Functional comparison of exome capture-based methods for transcriptomic profiling of formalin-fixed paraffin-embedded tumors.


Journal

NPJ genomic medicine
ISSN: 2056-7944
Titre abrégé: NPJ Genom Med
Pays: England
ID NLM: 101685193

Informations de publication

Date de publication:
12 Aug 2021
Historique:
received: 05 03 2021
accepted: 26 07 2021
entrez: 13 8 2021
pubmed: 14 8 2021
medline: 14 8 2021
Statut: epublish

Résumé

The availability of fresh frozen (FF) tissue is a barrier for implementing RNA sequencing (RNA-seq) in the clinic. The majority of clinical samples are stored as formalin-fixed, paraffin-embedded (FFPE) tissues. Exome capture platforms have been developed for RNA-seq from FFPE samples. However, these methods have not been systematically compared. We performed transcriptomic analysis of 32 FFPE tumor samples from 11 patients using three exome capture-based methods: Agilent SureSelect V6, TWIST NGS Exome, and IDT XGen Exome Research Panel. We compared these methods to the TruSeq RNA-seq of fresh frozen (FF-TruSeq) tumor samples from the same patients. We assessed the recovery of clinically relevant biological features. The Spearman's correlation coefficients between the global expression profiles of the three capture-based methods from FFPE and matched FF-TruSeq were high (rho = 0.72-0.9, p < 0.05). A significant correlation between the expression of key immune genes between individual capture-based methods and FF-TruSeq (rho = 0.76-0.88, p < 0.05) was observed. All exome capture-based methods reliably detected outlier expression of actionable gene transcripts, including ERBB2, MET, NTRK1, and PPARG. In urothelial cancer samples, the Agilent assay was associated with the highest molecular subtype concordance with FF-TruSeq (Cohen's k = 0.7, p < 0.01). The Agilent and IDT assays detected all the clinically relevant fusions that were initially identified in FF-TruSeq. All FFPE exome capture-based methods had comparable performance and concordance with FF-TruSeq. Our findings will enable the implementation of RNA-seq in the clinic to guide precision oncology approaches.

Identifiants

pubmed: 34385467
doi: 10.1038/s41525-021-00231-7
pii: 10.1038/s41525-021-00231-7
pmc: PMC8360986
doi:

Types de publication

Journal Article

Langues

eng

Pagination

66

Informations de copyright

© 2021. The Author(s).

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Auteurs

Kyrillus S Shohdy (KS)

Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA.
Department of Clinical Oncology, Kasr Alainy School of Medicine, Cairo University, Cairo, Egypt.

Rohan Bareja (R)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.

Michael Sigouros (M)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.

David C Wilkes (DC)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.

Princesca Dorsaint (P)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.

Jyothi Manohar (J)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.

Daniel Bockelman (D)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.

Jenny Z Xiang (JZ)

Genomic Resources Core Facility, Weill Cornell Medicine, New York, NY, USA.

Rob Kim (R)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.

Kentaro Ohara (K)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.

Kenneth Eng (K)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.

Juan Miguel Mosquera (JM)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.

Olivier Elemento (O)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.

Andrea Sboner (A)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.

Alicia Alonso (A)

Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.

Bishoy M Faltas (BM)

Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA. bmf9003@med.cornell.edu.
Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA. bmf9003@med.cornell.edu.
Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA. bmf9003@med.cornell.edu.
Department of Cell and Developmental Biology, Weill Cornell Medicine, New York, NY, USA. bmf9003@med.cornell.edu.

Classifications MeSH