Cancer is a very serious disease; it is the second leading cause of death worldwide. Most cancers can be effectively treated if discovered early, when the tumor is in its early stage. Current clinical cancer diagnose methods lack the necessary timelines and sensitivity, which needs 20-40 years to diagnose the cancer after first parental tumor cell genesis. However, according to the present method, it still needs more than ten years for the cancer to be diagnosed. In addition, most clinical blood biomarkers lack the requisite sensitivity and specificity to detect cancer at early stages. Therefore, there is a pressing need for discovering new biomarkers that possess higher sensitivity and specificity for diagnosing and prognosing aggressive cancers. We found that extracellular vesicle (EV) –exosomes contain many types of known biomarker proteins DNA and miRNA. The specific exosomes secreted by tumor cells normally contain the biomarkers that can be used to predict the existence of cancer. The biogenesis of exosomes is regulated by the machinery of endosomal sorting complexes required for lipid ceramide or transport (ESCRT). Since exosomes possess large enough density and size that can be isolated and purified from blood using ultracentrifugation or magnetic separation methods. After re-suspending the isolated exosomes in a pure solution, the concentration of the specific biomarkers in the exosomes can be significantly increased compared to the traditional blood based biomarker method. Moreover, the volume of re-suspension solution is controllable, which can adjust the concentration of the specific biomarkers accordingly. Therefore, exosomes based method is superior over the blood based biomarker method and other traditional cancer detection methods.
However, it is still unclear how early the exosomes based detection can diagnose cancer. In order to demonstrate the feasibility of exosomes based cancer diagnosis method, we developed a mathematical model to simulate the kinetics of cancer specific exosomes in relation to the growth of tumors that begin with a single parental cancer cell. To exemplify a more realistic scenario, we primed the model on pancreatic tumor growth using Glypican-1(GPC1) circulating exosomes (crExo). The simulation parameters are obtained from published literature.
As the result, after first parental tumor cell genesis, it requires 940 days for tumor cells to secrete 105 numbers of the GPC1 specific exosomes. Followed by isolation and purification of the secreted exosomes, the isolated and purified exosomes can be re-suspended in any desirable volume of solution, which can greatly increase the concentrations of all the biological contents including the biomarkers enclosed by the exosomes compared to the traditional blood based biomarker. Hence, the required number of secreted exosomes is significantly reduced in order to reach the same equipment detection limit. In comparision, exosome based method can diagnose the cancer in 2.58 years compared to about 10 years diagosis timeframe using traditional blood based biomarker.
My developed mathematical model is capable of predicting the cancer detection time and helping the researchers simulate the growth and size for any tumor cell type. The model presented here can be extended to virtually any solid cancer and associated biomarkers.
