Gide Tuba N, Mao Yizhe, Scolyer Richard A, Long Georgina V, Wilmott James S
Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.
Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Clin Cancer Res. 2024 Dec 2;30(23):5270-5280. doi: 10.1158/1078-0432.CCR-24-1109.
Immunotherapies targeting the programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4) checkpoint receptors have revolutionized the treatment of metastatic melanoma. However, half of the treated patients do not respond to or eventually progress on standard therapies and many experience adverse events as a result of drug toxicity. The identification of accurate biomarkers of clinical outcomes are required in order to move away from the one-size-fits-all treatment approach of standard clinical practice and toward a more personalized approach to enable the administration of the optimal therapy for any given patient and further improve patient outcomes. Recent clinical trials have proven the potential of multiomics analyses, including genomic, gene expression, and tumor immune profiling, of patients' tumor biopsies, to predict a patient's response to subsequently administered immunotherapies. However, reproducibility of such multiomics analyses, tissue requirements, and clinical validation have limited the practical application of these approaches in routine clinical workflows. In this review, we discuss several pivotal tissue-based profiling techniques that can be utilized to identify potential genomic, transcriptomic, and immune biomarkers predictive of clinical outcomes following treatment with immune checkpoint inhibitors in melanoma. Furthermore, we highlight the key opportunities and challenges associated with the use of each of these techniques. The development and implementation of multimodal predictive models that combine data derived from these various methods is the future for achieving precision medicine for patients with melanoma.
靶向程序性细胞死亡1（PD-1）和细胞毒性T淋巴细胞抗原4（CTLA-4）检查点受体的免疫疗法彻底改变了转移性黑色素瘤的治疗方式。然而，一半的接受治疗的患者对标准疗法无反应或最终病情进展，并且许多患者因药物毒性而出现不良事件。为了摆脱标准临床实践中一刀切的治疗方法，转向更个性化的方法，以便为任何给定患者提供最佳治疗并进一步改善患者预后，需要确定准确的临床结果生物标志物。最近的临床试验已经证明，对患者的肿瘤活检进行多组学分析，包括基因组、基因表达和肿瘤免疫谱分析，有潜力预测患者对随后给予的免疫疗法的反应。然而，这种多组学分析的可重复性、组织要求和临床验证限制了这些方法在常规临床工作流程中的实际应用。在这篇综述中，我们讨论了几种关键的基于组织的分析技术，这些技术可用于识别黑色素瘤患者接受免疫检查点抑制剂治疗后预测临床结果的潜在基因组、转录组和免疫生物标志物。此外，我们强调了与使用每种技术相关的关键机遇和挑战。结合这些不同方法得出的数据开发和实施多模态预测模型是实现黑色素瘤患者精准医学的未来方向。
