Savicell's proprietary Well-Shield™ technology has broad application in identifying disease status in multiple diseases that include cancer and autoimmune diseases. It may be used to track treatment effectiveness, including immunotherapy. Our initial focus is on lung cancer.
Savicell's lung cancer clinical study results were published in Cancer Immunology and Immunotherapy that validate the promise of Savicell’s Liquid ImmunoBiopsy™. The published study uses the Savicell Diagnostics, Ltd. platform to diagnose lung cancer, producing 91% sensitivity and 80% specificity in a 20-fold cross-validation. Diagnosis of Stage 1 lung cancer is as accurate as later stages.
Novel non-invasive early detection of lung cancer using liquid immunobiopsy metabolic activity profilesAdir, Y., Tirman, S., Abramovitch, S. et al. Cancer Immunol Immunother (2018). https://doi.org/10.1007/s00262-018-2173-5
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.