Exploring the cancer cell attractor in the Epstein-Barr virus infection model

Sammanfattning: It has been proposed, based on the theory of complex gene regulatory networks, that cell types, including cancer cells, represent attractor states of the network dynamics. In this study, we proposed an Epstein - Barr virus (EBV) latency I to latency III switch model. Exploiting this EBV latency switch model, we characterized for the first time the detailed dynamics of a cancer cell attractor at single-cell-resolution and found that the edge cells from a non-malignant cell line could transiently and stochastically adopt some malignant characteristics due to biological noise. This work might impact design of future rational cancer therapies by taking into account the dynamic robustness and high volatility of a heterogeneous cancer cell population. We also evaluated the impact of EBV load as a biomarker for hematopoietic stem cell transplantation (HSCT) patients. We established and validated a latency I to latency III switch model that is based on on-off status of the EBV C promoter during latent infection by competition between EBNA1 and Oct proteins together with co-regulators (Paper I). The dynamics of gene expression space, owing to a balance between homeostatic forces and stochastic fluctuations, has led to the cancer cell attractor conceptual model. Using the immortalized and malignant carrying EBV B-cell lines, we characterized the detailed structure of cell attractors. Any subpopulation selected from a population of cells repopulated the whole original basin of attraction within days to weeks. Cells at the basin edges were unstable and prone to apoptosis. Cells continuously changed states within their own attractor, thus driving the repopulation. Perturbations of key regulatory genes induced a jump to a nearby attractor. Using the Fokker-Planck equation, this cell population behavior could be described as two virtual, opposing influences on the cells: one attracting towards the center and the other promoting diffusion in state space. Transcriptome analysis suggests that these forces result from high-dimensional dynamics of the gene regulatory network. The clonal cell population heterogeneity was investigated by single cell RNA sequencing method. We sequenced different subpopulations within the clonal cell populations and found that edge cells from the non-malignant cell line (non-malignant attractor), represent a distinct population more close to the malignant attractor. This was based on mRNA expression pattern, single cell imaging, clustering analysis, and functional studies. We propose that these findings can be generalized to all cancer cell populations and represented intrinsic behaviors of tumors, offering new perspectives in the study of cancer. The results provide quantitative knowledge on non-genetic intercellular heterogeneity and its dynamics within an isogenic cell population of cancerous cells, affording insights at a new level of resolution, between molecular pathways and macroscopic tumor behaviors (Paper II and III). We evaluated the impact of EBV load on survival of 51 HSCT patients. Patients with very high or very low level of cell bound EBV-DNA levels had a shorter overall survival (OS) than those with moderate EBV load: OS at 5 years was 67% vs 90%, (P < 0.03). There was a conspicuous relationship between EBV load and the dynamics of reconstitution of total and EBV-specific T cells in a few patients. According to multivariate analysis, two other factors were also associated to early mortality: acute GVHD II-IV (p<0.02) and pre-transplant conditioning with total body irradiation (TBI) ≥6 Gy (p<0.03). All patients showing these three criteria died within two years after transplantation. This points to a subgroup of HSCT patients which deserve special attention aiming to improve their future treatment (Paper IV).