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Review
. 2017 Nov:189:93-104.
doi: 10.1016/j.trsl.2017.06.013. Epub 2017 Jul 8.

Integrating RNA sequencing into neuro-oncology practice

David S Rogawski  1 , Nicholas A Vitanza  2 , Angela C Gauthier  3 , Vijay Ramaswamy  4 , Carl Koschmann  5
Affiliations
Review

Integrating RNA sequencing into neuro-oncology practice

David S Rogawski et al. Transl Res. 2017 Nov.

Abstract

Malignant tumors of the central nervous system (CNS) cause substantial morbidity and mortality, yet efforts to optimize chemo- and radiotherapy have largely failed to improve dismal prognoses. Over the past decade, RNA sequencing (RNA-seq) has emerged as a powerful tool to comprehensively characterize the transcriptome of CNS tumor cells in one high-throughput step, leading to improved understanding of CNS tumor biology and suggesting new routes for targeted therapies. RNA-seq has been instrumental in improving the diagnostic classification of brain tumors, characterizing oncogenic fusion genes, and shedding light on intratumor heterogeneity. Currently, RNA-seq is beginning to be incorporated into regular neuro-oncology practice in the form of precision neuro-oncology programs, which use information from tumor sequencing to guide implementation of personalized targeted therapies. These programs show great promise in improving patient outcomes for tumors where single agent trials have been ineffective. As RNA-seq is a relatively new technique, many further applications yielding new advances in CNS tumor research and management are expected in the coming years.

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Figures

Figure 1
Figure 1
Key molecular alterations in CNS tumors. Many of the alterations identified in this schematic are targetable through approved or investigational drugs. RNA-seq is especially valuable for characterizing expressed fusion genes, but future applications may include the development of personalized immunotherapies and detection of extracellular RNAs for tumor diagnosis and monitoring. DC: dendritic cell; CTL: cytotoxic lymphocyte; TK: tyrosine kinase; KAc: acetylated lysine.
Figure 2
Figure 2
Techniques for characterizing RNA transcripts. (A) qRT-PCR (TaqMan®). Sample RNA is converted into cDNA and amplified by PCR in the presence of a target-specific oligonucleotide bound to a fluorescent probe and fluorescence quencher. As DNA polymerase synthesizes the new DNA strand, it cleaves the fluorescent probe off the oligonucleotide, freeing it to fluoresce. The fluorescence grows stronger with each PCR cycle as more fluorescent probes are freed. (B) Microarray. RNA is extracted from normal and tumor cells, reverse transcribed, and labeled with fluorescent probes (green for normal cDNA, red for tumor cDNA). The cDNAs are applied to a microarray chip, where they bind to complementary sequences from annotated genes. The relative amount of green versus red fluorescence corresponds to the relative expression of genes in normal versus tumor cells. (C) Nanostring. Extracted RNA is hybridized with target-specific capture probes and reporter probes. The reporter probes contain precisely ordered fluorescent barcodes to identify each target. The probe-target complexes are then aligned by an electric current, and the fluorescent barcodes are counted to digitally quantitate each target. (D) RNA-seq. Extracted RNA is fragmented, reverse-transcribed, and modified with linkers to aid sequencing. The cDNA is sequenced with NGS technology, and the resulting sequences are aligned against a reference genome to reveal the expression levels of various genes in the sample.
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