Glazar Daniel J, Johnson Matthew, Farinhas Joaquim, Steuer Conor E, Saba Nabil F, Bonomi Marcelo, Chung Christine H, Enderling Heiko
Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Oral Oncol. 2022 Apr;127:105787. doi: 10.1016/j.oraloncology.2022.105787. Epub 2022 Mar 4.
Recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is currently an incurable disease. To improve treatment strategies, combinations of cetuximab plus nivolumab or pembrolizumab were evaluated for efficacy and safety for incurable R/M HNSCC. While some patients had a significant clinical benefit with complete or partial response, most patients had stable or progressive disease (PD). To identify patients with a high likelihood of treatment failure and prevent futile treatments, we developed a mathematical model of early response dynamics as an early biomarker of treatment failure.
Demographics, RECIST assessment, and outcome were obtained from patients who were treated with combination of cetuximab and nivolumab on a previously published phase I/II clinical trial. We trained a tumor growth inhibition (TGI) ordinary differential equation (ODE) model describing patient-specific pre-treatment growth rate and uniform initial treatment sensitivity and rate of evolution of resistance. In a leave-one-out approach, we forecasted tumor burden and predicted time to progression (TTP) and PD.
The TGI model accurately represented tumor burden dynamics (R=0.98; RMSE=0.57 cm) and predicted PD with accuracy=0.71,sensitivity=1.00, and specificity=0.69 after three serial response assessment scans. Patient-specific pre-treatment growth rate correlated negatively with TTP (Spearman's ρ=-0.67,p=5.7e-05).
The TGI model can identify patients with high likelihood of PD based on early dynamics. Further studies including prospective validation are warranted.
复发和/或转移性（R/M）头颈部鳞状细胞癌（HNSCC）目前是一种无法治愈的疾病。为了改进治疗策略，评估了西妥昔单抗联合纳武利尤单抗或帕博利珠单抗治疗无法治愈的R/M HNSCC的疗效和安全性。虽然一些患者有显著的临床获益，达到完全或部分缓解，但大多数患者疾病稳定或进展（PD）。为了识别治疗失败可能性高的患者并避免无效治疗，我们开发了一种早期反应动力学数学模型作为治疗失败的早期生物标志物。
从先前发表的一项I/II期临床试验中接受西妥昔单抗和纳武利尤单抗联合治疗的患者获取人口统计学数据、RECIST评估结果和结局。我们训练了一个肿瘤生长抑制（TGI）常微分方程（ODE）模型，该模型描述患者特异性的治疗前生长速率以及统一的初始治疗敏感性和耐药性演变速率。采用留一法，我们预测了肿瘤负荷并预测了疾病进展时间（TTP）和PD。
TGI模型准确地反映了肿瘤负荷动态变化（R = 0.98；均方根误差 = 0.57 cm），并在三次连续反应评估扫描后以准确度 = 0.71、敏感性 = 1.00和特异性 = 0.69预测了PD。患者特异性的治疗前生长速率与TTP呈负相关（斯皮尔曼相关系数ρ = -0.67，p = 5.7×10⁻⁵）。
TGI模型可以根据早期动态变化识别PD可能性高的患者。需要进行包括前瞻性验证在内的进一步研究。
