| Author |
Cieslik, Marcin Chugh, Rashmi Wu, Yi-Mi Wu, Ming Brennan, Christine Lonigro, Robert Su, Fengyun Wang, Rui Siddiqui, Javed Mehra, Rohit Cao, Xuhong Lucas, David Chinnaiyan, Arul M. Robinson, Dan |
| Description |
Author Affiliation: Cieslik M ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Chugh R ( Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA); Wu YM ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Wu M ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Brennan C ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Lonigro R ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Su F ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Wang R ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Siddiqui J ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Mehra R ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Cao X ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Lucas D ( Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA); Chinnaiyan AM ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA); Robinson D ( Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA) |
| Abstract |
RNA-seq by poly(A) selection is currently the most common protocol for whole transcriptome sequencing as it provides a broad, detailed, and accurate view of the RNA landscape. Unfortunately, the utility of poly(A) libraries is greatly limited when the input RNA is degraded, which is the norm for research tissues and clinical samples, especially when specimens are formalin-fixed. To facilitate the use of RNA sequencing beyond cell lines and in the clinical setting, we developed an exome-capture transcriptome protocol with greatly improved performance on degraded RNA. Capture transcriptome libraries enable measuring absolute and differential gene expression, calling genetic variants, and detecting gene fusions. Through validation against gold-standard poly(A) and Ribo-Zero libraries from intact RNA, we show that capture RNA-seq provides accurate and unbiased estimates of RNA abundance, uniform transcript coverage, and broad dynamic range. Unlike poly(A) selection and Ribo-Zero depletion, capture libraries retain these qualities regardless of RNA quality and provide excellent data from clinical specimens including formalin-fixed paraffin-embedded (FFPE) blocks. Systematic improvements across key applications of RNA-seq are shown on a cohort of prostate cancer patients and a set of clinical FFPE samples. Further, we demonstrate the utility of capture RNA-seq libraries in a patient with a highly malignant solitary fibrous tumor (SFT) enrolled in our clinical sequencing program called MI-ONCOSEQ. Capture transcriptome profiling from FFPE revealed two oncogenic fusions: the pathognomonic NAB2-STAT6 inversion and a therapeutically actionable BRAF fusion, which may drive this specific cancer's aggressive phenotype. |
