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Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer

View Article: PubMed Central - PubMed

ABSTRACT

Background: Cancer reversion, converting the phenotypes of a cancer cell into those of a normal cell, has been sporadically observed throughout history. However, no systematic analysis has been attempted so far.

Results: To investigate this from a systems biological perspective, we have constructed a logical network model of colorectal tumorigenesis by integrating key regulatory molecules and their interactions from previous experimental data. We identified molecular targets that can reverse cancerous cellular states to a normal state by systematically perturbing each molecular activity in the network and evaluating the resulting changes of the attractor landscape with respect to uncontrolled proliferation, EMT, and stemness. Intriguingly, many of the identified targets were well in accord with previous studies. We further revealed that the identified targets constitute stable network motifs that contribute to enhancing the robustness of attractors in cancerous cellular states against diverse regulatory signals.

Conclusions: The proposed framework for systems analysis is applicable to the study of tumorigenesis and reversion of other types of cancer.

Electronic supplementary material: The online version of this article (doi:10.1186/s12918-017-0424-2) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus

The robustness analysis of the sequential mutation accumulations. A network is in general expected to be robust against the external signal when the number of attractors decreases or the average basin size of the five major attractors increases. Thus, the network becomes robust when the dot moves toward the left or upper part of the coordinate plane. The results of the sequential accumulation of mutations in colorectal cancer tumorigenesis have revealed that the robustness of the network against the external signals gradually increases in each step of mutation accumulation (top left). However, reversing the sequence of mutations accumulated in colorectal cancer tumorigenesis caused the increase of the robustness near the last step of the sequence (top right). Moreover, when we considered randomly selected mutation sequences (n = 30) and determined each mutation type to decrease the normal-like score, it also caused the increase of the robustness near the last step of the sequences (bottom left). A representative trajectory of random mutation sequences is shown in the figure. Furthermore, most of the random mutation accumulation sequences have not shown any significant increase of the robustness along with the accumulations of mutations (bottom right)
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Fig6: The robustness analysis of the sequential mutation accumulations. A network is in general expected to be robust against the external signal when the number of attractors decreases or the average basin size of the five major attractors increases. Thus, the network becomes robust when the dot moves toward the left or upper part of the coordinate plane. The results of the sequential accumulation of mutations in colorectal cancer tumorigenesis have revealed that the robustness of the network against the external signals gradually increases in each step of mutation accumulation (top left). However, reversing the sequence of mutations accumulated in colorectal cancer tumorigenesis caused the increase of the robustness near the last step of the sequence (top right). Moreover, when we considered randomly selected mutation sequences (n = 30) and determined each mutation type to decrease the normal-like score, it also caused the increase of the robustness near the last step of the sequences (bottom left). A representative trajectory of random mutation sequences is shown in the figure. Furthermore, most of the random mutation accumulation sequences have not shown any significant increase of the robustness along with the accumulations of mutations (bottom right)

Mentions: Our motif stability analysis has revealed that the major difference between colon cancer cells and normal cells is the stability of functional motifs. Because the stability of functional motifs represents the responsiveness to the external signals, cancer cells have the characteristics of higher state stability than normal cells. This indicates that the cancer cells become robust against external perturbation during tumorigenesis, and thus controlling the robustness can reverse cancerous state to normal-like state. The network becomes more robust against external signals when the number of attractors decreases or a basin size of attraction increases. So, we had further analyzed the changes of robustness during the tumorigenesis by tracking the number of attractors and the average basin size of attraction in each step when mutations were sequentially accumulated (see the Methods section for detailed explanations of robustness analysis). The most commonly observed mutation sequence in colorectal cancer gradually increased the robustness of the network at each step of mutation accumulation compared to the case of randomly selected mutation sequences, whereas reversing such a sequence caused a transient increase of the robustness near the last step of the sequence (Fig. 6). Moreover, when we considered randomly selected mutation sequences and determined each mutation type to decrease the normal-like score, we also obtained a similar pattern with the reverse of the most observed mutation sequence in colorectal tumorigenesis. Furthermore, most of the randomly selected mutation sequences have not shown significant increases of the robustness as mutations were accumulated. These analyses indicate that the specific mutation sequences accumulated during colorectal tumorigenesis might be preferentially selected for a cancer cell to avoid sensitive responses to external signals, and therefore such a robustness would be a major characteristics of cancer cells. Thus, perturbation of the stable motifs by altering the reversion targets may decrease the robustness of cancer cells against the external signals. Taken together, our results suggest that the key strategy for cancer reversion is to increase the responsiveness of cancer cells to external signals by disturbing stable functional motifs of cancer cells.Fig. 6


Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer
The robustness analysis of the sequential mutation accumulations. A network is in general expected to be robust against the external signal when the number of attractors decreases or the average basin size of the five major attractors increases. Thus, the network becomes robust when the dot moves toward the left or upper part of the coordinate plane. The results of the sequential accumulation of mutations in colorectal cancer tumorigenesis have revealed that the robustness of the network against the external signals gradually increases in each step of mutation accumulation (top left). However, reversing the sequence of mutations accumulated in colorectal cancer tumorigenesis caused the increase of the robustness near the last step of the sequence (top right). Moreover, when we considered randomly selected mutation sequences (n = 30) and determined each mutation type to decrease the normal-like score, it also caused the increase of the robustness near the last step of the sequences (bottom left). A representative trajectory of random mutation sequences is shown in the figure. Furthermore, most of the random mutation accumulation sequences have not shown any significant increase of the robustness along with the accumulations of mutations (bottom right)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5382366&req=5

Fig6: The robustness analysis of the sequential mutation accumulations. A network is in general expected to be robust against the external signal when the number of attractors decreases or the average basin size of the five major attractors increases. Thus, the network becomes robust when the dot moves toward the left or upper part of the coordinate plane. The results of the sequential accumulation of mutations in colorectal cancer tumorigenesis have revealed that the robustness of the network against the external signals gradually increases in each step of mutation accumulation (top left). However, reversing the sequence of mutations accumulated in colorectal cancer tumorigenesis caused the increase of the robustness near the last step of the sequence (top right). Moreover, when we considered randomly selected mutation sequences (n = 30) and determined each mutation type to decrease the normal-like score, it also caused the increase of the robustness near the last step of the sequences (bottom left). A representative trajectory of random mutation sequences is shown in the figure. Furthermore, most of the random mutation accumulation sequences have not shown any significant increase of the robustness along with the accumulations of mutations (bottom right)
Mentions: Our motif stability analysis has revealed that the major difference between colon cancer cells and normal cells is the stability of functional motifs. Because the stability of functional motifs represents the responsiveness to the external signals, cancer cells have the characteristics of higher state stability than normal cells. This indicates that the cancer cells become robust against external perturbation during tumorigenesis, and thus controlling the robustness can reverse cancerous state to normal-like state. The network becomes more robust against external signals when the number of attractors decreases or a basin size of attraction increases. So, we had further analyzed the changes of robustness during the tumorigenesis by tracking the number of attractors and the average basin size of attraction in each step when mutations were sequentially accumulated (see the Methods section for detailed explanations of robustness analysis). The most commonly observed mutation sequence in colorectal cancer gradually increased the robustness of the network at each step of mutation accumulation compared to the case of randomly selected mutation sequences, whereas reversing such a sequence caused a transient increase of the robustness near the last step of the sequence (Fig. 6). Moreover, when we considered randomly selected mutation sequences and determined each mutation type to decrease the normal-like score, we also obtained a similar pattern with the reverse of the most observed mutation sequence in colorectal tumorigenesis. Furthermore, most of the randomly selected mutation sequences have not shown significant increases of the robustness as mutations were accumulated. These analyses indicate that the specific mutation sequences accumulated during colorectal tumorigenesis might be preferentially selected for a cancer cell to avoid sensitive responses to external signals, and therefore such a robustness would be a major characteristics of cancer cells. Thus, perturbation of the stable motifs by altering the reversion targets may decrease the robustness of cancer cells against the external signals. Taken together, our results suggest that the key strategy for cancer reversion is to increase the responsiveness of cancer cells to external signals by disturbing stable functional motifs of cancer cells.Fig. 6

View Article: PubMed Central - PubMed

ABSTRACT

Background: Cancer reversion, converting the phenotypes of a cancer cell into those of a normal cell, has been sporadically observed throughout history. However, no systematic analysis has been attempted so far.

Results: To investigate this from a systems biological perspective, we have constructed a logical network model of colorectal tumorigenesis by integrating key regulatory molecules and their interactions from previous experimental data. We identified molecular targets that can reverse cancerous cellular states to a normal state by systematically perturbing each molecular activity in the network and evaluating the resulting changes of the attractor landscape with respect to uncontrolled proliferation, EMT, and stemness. Intriguingly, many of the identified targets were well in accord with previous studies. We further revealed that the identified targets constitute stable network motifs that contribute to enhancing the robustness of attractors in cancerous cellular states against diverse regulatory signals.

Conclusions: The proposed framework for systems analysis is applicable to the study of tumorigenesis and reversion of other types of cancer.

Electronic supplementary material: The online version of this article (doi:10.1186/s12918-017-0424-2) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus