Individualization of Treatment Improves the Survival of Children With High-Risk Solid Tumors: Comparative Patient Series Analysis in a Real-Life Scenario

Abstract

Introduction: The individualization of treatment is attractive, especially in children with high-risk cancer. In such a rare and very heterogeneous group of diseases, large population-based clinical randomized trials are not feasible without international collaboration. We therefore propose comparative patient series analysis in a real-life scenario. Methods: Open cohort observational study, comparative analysis. Seventy patients with high-risk solid tumors diagnosed between 2003 and 2015 and in whom the treatment was individualized either empirically or based on biomarkers were analyzed. The heterogeneity of the cohort and repeated measurements were advantageously utilized to increase effective sample size using appropriate statistical tools. Results: We demonstrated a beneficial effect of empirically given low-dose metronomic chemotherapy (HR 0.46 for relapses, p = 0.017) as well as various repurposed or targeted agents (HR 0.15 for deaths, p = 0.004) in a real-life scenario. However, targeted agents given on the basis of limited biological information were not beneficial. Conclusions: Comparative patient series analysis provides institutional-level evidence for treatment individualization in high-risk pediatric malignancies. Our findings emphasize the need for a comprehensive, multi omics assessment of the tumor and the host as well whenever molecularly driven targeted therapies are being considered. Low-dose metronomic chemotherapy or local control of the disease may be a more rational option in situations where targeted treatment cannot be justified by robust evidence and comprehensive biological information. “Targeted drugs” may be given empirically with a realistic benefit expectation when based on robust rationale.

N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine—A Review

“…Evidence from pooled and observational data or registries becomes more relevant for similar techniques. An example of such a combination of approaches can be found in our recent paper [ 58 ], in which the aggregation of heterogeneous data, and time series analyses of survival data, were performed.…”

“…Therefore, models with random effects are much more suitable for this purpose. We recently used the aggregation and random effects approach on observational survival data to demonstrate the benefit of individualized treatment in children with high-risk solid tumors [ 58 ].…”