Data from Predictive Biomarkers of Overall Survival in Patients with Metastatic Renal Cell Carcinoma Treated with IFNα ± Bevacizumab: Results from CALGB 90206 (Alliance)

<div>AbstractPurpose:<p>CALGB 90206 was a phase III trial of 732 patients with metastatic renal cell carcinoma (mRCC) comparing bevacizumab plus IFNα (BEV + IFN) with IFNα alone (IFN). No difference in overall survival (OS) was observed. Baseline samples were analyzed to identify predictive biomarkers for survival benefit.</p>Patients and Methods:<p>A total of 32 biomarkers were assessed in 498 consenting patients randomly assigned into training (<i>n</i> = 279) and testing (<i>n</i> = 219) sets. The proportional hazards model was used to test for treatment arm and biomarker interactions of OS. The estimated coefficients from the training set were used to compute a risk score for each patient and to classify patients by risk in the testing set. The resulting model was assessed for predictive accuracy using the time-dependent area under the ROC curve (tAUROC).</p>Results:<p>A statistically significant three-way interaction between IL6, hepatocyte growth factor (HGF), and bevacizumab treatment was observed in the training set and confirmed in the testing set (<i>P</i> < 0.0001). The model based on IL6, HGF, and bevacizumab treatment was predictive of OS (<i>P</i> < 0.001), with the high- and low-risk groups having a median OS of 10.2 [95% confidence interval (CI), 8.0–13.8] and 34.3 (95% CI, 28.5–40.5) months, respectively. The average tAUROC for the final model of OS based on 100 randomly split testing sets was 0.78 (first, third quartiles = 0.77, 0.79).</p>Conclusions:<p>IL6 and HGF are potential predictive biomarkers of OS benefit from BEV + IFN in patients with mRCC. The model based on key biological and clinical factors demonstrated predictive efficacy for OS. These markers warrant further validation in future anti-VEGF and immunotherapy in mRCC trials.</p><p><i><a href="http://dx.doi.org/10.1158/1078-0432.CCR-22-0750" target="_blank">See related commentaries by Mishkin and Kohn, p. 2722</a> and <a href="http://dx.doi.org/10.1158/1078-0432.CCR-22-0027" target="_blank">George and Bertagnolli, p. 2725</a></i></p></div>

Duke Scholars

Author Andrew Benjamin Nixon Medicine, Medical Oncology

Author Susan Halabi Biostatistics & Bioinformatics, Division of Biostatistics