A novel parallel targeted DNA/RNA sequencing method (P-Cap) for simultaneously detecting cancer-related variations in both DNA and RNA levels

Targeted DNA/RNA sequencing is critical for accurate cancer diagnosis, prognosis and adjuvant therapy. Targeted DNA sequencing (DNA-Cap) is widely used in identifying single-nucleotide variant (SNV), gene fusion and copy number variation (CNV), while targeted RNA sequencing (RNA-Cap) complements DNA-Cap in improving the accuracy of gene fusion detection and providing transcriptional data. However, traditional multi-omics sequencing usually performs DNA sequencing and RNA sequencing separately, which is costly and theoretically imperfect in revealing the true relationship between gene mutation and expression since they are not in the same sequence library. In this study, we acquired DNA and RNA data in a single assay, using parallel targeted DNA/RNA sequencing (P-Cap). This was done using a probe-capture-based method to enrich target regions from both DNA and cDNA libraries built from the same tissue sample with different indexes. The integrated bioinformatics analysis allowed the identification of cancer-related biomarkers from both genomic and transcriptomic profiles. To evaluate the performance of P-Cap, we applied P-Cap, DNA-Cap and RNA-Seq respectively on 31 tumor samples collected from collaborating hospitals. As a result, P-Cap and DNA-Cap showed 100% consistency in SNV detection, most of which were also verified at the RNA level. For gene fusion detection, the sensitivity and specificity of P-Cap were both 100% as verified by RT-PCR among the 21 fusion positive samples, significantly higher than those of DNA-Cap (with 73% sensitivity and 90% specificity, respectively). Similarly, the mean read count correlation between P-Cap and RNA-seq was about 0.96 across the 21 samples. In addition, 373 out of 519 targeted genes had correlation greater than 0.8 between P-cap and RNA-seq and most inconsistent genes were low-expressed. In summary, P-Cap is a promising method capable of simultaneously detecting cancer-related variations in both DNA and RNA levels.