Abstract 6560: Cell-free RNA analysis for non-invasive cancer detection and characterization

Abstract Blood-based liquid biopsies enable non-invasive characterization of cancers. Cell-free RNA (cfRNA) analysis could potentially complement circulating tumor DNA (ctDNA) analysis and allow broader molecular characterization of cancers but has not been extensively explored. Here, we describe RARE-Seq, a novel cfRNA sequencing method designed to maximize sensitivity by targeting rare, tissue-specific transcripts, and simultaneously detecting both tumor-derived expression signatures and somatic mutations in circulating tumor RNA (ctRNA). To interrogate samples for specific gene expression signatures, we developed and optimized a framework to measure tumor- and tissue-specific enrichment scores (ES) within cfRNA. We then empirically determined the limit of detection (LOD95) of RARE-Seq using admixtures of tumor cell line RNA and control cfRNA across a wide range of fractions. We found RARE-Seq to have an estimated LOD95 of 0. 05% for cancer-derived RNA, which was 52-fold better than whole transcriptome cfRNA profiling. To explore its potential clinical utility, we profiled 192 samples from 170 subjects with various cancers and non-cancer controls using RARE-Seq. Non-small cell lung cancer (NSCLC) expression signatures were significantly detected in 40 out of 51 NSCLC samples (78% sensitivity at 90% specificity). Detection increased with stage, ranging from 40% in stage I to 87% in stage IV. In a subset of stage IV NSCLC patients with matching ctDNA data (n=16), ctRNA levels were significantly correlated with ctDNA allele frequencies (Pearson R=0. 84, P=0. 005). Intriguingly, 86% of samples with undetectable ctDNA contained detectable ctRNA, suggesting potential complementarity of ctRNA and ctDNA analysis for cancer detection. We also developed and benchmarked a direct ctRNA genotyping approach, allowing noninvasive detection of driver mutations in 42% of patients with advanced NSCLC. When applied to other tumors, RARE-Seq demonstrated 80-100% sensitivity at 90% specificity for detection of pancreatic adenocarcinomas (PAAD), prostate adenocarcinomas (PRAD), and hepatocellular carcinomas (LIHC). Furthermore, when evaluating tissue of origin (TOO) classification performance, RARE-Seq achieved 85% median accuracy for the top predicted tissue and 96% median accuracy for the top two tissues. These results highlight the potential value of ultrasensitive cfRNA analysis, providing proof-of-principle for detection of multiple tumor types using RARE-Seq. Citation Format: Monica C. Nesselbush, Bogdan A. Luca, Young-Jun Jeon, Isabel Jabara, Catherine B. Meador, Michael S. Binkley, Nova Xu, Angela B. Hui, Andrea Garofalo, William Y. Shi, Kevin J. Liu, Takeshi Sugio, Noah Kastelowitz, Emily G. Hamilton, Rene F. Bonilla, Yi Peng Wang, Alice Jiang, Brianna Lau, Jordan Eichholz, Mandeep Banwait, Joseph Schroers-Martin, Jan Boegeholz, Daniel A. King, Mohammad S. Esfahani, Tyler Raclin, Robert Tibshirani, Sandy Srinivas, Viswam S. Nair, James M. Isbell, Bob T. Li, Zofia Piotrowska, Lecia V. Sequist, Aaron N. Hata, Joel W. Neal, Heather A. Wakelee, Andrew J. Gentles, Ash A. Alizadeh, Maximilian Diehn. Cell-free RNA analysis for non-invasive cancer detection and characterization abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 6560.