Related Experiment Video
Updated: Jan 13, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Heterogeneous treatment effect of immune checkpoint inhibitors by pretreatment prognosis in randomized controlled
Lee X Li1, Adel Shahnam2, Ganessan Kichenadasse1,3
1College of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, Australia.
Background:
Treatment response to immune checkpoint inhibitors varies considerably, a phenomenon known as heterogeneity of treatment effect. Heterogeneity of treatment effect is explored via one-variable-at-a-time subgroup analyses in randomized controlled trials (RCTs), however, this method has limitations, which the risk-modeling approach seeks to address.
Methods:
Applying the risk-modeling approach, individual patient data from 10 RCTs (6 supporting US Food and Drug Administration's atezolizumab label: OAK, IMpower130, IMpower150, IMpower133, IMbrave150, IMspire150; 4 unlabeled indications: IMpower131, IMpower132, IMmotion151, and IMvigor211) were analyzed by an extreme gradient-boosting algorithm to predict pretreatment prognosis for overall survival. The predicted risk scores were evaluated as efficacy modifiers categorically (high-, intermediate-, low-risk groups) and continuously in Cox models with treatment-by-risk-group interaction terms. Sensitivity and exploratory analyses investigated absolute and meta-analyzed treatment effect and compared the results with established prognostic tools and treatment effect predictors. Statistical significance tests are 2-sided.
Results:
Among the 10 RCTs (n = 7053), one trial (IMvigor211) showed statistically significant heterogeneity of treatment effect by pretreatment prognosis across all evaluations (risk groups, risk scores, sensitivity analyses: P < .001). Among other trials, no statistically significant heterogeneity of treatment effect was detected (risk group and risk score analysis interaction test: OAK P = .61 and P = .77; IMpower130 P = .13 and P = .52; IMpower131 P = .21 and P = .02; IMpower150 P = .14 and P = .36; IMpower133 P = .38 and P = .12; IMbrave150 P = .15 and P = .08; IMspire150 P = .24 and P = .6; IMpower132 P = .15 and P = .81; IMmotion151 P = .48 and P = .21, respectively).
Conclusions:
The risk-modeling approach showed no clear link between pretreatment prognosis and immune checkpoint inhibitor efficacy in most RCTs, particularly those supporting atezolizumab's Food and Drug Administration label. In IMvigor211, patients with better pretreatment prognosis were more likely to benefit from atezolizumab treatment for platinum-refractory metastatic urothelial carcinoma.
