Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom.
Department of Surgery and Cancer, Imperial College, London, United Kingdom.
J Thorac Oncol. 2023 Jun;18(6):718-730. doi: 10.1016/j.jtho.2023.01.089. Epub 2023 Feb 10.
INTRODUCTION: Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy. METHODS: This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively. RESULTS: LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 and hazard ratio = 2.45, 95% CI: 1.07-5.65, p = 0.035). CONCLUSIONS: A CT radiomics-based signature developed from response vector CD274 can aid in evaluating patients' suitability for PD-1 or PD-L1 checkpoint inhibitor immunotherapy in NSCLC.
简介：目前，检查点抑制剂免疫疗法的患者选择由肿瘤组织样本免疫组织化学染色获得的程序性死亡配体 1（PD-L1）表达指导。这种方法容易受到癌症细胞的动态和异质性以及组织取样过程的侵袭性带来的限制。为了解决这些挑战，我们开发了一种新的基于计算机断层扫描（CT）的放射组学特征，用于预测接受程序性细胞死亡蛋白 1（PD-1）或 PD-L1 检查点抑制剂免疫治疗的非小细胞肺癌（NSCLC）患者的疾病反应。
方法：这项回顾性研究共包括 340 名患者中 194 名具有合适 CT 扫描的患者。我们使用从在发现组的 85 名被诊断患有 NSCLC 的患者的增强胸部 CT 扫描上分割的肿瘤计算的放射组学特征，并将 CD274 计数作为反应向量，开发了一个复合放射组学特征，即肺癌免疫治疗放射组学预测向量（LCI-RPV）。该特征在接受 PD-1 或 PD-L1 抑制免疫治疗的 NSCLC 的两个独立测试队列中分别对 66 名和 43 名患者进行了验证。
结果：LCI-RPV 在两个 NSCLC 测试队列中均能预测 PD-L1 阳性（曲线下面积 [AUC] = 0.70，95%置信区间 [CI]：0.57-0.84 和 AUC = 0.70，95%CI：0.46-0.94）。在一个队列中，它还很好地预测了高 PD-L1 表达水平超过关键治疗阈值的病例（>50%：AUC = 0.72，95%CI：0.59-0.85 和>90%：AUC = 0.66，95%CI：0.45-0.88）、肿瘤在 3 个月时对治疗的客观反应（AUC = 0.68，95%CI：0.52-0.85）和肺炎发生（AUC = 0.64，95%CI：0.48-0.80）。LCI-RPV 实现了对患者进行高风险和低风险生存组的统计学显著分层（风险比=2.26，95%CI：1.21-4.24，p=0.011 和风险比=2.45，95%CI：1.07-5.65，p=0.035）。
结论：从反应向量 CD274 开发的 CT 放射组学特征可以帮助评估 NSCLC 患者对 PD-1 或 PD-L1 检查点抑制剂免疫治疗的适用性。
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