Extracellular vesicles (EVs) are double-layered phospholipid membrane vesicles that are released by most cells and can mediate intercellular communication through their RNA cargo. In this study, we tested if the NanoString nCounter platform can be used for the analysis of EV-mRNA. We developed and optimized a methodology for EV enrichment, EV-RNA extraction and nCounter analysis. Then, we demonstrated the validity of our workflow by analyzing EV-RNA profiles from the plasma of 19 cancer patients and 10 controls and developing a gene signature to differentiate cancer versus control samples. TRI reagent outperformed automated RNA extraction and, although lower plasma input is feasible, 500 μL provided highest total counts and number of transcripts detected. A 10-cycle pre-amplification followed by DNase treatment yielded reproducible mRNA target detection. However, appropriate probe design to prevent genomic DNA binding is preferred. A gene signature, created using a bioinformatic algorithm, was able to distinguish between control and cancer EV-mRNA profiles with an area under the ROC curve of 0.99. Hence, the nCounter platform can be used to detect mRNA targets and develop gene signatures from plasma-derived EVs.

Although many studies highlight the implication of circular RNAs (circRNAs) in carcinogenesis and tumor progression, their potential as cancer biomarkers has not yet been fully explored in the clinic due to the limitations of current quantification methods. Here, we report the use of the nCounter platform as a valid technology for the analysis of circRNA expression patterns in non‐small cell lung cancer (NSCLC) specimens. Under this context, our custom‐made circRNA panel was able to detect circRNA expression both in NSCLC cells and formalin‐fixed paraffin‐embedded (FFPE) tissues. CircFUT8 was overexpressed in NSCLC, contrasting with circEPB41L2, circBNC2, and circSOX13 downregulation even at the early stages of the disease. Machine learning (ML) approaches from different paradigms allowed discrimination of NSCLC from nontumor controls (NTCs) with an 8‐circRNA signature. An additional 4‐circRNA signature was able to classify early‐stage NSCLC samples from NTC, reaching a maximum area under the ROC curve (AUC) of 0.981. Our results not only present two circRNA signatures with diagnosis potential but also introduce nCounter processing following ML as a feasible protocol for the study and development of circRNA signatures for NSCLC.

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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.

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Background: 80% of non-small cell lung cancer (NSCLC) cases are diagnosed at stages IIIB-IV and have a dismal prognosis with a median life expectancy that does not exceed 2 years. In contrast, patients diagnosed at early and locally advanced stages (I-IIIA) can undergo surgery and have the potential to be totally cured. Imaging technologies often detect lung nodules of unknown significance that pose a diagnostic challenge; some patients with benign nodules are submitted to unnecessary surgical interventions while others with small tumors are just kept in observation, risking a significant delay for treatment. A diagnostic test that could differentiate between benign and malignant masses would be of great help in this setting. Methods: Circulating-free RNA (cfRNA) was isolated from the plasma of healthy individuals (N=21), early(I-II) stage (N=22) and stage IIIA (N=12) NSCLC patients, using an automatic extraction method(Qiasymphony, Qiagen). Purified cfRNA was quantified using Qubit, retrotranscribed and pre-amplified (14cycles) using the Low RNA Input Amplification kit (NanoString Technologies). Gene expression analysis was performed on the nCounter platform using the PanCancer IO360TM (NanoString Technologies), which can detect 770 transcripts related to tumor biology, micro-environment and the immune system. Results: Gene expression analysis revealed differential patterns for some cf-mRNAs from localized stage NSCLC patients versus healthy controls. A bioinformatics recursive feature elimination algorithm selected a 16-gene mRNA signature that was able to distinguish between localized NSCLC and control samples with an area under the ROC curve of 0.91 to 0.95. Furthermore, the signature scores derived from the algorithm were significantly different between the two cohorts. Conclusions: We have found an 16-gene signature that can differentiate between cfRNA of localized stages NSCLC patients and control individuals. Our results warrant validation studies in larger cohorts. Citation Format: Ana Giménez Capitán, Jillian Bracht, Nicolas Potie, María González-Cao, Santiago Viteri, Alejandro Martínez-Bueno, Carlos Cabrera-Gálvez, Pablo Rubinstein, Clara Mayo-de-las-Casas, Joselyn Valarezo, Chung-Ying Huang, Carlos Pedraz, Richard Boykind, Sarah Warren, Rafael Rosell, Miguel Ángel Molina-Vilaa, Andrés Aguilar-Hernández. A nCounter-Based mRNA signature in plasma associates with localized non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2606.

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