| Autor: |
Irit Arbel, Shoval Tirman, Fernando Patolsky, Michal Shteinberg, Eyal Davidovits Davidovits, Giora Davidovits, Sonia Schneer, Yochai Adir |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Lung Cancer. |
| DOI: |
10.1183/13993003.congress-2018.pa1756 |
| Popis: |
Introduction: Early diagnosis of lung cancer is a key for improving prognosis. We present an innovative, non-invasive, Liquid ImmunoBiopsy assay for lung cancer detection based on measurements of the metabolic activity profiles (MAPs) of immune system cells. Methods: Lung cancer and control subjects were enrolled in parallel. For each Liquid ImmunoBiopsy test, a 384 multi-well plate was loaded with freshly separated PBMCs. Each well contained one of 16 selected stimulants in several increasing concentrations. The extracellular acidity was measured in both air-open and hermetically-sealed states. Both states enable the measurement of real-time accumulation of ‘soluble’ versus ‘volatile’ metabolic products, thereby differentiating between oxidative phosphorylation and aerobic glycolysis. The MAPs are analyzed for cancer diagnosis by machine learning tools. A multivariable prediction model to differentiate between lung cancer and control blood samples was developed. Results: The model was developed and tested using a cohort of 200 subjects (100 lung cancer and 100 control), yielding 91% sensitivity and 80% specificity in a 20-fold cross-validation. 58% of patients had adeno while 10% had small cell carcinoma. No observable difference in sensitivity between lung cancer stages was found (p=0.74). The ability to identify lung cancer was not affected by the smoking status or COPD comorbidity. Conclusion: 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 |
| Externí odkaz: |
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