Reliable RNA-seq analysis from FFPE specimens as a means to accelerate cancer-related health disparities research

• DOI: 10.1371/journal.pone.0321631
• Publication Date: 2025-04-21T17:48:24Z
• Authors: Mitchell J. Frede...; Dannelys Perez-Bello; Pedram Yadollahi; Patricia Castro; Alan Frederick; Andrew Frederick; Rashid A. Osman; Fonma Essien; Imelda Yebra; Ashley Hamlin; Thomas J. Ow; Heath D. Skinner; Vlad C. Sandulache

Whole transcriptome sequencing (WTS/ RNA-Seq) is a ubiquitous tool for investigating cancer biology. RNA isolated from frozen sources limits possible studies for analysis of associations with phenotypes or clinical variables requiring long-term follow-up. Although good correlations are reported in RNA-Seq data from paired frozen and formalin fixed paraffin embedded (FFPE) samples, uncertainties regarding RNA quality, methods of extraction, and data reliability are hurdles to utilization of archival samples. We compared three different platforms for performing RNA-seq using archival FFPE oropharyngeal squamous carcinoma (OPSCC) specimens stored up to 20 years, as part of an investigation of transcriptional profiles related to health disparities. We developed guidelines to purify DNA and RNA from FFPE tissue and perform downstream RNA-seq and DNA SNP arrays. RNA was extracted from 150 specimens, with an average yield of 401.8 ng/cm2 of tissue. Most samples yielded sufficient RNA reads >13,000 protein coding genes which could be used to differentiate HPV-associated from HPV-independent OPSCCs. Co-isolated DNA was used to identify reliably define patient ancestry which correlated well with patient-reported race. Utilizing the methods described in this study provides a robust, reliable, and standardized means of DNA & RNA extraction from FFPE as well as a means by which to assure the quality of the data generated. Optimized RNA extraction techniques, combined with robust bioinformatic approaches designed to optimize data homogenization, analysis and biological validation can revolutionize our ability to transcriptomically profile large solid tumor sets derived from ancestrally varied patient populations.