UC Santa Cruz

A dramatic shift in diagnostic technologies is underway and RNA is central to the new paradigm taking hold. The potent combination of machine-learning, high-throughput sequencing, and increasing molecular knowledge has enabled the creation of minimally invasive yet maximally sensitive “liquid biopsies”. Rather than invasive tissue biopsy or imaging procedures that result in indirect diagnoses, liquid biopsies extract signal from human biofluids (e.g., blood plasma) that directly assess molecular events associated with disease. In addition to protein biomarkers, liquid biopsies have increasingly relied on the detection and parameterization of cell-free DNA to identify genetic and/or epigenetic changes at the root of diseases like cancer. The success of cell-free DNA has largely been driven by utilizing epigenetic signals associated with cell-free DNA molecules, often resulting in an indirect accounting of events that result in the transcription of RNA. Thus, the promise of cell- free or otherwise extra-cellular RNA is to capture the signal that has made cell-free DNA a successful platform but in a more dynamic, sensitive, and generalizable manner. I describe the prelude to and development of COMPLETE-Seq: a repeat-aware quantification approach I apply to detecting the diverse, pervasive, and informative repetitive RNAs secreted into peripheral blood. This approach is first applied to in vitro models where I demonstrate an uncharacterized connection between oncogenic KRAS signaling, present in roughly 30% of all cancers according to The Cancer Genome Atlas (TCGA), and uniform enrichment of extracellular repetitive RNA. I describe a novel perspective on both detecting efficacious KRAS inhibition via cell-free RNA and a proof of concept for the fine-tuned detection of oncogene-specific transcriptional events via RNA liquid biopsy. Finally, I show significant evidence that repeat-aware RNA liquid biopsies improve detection of numerous cancers compared to repeat-naïve classification.