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

Abstract : 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 biologicallyinformed 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.
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https://hal.inria.fr/hal-01928442
Contributor : Sebastien Benzekry <>
Submitted on : Tuesday, November 20, 2018 - 3:14:38 PM
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M Bilous, C Serdjebi, Arnaud Boyer, P. Tomasini, C Pouypoudat, et al.. Computational modeling reveals dynamics of brain metastasis in non-small cell lung cancer and provides a tool for personalized therapy. 2018. ⟨hal-01928442v1⟩

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