Single-cell RNA-sequencing reveals predictive features of response to pembrolizumab in Sézary syndrome.
Tianying SuGeorge E DuranAlexa C KwangNirasha RamchurrenSteven P FlingYoun H KimMichael S KhodadoustPublished in: Oncoimmunology (2022)
The PD-1 inhibitor pembrolizumab is effective in treating Sézary syndrome, a leukemic variant of cutaneous T-cell lymphoma. Our purpose was to investigate the effects of pembrolizumab on healthy and malignant T cells in Sézary syndrome and to discover characteristics that predict pembrolizumab response. Samples were analyzed before and after 3 weeks of pembrolizumab treatment using single-cell RNA-sequencing of 118,961 peripheral blood T cells isolated from six Sézary syndrome patients. T-cell receptor clonotyping, bulk RNA-seq signatures, and whole-exome data were integrated to classify malignant T-cells and their underlying subclonal heterogeneity. We found that responses to pembrolizumab were associated with lower KIR3DL2 expression within Sézary T cells. Pembrolizumab modulated Sézary cell gene expression of T-cell activation associated genes. The CD8 effector populations included clonally expanded populations with a strong cytotoxic profile. Expansions of CD8 terminal effector and CD8 effector memory T-cell populations were observed in responding patients after treatment. We observed intrapatient Sézary cell heterogeneity including subclonal segregation of a coding mutation and copy number variation. Our study reveals differential effects of pembrolizumab in both malignant and healthy T cells. These data support further study of KIR3DL2 expression and CD8 immune populations as predictive biomarkers of pembrolizumab response in Sézary syndrome.
Keyphrases
- single cell
- rna seq
- advanced non small cell lung cancer
- high throughput
- copy number
- gene expression
- case report
- ejection fraction
- peripheral blood
- poor prognosis
- genome wide
- newly diagnosed
- epidermal growth factor receptor
- dendritic cells
- regulatory t cells
- dna methylation
- patient reported outcomes
- healthcare
- genetic diversity
- binding protein
- mesenchymal stem cells
- machine learning
- working memory
- cell therapy
- health insurance
- tyrosine kinase
- nk cells
- type iii