Cancer metastasis networks and the prediction of progression patterns.
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.


CONCLUSIONS
Improvements over traditional techniques show that such a network-based modelling approach may be suitable for studying metastasis patterns.
Chen
L L
LL
Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.
Blumm
N
N
Christakis
N A
NA
Barabási
A-L
AL
Deisboeck
T S
TS
D000368:Aged; D000465:Algorithms; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D008954:Models, Biological; D009362:Neoplasm Metastasis; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000473:pathology*; D011237:Predictive Value of Tests; D011379:Prognosis
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