Circulating tumor cell and cell-free RNA capture and expression analysis identify platelet-associated genes in metastatic lung cancer

Abstract

Background Circulating tumor cells (CTC) and plasma cell-free RNA (cfRNA) can serve as biomarkers for prognosis and treatment response in lung cancer. One barrier to the selected or routine use of CTCs and plasma cfRNA in precision oncology is the limited quantity of both, and CTCs are only seen in metastatic disease. As capture of CTCs and plasma cfRNA presents an opportunity to monitor and assess malignancies without invasive procedures, we compared two methods for CTC capture and identification, and profiled mRNA from CTCs and plasma cfRNA to identify potential tumor-associated biomarkers. Methods Peripheral blood was collected from ten patients with small cell lung cancer (SCLC), ten patients with non-small cell lung cancer (NSCLC) and four healthy volunteers. Two methods were used for CTC capture: the standard epithelial cell adhesion molecule (EpCam) CellSearch kit (unicapture) and EpCAM plus HER2, EGFR and MUC-1 specific combined ferrofluid capture (quadcapture). For the quadcapture, anti-cytokeratin 7 (CK7) was additionally used to assist in CTC identification. NanoString analysis was performed on plasma cfRNA and on mRNA from combined ferrofluid isolated CTCs. Expression data was analyzed using STRING and Reactome. Results Unicapture detected CTCs in 40% of NSCLC and 60% of SCLC; whereas, quadcapture/CK7 identified CTCs in 20% of NSCLC and 80% of SCLC. Bioinformatic analysis of NanoString data identified high expression of a platelet factor 4 (PF4)-related group of transcripts. Conclusions Quadcapture ferrofluid reagent did not significantly improve CTC capture efficacy. NanoString analysis based on CTC and plasma cfRNA data highlighted an intriguing PF-4-centric network in patients with metastatic lung cancer. Electronic supplementary material The online version of this article (10.1186/s12885-019-5795-x) contains supplementary material, which is available to authorized users.

Circulating Cell-Free Nucleic Acids: Main Characteristics and Clinical Application

“…However, it requires larger amounts of input cell-free nucleic acids for analysis and data normalization for miRNAs is still problematic. The platform can also detect copy number variations in 87 genes known to be amplified or deleted in cancer [ 133 ]. The method still considered as the “gold standard” for diagnostic purposes is the RT-qPCR.…”

Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform

“…Since the authors made use of whole blood, the quantities of purified RNA were significantly higher than those in our EV-based study (3 μg/2.5 mL blood versus 0.01 μg/2.5 mL, respectively), avoiding the need for a pre-amplification step. Beck et al 21 used a combination of CTC-RNA and cfRNA to profile tumor-associated biomarkers and correlate them with diagnostics and survival. The quantities of RNA were similar to those obtained in our study and a pre-amplification step was also added.…”

“…These two assays, which utilize tissue samples, emphasize the potential of the nCounter platform as biomarker assay development tool, especially in diagnostic laboratories. Regarding the analysis of liquid biopsies on nCounter, several studies have investigated the potential of some materials, including cf- 18 and EV-DNA 19 , CTC-RNA 20 , 21 , leukocyte mRNA 22 , cfRNA 23 and EV-miRNA 24 , 25 , with different success rates. However, nCounter has never been tested for the analysis of EV-derived mRNA.…”

Circulating platelets as liquid biopsy sources for cancer detection

“…New technologies are also emerging and the RNA biomarker panel approach for testing is starting to be explored. Using a technology based on hybridization techniques, the NanoString, a study analyzed CTCs and plasma cfRNA from patients with metastatic lung cancer, and the objective was to identify potential tumor-associated biomarkers (Beck et al, 2019). NanoString analysis based on CTC and plasma cfRNA data highlighted an intriguing platelet-factor 4 (PF4)-centric network, including several possible interactions between PF4 and CLU, CCL5, TGFB1, SRGN, and SPARC.…”

“…NanoString analysis based on CTC and plasma cfRNA data highlighted an intriguing platelet-factor 4 (PF4)-centric network, including several possible interactions between PF4 and CLU, CCL5, TGFB1, SRGN, and SPARC. This model still needs careful validation through focused clinical and laboratory-based studies (Beck et al, 2019).…”