Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer

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 preamplification 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 L 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.European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant 76549

Digital multiplexed analysis of circular RNAs in FFPE and fresh non-small cell lung cancer specimens

We would like to thank Stephanie Davis for her language editing assistance. The investigators also wish to thank the patients for kindly agreeing to donate samples to this study. We thank all the physicians who collaborated by providing clinical information. Graphical Abstract, Figs 1A, 8A and Fig. S1 were created with Biorender.com. This project has received funding from a European Union's Horizon 2020 research and innovation program under the Marie SklodowskaCurie grant agreement ELBA No 765492.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 paraffinembedded (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.European Commission 76549

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

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

KRAS-mutant non-small cell lung cancer (NSCLC) therapy based on tepotinib and omeprazole combination

Background KRAS-mutant non-small cell lung cancer (NSCLC) shows a relatively low response rate to chemotherapy, immunotherapy and KRAS-G12C selective inhibitors, leading to short median progression-free survival, and overall survival. The MET receptor tyrosine kinase (c-MET), the cognate receptor of hepatocyte growth factor (HGF), was reported to be overexpressed in KRAS-mutant lung cancer cells leading to tumor-growth in anchorage-independent conditions. Methods Cell viability assay and synergy analysis were carried out in native, sotorasib and trametinib-resistant KRAS-mutant NSCLC cell lines. Colony formation assays and Western blot analysis were also performed. RNA isolation from tumors of KRAS-mutant NSCLC patients was performed and KRAS and MET mRNA expression was determined by real-time RT-qPCR. In vivo studies were conducted in NSCLC (NCI-H358) cell-derived tumor xenograft model. Results Our research has shown promising activity of omeprazole, a V-ATPase-driven proton pump inhibitor with potential anti-cancer properties, in combination with the MET inhibitor tepotinib in KRAS-mutant G12C and non-G12C NSCLC cell lines, as well as in G12C inhibitor (AMG510, sotorasib) and MEK inhibitor (trametinib)-resistant cell lines. Moreover, in a xenograft mouse model, combination of omeprazole plus tepotinib caused tumor growth regression. We observed that the combination of these two drugs downregulates phosphorylation of the glycolytic enzyme enolase 1 (ENO1) and the low-density lipoprotein receptor-related protein (LRP) 5/6 in the H358 KRAS G12C cell line, but not in the H358 sotorasib resistant, indicating that the effect of the combination could be independent of ENO1. In addition, we examined the probability of recurrence-free survival and overall survival in 40 early lung adenocarcinoma patients with KRAS G12C mutation stratified by KRAS and MET mRNA levels. Significant differences were observed in recurrence-free survival according to high levels of KRAS mRNA expression. Hazard ratio (HR) of recurrence-free survival was 7.291 (p = 0.014) for high levels of KRAS mRNA expression and 3.742 (p = 0.052) for high MET mRNA expression. Conclusions We posit that the combination of the V-ATPase inhibitor omeprazole plus tepotinib warrants further assessment in KRAS-mutant G12C and non G12C cell lines, including those resistant to the covalent KRAS G12C inhibitors

Multiplex analysis of circRNAs in lung cancer using the nCounter technology

El càncer de pulmó (CP) és un dels càncers més comunament diagnosticats i la principal causa de mort relacionada amb el càncer a tot el món, principalment degut al seu diagnòstic en estadis tardans i a la manca de tractaments efectius per a un nombre significatiu de pacients. En conseqüència, avanços en el diagnòstic precoç del CP són essencials per a poder millorar la supervivència global d’aquests pacients. Els ARN circulars (circRNA) són un tipus d’ARN amb una estructura estable i una expressió teixit específica. Diversos treballs científics indiquen com l’expressió anormal d’alguns circRNA pot exercir un paper en la carcinogènesis i la progressió del tumor. En aquest context, molts laboratoris estan investigant actualment el valor clínic dels circRNAs que es troben en les biòpsies líquides. Desafortunadament, la falta d’una metodologia estandarditzada per a l’estudi de circRNAs, a més de les limitacions inherents a les biòpsies líquides, està frenant la seva implementació clínica. El sistema nCounter de NanoString pot quantificar els nivells d’expressió de fins a 800 molècules de ARNs extretes de material fixat en formalina i inclòs en parafina (FFPE), lisats cel·lulars o biòpsies líquides. Les avantatges d’aquesta plataforma són la brevetat del seu protocol, així com la seva capacitat per a treballar amb molt poca quantitat de mostra altament degradada. No obstant això, fins on nosaltres sabem, no hi ha evidència sobre l’ús d’aquesta plataforma per a l’estudi dels circRNAs en mostres de CP. Per això, el principal objectiu d’aquesta tesi ha estat testar la tecnologia nCounter per a l’estudi de l’expressió dels circRNAs en mostres de CP, incloent-hi línies cel·lulars, mostres FFPE i biòpsies líquides. En el capítol 2, presentem un estudi de nCounter per a la detecció de circRNA en mostres de CP. Primerament, vam dissenyar i validar en línies cel·lulars de CP un panell de circRNAs per a nCounter. Després, el panell es va utilitzar per a estudiar l’expressió de circRNAs en biòpsies de teixit. Com a resultat, trobem un grup de circRNAs diferencialment expressats en CP (n = 53), enfront de controls sense càncer (n = 16). A més, l’anàlisi d’aprenentatge automàtic (Machine Learning, ML) ens va permetre desenvolupar dues signatures de circRNA per a discriminar biòpsies de CP, d’estadis primerencs i tardans, de mostres no tumorals. En el capítol 3, vam validar un protocol per a l’anàlisi dels circRNAs continguts en les vesícules extracel·lulars (EVs) per nCounter. Posteriorment, vam utilitzar el nostre protocol per analitzar els EV-circRNAs en mostres de plasma de pacients amb CP (n = 36) i controls sense càncer (n = 30). Novament, vam trobar un grup d’EV-circRNAs expressats diferencialment en mostres de pacients amb càncer que podrien usar-se potencialment com biomarcadors de CP. A més, ML ens va permetre trobar una signatura de 10 circRNAs que va permetre diferenciar les mostres de CP dels controls. La purificació dels EVs és un procés que pot resultar difícil d’implementar en l’entorn clínic. En conseqüència, en el capítol 4, vam analitzar nCounter en mostres d’RNA extretes directament de plasma, detectant una major quantitat de circRNAs en aquestes mostres en comparació amb aquelles enriquides en EV. Després, vam analitzar mostres de plasma de pacients amb CP en estadi primerenc (n=49) i de controls sense càncer (n=49), els quals incloïen també individus amb nòduls benignes (n=19/49). Posteriorment, a través de ML, vam poder generar una signatura per discriminar el CP en etapa primerenca amb un àrea sota la corba (AUC) ROC de 0.9. En conclusió, aquesta tesi ha demostrat que nCounter es pot utilitzar per a l’estudi de circRNAs en mostres de CP, establint les bases per al desenvolupament d’assajos de circRNA clínicament rellevants.El cáncer de pulmón (CP) es uno de los tipos de cáncer más comúnmente diagnosticados y la principal causa de muerte relacionada con cáncer en todo el mundo, principalmente debido a su diagnóstico en estadíos avanzados y a la falta de tratamientos efectivos para un número significativo de pacientes. En consecuencia, avances en el diagnóstico precoz de esta enfermedad son necesarios para poder mejorar la supervivencia de estos pacientes. Los ARN circulares (circRNA) son un tipo de ARN que muestra una estructura estable y una expresión específica a cada tejido. Numerosos trabajos científicos muestran cómo la expresión anormal de algunos circRNAs puede desempeñar un papel importante en la carcinogénesis y la progresión del tumor. En este contexto, muchos laboratorios están investigando el valor clínico de los circRNAs que se encuentran presentes en las biopsias líquidas. Desafortunadamente, la falta de un método estandarizado para el estudio de circRNAs, además de las limitaciones inherentes a las biopsias líquidas, está frenando su implementación clínica. El sistema nCounter de NanoString puede cuantificar los niveles de expresión de hasta 800 moléculas de ARN extraídas de material fijado en formalina e incluido en parafina (FFPE), lisados celulares o biopsias líquidas. Entre las diferentes ventajas de esta plataforma, destacan la brevedad de su protocolo, así como su capacidad para trabajar con muy poca cantidad de muestras altamente degradadas. Sin embargo, hasta donde nosotros sabemos, no hay evidencia sobre el uso de esta plataforma para el estudio de circRNAs en muestras de CP. Por ello, el principal objetivo de esta tesis ha sido testar la tecnología nCounter para el estudio de la expresión de circRNAs en muestras de CP, incluyendo líneas celulares, muestras FFPE y biopsias líquidas. En el capítulo 2, presentamos un estudio donde hacemos uso del nCounter para la detección de circRNAs en muestras de CP. Primero, diseñamos y validamos en líneas celulares un panel de circRNAs para nCounter. Después, utilizamos nuestro panel para estudiar la expresión de circRNAs en biopsias de tejido. Como resultado, encontramos un grupo de circRNAs diferencialmente expresado en CP (n = 53), frente a controles sin cáncer (n = 16). Además, análisis de aprendizaje automático (en inglés, Machine Learning, ML) nos permitió desarrollar dos firmas de circRNAs para discriminar biopsias de CP de muestras no tumorales. En el capítulo 3, establecimos y validamos un protocolo para el análisis de circRNAs contenidos en las vesículas extracelulares (EVs) por nCounter. Posteriormente, usamos nuestro protocolo para analizar EV-circRNAs en muestras de plasma de pacientes con CP (n = 36) y controles sin cáncer (n = 30). Nuevamente, encontramos un grupo de EV-circRNAs diferencialmente expresados en muestras de pacientes con cáncer que podrían usarse potencialmente como biomarcadores de CP. Además, ML nos permitió encontrar una firma de 10 circRNAs que permitió diferenciar las muestras de cáncer de los controles. La purificación de EVs es un proceso que puede resultar difícil de implementar en el entorno clínico. En consecuencia, en el capítulo 4 probamos nCounter en muestras de RNA extraídas directamente de plasma, detectando una mayor cantidad de circRNAs en estas muestras en comparación con preparados de EVs. Después, analizamos muestras de plasma de pacientes con CP en estadío temprano (n=49) y de controles sin cáncer (n=49), los cuales incluían también individuos con nódulos benignos (n=19/49). Finalmente, a través de ML, pudimos generar una firma para discriminar el CP en etapa temprana con un área bajo la curva (AUC) ROC de 0.9. En conclusión, esta tesis ha demostrado que la plataforma nCounter puede ser utilizada para el estudio de circRNAs en muestras de CP, sentando las bases para el desarrollo de ensayos de circRNAs clínicamente relevantes.Lung cancer stands as one of the two most commonly diagnosed types of cancer, and the foremost cause of cancer-related death worldwide, primarily due to a late-stage diagnosis and lack of effective treatments for a significant number of patients. Although innovative single-agent therapies and their combination are constantly being tested in clinical trials, the five-year survival rate of late-stage lung cancer remains only at 5% (Cancer Research, UK). Consequently, advances in the early diagnosis of lung cancer are critical for improving the overall survival. Circular RNAs (circRNAs) are a re-discovered type of RNA showing a stable structure and tissue-specific expression. Accumulating evidence indicates how abnormal expression of some circRNAs can play a role in carcinogenesis and tumor progression, positioning these molecules as putative lung cancer biomarkers. In this context, many laboratories are currently investigating the clinical value of circRNAs found in liquid biopsies. Unfortunately, the lack of standardized methodologies for the study of circRNAs, together with some limitations inherent to liquid biopsies, is holding back their clinical implementation. The NanoString nCounter system can quantify the expression levels of up to 800 RNAs extracted from formalin-fixed paraffin-embedded (FFPE) material, cell lysates or liquid biopsies. Among the different advantages of this platform, we can highlight its short turnaround time and the ability to work with very low quantity of highly degraded samples. However, to the best of our knowledge, there is no published evidence on the use of this platform for the study of circRNAs in lung cancer specimens. Therefore, the main objective of this doctoral thesis was to test the nCounter technology for the study of circRNA expression in lung cancer samples, including cell lines, FFPE samples and liquid biopsies. In Chapter 2, we present a proof-of-concept study of nCounter for the detection of circRNAs in lung cancer samples. First, we designed and validated a customized nCounter circRNA panel in lung cancer cell lines. Then, the panel was used to study circRNA expression in tissue biopsies. As a result, we found a cluster of differentially expressed circRNAs in early and late-stage lung cancer (n=53) vs. non-cancer controls (n=16). Also, machine learning (ML) analysis allowed us to develop two circRNA signatures to discriminate early and late-stage lung cancer biopsies from non-tumor samples with high accuracy. In Chapter 3, we transitioned from solid specimens to liquid biopsies. In this second proof-of-concept study, we established and validated a protocol for the analysis of extracellular vesicle (EV)-circRNAs by nCounter. To this end, we tested different key points such as initial volume of plasma, EV purification method, number of cycles of pre-amplification, data normalization procedures or ML approaches. Then, we used the protocol to analyze the EV-circRNAs in plasma samples of lung cancer patients (n=36) and non-cancer controls (n=30). Again, we found a cluster of differentially expressed EV-circRNAs that could potentially be used as biomarkers of lung cancer. Also, ML allowed us to find a 10-circRNA signature that discriminated lung cancer samples from controls. Purification of EVs may result challenging to implement in the clinical setting. Consequently, in Chapter 4, we tested nCounter in whole plasma and detected a higher number of circRNAs compared to EV-enriched samples. Then, we analyzed plasma samples of early-stage lung cancer patients (n=49) and non-cancer controls (n=49), which included individuals with benign nodules (n=19/49). Subsequently, ML techniques generated a signature to discriminate early-stage lung cancer with an area under the ROC curve (AUC ROC) of 0.9. In conclusion, this thesis has proved that nCounter can be used for the study of circRNAs in lung cancer samples, setting the ground for the development of clinically relevant circRNA assays.Universitat Autònoma de Barcelona. Programa de Doctorat en Bioquímica, Biologia Molecular i Biomedicin

Multiplex analysis of circRNAs in lung cancer using the nCounter technology

El càncer de pulmó (CP) és un dels càncers més comunament diagnosticats i la principal causa de mort relacionada amb el càncer a tot el món, principalment degut al seu diagnòstic en estadis tardans i a la manca de tractaments efectius per a un nombre significatiu de pacients. En conseqüència, avanços en el diagnòstic precoç del CP són essencials per a poder millorar la supervivència global d'aquests pacients. Els ARN circulars (circRNA) són un tipus d'ARN amb una estructura estable i una expressió teixit específica. Diversos treballs científics indiquen com l'expressió anormal d'alguns circRNA pot exercir un paper en la carcinogènesis i la progressió del tumor. En aquest context, molts laboratoris estan investigant actualment el valor clínic dels circRNAs que es troben en les biòpsies líquides. Desafortunadament, la falta d'una metodologia estandarditzada per a l'estudi de circRNAs, a més de les limitacions inherents a les biòpsies líquides, està frenant la seva implementació clínica. El sistema nCounter de NanoString pot quantificar els nivells d'expressió de fins a 800 molècules de ARNs extretes de material fixat en formalina i inclòs en parafina (FFPE), lisats cel·lulars o biòpsies líquides. Les avantatges d'aquesta plataforma són la brevetat del seu protocol, així com la seva capacitat per a treballar amb molt poca quantitat de mostra altament degradada. No obstant això, fins on nosaltres sabem, no hi ha evidència sobre l'ús d'aquesta plataforma per a l'estudi dels circRNAs en mostres de CP. Per això, el principal objectiu d'aquesta tesi ha estat testar la tecnologia nCounter per a l'estudi de l'expressió dels circRNAs en mostres de CP, incloent-hi línies cel·lulars, mostres FFPE i biòpsies líquides. En el capítol 2, presentem un estudi de nCounter per a la detecció de circRNA en mostres de CP. Primerament, vam dissenyar i validar en línies cel·lulars de CP un panell de circRNAs per a nCounter. Després, el panell es va utilitzar per a estudiar l'expressió de circRNAs en biòpsies de teixit. Com a resultat, trobem un grup de circRNAs diferencialment expressats en CP (n = 53), enfront de controls sense càncer (n = 16). A més, l'anàlisi d'aprenentatge automàtic (Machine Learning, ML) ens va permetre desenvolupar dues signatures de circRNA per a discriminar biòpsies de CP, d'estadis primerencs i tardans, de mostres no tumorals. En el capítol 3, vam validar un protocol per a l'anàlisi dels circRNAs continguts en les vesícules extracel·lulars (EVs) per nCounter. Posteriorment, vam utilitzar el nostre protocol per analitzar els EV-circRNAs en mostres de plasma de pacients amb CP (n = 36) i controls sense càncer (n = 30). Novament, vam trobar un grup d'EV-circRNAs expressats diferencialment en mostres de pacients amb càncer que podrien usar-se potencialment com biomarcadors de CP. A més, ML ens va permetre trobar una signatura de 10 circRNAs que va permetre diferenciar les mostres de CP dels controls. La purificació dels EVs és un procés que pot resultar difícil d'implementar en l'entorn clínic. En conseqüència, en el capítol 4, vam analitzar nCounter en mostres d'RNA extretes directament de plasma, detectant una major quantitat de circRNAs en aquestes mostres en comparació amb aquelles enriquides en EV. Després, vam analitzar mostres de plasma de pacients amb CP en estadi primerenc (n=49) i de controls sense càncer (n=49), els quals incloïen també individus amb nòduls benignes (n=19/49). Posteriorment, a través de ML, vam poder generar una signatura per discriminar el CP en etapa primerenca amb un àrea sota la corba (AUC) ROC de 0.9. En conclusió, aquesta tesi ha demostrat que nCounter es pot utilitzar per a l'estudi de circRNAs en mostres de CP, establint les bases per al desenvolupament d'assajos de circRNA clínicament rellevants.El cáncer de pulmón (CP) es uno de los tipos de cáncer más comúnmente diagnosticados y la principal causa de muerte relacionada con cáncer en todo el mundo, principalmente debido a su diagnóstico en estadíos avanzados y a la falta de tratamientos efectivos para un número significativo de pacientes. En consecuencia, avances en el diagnóstico precoz de esta enfermedad son necesarios para poder mejorar la supervivencia de estos pacientes. Los ARN circulares (circRNA) son un tipo de ARN que muestra una estructura estable y una expresión específica a cada tejido. Numerosos trabajos científicos muestran cómo la expresión anormal de algunos circRNAs puede desempeñar un papel importante en la carcinogénesis y la progresión del tumor. En este contexto, muchos laboratorios están investigando el valor clínico de los circRNAs que se encuentran presentes en las biopsias líquidas. Desafortunadamente, la falta de un método estandarizado para el estudio de circRNAs, además de las limitaciones inherentes a las biopsias líquidas, está frenando su implementación clínica. El sistema nCounter de NanoString puede cuantificar los niveles de expresión de hasta 800 moléculas de ARN extraídas de material fijado en formalina e incluido en parafina (FFPE), lisados celulares o biopsias líquidas. Entre las diferentes ventajas de esta plataforma, destacan la brevedad de su protocolo, así como su capacidad para trabajar con muy poca cantidad de muestras altamente degradadas. Sin embargo, hasta donde nosotros sabemos, no hay evidencia sobre el uso de esta plataforma para el estudio de circRNAs en muestras de CP. Por ello, el principal objetivo de esta tesis ha sido testar la tecnología nCounter para el estudio de la expresión de circRNAs en muestras de CP, incluyendo líneas celulares, muestras FFPE y biopsias líquidas. En el capítulo 2, presentamos un estudio donde hacemos uso del nCounter para la detección de circRNAs en muestras de CP. Primero, diseñamos y validamos en líneas celulares un panel de circRNAs para nCounter. Después, utilizamos nuestro panel para estudiar la expresión de circRNAs en biopsias de tejido. Como resultado, encontramos un grupo de circRNAs diferencialmente expresado en CP (n = 53), frente a controles sin cáncer (n = 16). Además, análisis de aprendizaje automático (en inglés, Machine Learning, ML) nos permitió desarrollar dos firmas de circRNAs para discriminar biopsias de CP de muestras no tumorales. En el capítulo 3, establecimos y validamos un protocolo para el análisis de circRNAs contenidos en las vesículas extracelulares (EVs) por nCounter. Posteriormente, usamos nuestro protocolo para analizar EV-circRNAs en muestras de plasma de pacientes con CP (n = 36) y controles sin cáncer (n = 30). Nuevamente, encontramos un grupo de EV-circRNAs diferencialmente expresados en muestras de pacientes con cáncer que podrían usarse potencialmente como biomarcadores de CP. Además, ML nos permitió encontrar una firma de 10 circRNAs que permitió diferenciar las muestras de cáncer de los controles. La purificación de EVs es un proceso que puede resultar difícil de implementar en el entorno clínico. En consecuencia, en el capítulo 4 probamos nCounter en muestras de RNA extraídas directamente de plasma, detectando una mayor cantidad de circRNAs en estas muestras en comparación con preparados de EVs. Después, analizamos muestras de plasma de pacientes con CP en estadío temprano (n=49) y de controles sin cáncer (n=49), los cuales incluían también individuos con nódulos benignos (n=19/49). Finalmente, a través de ML, pudimos generar una firma para discriminar el CP en etapa temprana con un área bajo la curva (AUC) ROC de 0.9. En conclusión, esta tesis ha demostrado que la plataforma nCounter puede ser utilizada para el estudio de circRNAs en muestras de CP, sentando las bases para el desarrollo de ensayos de circRNAs clínicamente relevantes.Lung cancer stands as one of the two most commonly diagnosed types of cancer, and the foremost cause of cancer-related death worldwide, primarily due to a late-stage diagnosis and lack of effective treatments for a significant number of patients. Although innovative single-agent therapies and their combination are constantly being tested in clinical trials, the five-year survival rate of late-stage lung cancer remains only at 5% (Cancer Research, UK). Consequently, advances in the early diagnosis of lung cancer are critical for improving the overall survival. Circular RNAs (circRNAs) are a re-discovered type of RNA showing a stable structure and tissue-specific expression. Accumulating evidence indicates how abnormal expression of some circRNAs can play a role in carcinogenesis and tumor progression, positioning these molecules as putative lung cancer biomarkers. In this context, many laboratories are currently investigating the clinical value of circRNAs found in liquid biopsies. Unfortunately, the lack of standardized methodologies for the study of circRNAs, together with some limitations inherent to liquid biopsies, is holding back their clinical implementation. The NanoString nCounter system can quantify the expression levels of up to 800 RNAs extracted from formalin-fixed paraffin-embedded (FFPE) material, cell lysates or liquid biopsies. Among the different advantages of this platform, we can highlight its short turnaround time and the ability to work with very low quantity of highly degraded samples. However, to the best of our knowledge, there is no published evidence on the use of this platform for the study of circRNAs in lung cancer specimens. Therefore, the main objective of this doctoral thesis was to test the nCounter technology for the study of circRNA expression in lung cancer samples, including cell lines, FFPE samples and liquid biopsies. In Chapter 2, we present a proof-of-concept study of nCounter for the detection of circRNAs in lung cancer samples. First, we designed and validated a customized nCounter circRNA panel in lung cancer cell lines. Then, the panel was used to study circRNA expression in tissue biopsies. As a result, we found a cluster of differentially expressed circRNAs in early and late-stage lung cancer (n=53) vs. non-cancer controls (n=16). Also, machine learning (ML) analysis allowed us to develop two circRNA signatures to discriminate early and late-stage lung cancer biopsies from non-tumor samples with high accuracy. In Chapter 3, we transitioned from solid specimens to liquid biopsies. In this second proof-of-concept study, we established and validated a protocol for the analysis of extracellular vesicle (EV)-circRNAs by nCounter. To this end, we tested different key points such as initial volume of plasma, EV purification method, number of cycles of pre-amplification, data normalization procedures or ML approaches. Then, we used the protocol to analyze the EV-circRNAs in plasma samples of lung cancer patients (n=36) and non-cancer controls (n=30). Again, we found a cluster of differentially expressed EV-circRNAs that could potentially be used as biomarkers of lung cancer. Also, ML allowed us to find a 10-circRNA signature that discriminated lung cancer samples from controls. Purification of EVs may result challenging to implement in the clinical setting. Consequently, in Chapter 4, we tested nCounter in whole plasma and detected a higher number of circRNAs compared to EV-enriched samples. Then, we analyzed plasma samples of early-stage lung cancer patients (n=49) and non-cancer controls (n=49), which included individuals with benign nodules (n=19/49). Subsequently, ML techniques generated a signature to discriminate early-stage lung cancer with an area under the ROC curve (AUC ROC) of 0.9. In conclusion, this thesis has proved that nCounter can be used for the study of circRNAs in lung cancer samples, setting the ground for the development of clinically relevant circRNA assays

Defining the landscape of circRNAs in non-small cell lung cancer and their potential as liquid biopsy biomarkers: a complete review including current methods

Despite the significant decrease in population-level mortality of lung cancer patients as reflected in the Surveillance Epidemiology and End Results program national database, lung cancer, with non-small cell lung cancer (NSCLC) in the lead, continues to be the most commonly diagnosed cancer and foremost cause of cancer-related death worldwide, primarily due to late-stage diagnosis and ineffective treatment regimens. Although innovative single therapies and their combinations are constantly being tested in clinical trials, the five-year survival rate of late-stage lung cancer remains only 5% (Cancer Research, UK). Henceforth, investigation in the early diagnosis of lung cancer and prediction of treatment response is critical for improving the overall survival of these patients. Circular RNAs (circRNAs) are a re-discovered type of RNAs featuring stable structure and high tissue-specific expression. Evidence has revealed that aberrant circRNA expression plays an important role in carcinogenesis and tumor progression. Further investigation is warranted to assess the value of EV- and platelet-derived circRNAs as liquid biopsy-based readouts for lung cancer detection. This review discusses the origin and biology of circRNAs, and analyzes their present landscape in NSCLC, focusing on liquid biopsies to illustrate the different methodological trends currently available in research. The possible limitations that could be holding back the clinical implementation of circRNAs are also analyzed

PIM-1 inhibition with AZD1208 to prevent osimertinib-induced resistance in EGFR-mutation positive non-small cell lung cancer

Aim: The progression free survival of non-small cell lung cancer (NSCLC) patients has been doubled over the last years, but still single epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) lead to incomplete responses. Compensatory signaling pathways are activated upon single EGFR TKIs. We have shown that compounds, which inhibit these pathways, are synergistic with EGFR TKIs. Proviral integration site for Moloney murine leukemia virus (PIM) has been connected to cancer therapy resistance, being involved in receptor tyrosine kinase, signal transducer and activator of transcription 3 (STAT3) and interleukin-6 signaling. We hypothesized that combined PIM and EGFR inhibition may improve the upfront therapy of EGFR-mutation positive NSCLC.Methods: We reviewed the literature on PIM kinases, and performed cell viability assays, gene expression analyses, and immunoblotting experiments to reveal the mechanisms of action of the PIM inhibitor (AZD1208) alone and combined with osimertinib in five EGFR-mutation positive NSCLC cell lines.Results: Osimertinib alone induced the activation of signal transducer and activator of transcription 3 (STAT3) as well as other signaling nodes. Combined osimertinib and AZD1208 yielded moderate synergistic effects in all NSCLC cell lines investigated. Among the EGFR-mutation positive cell lines examined, the H1975 and PC9 cell lines had the highest PIM1 and STAT3 mRNA expression. The combination decreased the osimertinib-induced STAT3 phosphorylation.Conclusion: This study provides a short review on PIM kinases, and shows our results of combined PIM and EGFR inhibition in EGFR-mutation positive NSCLC cell lines. The combination was moderately synergistic but decreased STAT3 phosphorylation, an important signaling node in therapy resistance

Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer

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