Computational Pharmacology and Clinical Oncology (COMPO), Inria Sophia Antipolis-Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, Marseille, France.
Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America.
PLoS Comput Biol. 2024 May 3;20(5):e1012088. doi: 10.1371/journal.pcbi.1012088. eCollection 2024 May.
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.
临床前试验通常不会涉及新辅助治疗、手术以及疾病手术后监测，而新辅助临床试验的目的是在手术前缩小肿瘤，同时允许对生物标志物、毒性和隐匿性（隐性）转移性疾病的抑制进行受控评估。在这里，我们使用了一种自发转移的小鼠模型，该模型发生在原发性肿瘤的原位植入手术后，以开发一种预测舒尼替尼（受体酪氨酸激酶抑制剂（RTKI））新辅助治疗反应的预测数学模型。我们使用 128 只接受不同新辅助治疗剂量和方案的小鼠的纵向术前原发性肿瘤大小和术后转移负担的测量数据，来验证一种新的预测围手术期疾病进展的数学动力学-药效动力学模型。（该模型的数据已在 https://zenodo.org/records/10607753 上公开）。非线性混合效应模型方法量化了转移性动力学和生存中的个体间变异性，并应用机器学习算法研究了切除时的几种生物标志物作为个体动力学预测因子的意义。生物标志物包括循环肿瘤和免疫细胞（循环肿瘤细胞和髓系来源的抑制细胞）以及免疫组织化学肿瘤蛋白（CD31 和 Ki67）。我们的计算模拟表明，新辅助 RTKI 治疗抑制原发性肿瘤生长，但在手术后和治疗停止时几乎没有预防（微）转移性疾病进展的效果。包括支持向量机、随机森林和人工神经网络在内的机器学习算法证实了缺乏明确的生物标志物，这表明临床前建模研究具有识别潜在失败的价值，这些失败在临床上应避免发生。
