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Cancer metastasis networks and the prediction of progression patterns.

Chen LL, Blumm N, Christakis NA, Barabási AL, Deisboeck TS - Br. J. Cancer (2009)

Bottom Line: Network modelling may allow the incorporation of the temporal dimension in the analysis of these patterns.These cancer metastasis networks capture both temporal and subtle relational information, the dynamics of which differ between cancer types.Using these networks as entities on which the metastatic disease of individual patients may evolve, we show that they may be used, for certain cancer types, to make retrograde predictions of a primary cancer type given a sequence of metastases, as well as anterograde predictions of future sites of metastasis.

View Article: PubMed Central - PubMed

Affiliation: Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.

ABSTRACT

Background: Metastasis patterns in cancer vary both spatially and temporally. Network modelling may allow the incorporation of the temporal dimension in the analysis of these patterns.

Methods: We used Medicare claims of 2,265,167 elderly patients aged > or = 65 years to study the large-scale clinical pattern of metastases. We introduce the concept of a cancer metastasis network, in which nodes represent the primary cancer site and the sites of subsequent metastases, connected by links that measure the strength of co-occurrence.

Results: These cancer metastasis networks capture both temporal and subtle relational information, the dynamics of which differ between cancer types. Using these networks as entities on which the metastatic disease of individual patients may evolve, we show that they may be used, for certain cancer types, to make retrograde predictions of a primary cancer type given a sequence of metastases, as well as anterograde predictions of future sites of metastasis.

Conclusion: Improvements over traditional techniques show that such a network-based modelling approach may be suitable for studying metastasis patterns.

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Related in: MedlinePlus

The cancer metastasis network for colon cancer (chosen as a representative example) and the dynamics of its links. (A), network at t=0, or the time of diagnosis of the primary tumour. (B), network at t=48 months. Nodes correspond to anatomical sites of metastases, the size of which represents their respective incidence rates. The widths of the links represent the strength of metastasis co-occurrence for two anatomical sites. Yellow nodes represent lymph node metastases; red nodes represent organ metastases. The curves in Figure 1 represent the monthly growth of these nodes, whereas the following (phi) represents the monthly growth of the links: (C), metastasis site co-occurrence associations as measured by phi over time. All the possible associations are lined up on the x axis, and their temporal dynamics are represented by the y axis. Only phi with P-value <0.01 are shown. (D), metastasis site co-occurrence associations as measured by relative risk over time. Only RR values with 99% confidence interval or RR <0.1 are shown.
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fig2: The cancer metastasis network for colon cancer (chosen as a representative example) and the dynamics of its links. (A), network at t=0, or the time of diagnosis of the primary tumour. (B), network at t=48 months. Nodes correspond to anatomical sites of metastases, the size of which represents their respective incidence rates. The widths of the links represent the strength of metastasis co-occurrence for two anatomical sites. Yellow nodes represent lymph node metastases; red nodes represent organ metastases. The curves in Figure 1 represent the monthly growth of these nodes, whereas the following (phi) represents the monthly growth of the links: (C), metastasis site co-occurrence associations as measured by phi over time. All the possible associations are lined up on the x axis, and their temporal dynamics are represented by the y axis. Only phi with P-value <0.01 are shown. (D), metastasis site co-occurrence associations as measured by relative risk over time. Only RR values with 99% confidence interval or RR <0.1 are shown.

Mentions: Metastasis conditional incidence (hazard) functions for cancers arising at six primary sites are shown in Figure 1. Each curve represents the hazard function for a particular secondary site. Similarly, with the metastasis network links, we can plot their dynamics over time. Figure 2A is the colon cancer-specific metastasis network at t=0, and Figure 2B shows the network at t=48 months. We can extract dynamical information from the evolution of the network links over time. Figures 2C and D show, for the array of all possible pair-wise links, the monthly increase in the link strength. For any given pair, link strength representing the likelihood metastases at one anatomical site will be found simultaneously with metastases at the other site. Only statistically significant links are shown. Figure 2C, which uses the phi measure to characterise link strength, creates a more detailed picture of the overall dynamics. Initially, a few links steadily and solidly increase. As cancers progress, many more links are added, and link addition becomes much more scattered, and thus covers many more link possibilities. As a consequence, at t=0, the strength distribution of these links is narrow and centred about a relatively low strength value (Supplementary Figure S2A). As the cancers progress, these distributions naturally shift toward higher strength values, and evolve toward a more uniform profile.


Cancer metastasis networks and the prediction of progression patterns.

Chen LL, Blumm N, Christakis NA, Barabási AL, Deisboeck TS - Br. J. Cancer (2009)

The cancer metastasis network for colon cancer (chosen as a representative example) and the dynamics of its links. (A), network at t=0, or the time of diagnosis of the primary tumour. (B), network at t=48 months. Nodes correspond to anatomical sites of metastases, the size of which represents their respective incidence rates. The widths of the links represent the strength of metastasis co-occurrence for two anatomical sites. Yellow nodes represent lymph node metastases; red nodes represent organ metastases. The curves in Figure 1 represent the monthly growth of these nodes, whereas the following (phi) represents the monthly growth of the links: (C), metastasis site co-occurrence associations as measured by phi over time. All the possible associations are lined up on the x axis, and their temporal dynamics are represented by the y axis. Only phi with P-value <0.01 are shown. (D), metastasis site co-occurrence associations as measured by relative risk over time. Only RR values with 99% confidence interval or RR <0.1 are shown.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2736851&req=5

fig2: The cancer metastasis network for colon cancer (chosen as a representative example) and the dynamics of its links. (A), network at t=0, or the time of diagnosis of the primary tumour. (B), network at t=48 months. Nodes correspond to anatomical sites of metastases, the size of which represents their respective incidence rates. The widths of the links represent the strength of metastasis co-occurrence for two anatomical sites. Yellow nodes represent lymph node metastases; red nodes represent organ metastases. The curves in Figure 1 represent the monthly growth of these nodes, whereas the following (phi) represents the monthly growth of the links: (C), metastasis site co-occurrence associations as measured by phi over time. All the possible associations are lined up on the x axis, and their temporal dynamics are represented by the y axis. Only phi with P-value <0.01 are shown. (D), metastasis site co-occurrence associations as measured by relative risk over time. Only RR values with 99% confidence interval or RR <0.1 are shown.
Mentions: Metastasis conditional incidence (hazard) functions for cancers arising at six primary sites are shown in Figure 1. Each curve represents the hazard function for a particular secondary site. Similarly, with the metastasis network links, we can plot their dynamics over time. Figure 2A is the colon cancer-specific metastasis network at t=0, and Figure 2B shows the network at t=48 months. We can extract dynamical information from the evolution of the network links over time. Figures 2C and D show, for the array of all possible pair-wise links, the monthly increase in the link strength. For any given pair, link strength representing the likelihood metastases at one anatomical site will be found simultaneously with metastases at the other site. Only statistically significant links are shown. Figure 2C, which uses the phi measure to characterise link strength, creates a more detailed picture of the overall dynamics. Initially, a few links steadily and solidly increase. As cancers progress, many more links are added, and link addition becomes much more scattered, and thus covers many more link possibilities. As a consequence, at t=0, the strength distribution of these links is narrow and centred about a relatively low strength value (Supplementary Figure S2A). As the cancers progress, these distributions naturally shift toward higher strength values, and evolve toward a more uniform profile.

Bottom Line: Network modelling may allow the incorporation of the temporal dimension in the analysis of these patterns.These cancer metastasis networks capture both temporal and subtle relational information, the dynamics of which differ between cancer types.Using these networks as entities on which the metastatic disease of individual patients may evolve, we show that they may be used, for certain cancer types, to make retrograde predictions of a primary cancer type given a sequence of metastases, as well as anterograde predictions of future sites of metastasis.

View Article: PubMed Central - PubMed

Affiliation: Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.

ABSTRACT

Background: Metastasis patterns in cancer vary both spatially and temporally. Network modelling may allow the incorporation of the temporal dimension in the analysis of these patterns.

Methods: We used Medicare claims of 2,265,167 elderly patients aged > or = 65 years to study the large-scale clinical pattern of metastases. We introduce the concept of a cancer metastasis network, in which nodes represent the primary cancer site and the sites of subsequent metastases, connected by links that measure the strength of co-occurrence.

Results: These cancer metastasis networks capture both temporal and subtle relational information, the dynamics of which differ between cancer types. Using these networks as entities on which the metastatic disease of individual patients may evolve, we show that they may be used, for certain cancer types, to make retrograde predictions of a primary cancer type given a sequence of metastases, as well as anterograde predictions of future sites of metastasis.

Conclusion: Improvements over traditional techniques show that such a network-based modelling approach may be suitable for studying metastasis patterns.

Show MeSH
Related in: MedlinePlus