Literature DB >> 28805806

Lung tumor exome files with T-cell receptor recombinations: a mouse model of T-cell infiltrates reflecting mutation burdens.

Yaping N Tu1, Wei Lue Tong1, Timothy J Fawcett2, George Blanck1,3.   

Abstract

Tumor exomes and RNASeq data were originally intended for obtaining tumor mutations and gene expression profiles, respectively. However, recent work has determined that tumor exome and RNAseq read files contain reads representing T-cell and B-cell receptor (TcR and BcR) recombinations, presumably due to infiltrating lymphocytes. Furthermore, the recovery of immune receptor recombination reads has demonstrated correlations with specific, previously appreciated aspects of tumor immunology. To further understand the usefulness of recovering TcR and BcR recombinations from tumor exome files, we developed a scripted algorithm for recovery of reads representing these recombinations from a previously described mouse model of lung tumorigenesis. Results indicated that exomes representing lung adenomas reveal significantly more TcR recombinations than do exomes from lung adenocarcinomas; and that exome files representing high mutation adenomas, arising from chemical mutagens, have more TcR recombinations than do exome files from low mutation adenomas arising from an activating Kras mutation. The latter results were also consistent with a similar analysis performed on human lung adenocarcinoma exomes. The mouse and human results for obtaining TcR recombination reads from tumor specimen exomes are consistent with human tumor biology results indicating that adenomas and high mutation cancers are sites of high immune activity. The results indicate hitherto unappreciated opportunities for the use of tumor specimen exome files, particularly from experimental animal models, to study the connection between the adenoma stage of tumorigenesis, or high cancer mutation rates, and high level lymphocyte infiltrates.

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Year:  2017        PMID: 28805806     DOI: 10.1038/labinvest.2017.80

Source DB:  PubMed          Journal:  Lab Invest        ISSN: 0023-6837            Impact factor:   5.662


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