Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics

Abstract

Cancer reflects the dysregulation of the underlying gene network, which is strongly related to the 3D genome organization. Numerous efforts have been spent on experimental characterizations of the structural alterations in cancer genomes. However, there is still a lack of genomic structural-level understanding of the temporal dynamics for cancer initiation and progression. Here, we use a landscape-switching model to investigate the chromosome structural transition during the cancerization and reversion processes. We find that the chromosome undergoes a non-monotonic structural shape-changing pathway with initial expansion followed by compaction during both of these processes. Furthermore, our analysis reveals that the chromosome with a more expanding structure than those at both the normal and cancer cell during cancerization exhibits a sparse contact pattern, which shows significant structural similarity to the one at the embryonic stem cell in many aspects, including the trend of contact probability declining with the genomic distance, the global structural shape geometry and the spatial distribution of loci on the chromosome. In light of the intimate structure-function relationship at the chromosomal level, we further describe the cell state transition processes by the chromosome structural changes, suggesting an elevated cell stemness during the formation of the cancer cells. We show that cell cancerization and reversion are highly irreversible processes in terms of the chromosome structural transition pathways, spatial repositioning of chromosomal loci and hysteresis loop of contact evolution analysis. Our model draws a molecular-scale picture of cell cancerization from the chromosome structural perspective. The process contains initial reprogramming towards the stem cell followed by the differentiation towards the cancer cell, accompanied by an initial increase and subsequent decrease of the cell stemness.

Uncovering the Quantitative Relationships Among Chromosome Fluctuations, Epigenetics, and Gene Expressions of Transdifferentiation on Waddington Landscape

“…The compartment A/B can be quantified with +1 and −1 (epigenetic quantity) in this study (see Figure 5), which is similar to the positively charged and negatively charged residues of a polyampholyte chain. [ 56–58 ] We found that there are much more loci with low local CFI values (or low gene expressions) have −1 epigenetic quantity than those with high local CFI values (or high gene expressions). Then in order to make clear comparison, we calculated the distribution of the epigenetic quantity on the gene expressions.…”

“…Moreover, the sequence charge decoration (SCD) value can reveal the property of the monomer distribution ($$\mathrm{SCD} = \frac{1}{N}\sum \nolimits _{i = 1}^N {\sum \nolimits _{j = i + 1}^N {{\alpha _i}{\alpha _j}\sqrt {j - i} } }$$). [ 56,58 ] The value of α is assigned according to the compartment or local CFI. SCD with large absolute value implies the sequence with charge‐blocky pattern, SCD with small absolute value implies the sequence with charge‐scrambled pattern (see sv15 and sv1 sequences in ref.…”

“…Then the sequence charge decoration (SCD) values were calculated for the chromosome chains of chr21 and chr22 ($$\mathrm{SCD} = \frac{1}{N}\sum \nolimits _{i = 1}^N {\sum \nolimits _{j = i + 1}^N {{\alpha _i}{\alpha _j}\sqrt {j - i} } }$$). [ 56,58 ] We also calculated the SCD values for the CFI spectrums of chr21 and chr22, with high local CFI part (CFI i > 12.3 in chr21, CFI i > 13 in chr22) set as +1 and low local CFI part set as −1. The threshold values correspond to about 60% level from the highest CFI i to the lowest CFI i .…”

“…We developed the chromosome structural ensemble switching model (CSESM), which is similar as the previous energy landscape‐switching model, [ 15 , 63 , 64 ] to explore the dynamical processes between different kind of cell‐types. By encoding the epigenetic information in the chromosome model, we performed the transdifferentiation processes from PBMC to BN and from BN to PBMC.…”

“…The non‐equilibrium effects in the landscape‐switching model are the driving forces for the transdifferentiation processes, which is often in the form of extended ATP hydrolysis for energy pumping in biology. As the previous simulation procedures, [ 15 , 63 , 64 ] here the CSESM simulations were performed under the potential of the beginning state of transdifferentiation. Then the potential was suddenly switched to that of the end state of transdifferentiation.…”