Name
Title | ||
|---|---|---|
A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment. |
Abstract | ||
|---|---|---|
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. Author summary We propose a stochastic model of metastasis formation, encompassing the release of cells from a growing tumor, their settlement at a distant site and their impact on patient outcome. We put forward a theory of the extreme bottleneck that this process encounters at the settlement step. We derive equations for several clinically relevant quantities as functions of primary tumor size for patients who underwent surgery: the probability of metastasis occurrence and of metastasis detection, the risk of death due to cancer, and the survival time. The model is fit to epidemiological data for 13 cancer types, and used to predict the impact of different treatment options on patient outcome. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1371/journal.pcbi.1008056 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 16 | 10 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ewa Szczurek | 1 | 49 | 6.75 |
| Tyll Krüger | 2 | 0 | 0.34 |
| Barbara Klink | 3 | 0 | 0.68 |
| Niko Beerenwinkel | 4 | 11 | 6.13 |