Analyzing the pivotal trial that compared sunitinib and interferon alfa in renal cell carcinoma, using a method that assesses tumor regression and growth

Abstract

Purpose

We applied a method that analyzes tumor response, quantifying the rates of tumor growth (g) and regression (d), using tumor measurements obtained while patients receive therapy. We used data from the phase III trial comparing sunitinib and interferon-alfa (IFN-α) in metastatic renal cell carcinoma (mRCC) patients.

Methods

The analysis used an equation that extracts d and g.

Results

For sunitinib, overall survival (OS) was strongly correlated with log g (Rsq=0.44, p<0.0001); much less with log d (Rsq=0.04; p=0.0002). The median g of tumors in these patients (0.00082 per days; log g=−3.09) was about half that (p<0.001) of tumors in patients receiving IFN-α (0.0015 per day; log g=−2.81). With IFN-α, the OS/log g correlation (Rsq=0.14) was weaker. Values of g from measurements obtained by study investigators or central review were highly correlated (Rsq=0.80). No advantage resulted in including data from central review in regressions. Further, g can be estimated accurately four months before treatment discontinuation. Extrapolating g in a model that incorporates survival generates the hypothesis that g increased after discontinuation of sunitinib but did not accelerate.

Conclusions

In patients with mRCC, sunitinib reduced tumor growth rate, g, more than did IFN-α. Correlating g with OS confirms earlier analyses suggesting g may be an important clinical trial endpoint, to be explored prospectively and in individual patients.

INTRODUCTION

In 2010, approximately 570,000 people died of cancer in the US, most from chemotherapy-refractory solid tumors, including >13,000 from metastatic renal cell carcinoma (mRCC)¹. With the advent of sorafenib, sunitinib, temsirolimus, bevacizumab with interferon, everolimus and pazopanib, therapy of mRCC has improved^2–7. However, these therapies are not curative, underscoring the need for alternative treatment strategies and novel decision paradigms⁸.

We developed a method to analyze tumor responses to therapy, quantifying the rate of tumor regression (decay, d) and growth (g), using measurements obtained while patients receive therapy^{9 –12}. The rate constants are derived using data collected in clinical trials. In responding tumors, regression dominates from start of therapy until nadir, growth dominating after nadir. If tumors do not respond, growth dominates throughout. Previous analyses of phase II studies in mRCC utilized CT measurements of tumors^{9, 11}; in prostate cancer, serum PSA^{10, 12} was used. For those, growth rate constants, g, correlated with overall survival (OS), while, surprisingly, regression rate constants, d, did not.

Here, we utilized randomized phase III trial data comparing sunitinib with interferon-α (IFN-α) in untreated mRCC^3,13. We illustrate the value of the growth rate constant as a clinical trial endpoint, reflecting the impact of sunitinib on tumor growth.

MATERIALS AND METHODS

Clinical Trial and Study Design

The study, an international, multicenter, randomized, phase III trial, compared sunitinib (SUTENT^®, Pfizer, New York, NY), with IFN-α ^3,13. OS was calculated from randomization date until date of death. Tumor measurements from CT scans were recorded as the sum of longest diameters (LDs) of target lesions. Responses and progressions were assessed according to RECIST 1.0¹⁴. Anonymized tumor measurement data, enrollment and off-study dates, and date of death data were provided in spreadsheet format by Pfizer, Inc.

Mathematical, Data and Statistical Analyses

Mathematical analysis

Our regression-growth equation is based on the assumption that change in tumor quantity during therapy, indicated by change in the sum of longest dimensions (LDs), results from two independent component processes (both following first order kinetics): an exponential decrease/regression, d, and an exponential growth/re-growth of the tumor, g. (To utilize this method in other data sets, tumor quantity data can be uploaded into http://www.tumorgrowthanalyses.com and the rate constants extracted). The equation is:

$$\text{f}(\text{t})=exp(-\mathbf{d}•\text{t})+exp(\mathbf{g}•\text{t})-1$$ Eq. (1)

where f(t) is the tumor quantity (sum of LDs) at time t in days, normalized to the sum of LDs at t=0, and d (decay, fraction per day) and g (growth, also per day) are the pertinent rate constants; exp is the base of the natural logarithms.

Theoretical curves depicting the separate components of Eq. (1) and how these combine together to give the time dependence of f, the tumor quantity, appear in Figure 1. For data showing a continuous decrease from start of treatment, g is eliminated:

$$\text{f}(\text{t})=exp(-\mathbf{d}•\text{t})$$ Eq. (2)

Figure 1. Time course of the decay and growth of a tumor.

Plots for the regression/growth model using the sum of the longest diameters (LDs) of target lesions in four representative patients treated with either sunitinib or interferon-α. The lines are curve-fits of a mathematical equation that describes in panels A and B the initial regression (dotted lines) and concomitant growth of the tumor (dashed lines). In all panels the solid circles are the sum of the actual measured LDs of target lesions obtained in the clinical setting; the solid line represents the sum of the fraction of tumor that is regressing and the fraction that is growing. A and B: Examples demonstrating a transient decrease in the sum of the LDs followed by an increase (growth). C: A case in which no growth is seen during the treatment period. D: An example of a case in which there is no apparent tumor shrinkage. A program that can be used to fit data using the equation can be found at: http://www.tumorgrowthanalyses.com

Similarly, when tumor measurements show a continuous increase, d is eliminated:

$$\text{f}(\text{t})=exp(\mathbf{g}•\text{t})$$ Eq. (3)

Data analysis

We attempted to fit each data set for which more than one data point was available. Curves were fit and parameters estimated using the procedure NLIN, the nonlinear regression model, in SAS¹¹ software. The routine extracts parameters g and d with associated Student’s t and p values.

Statistical analyses

Linear regressions used the polynomial linear routine of Sigmaplot 11.0 (Systat Software, Point Richmond, CA). Sample comparisons of data sets were performed on Sigmaplot 11.0 using Student’s t-test for normally distributed data, or the Mann-Whitney Rank Sum procedure for data that were not normally distributed. We report the appropriate p values for a two-sided assessment.

RESULTS

Analyzing the time course of tumor decay and growth

The data analyzed were obtained in the phase III registration trial of patients with mRCC randomized to either sunitinib or IFN-α^3,13. There were 379 patients assigned to sunitinib; 377 to IFN-α. Tumor measurements were assessed as the sum of the LDs of target lesions. Figure 1 depicts four examples of on-study change in LD sum, depicted as solid circles. Solid lines are curve-fits of the equations describing the sum of concomitant regression (decay) and growth fractions of the tumor, represented as dotted and dashed lines, respectively [Eqs. (1–3)]. Panels A and B demonstrate a decrease in LD sum followed by an increase. Panel C depicts a case where no growth is seen during treatment, while D shows a case with no apparent tumor shrinkage. We extracted g and d parameters, excluding patients with no tumor assessment data, with only baseline CT scan data, or with only one follow-up assessment that differed by less than 1.2-fold, these last patients not meeting criteria for progressive disease – treatment discontinued for toxicity or withdrawal of consent³. Exclusions left 331 patients in the sunitinib arm and 268 in the IFN-α arm. A consort diagram demonstrating patient numbers is in Supplementary Attached Material (SAM) Figure S1. Setting the significance for the derived parameters at p<0.1, we extracted acceptable g and/or d parameters in 319 (84%) of the patients assigned to sunitinib, and 240 (64%) of those assigned to IFN-α (differences in proportions, p=0.007, Chi-square).

To test if the difference in ability to extract a g value indicated different patient populations, we compared OS, initial tumor quantity Qo, nadir fraction, and time-to-nadir for patients with acceptable g/d parameters (i.e., extracted at p<0.1) with those for the whole population (SAM Table S1). For both IFN-α and sunitinib arms, for either deceased patients or those still living at study closure, neither OS, initial tumor quantity, nadir fraction, nor time-to-nadir were different (every p>0.25, Mann-Whitney Rank sum) between those with acceptable g/d and the whole population. In addition, Kaplan-Meier survivals plots were not different between those with acceptable g/d parameters and those without (Log rank: medians for sunitinib 130.6 and 122.7, p=0.79, for IFN-α, 137.7, 119.4, p=0.69). Thus, the cases with acceptable g/d parameters were representative of the whole population.

To validate the goodness-of-fit, we tabulated the Rsq between data points and fitted line for twenty cases selected randomly among those that had an acceptable g parameter. The median Rsq was 0.933, (25% to 75%: 0.891 to 0.958), indicating acceptable agreement between fitting equation and tumor quantity values over the data set. Additionally, the median of the p values for all acceptable g/d values was p=0.00003 (25% to 75%: 0.000 to 0.00192) for the sunitinib arm, and p=0.000540, (25% to 75%: 0.00002 to 0.00830) for interferon², again indicating the goodness of fit of our analytical approach.

Correlating growth and regression rate constants with overall survival in patients treated with sunitinib or interferon-α

Previous analyses^9–12 suggested the growth rate constant, g, could be added to response rate and PFS as a measure of efficacy. Here, the median g of tumors in patients receiving sunitinib (0.00082 per day; log g=−3.09) was 45% lower than that (p<0.001) of tumors in patients receiving IFN-α (0.0015 per day; log g=−2.81).

Recognizing OS as the “gold standard” of drug efficacy, we assessed the correlation between g or d and OS. The final analysis of OS¹³ was utilized, with 262 OS events (date of death [DOD] documented) that yielded acceptable g/d parameters (including 226 with acceptable g). The left upper panel of Figure 2 shows the OS/log g correlation for the 131 sunitinib patients having both a valid g (p<0.1) and DOD. The right upper panel is a similar plot for the 95 patients treated with IFN-α. For sunitinib, log g and OS were significantly negatively correlated (Rsq=0.44; p<0.001), whereas the data for IFN-α (Rsq=0.14; p<0.001), were less strongly correlated. These g values were calculated using tumor measurements obtained by study investigators. Regressions using measurements from central review were similar: for sunitinib, log g and OS correlated significantly negatively (Rsq=0.30; p<0.001) in contrast to those for patients randomized to IFN-α (Rsq=0.14; p<0.0044). The lower panels of Figure 2 compare regressions of tumor nadir (the measure used in response rate, defined as the ratio of minimum sum of LDs to initial sum of LDs) and PFS or OS in the sunitinib arm. In prostate cancer¹⁰, nadir depth and time to reach nadir were both surrogates of g, faster growth rates producing higher nadirs, shorter times to nadir, and, in turn, shorter PFS¹⁰. Accordingly, lower correlations (Rsq=0.19) were found when nadir, rather than log g, is regressed on OS in this dataset, the nadir being merely a surrogate of g. The regression of PFS with OS was comparable to the results obtained with g (data not shown).

Figure 2. The growth rate constants (log g) correlate (negatively) with overall survival in patients treated with sunitinib or interferon-α.

We extracted the rate constants from the data, using the model, and then asked whether d or g correlates with OS. Upper left panel: Treatment with sunitinib. Upper right panel: Treatment with IFN-α. We use log g on the y-axis rather than g itself since the dependence of OS on g is a hyperbolic function, not a straight line.

The growth rates (g) are significantly negatively correlated with OS (Rsq=0.44, p<0.0001) for sunitinib and less so (Rsq=0.14, p=0.0002) for IFN-α. The regression rates (d) have no significant correlation with OS (Rsq=0.04 for sunitinib and IFN-α, both, not shown). Lower panels: Regressions of tumor nadir (surrogate of the growth rate constant) with PFS and OS.

To test whether patients who died while on study were representative of the whole population, SAM Figure S2 shows log g versus OS for all patients for whom we had valid g parameters as solid circles, with the subset that had died as red open circles. The datasets fall on a continuum, suggesting that deceased patients are indeed representative of the whole population insofar as dependence of survival on g is concerned.

A step-wise regression of OS on log g, log d, and initial tumor quantity (f₀, sum of LDs) showed that only log g contributed to the regression for patients randomized to sunitinib (p<0.001), whereas both log g (p=0.010) and f₀ (p=0.028) contributed significantly to OS in patients randomized to IFN-α (data not shown).

Comparing growth rate constants extracted from the two study arms

The two panels on Figure 3 left, depict waterfall plots of tumor fraction after 12 treatment weeks as percent LD sum at enrollment: Sunitinib is more effective than IFN-α. Since calculations of g and d use the time elapsed between assessments, comparisons amongst studies can be made regardless of differences in assessment protocols. The right panel of Figure 3 depicts dot plots of log g values derived in our previous studies in patients with mRCC, treated with a placebo, bevacizumab or ixabepilone ^9,11,15,16 as well as the present four data sets (sunitinib or IFN-α, each measured by study investigators or central review). Confirming the waterfall plots, g values for sunitinib-treated patients are significantly slower at p<0.0001 [investigators/central review log g values were: sunitinib=−3.09/−2.94; IFN-α=−2.81/−2.78].

Figure 3.

Left panel Waterfall plot depicting on the y-axis, the percentage decrease in sum of the LDs from base line values at 12 weeks (as opposed to the best response assessment reported in Motzer et al³). Cases are stratified in terms of decreasing values of the change in the sum of LDs. Right panel: The y-axis is the logarithm of the derived growth rate constant (log g). The horizontal lines in each set are the median values and the 25% and 75% limits. The growth rate constants are shown from left to right for patients with RCC enrolled on the NCI placebo vs. bevacizumab study¹⁵, the NCI ixabepilone study¹⁶, and the two arms of the sunitinib Phase III study^{3, 13}. Using data from study investigators, the median g was 0.0015 per days and median log g was −2.81 (25% to 75%: −2.38 to −3.54) for IFN-α-treated patients compared with a median g of 0.00082 per days and median log g of −3.09 (25% to 75%: −2.70 to −3.46) for the sunitinib-treated cohort, significantly different at p<0.0001. The medians and spread of the g values are hardly different for data on tumor sizes measured by central review (−2.78, 25% to 75%: −2.44 to −3.23 for IFN-α and −2.94, 25% to 75%: −2.66 to −3.16 for sunitinib). The difference between the median log g values for bevacizumab and for sunitinib almost reaches significance at p=0.053. The median log g values for ixabepilone (−2.74) and IFN-α (−2.85) do not differ significantly. IR, data from central (independent) review; INV, data from study investigator measurements.

Comparing rate parameters derived from data obtained by study investigators or by independent central review

Figure 4 is a direct comparison of results obtained using data from study investigators or central review. (Log g values calculated using the central review on the x-axis, corresponding log g values using study investigator measurements on the y-axis). Rsq for the plot is 0.80; the regression slope is 1.14 ± 0.04, close to unity. Accordingly, no statistical advantage was gained by including central review data.

Figure 4. Tumor growth rate constants using two sources of sum of LD are similar.

Tumor growth rate constants determined using radiological measurements determined by either the study investigators (y-axis) or central review (x-axis) were highly correlated. The regression is y=0.23 (+/− 0.13) + 1.11 (+/− 0.04) *x (Rsq=0.80, p<0.0001, n=205).

g and d parameters can be extracted with accuracy before the nadir is reached

The eight panels of Figure 5, left, show successive values of the sum of LDs from one patient, obtained over time with starting quantity arbitrarily set at 1. In each plot, one additional time point is added. Applying Eq. (1) to the data in each plot, we obtained intermediary values for g and d (mean ± 95% confidence), plotting the values in panels on the right. By the fourth re-evaluation both g and d are obtained with accuracy; later values differ little. Thus, g can be accurately estimated long before tumor measurements demonstrate growth: the downward trajectory is “deviated upward” by the as yet “undetected” growing fraction. In similar analyses of twenty randomly chosen patients, an accurate estimate of g could, in most cases, be obtained before the data showed a >20% rise in the sum of LDs (not shown). For these 20 cases, the time between recording a g estimate and either a >20% rise in LD sum above the nadir or the end of assessment, was a median of 126 days (25% to 75%: 84 to 169 days). Thus, four months before treatment is discontinued, the tumor growth rate can be discerned.

Figure 5. The g and d parameters can be extracted from the data with good accuracy even before the nadir value is reached.

The eight panels on the left show successive data sets from one patient, obtained over time. The tumor quantity is plotted with the starting quantity arbitrarily set at 1. The panels on the right, plot the g and d parameters extracted using Eq. 1, each with its standard deviation. The g and d values obtained from the second reassessment onward are plotted. By the fourth evaluation, both g and d can be obtained with accuracy; later values differ little. Note that a g value can be accurately extracted long before tumor measurements demonstrate growth: the downward trajectory is “deviated upward” by the growing fraction. The greater the growth rate, the greater the deviation, allowing one to calculate g long before actual growth is measured.

The stability of the derived g parameter was examined, performing the same procedure on the entire data set. For the overwhelming majority of cases, g remained constant; only rarely was a final g value significantly above previously determined values. The first eight cases in each arm with valid g/d values, where data collection had continued above 320 days, are depicted in SAM Figure S3.

Estimating the time to death, had treatment continued beyond the arbitrary definition of progression

Patients (with known DOD) were divided into five groups (quintiles), of increasing g. The highest quintile comprises patients with no apparent benefit from sunitinib, whose on-study g is likely similar to their off-treatment g. Substituting median g and d values into Eqs. (1), using DOD as (t), we estimated the relative “tumor size” at time of death. For the highest g quintile, the median relative tumor size (sum of LDs, f) at DOD was 2.1-fold that of the median entry value (sum of LDs) for the whole population (for a sphere this represents a 9.3-fold increase in tumor volume). Assuming groups of patients die with similar tumor burdens (some reaching this faster than others), we used 2.1-fold the entry value as the tumor quantity at death. In Figure 6, we used median g and d values, in quintiles, for sunitinib-treated patients, to determine the time at which the projected tumor growth curve intersects a final expected tumor quantity of 2.1-fold. For the slowest growing quintile (largest negative log g value), where most patients showed only regression while on study, the model projects no tumor growth, although median OS was 516 days (We did not depict this quintile since the g values of nearly zero would give a large error in tumor growth projections). In the next two quintiles (panels A and B), the projected OS based on the on-study g and d values is considerably longer than actual OS. Our projected tumor curve assumes a constant g until the 2.1-fold tumor quantity is reached, so the shorter actual OS implies that g increased after discontinuation of drug. We hypothesize that patients with slow g values could have lived closer to the predicted OS had treatment continued beyond the conventional “20% above nadir” threshold. In panel C, the second fastest-growing quintile, predicted OS is 377 days, close to the 322 found, suggesting the on-study g accurately predicts the g when therapy ceases, perhaps indicating drug resistant tumor. For the fastest quintile (panel D) the predicted OS of 170 days is, of course, not different from the 174 days observed, since this observed OS was used to derive the value of 2.1, the fold-increase in the sum of LDs at death.

Figure 6. Predicted overall survival if therapy were continued after the conventional definition of disease progression (nadir + 20%).

Entering the study’s derived median g and d parameters into Eq. (1), the equation can be solved for a predicted time of death when tumor quantity, f, reaches 210% the on-study value (this being the tumor quantity relative to t=0, estimated for the 20% of patients with the fastest g values, i.e. fastest quintile). The panels depict the predicted time course of tumor quantity in patients treated with sunitinib divided into quintiles according to their on-study g values.

DISCUSSION

In the present study, using data from the pivotal trial of sunitinib versus IFN-α^3,13, we demonstrate that analysis of data routinely gathered during a clinical trial can provide estimates of the rates of tumor regression (d) and growth (g). The growth rate constant is an excellent “surrogate” for OS, correlating significantly with OS (p<0.001); d correlates less well. Both g and d can be extracted, and tumor response characterized, even before the nadir is reached. Sunitinib results in a significantly slower rate of tumor growth (p<0.0001) as compared with IFN-α, the sunitinib rate being almost better (p=0.053) than that achieved with bevacizumab in a smaller randomized trial¹⁵. The median g of tumors in patients receiving sunitinib (0.00082 per day; log g=−3.09) was about half that (p<0.001) of tumors in patients receiving IFN-α (0.0015 per day; log g=−2.81). This is consistent with the reported best response rates determined by RECIST, 47% for sunitinib, compared with 12% for IFN-α^3,13. The correlation of g with OS observed in patients randomized to sunitinib is consistent with the strong correlation of g with OS that has been observed in our previous analyses^9–12 (and on several additional histologies currently under study³), but was less so in those randomized to IFN-α. This may be due to major responses in patients on the IFN-α arm who went on to receive sunitinib after disease progression. While more patients had g/d values determined than had PFS scored (319 vs. 204 for sunitinib and 240 vs. 186 for IFN-α), one caveat is worth noting. Patients on the IFN-α arm were more likely to have “unacceptable” g values (63 had only 2 evaluations with <1.2 difference) than those on sunitinib (26 had only 2 evaluations with <1.2 difference) (SAM Figure S1), potentially introducing the same type of bias that can occur in Kaplan-Meier projections when patients are censored before progression.

We envision the g value to be particularly useful as a clinical trial endpoint that could provide an independent analysis of drug efficacy. Using Sigmaplot’s Size function in its statistics package, and the standard error of 0.0019 per day found for the g values in the sunitinib arm, 100 cases in each of two arms would be needed in some future trial in order to find a decrease of 50% in the mean of g with a power of 0.85 at p=0.05). We also compared tumor measurements determined by study investigators and by central review and observed that, despite absolute differences in measurements, the rate of change of these measurements –i.e., growth rate, g, – is nearly identical, and their correlation with OS indistinguishable. These analyses are in agreement with prior reports of overall concordance of independent and investigator review and suggestions that such review does not add value^17,18. As an efficacy endpoint, g could support investigator assessments and help eliminate need for central review.

Despite the overall correlation of on-study g with OS in the sunitinib arm, we could model a change in growth rate after study discontinuation in some patients who had marked slowing of growth while on study. Using the OS of enrolled patients and the derived g and d, we estimated a relative tumor burden at death 2.1-fold that at the time of enrollment (9.3-fold increase in tumor volume) in patients who did not benefit from therapy. This value is similar to that determined in a previous mRCC analysis (11.5-fold increase in tumor volume). Based on growth rates determined while on study, patients with slower on-study g values should have lived longer. The model suggests tumor growth rates increased after treatment was discontinued, to approximate that of a placebo rate (faster than the rate on therapy, but one that would not be characterized as “acceleration”¹⁹). We found no evidence that g accelerated as treatment continued. As shown in Figure 5 and SAM Figure S3, while on-study g did not rise appreciably – indeed at the time of treatment discontinuation, the value of g was comparable to that obtained months earlier.

An important consequence of the survival projection follows: continuation of sunitinib treatment beyond study criteria for progression might have extended survival substantially in patients whose g values indicated substantial slowing of tumor growth. For example, for the 30% of patients with the slowest calculable g value, the model predicts survival to 1365 days (to reach 2.1-relative size), while the actual median OS and 25% to 75 % limit was 633 (523–777) days. Since we lack curative therapies, using the g value to predict who might benefit from continued treatment, provided it is clinically feasible and tolerable, offers the possibility of prolonging survival, in the absence of good salvage or curative therapies – a hypothesis that needs to be prospectively tested.

Several caveats should be noted regarding the survival analysis. First, the survival modeling was performed only in patients who had died, rather than the entire study population; once the study was closed, collection of survival data ceased. Thus, a survival benefit due to extended therapy may be only seen in a subgroup with more advanced disease – the measured parameters in SAM Table S1 certainly suggest that the patients in the trial population who had died had more advanced disease, although the analysis of g in the two groups in SAM Figure S2 suggests dependence of OS on growth rate was similar whether patients were living or had died. Second, since many patients receiving interferon eventually received other therapies including sunitinib and other VEGF inhibitors, this may have reduced the survival difference between the two arms. In a similar way, the multiple lines of salvage therapy that are now available for renal carcinoma could confound the interpretation of a study aimed at proving the benefit of extending sunitinib therapy in renal cancer.

Finally, we would observe that in mRCC we have entered the era of “targeted therapies” leaving behind IL-2 and IFN-α, without understanding who benefited and why²⁰. Our analysis concurs with a recent review that concluded “genuine, if modest, effectiveness” of IFN-α in mRCC²¹, in that there were a number of patients in the IFN-α cohort with very slow growth rates – although proof that this could be ascribed to IFN-α requires pre-treatment tumor measurements.

In summary we demonstrate that a tumor’s growth rate constant can be reliably calculated from clinical data gathered during a randomized phase III trial and that this value can reliably predict the FDA gold standard, OS. We confirm the efficacy of sunitinib versus IFN-α and compare it to other agents used in mRCC. A similar analysis of data from other trials could reliably compare all existing therapies for mRCC. As regards individual patients, we show that very early in treatment, long before disease progression is scored, indeed before tumor growth is observed, one can determine a growth rate constant. This knowledge enables a reliable estimate of the predicted OS, which could be used in clinical trial designs to continue therapy past the arbitrary 20% progression in those with very slow g values. These results validate our previous analyses of the growth rate constant in renal and prostate cancer and will hopefully stimulate additional investigations of this novel clinical trial endpoint.

Translational Relevance.

Sunitinib was the first agent in metastatic renal cell cancer to show major clinical responses in a significant subset of patients; the randomized trial against interferon made it the first-line choice. However, survival analysis was complicated by the use of sunitinib and similar agents after disease progression. The growth rate constant, g, is a clinical trial endpoint that could have aided development of sunitinib by documenting reduced tumor growth rate early in the clinical trial. Results of the analysis of g are highly suggestive that reduced g can be equated with clinical benefit. A hypothesis-generating model incorporating g also suggests that continuing therapy after conventional measures of progression in a patient with marked reduction in g could increase survival. These analyses suggest that calculation of g can aid in both drug development and individual patient management.