Chronological genome and single-cell transcriptome integration characterizes the evolutionary process of adult T cell leukemia-lymphoma.
Makoto YamagishiMiyuki KubokawaYuta KuzeAyako SuzukiAkari YokomizoSeiichiro KobayashiMakoto NakashimaJunya MakiyamaMasako IwanagaTakahiro FukudaToshiki WatanabeYutaka SuzukiKaoru UchimaruPublished in: Nature communications (2021)
Subclonal genetic heterogeneity and their diverse gene expression impose serious problems in understanding the behavior of cancers and contemplating therapeutic strategies. Here we develop and utilize a capture-based sequencing panel, which covers host hotspot genes and the full-length genome of human T-cell leukemia virus type-1 (HTLV-1), to investigate the clonal architecture of adult T-cell leukemia-lymphoma (ATL). For chronologically collected specimens from patients with ATL or pre-onset individuals, we integrate deep DNA sequencing and single-cell RNA sequencing to detect the somatic mutations and virus directly and characterize the transcriptional readouts in respective subclones. Characteristic genomic and transcriptomic patterns are associated with subclonal expansion and switches during the clinical timeline. Multistep mutations in the T-cell receptor (TCR), STAT3, and NOTCH pathways establish clone-specific transcriptomic abnormalities and further accelerate their proliferative potential to develop highly malignant clones, leading to disease onset and progression. Early detection and characterization of newly expanded subclones through the integrative analytical platform will be valuable for the development of an in-depth understanding of this disease.
Keyphrases
- single cell
- rna seq
- genome wide
- gene expression
- high throughput
- acute myeloid leukemia
- copy number
- dna methylation
- bone marrow
- diffuse large b cell lymphoma
- endothelial cells
- cell proliferation
- mental health
- transcription factor
- circulating tumor
- induced pluripotent stem cells
- childhood cancer
- oxidative stress
- risk assessment
- genome wide identification
- human health
- network analysis
- liquid chromatography