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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Submitted March 24, 2007; accepted July 30, 2007.

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