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Verfasst von:Sikosek, Tobias [VerfasserIn]   i
 Horos, Rastislav [VerfasserIn]   i
 Trudzinski, Franziska [VerfasserIn]   i
 Jehn, Julia [VerfasserIn]   i
 Frank, Maurice [VerfasserIn]   i
 Rajakumar, Timothy [VerfasserIn]   i
 Klotz, Laura Valentina [VerfasserIn]   i
 Mercaldo, Nathaniel [VerfasserIn]   i
 Kahraman, Mustafa [VerfasserIn]   i
 Heuvelman, Marco [VerfasserIn]   i
 Taha, Yasser [VerfasserIn]   i
 Gerwing, Jennifer [VerfasserIn]   i
 Skottke, Jasmin [VerfasserIn]   i
 Daniel-Moreno, Alberto [VerfasserIn]   i
 Sanchez-Delgado, Marta [VerfasserIn]   i
 Bender, Sophie [VerfasserIn]   i
 Rudolf, Christina [VerfasserIn]   i
 Hinkfoth, Franziska [VerfasserIn]   i
 Tikk, Kaja [VerfasserIn]   i
 Schenz, Judith [VerfasserIn]   i
 Weigand, Markus A. [VerfasserIn]   i
 Feindt, Peter [VerfasserIn]   i
 Schumann, Christian [VerfasserIn]   i
 Christopoulos, Petros [VerfasserIn]   i
 Winter, Hauke [VerfasserIn]   i
 Kreuter, Michael [VerfasserIn]   i
 Schneider, Marc [VerfasserIn]   i
 Muley, Thomas [VerfasserIn]   i
 Walterspacher, Stephan [VerfasserIn]   i
 Schuler, Martin [VerfasserIn]   i
 Darwiche, Kaid [VerfasserIn]   i
 Taube, Christian [VerfasserIn]   i
 Hegedus, Balazs [VerfasserIn]   i
 Rabe, Klaus F. [VerfasserIn]   i
 Rieger-Christ, Kimberly [VerfasserIn]   i
 Jacobsen, Francine L. [VerfasserIn]   i
 Aigner, Clemens [VerfasserIn]   i
 Reck, Martin [VerfasserIn]   i
 Bankier, Alexander A. [VerfasserIn]   i
 Sharma, Amita [VerfasserIn]   i
 Steinkraus, Bruno R. [VerfasserIn]   i
Titel:Early detection of lung cancer using small RNAs
Verf.angabe:Tobias Sikosek, PhD, Rastislav Horos, PhD, DVM, Franziska Trudzinski, MD, Julia Jehn, PhD, Maurice Frank, MSc, Timothy Rajakumar, MD, PhD, Laura V. Klotz, MD, Nathaniel Mercaldo, PhD, Mustafa Kahraman, PhD, Marco Heuvelman, MSc, Yasser Taha, MSc, Jennifer Gerwing, MSc, Jasmin Skottke, MSc, Alberto Daniel-Moreno, PhD, Marta Sanchez-Delgado, PhD, Sophie Bender, BSc, Christina Rudolf, PhD, Franziska Hinkfoth, MSc, Kaja Tikk, PhD, Judith Schenz, PhD, Markus A. Weigand, MD, Peter Feindt, MD, Christian Schumann, MD, Petros Christopoulos, MD, PhD, Hauke Winter, MD, Michael Kreuter, MD, Marc A. Schneider, PhD, Thomas Muley, PhD, Stephan Walterspacher, MD, Martin Schuler, MD, Kaid Darwiche, MD, Christian Taube, MD, Balazs Hegedus, MD, Klaus F. Rabe, MD, Kimberly Rieger-Christ, MD, Francine L. Jacobsen, MD, Clemens Aigner, MD, Martin Reck, MD, PhD, Alexander A. Bankier, MD, Amita Sharma, MD, Bruno R. Steinkraus, PhD
E-Jahr:2023
Jahr:November 2023
Umfang:20 S.
Illustrationen:Illustrationen
Fussnoten:Online verfügbar 16 July 2023, Version des Artikels 23 October 2023 ; Gesehen am 17.05.2024
Titel Quelle:Enthalten in: Journal of thoracic oncology
Ort Quelle:Amsterdam : Elsevier, 2006
Jahr Quelle:2023
Band/Heft Quelle:18(2023), 11 vom: Nov., Seite 1504-1523
ISSN Quelle:1556-1380
Abstract:Introduction - Lung cancer remains the deadliest cancer in the world, and lung cancer survival is heavily dependent on tumor stage at the time of detection. Low-dose computed tomography screening can reduce mortality; however, annual screening is limited by low adherence in the United States of America and still not broadly implemented in Europe. As a result, less than 10% of lung cancers are detected through existing programs. Thus, there is a great need for additional screening tests, such as a blood test, that could be deployed in the primary care setting. - Methods - We prospectively recruited 1384 individuals meeting the National Lung Screening Trial demographic eligibility criteria for lung cancer and collected stabilized whole blood to enable the pipetting-free collection of material, thus minimizing preanalytical noise. Ultra-deep small RNA sequencing (20 million reads per sample) was performed with the addition of a method to remove highly abundant erythroid RNAs, and thus open bandwidth for the detection of less abundant species originating from the plasma or the immune cellular compartment. We used 100 random data splits to train and evaluate an ensemble of logistic regression classifiers using small RNA expression of 943 individuals, discovered an 18-small RNA feature consensus signature (miLung), and validated this signature in an independent cohort (441 individuals). Blood cell sorting and tumor tissue sequencing were performed to deconvolve small RNAs into their source of origin. - Results - We generated diagnostic models and report a median receiver-operating characteristic area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.86) in the discovery cohort and generalized performance of 0.83 in the validation cohort. Diagnostic performance increased in a stage-dependent manner ranging from 0.73 (95% CI: 0.71-0.76) for stage I to 0.90 (95% CI: 0.89-0.90) for stage IV in the discovery cohort and from 0.76 to 0.86 in the validation cohort. We identified a tumor-shed, plasma-bound ribosomal RNA fragment of the L1 stalk as a dominant predictor of lung cancer. The fragment is decreased after surgery with curative intent. In additional experiments, results of dried blood spot collection and sequencing revealed that small RNA analysis could potentially be conducted through home sampling. - Conclusions - These data suggest the potential of a small RNA-based blood test as a viable alternative to low-dose computed tomography screening for early detection of smoking-associated lung cancer.
DOI:doi:10.1016/j.jtho.2023.07.005
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1016/j.jtho.2023.07.005
 Volltext: https://www.sciencedirect.com/science/article/pii/S1556086423006706
 DOI: https://doi.org/10.1016/j.jtho.2023.07.005
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Biomarkers
 Blood test
 Early detection
 Liquid biopsy
 Small RNA
K10plus-PPN:1889500631
Verknüpfungen:→ Zeitschrift

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