A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy.
A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy.
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DOI：
10.1038/nature24473
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发表时间：
2017-11-23
期刊：
影响因子：
64.8
通讯作者：
Greenbaum BD
中科院分区：
文献类型：
--
作者：
Łuksza M;Riaz N;Makarov V;Balachandran VP;Hellmann MD;Solovyov A;Rizvi NA;Merghoub T;Levine AJ;Chan TA;Wolchok JD;Greenbaum BD
检查点封锁免疫疗法使宿主免疫系统能够识别并摧毁肿瘤细胞。它们的临床活性与激活的 T 细胞对新抗原的识别相关，新抗原是癌细胞表面呈现的肿瘤特异性突变肽。在这里，我们提出了一种基于新抗原免疫相互作用的肿瘤适应模型，可预测对免疫治疗的反应。两个主要因素决定新抗原的适应性：主要组织相容性复合物 (MHC) 呈现的可能性及其随后的 T 细胞识别。我们使用新抗原的相对 MHC 结合亲和力以及对其与已知抗原的序列相似性的非线性依赖性来估计这两个成分。为了描述异质肿瘤的进化，我们将其适应性评估为肿瘤亚克隆中主要新抗原的加权效应。我们的模型预测抗 CTLA-4 治疗的黑色素瘤患者和抗 PD-1 治疗的肺癌患者的生存率。重要的是，通过我们的方法鉴定的低适应性新抗原可用于开发新型免疫疗法。通过使用免疫适应性模型来研究免疫疗法，我们揭示了肿瘤进化和快速进化的病原体之间的广泛相似性。
Checkpoint blockade immunotherapies enable the host immune system to recognize and destroy tumor cells. Their clinical activity has been correlated with activated T-cell recognition of neoantigens, which are tumor-specific, mutated peptides presented on the surface of cancer cells. Here, we present a fitness model for tumors based on immune interactions of neoantigens that predicts response to immunotherapy. Two main factors determine neoantigen fitness: its likelihood of presentation by the major histocompatibility complex (MHC) and its subsequent T-cell recognition. We estimate these two components using a neoantigen’s relative MHC binding affinity and a non-linear dependence on its sequence similarity to known antigens. To describe the evolution of a heterogeneous tumor, we evaluate its fitness as a weighted effect of dominant neoantigens in the tumor’s subclones. Our model predicts survival in anti- CTLA-4 treated melanoma patients and anti-PD-1 treated lung cancer patients. Importantly, low-fitness neoantigens identified by our method may be leveraged for developing novel immunotherapies. By using an immune fitness model to study immunotherapy, we reveal broad similarities between the evolution of tumors and rapidly evolving pathogens.
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DOI：
10.1073/pnas.89.22.10915
发表时间：
1992-11-15
影响因子：
11.1
作者：
HENIKOFF, S;HENIKOFF, JG
通讯作者：
HENIKOFF, JG
DOI：
10.4049/jimmunol.1302101
发表时间：
2013-12-15
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Paul S;Weiskopf D;Angelo MA;Sidney J;Peters B;Sette A
通讯作者：
Sette A
影响因子：
32.4
作者：
Legoux FP;Lim JB;Cauley AW;Dikiy S;Ertelt J;Mariani TJ;Sparwasser T;Way SS;Moon JJ
通讯作者：
Moon JJ
影响因子：
64.5
作者：
Birnbaum ME;Mendoza JL;Sethi DK;Dong S;Glanville J;Dobbins J;Ozkan E;Davis MM;Wucherpfennig KW;Garcia KC
通讯作者：
Garcia KC
影响因子：
64.8
作者：
De Henau O;Rausch M;Winkler D;Campesato LF;Liu C;Cymerman DH;Budhu S;Ghosh A;Pink M;Tchaicha J;Douglas M;Tibbitts T;Sharma S;Proctor J;Kosmider N;White K;Stern H;Soglia J;Adams J;Palombella VJ;McGovern K;Kutok JL;Wolchok JD;Merghoub T
通讯作者：
Merghoub T