Transcriptome Analysis Identifies Accumulation of Natural Killer Cells with Enhanced Lymphotoxin-β Expression during Glioblastoma Progression.
Gianni MonacoAshkan KhavaranAdrià Dalmau GasullJonathan CahueauMartin DieboldChintan ChhatbarMirco FriedrichDieter Henrik HeilandRoman SankowskiPublished in: Cancers (2022)
Glioblastomas are the most common primary brain tumors. Despite extensive clinical and molecular insights into these tumors, the prognosis remains dismal. While targeted immunotherapies have shown remarkable success across different non-brain tumor entities, they failed to show efficacy in glioblastomas. These failures prompted the field to reassess the idiosyncrasies of the glioblastoma microenvironment. Several high-dimensional single-cell RNA sequencing studies generated remarkable findings about glioblastoma-associated immune cells. To build on the collective strength of these studies, we integrated several murine and human datasets that profiled glioblastoma-associated immune cells at different time points. We integrated these datasets and utilized state-of-the-art algorithms to investigate them in a hypothesis-free, purely exploratory approach. We identified a robust accumulation of a natural killer cell subset that was characterized by a downregulation of activation-associated genes with a concomitant upregulation of apoptosis genes. In both species, we found a robust upregulation of the Lymphotoxin-β gene, a cytokine from the TNF superfamily and a key factor for the development of adaptive immunity. Further validation analyses uncovered a correlation of lymphotoxin signaling with mesenchymal-like glioblastoma regions in situ and in TCGA and CGGA glioblastoma cohorts. In summary, we identify lymphotoxin signaling as a potential therapeutic target in glioblastoma-associated natural killer cells.
Keyphrases
- single cell
- natural killer cells
- genome wide
- poor prognosis
- rna seq
- cell proliferation
- machine learning
- signaling pathway
- rheumatoid arthritis
- oxidative stress
- high throughput
- gene expression
- dna methylation
- mesenchymal stem cells
- bone marrow
- deep learning
- long non coding rna
- endoplasmic reticulum stress
- climate change
- genetic diversity
- pi k akt