RNA sequencing-based identification of ganglioside GD2-positive cancer phenotype

Maxim Sorokin, Irina Kholodenko, Daniel Kalinovsky, Tatyana Shamanskaya, Igor Doronin, Dmitry Konovalov, Aleksei Mironov, Denis Kuzmin, Daniil Nikitin, Sergey Deyev, Anton Buzdin, Roman Kholodenko

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The tumor-associated ganglioside GD2 represents an attractive target for cancer immunotherapy. GD2-positive tumors are more responsive to such targeted therapy, and new methods are needed for the screening of GD2 molecular tumor phenotypes. In this work, we built a gene expression-based binary classifier predicting the GD2-positive tumor phenotypes. To this end, we compared RNA sequencing data from human tumor biopsy material from experimental samples and public databases as well as from GD2-positive and GD2-negative cancer cell lines, for expression levels of genes encoding enzymes involved in ganglioside biosynthesis. We identified a 2-gene expression signature combining ganglioside synthase genes ST8SIA1 and B4GALNT1 that serves as a more efficient predictor of GD2-positive phenotype (Matthews Correlation Coefficient (MCC) 0.32, 0.88, and 0.98 in three independent comparisons) compared to the individual ganglioside biosynthesis genes (MCC 0.02-0.32, 0.1-0.75, and 0.04-1 for the same independent comparisons). No individual gene showed a higher MCC score than the expression signature MCC score in two or more comparisons. Our diagnostic approach can hopefully be applied for pan-cancer prediction of GD2 phenotypes using gene expression data.

Original languageEnglish
Article number142
Issue number6
Publication statusPublished - 1 Jun 2020
Externally publishedYes


  • Ganglioside biosynthesis
  • Ganglioside GD2
  • GD2-positive tumors
  • Glioma
  • Immunotherapy
  • Molecular diagnostics
  • Neuroblastoma
  • NGS
  • RNA sequencing
  • Targeted therapy

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)

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