PDF(Pubmed)
Abstract：
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.
摘要：
涉及乳腺癌全身新辅助治疗的临床试验旨在在术前缩小肿瘤，同时允许对生物标志物进行控制评估。毒性,和抑制远处（隐匿性）转移性疾病。然而，在新辅助临床试验之前，很少有涉及新辅助治疗的临床前测试。手术,以及手术后对疾病的监测。在这里，我们使用了手术切除原位植入的原发性肿瘤后发生自发转移的小鼠模型，以建立新辅助治疗对舒尼替尼反应的预测性数学模型。受体酪氨酸激酶抑制剂（RTKI）。治疗结果用于验证预测围手术期疾病进展的新的数学动力学-药效学模型。使用128只接受可变新辅助治疗剂量和时间表的小鼠（公开发表于https://zenodo.org/records/10607753）汇编术前原发性肿瘤大小和术后转移负荷的纵向测量。非线性混合效应建模方法量化了动物间在转移动力学和存活方面的变异性，和机器学习算法被用于研究切除时几种生物标志物作为个体动力学预测因子的意义。生物标志物包括基于循环肿瘤和免疫的细胞(即，循环肿瘤细胞和髓源性抑制细胞)以及免疫组织化学肿瘤蛋白(即，CD31和Ki67）。我们的计算模拟显示，新辅助RTKI治疗抑制原发性肿瘤生长，但在手术和治疗停止后预防（微）转移性疾病进展方面效果甚微。包括支持向量机的机器学习算法，随机森林，和人工神经网络，证实缺乏明确的生物标志物，这表明了临床前建模研究的价值，以确定临床上应避免的潜在失败。
