Catherine Goudie1, Noelle Cullinan1, Anita Villani1, Natalie Mathews2, Kalene van Engelen3, David Malkin1, Meredith S Irwin1, William D Foulkes4. 1. Division of Haematology-Oncology, The Hospital for Sick Children, Department of Pediatrics, University of Toronto, Toronto, Canada. 2. Department of Pediatrics, McGill University Health Centre, Montreal, Quebec, Canada. 3. Department of Clinical and Metabolic Genetics, The Hospital for Sick Children, Department of Molecular Genetics, University of Toronto, Toronto, Canada. 4. Department of Medical Genetics, McGill University Health Centre, Montreal, Quebec, Canada.
Abstract
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.
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.
Authors: Catherine Goudie; Leora Witkowski; Noelle Cullinan; Lara Reichman; Ian Schiller; Melissa Tachdjian; Linlea Armstrong; Katherine A Blood; Josée Brossard; Ledia Brunga; Chantel Cacciotti; Kimberly Caswell; Sonia Cellot; Mary Egan Clark; Catherine Clinton; Hallie Coltin; Kathleen Felton; Conrad V Fernandez; Adam J Fleming; Noemi Fuentes-Bolanos; Paul Gibson; Ronald Grant; Rawan Hammad; Lynn W Harrison; Meredith S Irwin; Donna L Johnston; Sarah Kane; Lucie Lafay-Cousin; Irene Lara-Corrales; Valerie Larouche; Natalie Mathews; M Stephen Meyn; Orli Michaeli; Renée Perrier; Meghan Pike; Angela Punnett; Vijay Ramaswamy; Jemma Say; Gino Somers; Uri Tabori; My Linh Thibodeau; Annie-Kim Toupin; Katherine M Tucker; Kalene van Engelen; Stephanie Vairy; Nicolas Waespe; Meera Warby; Jonathan D Wasserman; James A Whitlock; Daniel Sinnett; Nada Jabado; Paul C Nathan; Adam Shlien; Junne Kamihara; Rebecca J Deyell; David S Ziegler; Kim E Nichols; Nandini Dendukuri; David Malkin; Anita Villani; William D Foulkes Journal: JAMA Oncol Date: 2021-12-01 Impact factor: 33.006
Authors: Thi Minh Kha Nguyen; Astrid Behnert; Torsten Pietsch; Christian Vokuhl; Christian Peter Kratz Journal: Fam Cancer Date: 2021-02-26 Impact factor: 2.375
Authors: Anna Byrjalsen; Illja J Diets; Jette Bakhuizen; Thomas van Overeem Hansen; Kjeld Schmiegelow; Anne-Marie Gerdes; Ulrik Stoltze; Roland P Kuiper; Johannes H M Merks; Karin Wadt; Marjolijn Jongmans Journal: Fam Cancer Date: 2021-06-01 Impact factor: 2.375