"Clinical trials are not able to study individual treatment effects as methods to do so have not yet matured into practical use. For decades, we have studied groups of patients enrolled on a trial, comparing groups of patients as large cohorts and losing signal for important, complex features that define individuals. Foundation models take a different approach. Using the CURE AI foundation model generated from clinical and multi-omics data from hundreds of thousands of patients, complex, non-linear biological patterns can be found in new datasets that can provide insights into disease biology. We previously identified a complex signature predictive of immunotherapy response relative to chemotherapy response by analysis of a set of non-small cell lung cancer clinical trials (Weiss et al., AI in Precision Oncology, 2025). In the current study, we theorized that we could apply predictors of immunotherapy response/nonresponse from adult clinical trials to pediatric cancer patients.
We will present our analysis of a major TIGIT clinical trial in non-small cell lung cancer comparing an anti-TIGIT/anti-PD-L1 versus anti-PD-L1 from where outcomes and omics data were available. We developed an omics-based benefit score that predicts benefit to TIGIT/PD-L1 combination therapy over PD-L1 monotherapy that is valid in adult non-small cell lung cancer. From this prediction, we then asked whether we could use this score to stratify pediatric patients by predicted benefit to anti-TIGIT therapy. Neuroblastoma stood out as a pediatric cancer with high benefit prediction to anti-TIGIT therapy, which we are currently further mechanistically exploring in the high-risk space. As an indicati"