TRB-J1 usage, in combination with the HLA-A*01:01 allele, represents an apparent survival advantage for uterine corpus endometrial carcinoma: Comparisons with microscopic assessments of lymphocyte infiltrates.
Kendall R ClarkWei Lue TongBlake M CallahanJohn M YavorskiYaping N TuGeorge BlanckPublished in: International journal of immunogenetics (2018)
The opportunity for the highly efficient recovery of immune receptor recombination data from cancer specimens, including the ready assessment of immune receptor V and J usage, raises the issue of establishing precise values of assessing the immune receptor status as opposed to obtaining basic information regarding lymphocyte infiltration, in the cancer setting. In this report, we obtained the lymphocyte infiltration percentages from the cancer digital slide archive representing uterine corpus endometrial carcinoma (UCEC) and correlated these data with recovery of the immune receptor recombination reads from corresponding UCEC exome files. Results indicated a basic correlation of the recovery of productive T-cell receptor beta (TRB) recombination reads with lymphocyte infiltration percentages. However, the recovery of specific immune receptor recombination reads did not indicate the same survival outcomes as microscope detection of lymphocyte infiltrate percentages. To further exploit the value of recovery of the TRB recombination reads from the UCEC exome files, we determined the survival outcomes for combinations of TRB gene segment usage and HLA class I alleles, with the most important result being that the combination of HLA-A*01:01 and TRB-J1 segment usage reflected a strikingly high survival rate. Overall, this report emphasized the increased value of the knowledge of the immune receptor recombinations, in comparison with basic lymphocyte infiltration percentages, in assessing cancer survival rates.
Keyphrases
- papillary thyroid
- dna damage
- peripheral blood
- highly efficient
- dna repair
- healthcare
- squamous cell
- computed tomography
- magnetic resonance imaging
- binding protein
- electronic health record
- dna methylation
- machine learning
- genome wide
- health information
- childhood cancer
- big data
- ultrasound guided
- transcription factor
- quantum dots
- artificial intelligence
- free survival
- diffusion weighted imaging
- real time pcr