将新抗原发现与SNAF结合,揭示了癌症免疫治疗的共同目标
Guangyuan Li1,2, Shweta Mahajan3, Siyuan Ma3
1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
研究人员开发了Splicing Neo Antigen Finder (SNAF) 来从RNA测序数据中识别新的癌症标. 该工具预测了拼接衍生的新抗原,为各种癌症提供了潜在的共享免疫治疗点.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 免疫疗法是癌症的关键治疗方法,通常用于突变率高的瘤.
- 后转录调节产生新抗原,提供超出高突变负担的潜在共同目标.
- 识别这些新抗原对于开发新的向疗法至关重要.
研究的目的:
- 开发一个计算工作流程,Splicing Neo Antigen Finder (SNAF),用于从患者RNA测序数据中预测拼接衍生的新抗原.
- 识别T细胞抗原 (MHC结合) 和B细胞抗原 (改变的跨膜蛋白).
- 为全面的新抗原发现提供一个易于使用的开源工具.
主要方法:
- 利用深度学习 (DeepImmuno) 进行免疫性预测.
- 采用贝叶斯TS算法用于瘤特异性和RNA-SPRINT用于剪接调节器预测.
- 综合质谱和长读异型序列测定用于验证.
主要成果:
- SNAF准确地预测了T细胞抗原,通过质谱验证并与黑色素瘤的免疫疗法反应相关.
- 在多达90%的黑色素瘤患者中发现了共享拼接新抗原,与生存相关.
- 鉴定了一种新的瘤特异性B细胞新型细胞类 (ExNeoEpitopes),具有经过验证的瘤特异性和定位.
结论:
- 拼接新抗原代表了癌症中常见的免疫原性点的重要,大部分尚未开发的来源.
- SNAF为识别T细胞和B细胞新抗原提供了一个强大的平台.
- 这些发现表明,拼接新抗原可以被利用来开发广泛适用的癌症免疫疗法.
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