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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.

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.

Monotherapy with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) still leads to incomplete responses in most EGFR-mutation positive non-small cell lung cancer (NSCLC) patients, often due to acquired resistance through activation of parallel compensatory pathways. We have previously shown that co-targeting EGFR, signal transducer and activator of transcription 3 (STAT3), and Src-yes-associated protein 1 (YAP1) was highly synergistic in vitro and in vivo. In the present study, we treated EGFR-mutation positive cell lines with the combination of osimertinib plus a natural compound, pterostilbene, which has been reported to abrogate Src and STAT3 activation. Methods: Cell viability assays and immunoblotting were performed to reveal the mechanisms of action of pterostilbene, osimertinib and pterostilbene plus osimertinib in five EGFR-mutation positive NSCLC and one triple negative breast cancer (TNBC) cell lines. Results: Osimertinib plus pterostilbene yielded synergistic effects in all EGFR-mutation positive NSCLC cell lines investigated. Surprisingly, pterostilbene alone did not inhibit, nor downregulate Src phosphorylation in the EGFR-mutation positive NSCLC cell lines or the TNBC cell line, MDA-MB-231. However, the double combination of osimertinib plus pterostilbene reversed the osimertinib-induced STAT3, YAP1, and CUB domain-containing protein-1 (CDCP1) phosphorylation and slightly suppressed Src phosphorylation in PC9 and H1975 cells. Conclusion: The results of this study indicate that pterostilbene may be used to abrogate the activated resistance pathways of single osimertinib treatment in EGFR-mutation positive NSCLC. Future studies should focus on in vivo translation and confirmation of these results.

Introduction: Biological therapies, with immunotherapy leading the field, have arisen as one of the quickest expanding areas of research for cancer treatment in the last few years. The clear benefits for patients are undeniable, satisfying the long-awaited necessity of a target-specific therapy. However, its full potential remains still unexploited due to a lack of response in a majority of patients and pending reliable biomarkers.Areas covered: This review provides a summarizing view of the current biological therapies for lung cancer, focusing on immunotherapy - including immune checkpoint inhibitors, adoptive cell therapy and vaccines available in clinical/pre-clinical settings or currently in development. A thorough analysis of the technical and functional differences among all therapies is provided, along with a critical discussion of prospective treatments and potential biomarkers.Expert opinion: The use of immunotherapy in the treatment of cancer has provided clear benefits for patients. Still, exploitation of the full potential of immune checkpoint inhibitors alone or in combination, or adoptive cell therapies is hampered by, amongst other reasons, the lack of reliable biomarkers and possible adverse immune effects. We postulate that the development of liquid biopsy-based diagnostics will help to overcome these limitations in the near future.

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.

In the last two decades, many efforts have been made towards the development of innovative and targeted therapeutic approaches for cancer treatment. Nonetheless, in contrast to the several hallmarks achieved in recent years, drug resistance to cancer therapies has arisen as the real challenge.