Enhanced Detection of Treatment Effects on Metastatic Colorectal Cancer with Volumetric CT Measurements for Tumor Burden Growth Rate Evaluation

Abstract

Purpose: Mathematical models combined with new imaging technologies could improve clinical oncology studies. To improve detection of therapeutic effect in patients with cancer, we assessed volumetric measurement of target lesions to estimate the rates of exponential tumor growth and regression as treatment is administered.Experimental Design: Two completed phase III trials were studied (988 patients) of aflibercept or panitumumab added to standard chemotherapy for advanced colorectal cancer. Retrospectively, radiologists performed semiautomated measurements of all metastatic lesions on CT images. Using exponential growth modeling, tumor regression (d) and growth (g) rates were estimated for each patient's unidimensional and volumetric measurements.Results: Exponential growth modeling of volumetric measurements detected different empiric mechanisms of effect for each drug: panitumumab marginally augmented the decay rate [tumor half-life; d [IQR]: 36.5 days (56.3, 29.0)] of chemotherapy [d: 44.5 days (67.2, 32.1), two-sided Wilcoxon P ¼ 0.016], whereas aflibercept more significantly slowed the growth rate [doubling time; g ¼ 300.8 days (154.0, 572.3)] compared with chemotherapy alone [g ¼ 155.9 days (82.2, 347.0), P ≤ 0.0001]. An association of g with overall survival (OS) was observed. Simulating clinical trials using volumetric or unidimensional tumor measurements, fewer patients were required to detect a treatment effect using a volumetric measurement-based strategy (32-60 patients) than for unidimensional measurement-based strategies (124-184 patients).Conclusions: Combined tumor volume measurement and estimation of tumor regression and growth rate has potential to enhance assessment of treatment effects in clinical studies of colorectal cancer that would not be achieved with conventional, RECIST-based unidimensional measurements.

Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition–overall survival modeling framework

“…Model-based estimates of on-treatment growth rate (KG) have been found to predict for OS in a variety of tumor types and treatments [6,10,15,18,25]. More recently KG was found to be the only tumor dynamic metric able to predict survival benefit in one Phase II and one Phase III studies of atezolizumab vs. chemotherapy in second and later lines of therapy for NSCLC, while other model-based metrics or classical endpoints (ORR, PFS) did not [10].…”

“…The use of tumor dynamics model-based approaches has become increasingly attractive to evaluate treatment response for decision-making through the course of clinical development in oncology [1][2][3]. Model-based tumor dynamics metrics (including early shrinkage, time to regrowth, on-treatment growth rate, or the full dynamic profile) have been demonstrated to predict overall survival (OS) in different types of solid tumors, including colorectal cancer [4][5][6], breast cancer [7,8], non-small cell lung cancer (NSCLC) [9][10][11], locally advanced and metastatic urothelial carcinoma (mUC) [12,13], renal cell carcinoma (RCC) [14,15], and several other…”

“…Although the link between tumor dynamics and OS was shown for other CITs, 12,13,16,25 this is the only analysis where the difference in TGI metrics (namely KG) across treatment arms in randomized studies was shown to predict treatment effect (HR of investigational treatment vs. control) on OS. Among the various tumor dynamic‐based approaches to predict OS, the use of KG estimates seems to be quite promising as demonstrated in many studies 6,10,15,18,25 …”

“…The use of tumor dynamics model‐based approaches has become increasingly attractive to evaluate treatment response for decision‐making through the course of clinical development in oncology. 1 , 2 , 3 Model‐based tumor dynamics metrics (including early shrinkage, time to regrowth, on‐treatment growth rate, or the full dynamic profile) have been demonstrated to predict overall survival (OS) in different types of solid tumors, including colorectal cancer, 4 , 5 , 6 breast cancer, 7 , 8 non‐small cell lung cancer (NSCLC), 9 , 10 , 11 locally advanced and metastatic urothelial carcinoma (mUC), 12 , 13 renal cell carcinoma (RCC), 14 , 15 and several other tumor types 16 , 17 , 18 , 19 for a variety of treatments. Leveraging tumor dynamics as a biomarker to predict OS in phase II trials with cancer immunotherapy (CIT) is not a novel concept, but longitudinal tumor response to CIT treatment may elicit different patterns compared with treatments with other mechanisms of action, such as delayed responses or increased tumor burden before regression.…”

Tumor Growth Rate After Nadir Is Associated With Survival in Patients With EGFR-Mutant Non–Small-Cell Lung Cancer Treated With Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor

“…5,[29][30][31][32][33] Previous studies evaluated tumor growth rate as a marker for defining meaningful trial end points and documenting drug efficacy in advanced solid tumors. 30,[32][33][34][35] Although the potential role of tumor growth rates in enhancing detection of treatment effects is described in these studies, only a few studies evaluated the impact of tumor growth rates on the survival of patients. In clinical trials of renal cell carcinomas (RCC) and prostate cancers, the growth rate constant, obtained as log e 2/doubling time using tumor size, showed a negative correlation with survival, 32,33 indicating that faster tumor growth is associated with shorter survival in these tumors.…”