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Computational modeling reveals dynamics of brain metastasis in non-small cell lung cancer and provides a tool for personalized therapy

Brain metastases (BMs) are the largest disabling site for non-small cell lung cancers, but are only visible when sizeable. Individualized prediction of the BM risk and extent is a major challenge for therapeutic decision. This study assesses mechanistic models of BM apparition and growth against clinical imaging data. We implemented a quantitative computational method to confront biologically-informed mathematical models to clinical data of BMs. Primary tumor growth parameters were estimated from size at diagnosis and histology. Metastatic dissemination and growth parameters were fitted to either population data of BM probability (n=183 patients) or longitudinal measurements of number and size of visible BMs (63 size measurements in two patients). Pre-clinical phases from first cancer cell to detection were estimated to 2.1-5.3 years. A model featuring dormancy was best able to describe the longitudinal data, as well as BM probability as a function of primary tumor size at diagnosis. It predicted first appearance of BMs at 14-19 months pre-diagnosis. Model-informed predictions of invisible cerebral disease burden could be used to inform therapeutic intervention. Author summary Management of brain metastasis in lung cancer is a major clinical challenge. This study reports on a quantitative modeling analysis of patient-specific time courses of the apparition and growth of brain metastases in lung cancer. Several biological theories were tested by confronting mechanistic mathematical models to clinical data. The best of these models, which features periods of stable metastatic size (dormancy), provides a valuable computational tool for personalized therapy, by informing on the extent of invisible cerebral metastases at various time points during therapeutic management. This information is critical for clinical interventions such as the use of whole brain radiation therapy.