Cancer-related Immunomics Analysis Based On Single-cell Transcriptome Sequencing Data

Cancer is one of the major threats to human health,but cancer immunotherapy is also revolutionizing oncology.The tumor-infiltrating immune cells have a profound impact on the clinical response to immunotherapy.The Ig/Tc R diversity of immune repertoire reflects the clonal expansion of B/T cells and the immune status of the body,and is closely related to solid tumors,autoimmune diseases and infections.The development of single-cell sequencing technology has promoted the maturity and perfection of immune database sequencing technology,and the increasing immune repertoire data requires efficient immune repertoire analysis technology to quickly and accurately realize high-throughput data analysis,so as to further explore the cell heterogeneity in the complex immune system.In addition,immunomics research can promote the development of cancer immunotherapy.Exploring early cancer screening from immunomics data is of great significance for cancer treatment,and how to develop cancer prediction models based on immunomics data using machine learning methods is a very challenging problem.Therefore,this thesis mainly divided into three aspects: in the first part,in view of the growing single-celled immune repertoire data,analyze the existing problems of analysis pipeline,design a set of relatively complete immune repertoire analysis pipeline based on the single-cell transcriptome sequencing data,develop a set of effective and practical analysis tool based on immune repertoire.In the second part,based on non-small cell lung cancer,colorectal cancer,melanoma cancer three single-celled immune repertoire,with the analysis tools of the immune repertoire,select CDR3 protein sequences of TCR beta chain for relevant post-analysis,such as V/J gene expression analysis,V/J sharing,to find T cell immune commonality and difference among various cancers.In the third part,a cancer prediction model of non-small cell lung cancer and colorectal cancer was established by using the convolutional neural network model,and cancer was detected according to the immune information of peripheral blood samples.This study not only improved the existing immune analysis pipelines,but also provided more data sources for the single-cell immune data.The analysis of cancer immune repertoire and the establishment of the cancer prediction model also provided theoretical and practical significance for the early diagnosis,personalized treatment and good prognosis of cancer.