Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome

Abstract

ABSTRACTBackgroundIntracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available.The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event.MethodsData included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N=31).We propose a mechanistic mathematical model to estimate the amount and sizes of (visible and invisible) BMs. The two key parameters of the model areα, the proliferation rate of a single tumor cell; andμ, the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated.FindingsThe model was able to correctly describe the number and size of metastases at the time of first BM relapse for 20 patients. Parametersαandμwere significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p=0.0029 and HR 1.95 (1.31-2.91) p=0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p<0.0001).InterpretationWe demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.SIGNIFICANCE STATEMENTNon-small cell lung cancer is difficult to manage when brain metastases are present. This study presents a mathematical model that can be calibrated on individual patients’ data early in the treatment course to explain the growth dynamics of brain metastases and demonstrates that the mathematically derived parameters can serve as predictive tool in clinical routine care.Highlights-Mechanistic mathematical modeling allows individualized prognosis for lung cancer patients at first brain metastatic relapse-Individual model-derived computational parameters identifies high-risk patients in terms of brain metastasis progression and survival-Prognostic features include quantification of the number and sizes of both clinically visible and invisible brain metastases