Background: Intercellular communication is mediated by extracellular vesicles (EVs), as they enclose selectively packaged biomolecules that can be horizontally transferred from donor to recipient cells. Because all cells constantly generate and recycle EVs, they provide accurate timed snapshots of individual pathophysiological status. Since blood plasma circulates through the whole body, it is often the biofluid of choice for biomarker detection in EVs. Blood collection is easy and minimally invasive, yet reproducible procedures to obtain pure EV samples from circulating biofluids are still lacking. Here, we addressed central aspects of EV immunoaffinity isolation from simple and complex matrices, such as plasma. Methods: Cell-generated EV spike-in models were isolated and purified by size-exclusion chromatography, stained with cellular dyes and characterized by nano flow cytometry. Fluorescently-labelled spike-in EVs emerged as reliable, high-throughput and easily measurable readouts, which were employed to optimize our EV immunoprecipitation strategy and evaluate its performance. Plasma-derived EVs were captured and detected using this straightforward protocol, sequentially combining isolation and staining of specific surface markers, such as CD9 or CD41. Multiplexed digital transcript detection data was generated using the Nanostring nCounter platform and evaluated through a dedicated bioinformatics pipeline. Results: Beads with covalently-conjugated antibodies on their surface outperformed streptavidin-conjugated beads, coated with biotinylated antibodies, in EV immunoprecipitation. Fluorescent EV spike recovery evidenced that target EV subpopulations can be efficiently retrieved from plasma, and that their enrichment is dependent not only on complex matrix composition, but also on the EV surface phenotype. Finally, mRNA profiling experiments proved that distinct EV subpopulations can be captured by directly targeting different surface markers. Furthermore, EVs isolated with anti-CD61 beads enclosed mRNA expression patterns that might be associated to early-stage lung cancer, in contrast with EVs captured through CD9, CD63 or CD81. The differential clinical value carried within each distinct EV subset highlights the advantages of selective isolation. Conclusions: This EV isolation protocol facilitated the extraction of clinically useful information from plasma. Compatible with common downstream analytics, it is a readily implementable research tool, tailored to provide a truly translational solution in routine clinical workflows, fostering the inclusion of EVs in novel liquid biopsy settings.

Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.

Background: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. Methods: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. Results: A combination of 500 μL of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0.86. Conclusions: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures.

The detection of ALK receptor tyrosine kinase (ALK), ROS proto-oncogen1, receptor tyrosine kinase (ROS1), ret proto-oncogen (RET), and MET proto-oncogen exon 14 skipping (METΔex14) allows for the selection of specific kinase inhibitor treatment in patients with non-small cell lung cancer (NSCLC). Multiplex technologies are recommended in this setting. We used nCounter, a multiplexed technology based on RNA hybridization, to detect ALK, ROS1, RET, and METΔex14 in RNA purified from cytological specimens (n = 16) and biopsies (n = 132). Twelve of the 16 cytological samples (75.0%) were evaluable by nCounter compared to 120 out of 132 (90.9%) biopsies. The geometrical mean (geomean) of the housekeeping genes of the nCounter panel, but not the total amount of RNA purified, was significantly higher in biopsies vs. cytological samples. Among cytological samples, we detected ALK (n = 3), METΔex14 (n = 1) and very high MET expression (n = 1) positive cases. The patient with METΔex14 had a partial response to tepotinib, one of the patients with ALK fusions was treated with crizotinib with a complete response. Cell blocks and cytological extensions can be successfully used for the detection of fusions and splicing variants using RNA-based methods such as nCounter.

Genetic and drug sensitivity assays on primary cultures are not only of basic but also of translational interest and could eventually aid oncologists in the selection of treatments. However, cancer cells need to be identified and differentiated from the non-tumor cells always present in primary cultures. Also, successive passages can change the proportions of these two subpopulations. In this study, we propose fluorescence in situ hybridization (FISH) analysis on cell smears to determine the presence of tumor cells in primary cultures obtained from patients carrying translocations or copy number gains. FISH proved to be an easy, fast, economic, and reliable method of characterizing cell populations, which could be used repeatedly at different passages to monitor variations and to confirm the maintenance of translocations and copy number gains throughout the culture process.