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Multi-institution analysis of tumor mutational burden and outcomes in pediatric central nervous system tumor patients.
Parisi, Rose; Patel, Roshal R; Rood, Gavrielle; Bowden, Acacia; Turco, George; Korones, David N; Andolina, Jeffrey R; Comito, Melanie; Barth, Matthew; Weintraub, Lauren.
Afiliação
  • Parisi R; Albany Medical College, Albany, New York, USA.
  • Patel RR; Albany Medical College, Albany, New York, USA.
  • Rood G; Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • Bowden A; Upstate Medical University College of Medicine, Syracuse, New York, USA.
  • Turco G; University of Rochester School of Medicine, Rochester, New York, USA.
  • Korones DN; Pediatric Hematology/Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
  • Andolina JR; Pediatric Hematology/Oncology, University of Rochester Medical Center, Rochester, New York, USA.
  • Comito M; Pediatric Hematology/Oncology, University of Rochester Medical Center, Rochester, New York, USA.
  • Barth M; Pediatric Hematology/Oncology, Upstate University Hospital, Syracuse, New York, USA.
  • Weintraub L; Pediatric Hematology/Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
Pediatr Blood Cancer ; 70(3): e30139, 2023 03.
Article em En | MEDLINE | ID: mdl-36573296
ABSTRACT

BACKGROUND:

Pediatric central nervous system (CNS) tumors are the leading cause of pediatric cancer mortality. Research addressing genomic biomarkers and clinical outcomes is needed to inform therapeutic decision-making.

METHODS:

We conducted a retrospective analysis of pediatric patients (age <21) diagnosed with a primary CNS tumor at four upstate New York hospitals from 2008 to 2021. Clinical and histopathologic data were identified from each patient, including genomic analysis of somatic mutations and tumor mutational burden (TMB) where available. These variables were each compared with overall survival using Cox regression analyses. Multivariable analysis was conducted to identify patient characteristics that may independently predict survival.

RESULTS:

We identified 119 patients. Common tumor types included low-grade glioma (N = 51), high-grade glioma (N = 29), and medulloblastoma (N = 11). Common driver mutations included TP53 inactivation (N = 16), BRAF-KIAA1549 fusion (N = 16), FGFR1 amplification (N = 12), BRAF V600E mutation (N = 12), NF1 loss (N = 12), and H3F3A K28M mutation (N = 6). Median TMB was one mutation/megabase (mut/Mb, range = 0-132). Overall survival was 79.9%. Variables associated with poorer survival on univariable analysis were higher TMB (p = .002, HR 4.97), high-grade tumors (p = .009, HR 84.3), and high-grade glioma histology (p = .021, HR 3.14). Multivariable analyses further identified TMB (p = .011, HR 4.46) and high-grade histology (p = .015, HR 5.28) as independently predictive of worse survival. Tumor progression was more common in high-TMB (N = 15, 44%) than in low-TMB tumors (N = 19, 35%).

CONCLUSIONS:

High TMB is correlated with higher rates of progression and death as compared to low-TMB tumors. These findings may help identify patients who may benefit from alternative treatments, such as immunotherapies.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Cerebelares / Neoplasias do Sistema Nervoso Central / Glioma Tipo de estudo: Prognostic_studies Limite: Child / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Cerebelares / Neoplasias do Sistema Nervoso Central / Glioma Tipo de estudo: Prognostic_studies Limite: Child / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article