Garutti Mattia, Bonin Serena, Buriolla Silvia, Bertoli Elisa, Pizzichetta Maria Antonietta, Zalaudek Iris, Puglisi Fabio
CRO Aviano National Cancer Institute IRCCS, 33081 Aviano, Italy.
DSM-Department of Medical Sciences, University of Trieste, 34123 Trieste, Italy.
Cancers (Basel). 2021 Apr 10;13(8):1819. doi: 10.3390/cancers13081819.
Immunotherapy has revolutionized the therapeutic landscape of melanoma. In particular, checkpoint inhibition has shown to increase long-term outcome, and, in some cases, it can be virtually curative. However, the absence of clinically validated predictive biomarkers is one of the major causes of unpredictable efficacy of immunotherapy. Indeed, the availability of predictive biomarkers could allow a better stratification of patients, suggesting which type of drugs should be used in a certain clinical context and guiding clinicians in escalating or de-escalating therapy. However, the difficulty in obtaining clinically useful predictive biomarkers reflects the deep complexity of tumor biology. Biomarkers can be classified as tumor-intrinsic biomarkers, microenvironment biomarkers, and systemic biomarkers. Herein we review the available literature to classify and describe predictive biomarkers for checkpoint inhibition in melanoma with the aim of helping clinicians in the decision-making process. We also performed a meta-analysis on the predictive value of PDL-1.
免疫疗法彻底改变了黑色素瘤的治疗格局。特别是,检查点抑制已显示可改善长期预后,在某些情况下,几乎可以治愈。然而,缺乏经过临床验证的预测性生物标志物是免疫疗法疗效不可预测的主要原因之一。事实上,预测性生物标志物的存在可以使患者得到更好的分层,提示在特定临床情况下应使用哪种类型的药物,并指导临床医生调整治疗方案。然而,获得临床有用的预测性生物标志物的困难反映了肿瘤生物学的深度复杂性。生物标志物可分为肿瘤内在生物标志物、微环境生物标志物和全身生物标志物。在此,我们回顾现有文献,对黑色素瘤检查点抑制的预测性生物标志物进行分类和描述,以帮助临床医生进行决策。我们还对PDL-1的预测价值进行了荟萃分析。