Melanoma Single-Cell Biology in Experimental and Clinical Settings.
Hans BinderMaria SchmidtHenry Loeffler-WirthLena Suenke MortensenManfred KunzPublished in: Journal of clinical medicine (2021)
Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.
Keyphrases
- single cell
- gene expression
- tyrosine kinase
- clinical trial
- rna seq
- transcription factor
- protein kinase
- dna damage
- signaling pathway
- stem cells
- epithelial mesenchymal transition
- high throughput
- machine learning
- drug delivery
- oxidative stress
- bone marrow
- dna methylation
- artificial intelligence
- replacement therapy
- deep learning
- double blind
- phase ii
- cancer therapy
- genome wide
- big data