UC San Diego

Though numerous treatments for breast cancer have been developed, recurrence still exists, biomarkers and risk assessment tools are thus desired by the field. Cell-free RNA in blood or serum is now receiving more and more interest as a pool of biomarkers and RNA sequencing was believed to be a powerful tool for its analysis. However, development of the field was hindered by large volume of serum required for RNA extraction.

In this study, by circumventing RNA extraction and with the inspiration from single-cell RNA sequencing, we developed a technology being able to construct RNA sequencing libraries in large scale with microliters of direct unprocessed serum input. Optimizations for sequencing and mapping qualities, library complexity and library construction efficiency were conducted by varying serum input volume, adding custom sequencing primer and applying liquid handling instrument epMotion 5075. The optimized strategy “construct 96 different libraries in one single automated batch with 7ul serum input” was proposed accordingly. A huge diversity of genes could be found in serum with this technology. The developed technology was then applied to 96 breast cancer patients’ serums for its performance and potential in clinical cases. Based on the sequencing data: 465, 601 and 1259 genes were identified by three methods respectively to behave differently in recurrence and non-recurrence patients; The two populations could be separated by principle component analysis with all three sets of genes mentioned above; Preliminary recurrence risk assessment was conducted successfully with the classifier random forest.