Biology subjects

Reznik, E.

Publications and source records attributed to Reznik, E..

2 recordsLinked to original sources

bioRxiv2018

Isoform switching as a mechanism of acquired resistance to isocitrate dehydrogenase inhibition

Somatic mutations in cytosolic or mitochondrial isoforms of isocitrate dehydrogenase (IDH1 or IDH2, respectively) contribute to oncogenesis via production of the metabolite 2-hydroxyglutarate (2HG). Isoform-selective IDH inhibitors suppress 2HG production and induce clinical responses in patients with IDH1- and IDH2-mutant malignancies. Despite the promising activity of IDH inhibitors, the mechanisms that mediate resistance to IDH inhibition are poorly understood. Here, we describe four clinical cases that identify mutant IDH isoform switching, either from mutant IDH1 to mutant IDH2 or vice versa, as a mechanism of acquired clinical resistance to IDH inhibition in solid and liquid tumors.\n\nSignificanceIDH-mutant cancers can develop resistance to isoform-selective IDH inhibition by \"isoform switching\" from mutant IDH1 to mutant IDH2 or vice versa, thereby restoring 2-hydroxyglutarate (2HG) production by the tumor. These findings underscore a role for continued 2HG production in tumor progression and suggest therapeutic strategies to prevent or overcome resistance.

cancer biology↗

bioRxiv2017

Enabling cross-study analysis of RNA-Sequencing data

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).

bioinformatics↗