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Available treatments for glioma have limited effectiveness rendering this a disease of poor clinical outcome. Gene expression profiling can uncover the biological mechanisms underlying disease, information that is crucial for drug development or repurposing. RNA sequencing (RNA-seq) is routinely used to assess gene expression but costs remain high. Whilst sample multiplexing reduces RNA-seq costs, the lowered cDNA sequencing depth of multiplexed samples may hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq-based downstream analyses such as gene expression connectivity mapping is not known. Connectivity mapping is a method used to identify potential therapeutic compounds for drug repurposing. In this study, published RNA-seq from brain tumour (glioma) patients were analysed and assembled into two gene signature contrasts for astrocytoma disease progression (WHO Grade II-III; III-IV). Gene signatures were subsampled to simulate sequencing depth alterations and analysed in connectivity mapping to investigate target compound robustness. Data loss to gene signatures led to the loss, gain and consistent identification of significant connections to target compounds. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (e.g. Suramin, Dasatinib). Lost connections may have been linked to low abundance genes in the gene signature that closely characterised the disease phenotype. Consistently identified connections may have related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false positive connections that were gained as a result of gene signature modification with data loss. Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compounds identified for drug repurposing.

BACKGROUND: Gene expression profiling can uncover biological mechanisms underlying disease and is important in drug development. RNA-seq is routinely used to assess gene expression but costs remain high. Sample multiplexing reduces RNA-seq costs, however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq-based downstream analyses such as gene expression connectivity mapping is not known, where connectivity mapping can be used to identify potential therapeutic compounds for repurposing. MATERIAL AND METHODS: In this study, published RNA-seq profiles from brain tumour (glioma) patients were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analysed in connectivity mapping to investigate target compound robustness. RESULTS: Data loss to gene signatures led to the loss, gain and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (e.g. Suramin, Dasatinib). Lost connections may have been linked to low abundance genes in the gene signature that closely characterised the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false positive connections that were gained as a result of gene signature modification with data loss. CONCLUSION: Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.