Novel non-invasive early detection of lung cancer using liquid immunobiopsy metabolic activity profiles
| Autor: | Giora Davidovits, Aviv Lutaty, Cynthia Botbol, Irit Arbel, Hagit Peretz Soroka, Tali Scheinmann, Shoval Tirman, Ruven Tirosh, Sonia Schneer, Michal Shteinberg, Shirley Abramovitch, Yochai Adir, Fernando Patolsky, Eyal Davidovits Davidovits |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Adult Male Cancer Research Lung Neoplasms Adolescent Immunology Peripheral blood mononuclear cell 03 medical and health sciences Young Adult 0302 clinical medicine Immune system Biomarkers Tumor Immunology and Allergy Medicine Humans Stage (cooking) Liquid biopsy Lung cancer Early Detection of Cancer Aged Neoplasm Staging Aged 80 and over business.industry Cancer Middle Aged medicine.disease Prognosis 030104 developmental biology Oncology Anaerobic glycolysis 030220 oncology & carcinogenesis Localized disease Case-Control Studies Cancer research Metabolome Female business Follow-Up Studies |
| Zdroj: | Cancer immunology, immunotherapy : CII. 67(7) |
| ISSN: | 1432-0851 |
| Popis: | Lung cancer is the leading cause of cancer death worldwide. Survival is largely dependent on the stage of diagnosis: the localized disease has a 5-year survival greater than 55%, whereas, for spread tumors, this rate is only 4%. Therefore, the early detection of lung cancer is key for improving prognosis. In this study, we present an innovative, non-invasive, cancer detection approach based on measurements of the metabolic activity profiles of immune system cells. For each Liquid ImmunoBiopsy test, a 384 multi-well plate is loaded with freshly separated PBMCs, and each well contains 1 of the 16 selected stimulants in several increasing concentrations. The extracellular acidity is measured in both air-open and hermetically-sealed states, using a commercial fluorescence plate reader, for approximately 1.5 h. Both states enable the measurement of real-time accumulation of ‘soluble’ versus ‘volatile’ metabolic products, thereby differentiating between oxidative phosphorylation and aerobic glycolysis. The metabolic activity profiles are analyzed for cancer diagnosis by machine-learning tools. We present a diagnostic accuracy study, using a multivariable prediction model to differentiate between lung cancer and control blood samples. The model was developed and tested using a cohort of 200 subjects (100 lung cancer and 100 control subjects), yielding 91% sensitivity and 80% specificity in a 20-fold cross-validation. Our results clearly indicate that the proposed clinical model is suitable for non-invasive early lung cancer diagnosis, and is indifferent to lung cancer stage and histological type. |
| Databáze: | OpenAIRE |
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