Systemic cancer progression and tumor dormancy: mathematical models meet single cell genomics.
Metastatic progression is thought to result from genetically advanced "fully-malignant" tumor cells. Within the concept the prevailing view holds that such cells disseminate mostly from large tumors and are capable of growing into metastases once they arrive at a distant site. Support for this scenario comes from numerous mouse models in which transplanted tumor cells grow into metastases within days or weeks. However, the assumption of such fully-malignant disseminating cells in human cancer is misleading and is neither supported by mathematical modeling of survival data from cancer patients nor by ex-vivo genomic data from disseminated cancer cells. For example, in breast cancer the growth of metastases is highly homogeneous and takes on average six years, the number of disseminated tumor cells before diagnosis of metastasis is similar for different tumor stages, and the genomic aberrations of disseminated cancer cells do rarely correspond to those in the primary tumor. Since these facts question conventional concepts of metastatic progression we provide a model of cancer progression in which time considerations and direct ex-vivo data form a starting point. In the proposed model tumor dormancy is a characteristic of almost all migrated tumor cells and metastatic growth is a rare, stochastic, evolutionary process of selection and mutation of cells that often disseminate shortly after transformation at the primary site.
Klein
Christoph A
CA
Institut für Immunologie, Ludwig-Maximilians-Universität, München, Germany. christoph.klein@med.uni-muenchen.de
Hölzel
Dieter
D
D000818:Animals; D001943:Breast Neoplasms / Q000453:epidemiology / Q000235:genetics* / Q000473:pathology; D049109:Cell Proliferation; D005260:Female; D006801:Humans; D016014:Linear Models; D051379:Mice; D023421:Models, Animal; D008957:Models, Genetic; D008962:Models, Theoretical; D009362:Neoplasm Metastasis / Q000235:genetics; D014411:Neoplastic Stem Cells / Q000473:pathology*; D013269:Stochastic Processes; D016019:Survival Analysis; D013997:Time Factors