RESUMO
The capacity of T cells to initiate anti-leukemia immune responses is determined by the ability of their receptors (TCRs) to recognize leukemia neoantigens. Epigenetic mechanisms including DNA methylation contribute to shaping the TCR repertoire composition and diversity. The DNA hypomethylating agents (HMAs) have been widely used in the treatment of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Whether DNA HMAs directly influence TCR gene loci methylation patterns remains unknown. By analyzing public datasets, we compared methylation patterns across TCR loci in AML patients and healthy controls. We also explored how HMAs influence TCR loci DNA methylation in patients with AML. While methylation patterns are largely conserved across the TCR loci, certain V genes exhibit high interindividual variability. Although overall methylation levels within the TCR loci did not show significant differences, specific sites, including 32 TRAV and 12 TRBV sites exhibited distinct methylation patterns when comparing T cells from healthy donors to those from patients with AML. In leukemic cells, decitabine treatment demethylates sites across the TRAV and TRBV genes. While not as significant, a similar pattern of demethylation is observed in T cells. Pretreatment AML samples exhibit higher methylation beta values in differentially methylated positions (DMPs) compared with non-DMPs. Methylation levels of certain TRAV and TRBV genes in leukemic cells are associated with patients' risk status. The presence of disease specific TCR loci methylated signatures that are associated with clinical outcome presents an opportunity for therapeutic intervention. HMAs can modulate the TCR loci methylation patterns, yet whether they could reprogram the TCR repertoire composition remains to be explored.
RESUMO
Background: Allogeneic hematopoietic stem cell transplant remains the most effective strategy for patients with high-risk acute myeloid leukemia (AML). Leukemia-specific neoantigens presented by the major histocompatibility complexes (MHCs) are recognized by the T cell receptors (TCR) triggering the graft-versus-leukemia effect. A unique TCR signature is generated by a complex V(D)J rearrangement process to form TCR capable of binding to the peptide-MHC. The generated TCR repertoire undergoes dynamic changes with disease progression and treatment. Method: Here we applied two different computational tools (TRUST4 and MIXCR) to extract the TCR sequences from RNA-seq data from The Cancer Genome Atlas (TCGA) and examine the association between features of the TCR repertoire in adult patients with AML and their clinical and molecular characteristics. Results: We found that only ~30% of identified TCR CDR3s were shared by the two computational tools. Yet, patterns of TCR associations with patients' clinical and molecular characteristics based on data obtained from either tool were similar. The numbers of unique TCR clones were highly correlated with patients' white blood cell counts, bone marrow blast percentage, and peripheral blood blast percentage. Multivariable regressions of TCRA and TCRB median normalized number of unique clones with mutational status of AML patients using TRUST4 showed significant association of TCRA or TCRB with WT1 mutations, WBC count, %BM blast, and sex (adjusted in TCRB model). We observed a correlation between TCRA/B number of unique clones and the expression of T cells inhibitory signal genes (TIGIT, LAG3, CTLA-4) and foxp3, but not IL2RA, CD69 and TNFRSF9 suggestive of exhausted T cell phenotypes in AML. Conclusion: Benchmarking of computational tools is needed to increase the accuracy of the identified clones. The utilization of RNA-seq data enables identification of highly abundant TCRs and correlating these clones with patients' clinical and molecular characteristics. This study further supports the value of high-resolution TCR-Seq analyses to characterize the TCR repertoire in patients.