Predicting clinical outcomes in neuroblastoma with genomic data integration


Baali I., Acar D. A. E., Aderinwale T. W., HafezQorani S., Kazan H.

BIOLOGY DIRECT, vol.13, 2018 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 13
  • Publication Date: 2018
  • Doi Number: 10.1186/s13062-018-0223-8
  • Journal Name: BIOLOGY DIRECT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Neuroblastoma, Data integration, Cancer subtypes, Kernel k-means, EXPRESSION-BASED CLASSIFICATION, CLUSTER VALIDATION
  • Middle East Technical University Affiliated: Yes

Abstract

Background: Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis.