Long Shichao, Li Mengsi, Chen Juan, Zhong Linhui, Abudulimu Aerzuguli, Zhou Lan, Liu Wenguang, Pan Deng, Dai Ganmian, Fu Kai, Chen Xiong, Pei Yigang, Li Wenzheng
Department of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, China.
Department of Nuclear Medicine, Hainan Cancer Hospital of Hainan Medical University, Haikou, Hainan, China.
J Immunother Cancer. 2024 Dec 15;12(12):e009879. doi: 10.1136/jitc-2024-009879.
Tertiary lymphoid structures (TLS) within the tumor microenvironment have been associated with cancer prognosis and therapeutic response. However, the immunological pattern of a high peritumoral TLS (pTLS) density and its clinical potential in hepatocellular carcinoma (HCC) remain poor. This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.
Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. Baseline MRI images from 660 patients with HCC who had undergone surgery treatment between October 2015 and January 2023 were retrospectively recruited for model development and validation. This included training (n=307) and temporal validation (n=76) cohorts from Xiangya Hospital, and external validation cohorts from three independent hospitals (n=277). Radiomic features were extracted from intratumoral and peritumoral regions of interest and analyzed using machine learning algorithms to develop a predictive classifier. The classifier's performance was evaluated using the area under the curve (AUC), with prognostic and predictive value assessed across four independent cohorts and in a dual-center outcome cohort of 41 patients who received immunotherapy.
Patients with HCC and a high pTLS density experienced prolonged median overall survival (p<0.05) and favorable immunotherapy response (p=0.03). Moreover, immune infiltration by mature B cells was observed in the high pTLS density region. Spatial pseudotime analysis and immunohistochemistry staining revealed that expansion of pTLS in HCC was associated with elevated CXCL9 and CXCL10 co-expression. We developed an optimal radiomic-based classifier with excellent discrimination for predicting pTLS density, achieving an AUC of 0.91 (95% CI 0.87, 0.94) in the external validation cohort. This classifier also exhibited promising stratification ability in terms of overall survival (p<0.01), relapse-free survival (p<0.05), and immunotherapy response (p<0.05).
We identified key regulators of pTLS density in patients with HCC and proposed a non-invasive radiomic classifier capable of assisting in stratification for prognosis and treatment.
肿瘤微环境中的三级淋巴结构（TLS）与癌症预后及治疗反应相关。然而，高瘤周TLS（pTLS）密度的免疫模式及其在肝细胞癌（HCC）中的临床潜力仍不清楚。本研究旨在阐明与pTLS密度相关的生物学差异，并开发一种用于预测HCC中pTLS密度的放射组学分类器，为临床诊断和治疗提供新见解。
利用空间转录组学（n = 4）和RNA测序数据（n = 952）来确定pTLS密度的关键调节因子，并评估其在HCC中的预后意义。回顾性纳入2015年10月至2023年1月间接受手术治疗的660例HCC患者的基线MRI图像，用于模型开发和验证。这包括来自湘雅医院的训练队列（n = 307）和时间验证队列（n = 76），以及来自三家独立医院的外部验证队列（n = 277）。从肿瘤内和瘤周感兴趣区域提取放射组学特征，并使用机器学习算法进行分析，以开发预测分类器。使用曲线下面积（AUC）评估分类器的性能，并在四个独立队列以及41例接受免疫治疗的患者的双中心结果队列中评估其预后和预测价值。
HCC患者且pTLS密度高者的中位总生存期延长（p < 0.05），免疫治疗反应良好（p = 0.03）。此外，在高pTLS密度区域观察到成熟B细胞的免疫浸润。空间伪时间分析和免疫组化染色显示，HCC中pTLS的扩展与CXCL9和CXCL10共表达升高相关。我们开发了一种基于放射组学的最佳分类器，对预测pTLS密度具有出色的区分能力，在外部验证队列中AUC为0.91（95%CI 0.87，0.94）。该分类器在总生存期（p < 0.01）、无复发生存期（p < 0.05）和免疫治疗反应（p < 0.05）方面也表现出有前景的分层能力。
我们确定了HCC患者中pTLS密度的关键调节因子，并提出了一种能够辅助预后和治疗分层的非侵入性放射组学分类器。
