Abstract LB316: Enabling long-read mRNA-seq for oncology biomarker discovery using limited clinical sample inputs

Abstract RNAs serve a central role in cellular biology by converting genomic information into effector molecules, either as mRNAs or directly functional RNAs. Measurement of RNA through RNA Sequencing has become an important method to understand biological processes. While it has been long appreciated that a vast diversity of RNA transcripts can be produced through alternative splicing, more recent work has defined a significant contribution of alternate and aberrant splicing to diseases like cancer, neurodegeneration, and autoimmunity. Thus, understanding the diversity of RNA transcripts is a key emerging topic in pathophysiology and biomarker discovery. Third-generation sequencing technologies provide the opportunity to sequence full-length cDNA without the need for fragmentation and hence provide a more complete picture of isoform structure and transcript abundance. However, a current limitation of long-read RNA sequencing (LR-RNA-seq) is the requirement for high input amounts of RNA that can be unachievable for some primary sample types, like RNA isolated from small amounts of tumor samples or sorted blood cancer cells. Here we describe a new LR-RNA-seq product enabling full-length RNA sequencing from single cells (∼10pg) up to 100 ng total RNA. With a 10ng total RNA input we demonstrate the ability to reliably sequence at an average length (N50) of 2kb and detection of full-length transcripts as long as 10kb. Performance at the single-cell level substantially outperforms existing single-cell LR-RNA-seq methods. Further, our data provides a more complete picture of isoform-specific changes compared to other commercially available technologies. With the ability to process up to 96 samples at a time, this technology will enable the processing of rare or valuable samples to uncover novel biomarkers beyond gene expression. Citation Format: Bryan Bell, Lisa Welter, Kazuo Tori, Jackson Peterson, Alan Du, Gilma Sevilla, Yana Ryan, Ploy Setthasap, Sherry Wei, Tomoya Uchiyama, Shiyi Yin, Mike Covington, Mohammad Fallahi, Saloni Pasta, Yue Yun, Andrew Farmer. Enabling long-read mRNA-seq for oncology biomarker discovery using limited clinical sample inputs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB316.