Abstract 868P
Background
About 20% of human population recovers from asymptomatic malignancy without treatment, this process is known as spontaneous regression of tumors. There the host has effective immune system, eliminating tumor cells, but protecting normal tissue. Here we develop systems biology approach for analyzing normal tissue protection process, synchronized with tumor regression. We validate our approach by experimental findings.
Methods
We develop a computational System Biology model of coupled differential equations for tumor lysis, keeping normal tissue protected, as estimated by minimization of quadratic toxicity function. We mathematically obtain the temporal variation in level of 3 components which preserve normal tissue: Natural Killer (NK) cells, Circulating Leucocytes, and Interleukin (IL-2). Using microarray analysis of melanoma regression, and bio-informatics modelling, we investigate the temporal profiling and signaling pathways of normal tissue protection as tumor regresses. We also find out the gene expression signature related to the above 3 components.
Results
From mathematical model we find temporal behavior of the normal cell protecting components: (1) Natural Killer cell activation: Saturation function; (2) Interleukin-2 activation: Uniform function. (3) Circulating Leucocyte activation: Saturation function. We use quadratic least damage principle to characterise tumor regression dynamics that would be optimal for host, producing minor damage to normal tissue. Utilizing IPA assessment, our microarray analysis shows temporal behavior of gene expression levels corroborating the above 3 components: NK Signaling pathway (KLRK1, TVB1 genes), IL-2 Signaling pathway (genes IL2RG, CD74), Leukocyte vascular Signaling activity (genes CCL5, TAC). Finally, we validate our mathematical model by the experimental findings (Smirnov statistical test satisfied; 5% a).
Conclusions
Normal tissue protection is enabled by chronologically phased alteration of IL-2, NK cells, and circulation leucocytes. Using minimization-maximization algorithm, one may optimize temporal scheduling of chemotherapy/immunotherapy, so that drug-induced side-effects minimize.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.