Integrative cancer omics analysis in adoptive cell therapy and chemotherapy

Abstract

With the development of chemotherapy and immunotherapy, the recent clinical and pre-clinical cancer studies has been based on securing large amounts of genetic data, linking clinical meta-information, and statistical and scientific interpretation. Thus, I have conducted meaningful integrated studies based on the bioinformatic approach in line with this trend. In the first part, I performed the integrated analysis of whole-genome sequence and transcriptome for a rare tumor to show the relationship between genome and clinical response. To treat the desmoid tumor, one of the rare sarcomas, imatinib has been used as a clinical option, however, the molecular mechanism of why imatinib works remain unclear. Here, I described the potential role of major genes in clinical response to imatinib at genome and transcriptome levels. In the second part, the integrated analysis of six carcinomas of epithelial origin at the single-cell level was performed for the target study. Identification of optimal target antigens remains a key challenge in cellular immunotherapy due to the tumor heterogeneity. In this study, to dissect tissue complexity to the level of individual cells, I constructed a single-cell expression meta-atlas integrating ~1.3 million cells from 17 types of tumors and 12 normal organs. Deep learning was employed to search this meta-atlas for logical combinations of surface antigens that best discriminate between individual malignant and normal cells. Taken together, through the integrated approach according to the sequencing or data sources, important results for various cancer types could be derived. Therefore, it is believed that bioinformatic integrated analysis in the intermediary medical field will play an important role in the development of treatments and follow-up studies, clinically and pre-clinically.