Background: Neuroblastoma is the most common pediatric extracranial solid tumor. Germline pathogenic variants in ALK and PHOX2B, as well as other cancer predisposition genes, are increasingly implicated in the pathogenesis of neuroblastic tumors. A challenge for clinicians is the identification of children with neuroblastoma who require genetics evaluation for underlying cancer predisposition syndromes (CPS).
Procedure: We developed a decisional algorithm (MIPOGG) to identify which patients with neuroblastic tumors have an increased likelihood of an underlying CPS. This algorithm, comprising 11 Yes/No questions, evaluates features in the tumor, personal and family history that are suggestive of an underlying CPS. We assessed the algorithm's performance in a retrospective cohort.
Results: Two hundred and nine of 278 consecutive patients with neuroblastic tumors at The Hospital for Sick Children (2007-2016) had sufficient clinical data for retrospective application of the decisional algorithm. Fifty-one of 209 patients had been referred to genetics for CPS evaluation; 6/51 had a genetic or clinical confirmation of a CPS. The algorithm correctly identified all six children (Beckwith-Wiedemann (n = 2), Fanconi anemia, RB1, PHOX2B, chromosome duplication involving ALK) as requiring a genetic evaluation by using clinical features present at diagnosis. The level of agreement between the algorithm and physicians was 83.9%, with 15 more patients identified by the algorithm than by physicians as requiring a genetics referral.
Conclusions: This decisional algorithm appropriately detected all patients who, following genetic evaluation, were confirmed to have a CPS and may improve the detection of CPS in patients with neuroblastic tumors compared with current practice.
Keywords: MIPOGG study; cancer predisposition syndrome; decision-support tool; genetic; neuroblastoma; pediatric.
© 2018 Wiley Periodicals, Inc.