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Ming Liu

Publications and source records attributed to Ming Liu.

2 recordsLinked to original sources

The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression

One sentence summaryIn silico decomposition of the immune microenvironment among common tumor types identified clear cell renal cell carcinoma as the most highly infiltrated by T-cells and further analysis of this tumor type revealed three distinct and clinically relevant clusters which were validated in an independent cohort.\n\nAbstractInfiltrating T cells in the tumor microenvironment have crucial roles in the competing processes of pro-tumor and anti-tumor immune response. However, the infiltration level of distinct T cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery (APM) genes, remain poorly characterized across human cancers. Here, we define a novel mRNA-based T cell infiltration score (TIS) and profile infiltration levels in 19 tumor types. We find that clear cell renal cell carcinoma (ccRCC) is the highest for TIS and among the highest for the correlation between TIS and APM expression, despite a modest mutation burden. This finding is contrary to the expectation that immune infiltration and mutation burden are linked. To further characterize the immune infiltration in ccRCC, we use RNA-seq data to computationally infer the infiltration levels of 24 immune cell types in a discovery cohort of 415 ccRCC patients and validate our findings in an independent cohort of 101 ccRCC patients. We find three clusters of tumors that are primarily separated by levels of T cell infiltration and APM gene expression. In ccRCC, the levels of Th17 cells and the ratio of CD8+ T/Treg levels are associated with improved survival whereas the levels of Th2 cells and Tregs are associated with negative clinical outcome. Our analysis illustrates the utility of computational immune cell decomposition for solid tumors, and the potential of this method to guide clinical decision-making.

Cancer Biology

Mutation rate estimation for 15 autosomal STR loci in a large population from Mainland China

STR, short trandem repeats, is well known as a type of powerful genetic marker and widely used in studying human population genetics. Compared with the conventional genetic markers, the mutation rate of STR is higher. Additionally, the mutations of STR loci do not lead to genetic inconsistencies between the genotypes of parents and children; therefore, the analysis of STR mutation is more suited to assess the population mutation. In this study, we focused on 15 autosomal STR loci (D8S1179, D21S11, D7S820, CSF1PO, D3S1358, TH01, D13S317, D16S539, D2S1338, D19S433, vWA, TPOX, D18S51, D5S818, FGA). DNA samples from a total of 42416 unrelated healthy individuals (19037 trios) from the population of Mainland China collected between Jan 2012 and May 2014 were successfully investigated. In our study, the allele frequencies, paternal mutation rates, maternal mutation rates and average mutation rates were detected in the 15 STR loci. Furthermore, we also investigated the relationship between paternal ages, maternal ages, pregnant time, area and average mutation rate. We found that paternal mutation rate is higher than maternal mutation rate and the paternal, maternal, and average mutation rates have a positive correlation with paternal ages, maternal ages and times respectively. Additionally, the average mutation rates of coastal areas are higher than that of inland areas. Overall, these results suggest that the 15 autosomal STR loci can provide highly informative polymorphic data for population genetic assessment in Mainland China, as well as confirm and extend the application of STR analysis in population genetics.

Genetics