Kai Jiang, Lili Zhu, Huizhen Huang, Liu Zheng, Zhuqing Wang, Xiaonan Kang
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引用次数: 0
摘要
目的：肝细胞癌（HCC）对免疫疗法的反应较差，持久反应率为10%-20%。在此，我们旨在根据乳酸基因对 HCC 进行分类，以识别可能从免疫疗法中获益的患者：方法：本研究将乳酸相关基因用于HCC分类，并定义了乳酸簇1（LC1）和乳酸簇2（LC2）。LC1和LC2的差异基因帮助定义了以下乳酸表型群：乳酸表型群1（LPC1）、乳酸表型群2（LPC2）和乳酸表型群3（LPC3）。根据聚类注释，定义并分析乳酸评分，以评估免疫治疗反应：结果：对所有分类群组进行了分析，它们显示出不同的免疫特征。LPC3的存活率高于LPC2（LPC3 vs. LPC2，P = 0.027）和LPC1（LPC3 vs. LPC1，P = 0.027）。然后，对乳酸评分进行了注释，证实其能有效预测免疫检查点阻断疗法的反应：在当前的研究中，我们开发了一套HCC分类系统，并定义了乳酸评分，该评分被证实对估计肿瘤患者的反应部分有效。
Lactate score classification of hepatocellular carcinoma helps identify patients with tumors that respond to immune checkpoint blockade therapy.
Purpose: Hepatocellular carcinoma (HCC) responds poorly to immunotherapy, and the durable response rate is 10-20%. Here, we aim to characterize HCC classifications based on lactate genes to identify patients who may benefit from immunotherapy.
Methods: Lactate-related genes were applied for HCC classification in the current study, and lactate Cluster 1 (LC1) and lactate Cluster 2 (LC2) were defined. Differential genes from LC1 and LC2 helped define the following lactate phenotype clusters: lactate phenotype Cluster 1 (LPC1), lactate phenotype Cluster 2 (LPC2) and lactate phenotype Cluster 3 (LPC3). Based on the cluster annotation, the lactate score was defined and analyzed to evaluate the immunotherapy response.
Results: All the classified clusters were analyzed, and they showed different immune signatures. The survival rate of LPC3 was higher than that of LPC2 (LPC3 vs. LPC2, P = 0.027) and LPC1 (LPC3 vs. LPC1, P = 0.027). Then, the lactate score was annotated and confirmed to be effective in predicting responses to immune checkpoint blockade therapy.
Conclusion: In the current study, we developed a classification system for HCC and defined the lactate score, which was validated to be partially effective in estimating responses among tumor patients.
期刊介绍：
The Official Journal of the International Society for Cellular Oncology
Focuses on translational research
Addresses the conversion of cell biology to clinical applications
Cellular Oncology publishes scientific contributions from various biomedical and clinical disciplines involved in basic and translational cancer research on the cell and tissue level, technical and bioinformatics developments in this area, and clinical applications. This includes a variety of fields like genome technology, micro-arrays and other high-throughput techniques, genomic instability, SNP, DNA methylation, signaling pathways, DNA organization, (sub)microscopic imaging, proteomics, bioinformatics, functional effects of genomics, drug design and development, molecular diagnostics and targeted cancer therapies, genotype-phenotype interactions.
A major goal is to translate the latest developments in these fields from the research laboratory into routine patient management. To this end Cellular Oncology forms a platform of scientific information exchange between molecular biologists and geneticists, technical developers, pathologists, (medical) oncologists and other clinicians involved in the management of cancer patients.
In vitro studies are preferentially supported by validations in tumor tissue with clinicopathological associations.
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