RNA Sequencing-Based Identification of Ganglioside GD2-Positive Cancer Phenotype

• Published in: Biomedicines 2020-05, Vol.8 (6), p.142
• Main Authors: Sorokin, Maxim, Kholodenko, Irina, Kalinovsky, Daniel, Shamanskaya, Tatyana, Doronin, Igor, Konovalov, Dmitry, Mironov, Aleksei, Kuzmin, Denis, Nikitin, Daniil, Deyev, Sergey, Buzdin, Anton, Kholodenko, Roman
• Format: Article
• Language: English
• Subjects: ganglioside GD2; GD2-positive tumors; glioma; immunotherapy; molecular diagnostics; neuroblastoma
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines8060142

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 and 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.