Project description:Cell-free RNA (cfRNA) in plasma reflects gene expression profiles of both localized sites of cancer and at the systemic host response. Here we explore the diagnostic potential of cell-free transcriptomes by mRNA sequencing. We sequenced total cell-free plasma RNA from 90 plasma samples from two independent sample cohorts representing two cancer types, two pre-cancerous conditions and non-cancer donors. We identified distinct gene sets and built classification models that could distinguish cancer patients with specific cancer types from premalignant conditions and non-cancer individuals with high accuracy. Determination of multiple myeloma from its pre-cancerous monoclonal gammopathy of undetermined significance (MGUS) yielded an accuracy of 90% (17/19). Detection of primary liver cancer from its premalignant condition cirrhosis yielded an accuracy of 100% (12/12). This work lays the foundation for developing low cost assays by measuring mRNA transcript levels in plasma using a small panel of genes for detection that can distinguish the presence of cancer from pre-cancerous conditions and non-cancer individuals.
Project description:Cell-free RNAs in biofluids provide opportunities to monitor cancer in a non-invasive manner. Although extracellular microRNAs are extensively characterized, fragmented cell-free long RNAs are not well investigated. Here, we developed Detector-seq (depletion-assisted multiplexing cell-free total RNA sequencing) to enable the deciphering of the cell-free transcriptome. After demonstrating the superior performance of detecting fragmented cell-free long RNAs, we applied Detector-seq to compare cell-free RNAs in human plasma and its extracellular vesicle (EV). Distinct human and microbial RNA signatures were revealed. Structured circular RNA, tRNA, and Y RNA were enriched in plasma, while mRNA and srpRNA were enriched in EV. Meanwhile, cell-free RNAs derived from the virus were more enriched in plasma than in EV. We identified RNAs that showed a selective distribution between plasma and EV and uncovered their distinct functional pathways, that is RNA splicing, antimicrobial humoral response enriched in plasma and transcriptional activity, cell migration, and antigen receptor-mediated immune signals enriched in EV. Although distinctive cancer-relevant RNA signals were identified in plasma and EV, a comparable performance of distinguishing cancer patients from normal individuals could be achieved. Compared to human RNAs, microbe-derived RNA features enabled better classification between colorectal and lung cancer. And for these microbial RNAs, plasma RNAs outperformed EV RNAs for the discrimination of cancer types. Overall, our work provides insights into the unexplored difference of cell-free RNA signals between plasma and EV, thus offering practical guidance for proper selection (with/without EV enrichment) when launching an RNA-based liquid biopsy study. Furthermore, with the ability to capture understudied cell-free long RNA fragments, Detector-seq offers new possibilities for transcriptome-wide characterization of cell-free RNAs to facilitate the understanding of extracellular RNA biology and clinical advances of liquid biopsy.
Project description:Liquid biopsies provide a means for the profiling of cell-free RNAs secreted by cells throughout the body. Although well-annotated coding and noncoding transcripts in blood are readily detectable and can serve as biomarkers of disease, the overall diagnostic utility of the cell-free transcriptome remains unclear. Here we show that transposable elements and other repeat RNA elements are enriched in the cell-free transcriptome of patients with cancer, and that they serve as signatures for the accurate classification of the disease. We used repeat-element-aware sequencing and analysis technology and single-molecule nanopore sequencing to profile the cell-free transcriptome in plasma from patients with cancer and to examine millions of genomic features comprised of all annotated genes and of repeat elements throughout the genome. By aggregating individual repeat elements to the subfamily level, we found that samples with pancreatic cancer are enriched with specific Alu subfamilies, whereas other cancers exhibit their own characteristic cell-free RNA profile. Our findings show that repetitive RNA sequences are abundant in blood and can be used as disease-specific diagnostic biomarkers.