CD4+ T cells in classical Hodgkin lymphoma express exhaustion associated transcription factors TOX and TOX2: Characterizing CD4+ T cells in Hodgkin lymphoma.
Johanna VeldmanJessica Rodrigues PlaçaLauren ChongMiente Martijn TerpstraMirjam MastikLéon C van KempenKlaas KokTomohiro AokiChristian SteidlAnke van den BergLydia VisserArjan DiepstraPublished in: Oncoimmunology (2022)
In classical Hodgkin lymphoma (cHL), the highly abundant CD4+ T cells in the vicinity of tumor cells are considered essential for tumor cell survival, but are ill-defined. Although they are activated, they consistently lack expression of activation marker CD26. In this study, we compared sorted CD4+CD26- and CD4+CD26+ T cells from cHL lymph node cell suspensions by RNA sequencing and T cell receptor variable gene segment usage analysis. This revealed that although CD4+CD26- T cells are antigen experienced, they have not clonally expanded. This may well be explained by the expression of exhaustion associated transcription factors TOX and TOX2 , immune checkpoints PDCD1 and CD200 , and chemokine CXCL13 , which were amongst the 100 significantly enriched genes in comparison with the CD4+CD26+ T cells. Findings were validated in single-cell RNA sequencing data from an independent cohort. Interestingly, immunohistochemistry revealed predominant and high frequency of staining for TOX and TOX2 in the T cells attached to the tumor cells. In conclusion, the dominant CD4+CD26- T cell population in cHL is antigen experienced, polyclonal, and exhausted. This population is likely a main contributor to the very high response rates to immune checkpoint inhibitors in cHL.
Keyphrases
- hodgkin lymphoma
- single cell
- high frequency
- rna seq
- lymph node
- transcription factor
- poor prognosis
- nk cells
- transcranial magnetic stimulation
- genome wide
- genome wide identification
- binding protein
- squamous cell carcinoma
- copy number
- gene expression
- neoadjuvant chemotherapy
- electronic health record
- radiation therapy
- deep learning
- bone marrow
- artificial intelligence
- mesenchymal stem cells