Other Paradigms: Growth Rate Constants and Tumor Burden Determined Using Computed Tomography Data Correlate Strongly With the Overall Survival of Patients With Renal Cell Carcinoma

Stein, Wilfred D.; Huang, Hui; Menefee, Michael E.; Edgerly, Maureen; Kotz, Herbert L.; Dwyer, Andrew J.; Yang, James Chih‐Hsin; Bates, Susan E.

doi:10.1097/ppo.0b013e3181be1b90

Cited by 33 publications

(41 citation statements)

References 33 publications

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“…In agreement with Skipper ( Fig. 1), we have previously shown through analysis of tumor measurements obtained from patients that tumor growth occurs exponentially and that the rate of this exponential growth is constant (4)(5)(6)(7)(8). The rates are far slower than those of the leukemia models used by Skipper, but the exponential nature of growth was confirmed.…”

Section: Introductionsupporting

confidence: 63%

“…All the analyses of the killing process include the exponentially growing fraction (1 À f) * e gt , where (1 À f) is the fraction of the tumor resistant or relatively resistant to the therapy in each model. Although the constant fraction or exponential model represents the exponential decrease we have reported before (4)(5)(6)(7)(8), the other two models-constant number and tumor surface-represent nonexponential models.…”

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confidence: 99%

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Therapies with Diverse Mechanisms of Action Kill Cells by a Similar Exponential Process in Advanced Cancers

et al. 2014

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Successful cancer treatments are generally defined as those that decrease tumor quantity. In many cases, this decrease occurs exponentially, with deviations from a strict exponential being attributed to a growing fraction of drug-resistant cells. Deviations from an exponential decrease in tumor quantity can also be expected if drugs have a nonuniform spatial distribution inside the tumor, for example, because of interstitial pressure inside the tumor. Here, we examine theoretically different models of cell killing and analyze data from clinical trials based on these models. We show that the best description of clinical outcomes is by first-order kinetics with exponential decrease of tumor quantity. We analyzed the total tumor quantity in a diverse group of clinical trials with various cancers during the administration of different classes of anticancer agents and in all cases observed that the models that best fit the data describe the decrease of the sensitive tumor fraction exponentially. The exponential decrease suggests that all drug-sensitive cancer cells have a single rate-limiting step on the path to cell death. If there are intermediate steps in the path to cell death, they are not rate limiting in the observational time scale utilized in clinical trials-tumor restaging at 6-to 8-week intervals. On shorter time scales, there might be intermediate steps, but the rate-limiting step is the same. Our analysis, thus, points to a common pathway to cell death for cancer cells in patients.

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Section: Introductionsupporting

confidence: 63%

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confidence: 99%

Therapies with Diverse Mechanisms of Action Kill Cells by a Similar Exponential Process in Advanced Cancers

et al. 2014

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“…There have been recent suggestions that the percentage change from baseline in tumor burden, as measured by the sum of longest diameters (SLD) of the target lesions, can be used for the assessment of comparative efficacy in randomized trials (9)(10)(11)(12)(13)(14)(15)(16)(17)(18). The proposals fall into 2 categories.…”

Section: Introductionmentioning

confidence: 99%

“…One (9,10) suggests use of the percentage change from baseline in SLD, denoted as SLD%, as a better endpoint than response rate, which is essentially a dichotomized minimum SLD%. The other category (11)(12)(13)(14)(15)(16)(17)(18) models SLD as a function of time. Buyse and colleagues (11) suggest that "a model that uses all tumor size measurements for each patient may be preferable to a model that uses PFS, given that the latter design makes less efficient use of these data."…”

Section: Introductionmentioning

confidence: 99%

“…Buyse and colleagues (11) suggest that "a model that uses all tumor size measurements for each patient may be preferable to a model that uses PFS, given that the latter design makes less efficient use of these data." Modeling of SLD has also been used to * predict overall survival as a function of early SLD% and other variables, which is then used to predict Phase III outcomes (12)(13)(14), * assess the response of tumors to time-varying dose levels of an experimental drug (15), and * evaluate the relative efficacy of study treatments through the comparison of fitted tumor growth parameters between treatment arms (16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

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Tumor Burden Modeling Versus Progression-Free Survival for Phase II Decision Making

Kaiser¹

2013

Clinical Cancer Research

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Randomized Phase II oncology trial endpoints for decision making include both progression-free survival (PFS) and change in tumor burden as measured by the sum of longest diameters (SLD) of the target lesions. In addition to observed SLD changes, tumor shrinkage and growth parameters can be estimated from the patient-specific SLD profile over time. The ability of these SLD analyses to identify an active drug is contrasted with that of a PFS analysis through the simulation of Phase II trials via resampling from each of 6 large, Phase II and III trials, 5 of which were positive and one negative. From each simulated Phase II trial, a P value was obtained from 4 analyses-a log-rank test on PFS, a Wilcoxon rank-sum test on the minimum observed percentage change from baseline in SLD, and 2 nonlinear, mixed-effects model analyses of the SLD profiles. All 4 analyses led to approximately uniformly distributed P values in the negative trial. The PFS analysis was the best or nearly the best analysis in the other 5 trials. In only one of the positive studies did the modeling analysis outperform the analysis of the minimum SLD. In conclusion, for the decision to start a Phase III trial based on the results of a randomized Phase II trial of an oncology drug, PFS appears to be a better endpoint than does SLD, whether analyzed through simple SLD endpoints, such as the minimum percentage change from baseline, or through the modeling of the SLD time course to estimate tumor dynamics.

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The impact of tumor burden characteristics in patients with metastatic renal cell carcinoma treated with sunitinib

Basappa¹,

Elson²,

Golshayan³

et al. 2010

Cancer

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BACKGROUND: An important goal of noncurative therapy for metastatic renal cell carcinoma (mRCC) is tumor burden (TB) control. However, to the authors' knowledge, the impact of TB characteristics on clinical outcome has not been studied in patients with mRCC who were treated with vascular endothelial growth factor-targeted therapy. METHODS: Patients with clear cell mRCC who were treated with sunitinib between June 2004 and October 2007 were retrospectively identified. Computed tomography scans were re-reviewed from baseline, at the time of maximal TB shrinkage (TS) while receiving sunitinib, and at the time of progressive disease (PD). Measurements were recorded as per Response Evaluation Criteria In Solid Tumors (RECIST). RESULTS: A total of 69 patients were identified. The majority (54%) were classified as being of favorable risk using Cleveland Clinic Foundation Tyrosine Kinase Inhibitor (CCF TKI) risk group criteria. All patients underwent prior nephrectomy and 77% received prior systemic therapy. There were a median of 8 metastatic deposits across all organs (range, 1-27 deposits). The median TB at the initiation of therapy was 14.0 cm (range, 3.0 cm-42.2 cm). On multivariable analysis, baseline characteristics of disease confined to above the diaphragm (P ¼ .03) and a total TB <13 cm (P ¼ .09) were found to be independent positive predictors of progression-free survival. Aþ baseline, total number of metastases <10 (P < .001) and TB above the diaphragm <6.5 cm (P ¼ .05) were found to be independent positive predictors of overall survival (OS). Increased TS while receiving sunitinib was found to be significantly associated with OS (P < .001). At the time of PD, tumor location and pattern of disease progression were not found to be associated with survival as measured from the date of PD. However, total TB (P ¼ .003) and total number of metastatic deposits ( 12 vs >12; P < .001) were found to be significant predictors of survival after PD. CONCLUSIONS: The results of the current study indicate that TB characteristics are associated with clinical outcome in patients with mRCC who are treated with sunitinib.

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Other Paradigms: Growth Rate Constants and Tumor Burden Determined Using Computed Tomography Data Correlate Strongly With the Overall Survival of Patients With Renal Cell Carcinoma

Cited by 33 publications

References 33 publications

Therapies with Diverse Mechanisms of Action Kill Cells by a Similar Exponential Process in Advanced Cancers

Therapies with Diverse Mechanisms of Action Kill Cells by a Similar Exponential Process in Advanced Cancers

Tumor Burden Modeling Versus Progression-Free Survival for Phase II Decision Making

The impact of tumor burden characteristics in patients with metastatic renal cell carcinoma treated with sunitinib

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