Bigarré Célestin, Bertucci François, Finetti Pascal, Macgrogan Gaëtan, Muracciole Xavier, Benzekry Sébastien
COMPO, Inria Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 13385 Marseille, France.
Predictive Oncology Laboratory, Marseille Cancer Research Centre (CRCM), Inserm U1068, CNRS UMR7258, Institut Paoli-Calmettes, Equipe labellisée Ligue Nationale Contre Le Cancer, Aix-Marseille University, Marseille, France; Department of Medical Oncology, CRCM, Institut Paoli-Calmettes, CNRS, Inserm, Aix-Marseille University, Marseille, France.
Comput Methods Programs Biomed. 2023 Apr;231:107401. doi: 10.1016/j.cmpb.2023.107401. Epub 2023 Feb 3.
Estimating the risk of metastatic relapse is a major challenge to decide adjuvant treatment options in early-stage breast cancer (eBC). To date, distant metastasis-free survival (DMFS) analysis mainly relies on classical, agnostic, statistical models (e.g., Cox regression). Instead, we propose here to derive mechanistic models of DMFS.
The present series consisted of eBC patients who did not receive adjuvant systemic therapy from three datasets, composed respectively of 692 (Bergonié Institute), 591 (Paoli-Calmettes Institute, IPC), and 163 (Public Hospital Marseille, AP-HM) patients with routine clinical annotations. The last dataset also contained expression of three non-routine biomarkers. Our mechanistic model of DMFS relies on two mathematical parameters that represent growth (α) and dissemination (μ). We identified their population distributions using mixed-effects modeling. Critically, we propose a novel variable selection procedure allowing to: (i) identify the association of biological parameters with either α, μ or both, and (ii) generate an optimal candidate model for DMFS prediction.
We found that Ki67 and Thymidine Kinase-1 were associated with α, and nodal status and Plasminogen Activator Inhibitor-1 with μ. The predictive performances of the model were excellent in calibration but moderate in discrimination, with c-indices of 0.72 (95% CI [0.48, 0.95], AP-HM), 0.63 ([0.44, 0.83], Bergonié) and 0.60 (95% CI [0.54, 0.80], IPC).
Overall, we demonstrate that our novel method combining mechanistic and advanced statistical modeling is able to unravel the biological roles of clinicopathological parameters from DMFS data.
评估转移性复发风险是决定早期乳腺癌（eBC）辅助治疗方案的一项重大挑战。迄今为止，无远处转移生存期（DMFS）分析主要依赖于传统的、无特定假设的统计模型（如Cox回归）。相反，我们在此提出推导DMFS的机制模型。
本研究系列包括来自三个数据集的未接受辅助全身治疗的eBC患者，分别由692例（贝戈涅研究所）、591例（保利 - 卡美特研究所，IPC）和163例（马赛公立医院，AP - HM）有常规临床注释的患者组成。最后一个数据集还包含三种非常规生物标志物的表达。我们的DMFS机制模型依赖于两个代表生长（α）和播散（μ）的数学参数。我们使用混合效应模型确定了它们的总体分布。至关重要的是，我们提出了一种新颖的变量选择程序，该程序能够：（i）确定生物学参数与α、μ或两者的关联，以及（ii）生成用于DMFS预测的最佳候选模型。
我们发现Ki67和胸苷激酶-1与α相关，而淋巴结状态和纤溶酶原激活物抑制剂-1与μ相关。该模型在校准方面的预测性能出色，但在区分能力方面中等，AP - HM的c指数为0.72（95%CI[0.48, 0.95]），贝戈涅研究所的为0.63（[0.44, 0.83]），IPC的为0.60（95%CI[0.54, 0.80]）。
总体而言，我们证明了我们结合机制和先进统计建模的新方法能够从DMFS数据中揭示临床病理参数的生物学作用。
