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

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...

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Autores principales: Szczurek, Ewa, Krüger, Tyll, Klink, Barbara, Beerenwinkel, Niko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591057/
https://www.ncbi.nlm.nih.gov/pubmed/33006977
http://dx.doi.org/10.1371/journal.pcbi.1008056
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author Szczurek, Ewa
Krüger, Tyll
Klink, Barbara
Beerenwinkel, Niko
author_facet Szczurek, Ewa
Krüger, Tyll
Klink, Barbara
Beerenwinkel, Niko
author_sort Szczurek, Ewa
collection PubMed
description 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.
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spelling pubmed-75910572020-10-30 A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment Szczurek, Ewa Krüger, Tyll Klink, Barbara Beerenwinkel, Niko PLoS Comput Biol Research Article 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. Public Library of Science 2020-10-02 /pmc/articles/PMC7591057/ /pubmed/33006977 http://dx.doi.org/10.1371/journal.pcbi.1008056 Text en © 2020 Szczurek et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Szczurek, Ewa
Krüger, Tyll
Klink, Barbara
Beerenwinkel, Niko
A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment
title A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment
title_full A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment
title_fullStr A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment
title_full_unstemmed A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment
title_short A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment
title_sort mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591057/
https://www.ncbi.nlm.nih.gov/pubmed/33006977
http://dx.doi.org/10.1371/journal.pcbi.1008056
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