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- Dokumenttyp:
- Zeitschriftenaufsatz
- Autor(en):
- Benzekry, S.; Schlicke, P.; Tomasini, P.; Simon, E.
- Titel:
- Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome
- Abstract:
- Background 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.Methods 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 to estimate the amount and sizes of (visible and invisible) BMs. The two key parameters of the model are α, the proliferation rate of a single tumor cell; and μ, the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated.Findings The model was able to correctly describe the number and size of metastases at the time of first BM relapse for 20 patients. Parameters α and μ 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).Interpretation 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.SIGNIFICANCE STATEMENT 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\textquoteright 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 metastasesCompeting Interest StatementThe authors have declared no competing interest.Funding StatementSupported by Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE), GSC 81 (Pirmin Schlicke).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:We retrospectively investigated all early-stage NSCLC patients treated with a curative intent who had a brain relapse at Public Assistance from Marseille Hospitals (APHM) in the department of Multidisciplinary Oncology and Therapeutic Innovations. We enrolled all patients with BM as the first relapse site. Primary tumor progression or metastatic extracranial progression at the time of the brain oligo-progression were excluded. Patients with ongoing systemic treatment at the time of oligo-progression were also excluded. The non-interventional retrospective study did not require opinion of a CPP in accordance with the requirements of the Jarde 2016 law regarding studies qualified as internal by the CNIL. This study was ethically approved by the Instutional Review Board of APHM ($#$2021-25). The patient idetifiers are anonymized patient handles and do not allow to reveal patient identity. The patient consentment file is available upon request.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe datasets used and analyzed during this study are available from the corresponding author on reasonable request.
- Zeitschriftentitel:
- medRxiv
- Jahr:
- 2023
- Volltext / DOI:
- doi:10.1101/2023.01.10.23284189
- WWW:
- https://www.medrxiv.org/content/early/2023/01/11/2023.01.10.23284189
- Verlag / Institution:
- Cold Spring Harbor Laboratory Press
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