Expanding the clinical utility of liquid biopsy by using liquid transcriptome and artificial intelligence

Abstract

Most of the current utilization of liquid biopsy (LBx) is based on analyzing cell-free DNA(cfDNA). There is limited data on using cell-free RNA (cfRNA) levels (liquid transcriptome) in LBx. The major hurdles for using liquid transcriptome is its low level in circulation and the dilutional effects of various tissues that may pour their RNA into circulation. We explored the potential of using artificial intelligence (AI) to normalize the cancer-specific cfRNA and to enable liquid transcriptome to predict diagnosis. cfRNA transcriptomic data from 1009 peripheral blood samples was generated by hybrid capture next generation sequencing (NGS). Using two-thirds of samples for training and one third for testing, we demonstrate that AI is able to distinguish between normal control (N = 368) and patients with solid tumors (N = 404) with AUC = 0.820 (95 % CI: 0.760–0.879), patients with myeloid neoplasms (N = 99) with AUC = 0.858 (95 % CI: 0.793–0.924) and patients with lymphoid neoplasms (N = 128) with AUC = 0.788 (95 % CI: 0.687–0.888). Specific diagnosis was also possible when patients with lung, breast, colorectal, and myelodysplastic subgroups were tested. This data suggests that liquid transcriptomics when used with AI has the potential of transforming “liquid biopsy” to “true” biopsy, replacing the need for tissue biopsy.

Keywords

• Liquid biopsy
• cell-free DNA (cfDNA)
• cell-free RNA (cfRNA)
• Cancer diagnosis
• Next generation sequencing
• Artificial intelligence (AI)