INFORMING TREATMENT DECISIONS IN RELAPSED PEDIATRIC MALIGNANCIES USING NEXT-GENERATION DIAGNOSTICS

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Abstract

BACKGROUND: Although childhood malignancies have become curable in about 75% of cases due to empirically developed multi-modal therapeutic concepts applied in nation-wide collaborative trials, for children with a relapse, cure remains the exception. In the framework of the ICGC project PedBrain many new potentially druggable genetic lesions have been identified. However, it will not be feasible to conduct traditional phase I trials for all these new drugs in these overall rare entities. To still have our young patients participating in the recent advances in molecular targeted drug treatment, we initiated a novel innovative way of introducing these drugs in a clinical setting based on an individualized molecular rationale, a concept called INFORM (INdividualized therapy For Relapsed Malignancies in childhood). METHODS: Exome- and low-coverage whole-genome sequencing, RNA sequencing, gene expression profiling, DNA methylation profiling, bioinformatic prediction of drug targets and compound selection are carried out aiming at a turnaround time of 4 weeks or less after re-biopsy of the tumor. In the current feasibility phase, drug targets and prioritization are offered to the treating physician in the local hospital for individual treatment decisions. RESULTS: Ten patients were recruited to the INFORM-pilot study by now. For all but two, druggable targets have been identified. The first two patients with early follow-up MRIs after 6 weeks had stable disease (medulloblastoma) and 50% tumor volume reduction (myofibroblastc tumor), respectively. In an additional case with pontine glioma, molecular diagnostics significantly contributed to the establishment of an unambiguous diagnosis. CONCLUSIONS: This is the first population-based study using next-generation sequencing technologies to guide treatment decisions in a clinical setting. In addition to this advance in “next-generation” clinical oncology, INFORM will also reveal the largest comprehensive molecular datasets of relapsed tumors to date, and since primary tumor material from the same patient will also be analyzed whenever available, will likely identify key biological properties of relapsing malignancies and recurrent mechanisms of drug resistance across entities. SECONDARY CATEGORY: Pediatrics.

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