RNA expression profiling
tumor, tumors
REGRESSION, TUMOR, TUMORS, TUMOR GROWTH
Transcriptional profiling (RNA-seq) of frozen surgical samples was conducted using the ACE Research Transcriptome assay (Personalis). Paired-end reads in FASTQ format were aligned to the human genome (GRCh38, release 85 (ref. To evaluate the association between gene expression and 2-HG concentrations, differential expression analysis was conducted on raw count expression values for a complete set of 33,121 genes. A negative binomial generalized linear model, as implemented in the DESeq2 v.1.24.0R packageTo visually demonstrate the relationship between gene expression and 2-HG concentration, a heatmap of To identify genes that are associated with To identify genes differentially expressed between samples from patients whose tumors responded to treatment (tumor regression) and those whose tumors did not (continued tumor growth), gene expression analysis was conducted on raw count expression values for a complete set of 33,121 genes. We used DESeq2 v.1.24.0 to fit a negative binomial generalized linear model, correcting for histology. To visually demonstrate the relationship between gene expression and tumor response, a heatmap of NanoString gene expression assay was performed on RNA extracted from FFPE tumor biopsies after macrodissection to enrich tumor content and then scanned using the nCounter Digital Analyzer as per the manufacturer’s instructions (NanoString Technologies). Gene expression was analyzed using nSolver software 4.0 (NanoString Technologies) and the expression levels of each gene were normalized to those of control genes.