Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.
Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.
Mol Carcinog. 2023 Jan;62(1):77-89. doi: 10.1002/mc.23442. Epub 2022 Jul 4.
Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published predictive biomarkers of ICIs based on gene expression signatures in eight previously published studies with available RNA-sequencing data in public repositories. We found that signatures related to IFN-γ-responsive genes, T and B cell markers, and chemokines in the tumor immune microenvironment are generally predictive of response to ICIs in these patients. In addition, we identified that these predictive biomarkers have higher predictive values in on-treatment samples as compared to pretreatment samples in metastatic melanoma. The most frequently overlapping genes among the top 18 predictive signatures were CXCL10, CXCL9, PRF1, RANTES, IFNG, HLA-DRA, GZMB, and CD8A. From gene set enrichment analysis and cell type deconvolution, we estimated that the tumors of responders were enriched with infiltrating cytotoxic T-cells and other immune cells and the upregulation of genes related to interferon-γ signaling. Conversely, the tumors of non-responders were enriched with stromal-related cell types such as fibroblasts and myofibroblasts, as well as enrichment with T helper 17 cell types across all cohorts. In summary, our approach of validating and integrating multi-omics data can help guide future biomarker development in the field of ICIs and serve the quest for a more personalized therapeutic approach for melanoma patients.
免疫疗法的进展，包括免疫检查点抑制剂（ICIs），已经改变了许多类型癌症的治疗标准，包括黑色素瘤。ICIs 改善了黑色素瘤患者的总体预后；然而，相当一部分患者存在原发性或继发性肿瘤耐药。因此，迫切需要开发预测性生物标志物，以便更好地选择接受 ICI 治疗的患者。已经研究了许多预测黑色素瘤对 ICI 反应的生物标志物，包括基于基因组学或转录组学的生物标志物特征。这些预测生物标志物中的大多数尚未在不同队列中进行系统评估，以确定这些特征在转移性黑色素瘤中的可重复性。我们评估了 8 项先前发表的研究中基于基因表达特征的 28 种先前发表的预测 ICI 的生物标志物，这些研究在公共存储库中都有可用的 RNA 测序数据。我们发现，与肿瘤免疫微环境中 IFN-γ 反应基因、T 和 B 细胞标志物以及趋化因子相关的特征通常可预测这些患者对 ICI 的反应。此外，我们还发现，与预处理样本相比，这些预测生物标志物在转移性黑色素瘤的治疗中样本中具有更高的预测值。在 18 个预测性签名中最常重叠的基因是 CXCL10、CXCL9、PRF1、RANTES、IFNG、HLA-DRA、GZMB 和 CD8A。从基因集富集分析和细胞类型去卷积来看，我们估计应答者的肿瘤富含浸润性细胞毒性 T 细胞和其他免疫细胞，以及与干扰素-γ 信号相关的基因上调。相反，非应答者的肿瘤富含成纤维细胞和肌成纤维细胞等基质相关细胞类型，以及所有队列中富含辅助性 T 细胞 17 型。总之，我们验证和整合多组学数据的方法可以帮助指导免疫治疗领域的未来生物标志物开发，并为黑色素瘤患者寻求更个性化的治疗方法服务。
