Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer.
View / Open Files
Authors
Ascolani, Gianluca
Occhipinti, Annalisa
Liò, Pietro
Publication Date
2015-05Journal Title
PLoS Comput Biol
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Volume
11
Issue
5
Pages
e1004199
Language
eng
Type
Article
Physical Medium
Electronic-eCollection
Metadata
Show full item recordCitation
Ascolani, G., Occhipinti, A., & Liò, P. (2015). Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer.. PLoS Comput Biol, 11 (5), e1004199. https://doi.org/10.1371/journal.pcbi.1004199
Abstract
Ductal carcinoma is one of the most common cancers among women, and the main cause of death is the formation of metastases. The development of metastases is caused by cancer cells that migrate from the primary tumour site (the mammary duct) through the blood vessels and extravasating they initiate metastasis. Here, we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct, in the circulatory system and in the bone. Through a branching process model, we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer (EPCAM, CD47, CD44 and MET). In particular, the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability. We also include the administration of drugs as bisphosphonates, which reduce the formation of circulating tumour cells and their survival in the blood vessels, in order to analyse the dynamic changes induced by the therapy. We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue. Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics.
Keywords
Humans, Carcinoma, Ductal, Breast, Breast Neoplasms, Disease Progression, Transforming Growth Factor beta, Survival Rate, Gene Expression Profiling, Models, Biological, Computer Simulation, Female, Neoplastic Cells, Circulating, Kaplan-Meier Estimate, Biomarkers, Tumor
Identifiers
External DOI: https://doi.org/10.1371/journal.pcbi.1004199
This record's URL: https://www.repository.cam.ac.uk/handle/1810/284994
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk