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Introduction: The evaluation of suspicious lung nodules detected by imaging techniques is currently being performed by invasive methods, such as surgical biopsy or fine-needle aspiration (FNA). Liquid biopsies could offer a minimally invasive alternative in this setting. Some circRNAs have recently been investigated as biomarkers for the early detection of lung cancer and other solid tumors. In this project, we are evaluating the levels of whole plasma circRNAs using the nCounter technology in order to develop a prognostic signature that can aid the clinician to differentiate benign vs. malign nodules. Methods: Plasma samples from patients with malign lung nodules (n=135) and controls (n=149), including patients with benign nodules (n=66/149) have been collected. RNA was purified using the QIAsymphony DSP Virus/Pathogen Midi Kit in a QIAsymphony robot (Qiagen). The nCounter Low RNA Input Amplification Kit (NanoString) was used to retrotranscribe and pre-amplify 4 μL of plasma RNA. Overnight hybridization and posterior nCounter processing (NanoString) steps were carried out following the manufacturer’s instructions. Differential expression and machine learning (ML) methods were performed for the development of a lung cancer signature. Results: Preliminary analysis of the 88 samples first included in the study shown a cluster of 6 circRNAs differentially expressed in lung cancer. Among these circRNAs, circSMAD2, circCOL11A1, circCHST15, and circFUT8 were upregulated, while circACP3 and circLYPLAL1 were found downregulated in plasma of lung cancer patients. In addition, a 64-circRNA signature selected by ML was able to differentiate NSCLC patients from controls, including those with benign nodules, The algorithm assigned signature scores to samples, which ranged from 0 to 1. Using a cut-off value of 0.5, preliminary signature was able to classify samples with an AUC ROC of 0.90 and accuracy of 78.41% (CI = 68.35% - 86.47%) with the final model. Conclusion: Using ML on nCounter analysis of 88 samples, we have generated a preliminary 64-circRNA signature in plasma predictive of early-stage NSCLC. Final results in the entire cohort of 294 samples will be presented at the meeting. Citation Format: Carlos Pedraz Valdunciel, Giovanna Casagrande, Elizabeth Martínez-Pérez, Ana Gimenez-Capitán, Joselyn Valarezo, Pablo Rubinstein, Andrés Aguilar-Hernández, Leticia Ferro-Leal, Cristina Marino-Buslje, Rui Reis, Rafael Rosell, Miguel Ángel Molina-Vila. Development of a plasma circRNA signature for the discrimination of malign lung nodules using the nCounter platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1032.

Background: Non-small cell lung cancer (NSCLC) is usually diagnosed at stages IIIB-IV, with a median overall survival that does not exceed two years. In contrast, patients diagnosed at early and locally advanced stages (I-IIIA) can undergo surgery and have a significantly better prognosis. Imaging technologies often detect lung nodules of unknown significance that pose a diagnostic challenge. In a proof-of-concept study, based on a 76-patient cohort, we developed a preliminary mRNA expression signature in plasma that discriminated healthy individuals from early-stage NSCLC patients with AUC=0.98. Here, we aimed to expand the training cohort, to refine the diagnostic signature and to prospectively validate the final signature in the clinical setting. Methods: Two hundred and thirty individuals with pulmonary nodules suspicious of lung cancer have been enrolled in the training cohort. All of them underwent bronchoscopy, fine needle aspiration, percutaneous or surgical biopsy to confirm the diagnosis. Circulating-free RNA (cfRNA) has been isolated from plasma using an automatic extraction method (Qiasymphony, Qiagen). Purified cfRNA has been quantified using Qubit®, retrotranscribed and pre-amplified with 14 cycles using the Low RNA Input Amplification kit (NanoString Technologies). Gene expression analysis has been performed on the nCounter platform using the PanCancer IO360࣪ panel (NanoString Technologies), which can detect 770 transcripts related to tumor biology, micro-environment and the immune system. Results: One hundred twenty-six patients have been analyzed so far; plasma samples have been successfully analyzed by nCounter in all cases. Ongoing analysis reveal differential patterns of gene expression in early-stage NSCLC patients versus non-cancer individuals. Using a bioinformatics recursive feature elimination algorithm, we have selected a diagnostic signature with an area under the ROC curve of 0.89. The signature scores derived from the algorithm are significantly different between the non-cancer and NSCLC cases. Final results of the training and validation cohort will be presented at the meeting Conclusions: Plasma RNA expression signatures can be a useful tool to guide clinical decision in patients with pulmonary nodules suspicious of malignancy, orienting towards surgery or observation. Citation Format: Ana María Giménez Capitán, Pablo Rubisntein, Andrés Aguilar-Hernández, María González-cao, Irene Moya, Santiago Viteri, Carlos Cabrera, Santiago Ramón y Cajal, Karina Loor, Mario Culebras, Irene Sansano, Federico Rubisntein, Joselyn Valarezo, Clara Mayo-de las-Casas, Carlos Pedraz, Joseph Beechem, Sarah Warren, Rafael Rosell, Miguel Ángel Molina-Vila. Prospective validation of a mRNA signature in plasma for the diagnosis of early stage lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1424.

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.

Introduction: Although genetic and transcriptomic analysis of tumor tissue can provide useful information for prognosis and treatment decision making, 5-20% of advanced-stage lung cancer patients cannot be biopsied, or the amount of tumor tissue is insufficient for successful analysis. In addition, repeated sampling is often not possible. Liquid biopsies have shown potential to be used as minimally invasive, safe and sensitive alternative for tissue biopsies, but lack of standardized protocols is hampering implementation in the clinic. The nCounter platform could provide the solution for this problem, with an easy-to-use technical workflow and straightforward data analysis. Extracellular vesicles (EVs) are mediators of intercellular communication and may play a role in early cancer development. Therefore, RNA found within EVs can be used as a biomarker for cancer development and progression. In addition, the lipid bilayer of EVs makes their cargo particularly stable and allows the use of biobank stored samples. Methods: EVs were isolated from 600 μL plasma of 19 cancer patients and 10 healthy donors, using the miRCURY® Exosome Serum/Plasma Kit (Qiagen), and total RNA was extracted using TRI Reagent® (MRC Inc) or the automated QIAsymphony® System (Qiagen) with the DSP Virus/Pathogen Kit, after RNAse A (Sigma-Aldrich) treatment to remove plasma cell-free RNA. The Human Immune V2 panel (NanoString Technologies), including 600 mRNA targets, was used to analyze EV-derived mRNA after a pre-amplification (pre-amp) step with the Low RNA Input Amplification Kit. In addition, the Human V3 miRNA panel (NanoString Technologies), including 800 miRNA targets, was used to analyze the same EV samples without pre-amp. Results: Total amount of RNA isolated from EVs was found to be significantly higher using TRI Reagent®, versus automated RNA isolation. In addition, the conditions for the pre-amp step were tested and optimized. A pre-amp of 10 cycles for the mRNA panel was shown to be sufficient to detect mRNA targets in EVs without saturation, and the NanoString retrotranscription (RT) enzyme outperformed the other RT enzyme tested. In addition, supernatant collected during EV isolation was also analyzed, and results showed that the RNA targets were derived from within the EVs. On average, 337 mRNA targets were detected within the EVs, while 157 miRNA targets were detected in the same samples without pre-amp, with no significant differences between cancer patients and healthy donors. Interestingly, most differentially expressed (DE) mRNAs were shown to be lower expressed in cancer patients, while most DE miRNAs were found to be higher expressed in cancer patients. Conclusion: Our results demonstrate that the nCounter NanoString platform can be used for miRNA and mRNA detection in plasma-derived EVs from cancer patients and healthy donors. Further studies will focus on specific mRNA and miRNA expression differences between these two cohorts. Citation Format: Jillian Wilhelmina Bracht, Ana Gimenez-Capitan, Chung-Ying Huang, Carlos Pedraz-Valdunciel, Joselyn Valarezo, Sarah Warren, Rafael Rosell, Miguel Angel Molina-Vila. miRNA and mRNA detection in plasma-derived extracellular vesicles (EVs) using the nCounter NanoString platform [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 760.