LENS: Landscape of Effective Neoantigens Software.
LENS: Landscape of Effective Neoantigens Software.
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DOI：
10.1093/bioinformatics/btad322
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发表时间：
2023-05-04
期刊：
影响因子：
--
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中科院分区：
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T细胞清除肿瘤细胞是抗肿瘤免疫和肿瘤免疫治疗应答的重要机制。T细胞通过T细胞受体与癌细胞表面主要组织相容性复合体分子呈现的多肽表位结合来识别癌细胞。多肽表位可以由多种基因组来源编码的抗原蛋白衍生而来。用于通过分析DNA和RNA测序数据来识别肿瘤特异性表位的生物信息学工具主要集中在来自体细胞变异的表位上，尽管有一小部分人评估了来自其他基因组来源的潜在抗原。我们在这里报告了一个利用Nextflow DSL2工作流管理器的开源工作流，有效新抗原软件(LENS)，它从单核苷酸变体、插入和缺失、融合事件、剪接变体、癌症-睾丸抗原、过度表达的自身抗原、病毒和内源性逆转录病毒预测肿瘤特异性和肿瘤相关抗原。Lens的主要优势是它利用基因组学数据扩大了可发现的肿瘤抗原的基因组来源的广度。其他优势包括模块化、可扩展性、易用性，以及跨多个基因组来源的相对表达水平和免疫原性预测的协调。我们提出了115个急性髓系白血病样本的分析，以证明晶状体的实用性。我们预计LENS将成为发现T细胞表位的生物信息学的有价值的平台和资源，特别是在体细胞变异很少的癌症中，来自替代基因组来源的肿瘤特异性表位是更高的优先级。有关LENS的更多信息，包括代码、工作流文档和说明，请访问(https://gitlab.com/landscape-of-effective-neoantigens-software).
Elimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA-sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. We report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigens Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer-testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. More information about LENS, including code, workflow documentation, and instructions, can be found at (https://gitlab.com/landscape-of-effective-neoantigens-software).
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DOI：
10.1093/annonc/mdu479
发表时间：
2015-01
期刊：
Annals of oncology : official journal of the European Society for Medical Oncology
影响因子：
--
作者：
Favero F;Joshi T;Marquard AM;Birkbak NJ;Krzystanek M;Li Q;Szallasi Z;Eklund AC
通讯作者：
Eklund AC
影响因子：
64.5
作者：
Litchfield K;Reading JL;Puttick C;Thakkar K;Abbosh C;Bentham R;Watkins TBK;Rosenthal R;Biswas D;Rowan A;Lim E;Al Bakir M;Turati V;Guerra-Assunção JA;Conde L;Furness AJS;Saini SK;Hadrup SR;Herrero J;Lee SH;Van Loo P;Enver T;Larkin J;Hellmann MD;Turajlic S;Quezada SA;McGranahan N;Swanton C
通讯作者：
Swanton C
DOI：
10.4049/jimmunol.1700893
发表时间：
2017-11-01
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Jurtz V;Paul S;Andreatta M;Marcatili P;Peters B;Nielsen M
通讯作者：
Nielsen M
影响因子：
14.9
作者：
Almeida LG;Sakabe NJ;deOliveira AR;Silva MC;Mundstein AS;Cohen T;Chen YT;Chua R;Gurung S;Gnjatic S;Jungbluth AA;Caballero OL;Bairoch A;Kiesler E;White SL;Simpson AJ;Old LJ;Camargo AA;Vasconcelos AT
通讯作者：
Vasconcelos AT
DOI：
10.1007/978-1-0716-0327-7_10
发表时间：
2020-01-01
期刊：
BIOINFORMATICS FOR CANCER IMMUNOTHERAPY
影响因子：
--
作者：
Kodysh, Julia;Rubinsteyn, Alex
通讯作者：
Rubinsteyn, Alex