Leisegang Matthias, Kammertoens Thomas, Uckert Wolfgang, Blankenstein Thomas
J Clin Invest. 2016 Mar 1;126(3):854-8. doi: 10.1172/JCI83465. Epub 2016 Jan 25.
In successful cancer immunotherapy, T cell responses appear to be directed toward neoantigens created by somatic mutations; however, direct evidence that neoantigen-specific T cells cause regression of established cancer is lacking. Here, we generated T cells expressing a mutation-specific transgenic T cell receptor (TCR) to target different immunogenic mutations in cyclin-dependent kinase 4 (CDK4) that naturally occur in human melanoma. Two mutant CDK4 isoforms (R24C, R24L) similarly stimulated T cell responses in vitro and were analyzed as therapeutic targets for TCR gene therapy. In a syngeneic HLA-A2-transgenic mouse model of large established tumors, we found that both mutations differed dramatically as targets for TCR-modified T cells in vivo. While T cells expanded efficiently and produced IFN-γ in response to R24L, R24C failed to induce an effective antitumor response. Such differences in neoantigen quality might explain why cancer immunotherapy induces tumor regression in some individuals, while others do not respond, despite similar mutational load. We confirmed the validity of the in vivo model by showing that the melan-A-specific (MART-1-specific) TCR DMF5 induces rejection of tumors expressing analog, but not native, MART-1 epitopes. The described model allows identification of those neoantigens in human cancer that serve as suitable T cell targets and may help to predict clinical efficacy.
在成功的癌症免疫治疗中,T细胞反应似乎是针对由体细胞突变产生的新抗原;然而,缺乏新抗原特异性T细胞导致已形成的癌症消退的直接证据。在这里,我们生成了表达突变特异性转基因T细胞受体(TCR)的T细胞,以靶向人类黑色素瘤中自然发生的细胞周期蛋白依赖性激酶4(CDK4)中的不同免疫原性突变。两种突变的CDK4异构体(R24C、R24L)在体外同样刺激T细胞反应,并作为TCR基因治疗的治疗靶点进行分析。在一个大型已形成肿瘤的同基因HLA-A2转基因小鼠模型中,我们发现这两种突变作为体内TCR修饰T细胞的靶点有显著差异。虽然T细胞对R24L有效扩增并产生IFN-γ,但R24C未能诱导有效的抗肿瘤反应。新抗原质量的这种差异可能解释了为什么癌症免疫治疗在一些个体中诱导肿瘤消退,而另一些个体尽管具有相似的突变负荷却没有反应。我们通过证明黑色素瘤抗原A特异性(MART-1特异性)TCR DMF5诱导表达类似物而非天然MART-1表位的肿瘤排斥反应,证实了体内模型的有效性。所描述的模型允许识别人类癌症中那些作为合适T细胞靶点的新抗原,并可能有助于预测临床疗效。