ArticleJNCI cancer spectrum2026
Heterogeneous treatment effect of immune checkpoint inhibitors by pretreatment prognosis in randomized controlled trials.
Article in JNCI cancer spectrum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Heterogeneity of treatment effect? What baseline prognostic factors tell us about immunotherapy benefit.JNCI cancer spectrum · 2026Article
Corrections and comments
- Commented on by
Authors and funding
10 authors.
Funding
Abstract
backgroundTreatment 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.
methodsApplying 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.
resultsAmong 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).
conclusionsThe 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.