Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
BMC Med Genomics. 2020 Aug 27;13(Suppl 6):62. doi: 10.1186/s12920-020-0708-z.
High-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate the immune response of the T cell. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response.
Numerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Based on our previous discoveries, we propose to investigate the distribution of neoantigens with different immunogenicity abilities in 3D genome and propose to adopt this important information into neoantigen prediction.
We retrospect the DNA origins of the immuno-positive and immuno-negative neoantigens in the context of 3D genome and discovered that DNA loci of the immuno-positive neoantigens and immuno-negative neoantigens have very different distribution pattern. Specifically, comparing to the background 3D genome, DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We thus used this information into neoantigen prediction and demonstrated the effectiveness of this approach.
We believe that the 3D genome information will help to increase the precision of neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.
高通量测序技术已经为 DNA 和 RNA 提供了可靠和超快速的测序。对于癌症患者的肿瘤细胞，当将 DNA 和 RNA 测序的结果结合起来时，可以识别出潜在的能够刺激 T 细胞免疫反应的新抗原。然而，当体细胞突变丰富时，根据它们激活 T 细胞免疫反应的能力，有效地对鉴定出的新抗原候选物进行优先级排序在计算上具有挑战性。
已经提出了许多优先级排序或预测方法来解决这个问题，但没有一种方法从 3D 基因组的角度考虑新抗原的原始 DNA 基因座。基于我们之前的发现，我们建议研究具有不同免疫原性能力的新抗原在 3D 基因组中的分布，并提出将这一重要信息纳入新抗原预测。
我们回顾了 3D 基因组背景下免疫阳性和免疫阴性新抗原的 DNA 起源，并发现免疫阳性新抗原和免疫阴性新抗原的 DNA 基因座具有非常不同的分布模式。具体来说，与背景 3D 基因组相比，免疫阳性新抗原的 DNA 基因座倾向于位于 3D 基因组的特定区域。因此，我们将此信息用于新抗原预测，并证明了该方法的有效性。
我们相信 3D 基因组信息将有助于提高新抗原优先级排序和发现的准确性，并最终有益于癌症免疫治疗中的精准和个性化医学。
