Data on the recurrence of breast tumors fit a model in which dormant cells are subject to slow attrition but can randomly awaken to become malignant
Research output: Contribution to journal › Journal article › Research › peer-review
We successfully modeled the recurrence of tumors in breast cancer patients, assuming that: (i) A breast cancer patient is likely to have some circulating metastatic cells, even after initial surgery. (ii) These metastatic cells are dormant. (iii) The dormant cells are subject to attrition by the body's immune system, or by random apoptosis or senescence. (iv) Recurrence suppressor mechanisms exist. (v) When such genes are disabled by random mutations, the dormant metastatic cell is activated, and will develop to a cancer recurrence. The model was also fitted to data on the survival of pancreatic cancer patients. The time course of cancer recurrence in a group of poor prognosis breast cancer patients could not be linked to the over- (or under-) expression of any gene in the primary tumors from which the recurrent tumors derived. Thus, the recurrence of the tumor in breast cancer patients appears to be a random event. Inasmuch as the kinetics of cancer recurrence in published data sets closely follows the model found for the appearance of sporadic retinoblastoma, tumor recurrence could be triggered by mutations in awakening- suppressor mechanisms. The retinoblastoma tumor suppressor gene was identified by tracing its occurrence in familial retinoblastoma pedigrees. Will it be possible to track the postulated cancer recurrence, awakening suppressor gene(s) in early recurrence breast cancer patients?
Original language | English |
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Journal | Cell Cycle |
Volume | 5 |
Issue number | 20 |
Pages (from-to) | 2348-53 |
Number of pages | 6 |
ISSN | 1538-4101 |
Publication status | Published - 2006 |
- Breast Neoplasms, Female, Follow-Up Studies, Genes, Neoplasm, Humans, Models, Biological, Neoplasm Metastasis, Neoplastic Cells, Circulating, Pancreatic Neoplasms, Prognosis, Recurrence, Survival Analysis
Research areas
ID: 119646331