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

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Abstract

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.

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Data availability

The datasets used and analyzed during this study are available from the corresponding author on reasonable request.

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Acknowledgements

PS wants to thank Christina Kuttler for valuable discussions on the mathematical modelling method. Supported by Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE), GSC 81.

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Author notes

  1. Sébastien Benzekry, Pirmin Schlicke, Pascale Tomasini, Eléonore Simon have contributed equally and are co-first authors in alphabetical order.

Authors and Affiliations

  1. COMPutational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis – Méditerranée, Faculté de Pharmacie, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 27 Boulevard Jean Moulin, 13005, Marseille, France

    Sébastien Benzekry

  2. Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Garching (Munich), Germany

    Pirmin Schlicke

  3. Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France

    Alice Mogenet, Laurent Greillier, Pascale Tomasini & Eléonore Simon

  4. Aix Marseille University, CNRS, INSERM, CRCM, Marseille, France

    Laurent Greillier & Pascale Tomasini

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Contributions

Conceived the research idea: SB, PT. Model setup: PS, SB. Collected the data: ES, PT. Performed data analysis and presentation: PS. Wrote software to estimate parameters and simulate: PS, SB. The paper was written by ES and PS with editorial input from all authors. All authors read and approved the final manuscript.

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Correspondence to Sébastien Benzekry.

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Non-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.

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Benzekry, S., Schlicke, P., Mogenet, A. et al. Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases. Clin Exp Metastasis 41, 55–68 (2024). https://doi.org/10.1007/s10585-023-10245-3

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