Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Intracranial 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 occurring at a time $${T}_{BM}$$ . Data 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 able to derive computational markers from primary tumor and BM data at $${T}_{BM}$$ and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are $$\alpha $$ , the proliferation rate of a single tumor cell; and $$\mu $$ , the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at $${T}_{BM}$$ for 20 patients. Parameters $$\alpha $$ and $$\mu $$ were 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). We 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.