Dataset Information

High-throughput T cell receptor sequencing reveals distinct repertoires between tumor and adjacent non-tumor tissues in HBV-associated HCC.

ABSTRACT: T lymphocytes, which recognize antigen peptides through specific T cell receptors (TCRs), play an important role in the human adaptive immune response. TCR diversity is closely associated with host immune response and cancer prognosis. Although tumor-infiltrating T lymphocytes have implications for tumor prognosis, few studies have performed a detailed characterization of TCR diversity in both tumor and non-tumor tissues in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC). Here, we performed high-throughput sequencing of the TCR? chain complementarity determining region 3 (CDR3) of liver-infiltrating T cells from 48 HBV-associated HCC patients. A significantly higher average number of CDR3 aa clonotypes (2259 vs. 1324, p < 0.001), and significantly higher TCR diversity (Gini coefficient, p < 0.001; Simpson index, p < 0.01; Shannon entropy, p < 0.001) were observed in tumor tissues compared with adjacent non-tumor tissues. The ratio of highly expanded clones (HECs) was significantly higher in non-tumor tissues than in tumor tissues when the HEC threshold was defined as 2% or greater (p < 0.05). Our analysis of the median Morisita-Horn index indicated weak TCR repertoire similarity between tumor and matched non-tumor tissues. The median number of shared clones in tumor tissue and matched non-tumor tissue from each patient was 360.5, representing 5.1-15.8% (10.6 ± 0.4%) of all clones in each patient. We observed extensive heterogeneity of T lymphocytes in tumors and higher HEC ratios in adjacent non-tumor tissues of HCC patients. The differential T cell repertoires in tumor and non-tumor tissues suggest a distinct T cell immune microenvironment in patients with HBV-associated HCC.

SUBMITTER: Chen Y

PROVIDER: S-EPMC5087304 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High-throughput T cell receptor sequencing reveals distinct repertoires between tumor and adjacent non-tumor tissues in HBV-associated HCC.

Chen Yunqing Y Xu Ying Y Zhao Miaoxian M Liu Yu Y Gong Mingxing M Xie Cantao C Wu Hongkai H Wang Zhanhui Z

Oncoimmunology 20160805 10

T lymphocytes, which recognize antigen peptides through specific T cell receptors (TCRs), play an important role in the human adaptive immune response. TCR diversity is closely associated with host immune response and cancer prognosis. Although tumor-infiltrating T lymphocytes have implications for tumor prognosis, few studies have performed a detailed characterization of TCR diversity in both tumor and non-tumor tissues in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC). Here, ...[more]

PMID: 27853640

Similar Datasets

Project description:Hepatitis B virus (HBV) infection is a common problem in the world, especially in China. More than 60-80% of hepatocellular carcinoma (HCC) cases can be attributed to HBV infection in high HBV prevalent regions. Although traditional Sanger sequencing has been extensively used to investigate HBV sequences, NGS is becoming more commonly used. Further, it is unknown whether word pattern frequencies of HBV reads by Next Generation Sequencing (NGS) can be used to investigate HBV genotypes and predict HCC status. In this study, we used NGS to sequence the pre-S region of the HBV sequence of 94 HCC patients and 45 chronic HBV (CHB) infected individuals. Word pattern frequencies among the sequence data of all individuals were calculated and compared using the Manhattan distance. The individuals were grouped using principal coordinate analysis (PCoA) and hierarchical clustering. Word pattern frequencies were also used to build prediction models for HCC status using both K-nearest neighbors (KNN) and support vector machine (SVM). We showed the extremely high power of analyzing HBV sequences using word patterns. Our key findings include that the first principal coordinate of the PCoA analysis was highly associated with the fraction of genotype B (or C) sequences and the second principal coordinate was significantly associated with the probability of having HCC. Hierarchical clustering first groups the individuals according to their major genotypes followed by their HCC status. Using cross-validation, high area under the receiver operational characteristic curve (AUC) of around 0.88 for KNN and 0.92 for SVM were obtained. In the independent data set of 46 HCC patients and 31 CHB individuals, a good AUC score of 0.77 was obtained using SVM. It was further shown that 3000 reads for each individual can yield stable prediction results for SVM. Thus, another key finding is that word patterns can be used to predict HCC status with high accuracy. Therefore, our study shows clearly that word pattern frequencies of HBV sequences contain much information about the composition of different HBV genotypes and the HCC status of an individual.

Dataset Information

High-throughput T cell receptor sequencing reveals distinct repertoires between tumor and adjacent non-tumor tissues in HBV-associated HCC.

Publications

High-throughput T cell receptor sequencing reveals distinct repertoires between tumor and adjacent non-tumor tissues in HBV-associated HCC.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets