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.
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Area under the curve
Chronic obstructive pulmonary disease
Low-dose computed tomography
Metabolic activity profile
Small cell lung cancer
Support vector machine
US Preventive Services Task Force
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The authors wish to thank the principal investigators, Prof. Fernando Patolsky, Prof. Nadir Arber, Dr. Mirjana Wollner, and Dr. Ran Kremer; the coordinators of this study, including multi-site coordinator Janna Bronstein, for patient recruitment, collection of specimens, and clinical data; Dr. Sara Bar Yehuda for regulatory support; Dr. Uri Itay and Chaim Singal for their help in the statistical analysis of the data; and furthermore, all the donor patients for their contribution to this clinical study.
This work was supported by Savicell Diagnostics and by Grants from the Israel Science Foundation (ISF) through the Legacy Program.
Conflict of interest
Fernando Patolsky received consulting fees from Savicell Diagnostics. Shoval Tirman, Aviv Lutaty, Tali Scheinmann, Eyal Davidovits, Irit Arbel, and Giora Davidovits are employed by Savicell Diagnostics. Shirley Abramovitch and Cynthia Botbol were employed by Savicell Diagnostics. Eyal Davidovits, Irit Arbel, and Giora Davidovits are officers in Savicell Diagnostics. Eyal Davidovits and Giora Davidovits are on the board of directors of Savicell Diagnostics. Yochai Adir, Shoval Tirman, Shirley Abramovitch, Cynthia Botbol, Aviv Lutaty, Tali Scheinmann, Eyal Davidovits, Irit Arbel, Giora Davidovits, and Fernando Patolsky own stock and/or options in Savicell Diagnostics’ parent company. The other authors declare that they have no conflict of interest.
Ethical approval and ethical standards
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committees and with the 1964 Helsinki declaration and its later amendments. Institutional review board approval numbers are: 0105-13-CMC for Carmel Medical Center, Haifa; 0274-15-RMB for Rambam Medical Center, Haifa; and 0009-13-TLV for Sourasky Medical Center, Tel Aviv.
Informed consent was obtained from all individual participants included in the study.
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Adir, Y., Tirman, S., Abramovitch, S. et al. Novel non-invasive early detection of lung cancer using liquid immunobiopsy metabolic activity profiles. Cancer Immunol Immunother 67, 1135–1146 (2018). https://doi.org/10.1007/s00262-018-2173-5
- Liquid biopsy
- Metabolic profile
- Lung cancer
- Early detection
- Cancer diagnosis