A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment

Ewa Szczurek, Tyll Krüger, Barbara Klink, Niko Beerenwinkel*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


Metastases are the main reason for cancer-related deaths. Initiation of metastases, where newly seeded tumor cells expand into colonies, presents a tremendous bottleneck to metastasis formation. Despite its importance, a quantitative description of metastasis initiation and its clinical implications is lacking. Here, we set theoretical grounds for the metastatic bottleneck with a simple stochastic model. The model assumes that the proliferation-to-death rate ratio for the initiating metastatic cells increases when they are surrounded by more of their kind. For a total of 159,191 patients across 13 cancer types, we found that a single cell has an extremely low median probability of successful seeding of the order of 10-8. With increasing colony size, a sharp transition from very unlikely to very likely successful metastasis initiation occurs. The median metastatic bottleneck, defined as the critical colony size that marks this transition, was between 10 and 21 cells. We derived the probability of metastasis occurrence and patient outcome based on primary tumor size at diagnosis and tumor type. The model predicts that the efficacy of patient treatment depends on the primary tumor size but even more so on the severity of the metastatic bottleneck, which is estimated to largely vary between patients. We find that medical interventions aiming at tightening the bottleneck, such as immunotherapy, can be much more efficient than therapies that decrease overall tumor burden, such as chemotherapy.

Original languageEnglish
Article numbere1008056
JournalPLoS Computational Biology
Issue number10
Publication statusPublished - 2 Oct 2020
Externally publishedYes


Dive into the research topics of 'A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment'. Together they form a unique fingerprint.

Cite this