MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.
MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.
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DOI：
10.1038/s41467-023-44460-z
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发表时间：
2024-01-22
影响因子：
16.6
通讯作者：
Ternette, Nicola
中科院分区：
文献类型：
--
作者：
Liao, Hanqing;Barra, Carolina;Zhou, Zhicheng;Peng, Xu;Woodhouse, Isaac;Tailor, Arun;Parker, Robert;Carre, Alexia;Borrow, Persephone;Hogan, Michael J.;Paes, Wayne;Eisenlohr, Laurence C.;Mallone, Roberto;Nielsen, Morten;Ternette, Nicola
了解肿瘤细胞中非典型人类白细胞抗原 (HLA) 呈递的性质和程度是开发下一代癌症免疫疗法的靶抗原发现的首要任务。我们在这里采用从头质谱测序方法和以 MHC 为中心的精细分析策略来检测癌症特异性的非典型 MHC 相关肽，而无需事先了解基因组或 RNA 测序数据中的目标序列。我们的策略集成了 MHC 结合排名、平均局部置信度得分和肽保留时间预测，以改进从头候选选择；最终形成了机器学习模型 MARS。我们在大型合成肽库数据集上对我们的模型进行了基准测试，并对已发布的人类癌症中高质量非规范 MHC 相关肽鉴定数据集进行了重新分析。与单独从头测序相比，我们在高质量光谱分配方面实现了近 2 倍的改进，与逐步肽序列作图策略集成时，估计准确度超过 85.7%。最后，我们利用 MARS 检测和验证人宫颈肿瘤切除术中的 lncRNA 衍生肽，证明其适合在原发性肿瘤组织中发现新颖的、免疫原性的、非规范的肽序列。检测肿瘤中的新表位非常耗时，并且需要整合基因组和/或 RNA 测序表达数据。在这里，作者提出了一种机器学习方法，通过整合从头肽测序评分、MHC I 类结合预测和肽保留时间预测，使用质谱法直接识别其他肿瘤特异性序列。
Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue. Detection of neoepitopes from tumours is time consuming and requires the integration of genomic and/or RNA sequencing expression data. Here, the authors propose a machine learning method to enable direct identification of additional, tumour-specific sequences using mass spectrometry through integration of de novo peptide sequencing scores, MHC class I binding prediction, and peptide retention time prediction.
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影响因子：
3.7
作者：
Akrami R;Jacobsen A;Hoell J;Schultz N;Sander C;Larsson E
通讯作者：
Larsson E
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Li K;Jain A;Malovannaya A;Wen B;Zhang B
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Zhang B
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Schlosser, Andreas
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影响因子：
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作者：
Gonzalez-Duque, Sergio;Azoury, Marie Eliane;Mallone, Roberto
通讯作者：
Mallone, Roberto