Conclusions
Single-cell analysis of CTCL skin tumor samples reveals patient-specific landscapes of malignant and reactive lymphocytes within the local microenvironment of each tumor, giving an unprecedented view of lymphocyte heterogeneity and identifying tumor-specific molecular signatures, with important implications for diagnosis and personalized disease treatment.
Purpose
The heterogeneity of tumor cells presents a major challenge to cancer diagnosis and therapy. Cutaneous T-cell lymphomas (CTCL) are a group of T lymphocyte malignancies that primarily affect skin. Lack of highly specific markers for malignant lymphocytes prevents early diagnosis, while only limited treatment options are available for patients with advanced stage CTCL. Droplet-based single-cell transcriptome analysis of CTCL skin biopsies opens avenues for dissecting patient-specific T lymphocyte heterogeneity, providing a basis for identifying specific markers for diagnosis and cure of CTCL. Experimental design: Single-cell RNA-sequencing was performed by Droplet-based sequencing (10X Genomics), focusing on 14,056 CD3+ lymphocytes (448 cells from normal and 13,608 cells from CTCL skin samples) from skin biopsies of 5 patients with advanced-stage CTCL and 4 healthy donors. Protein expression of identified genes was validated in advanced stage CTCL skin tumors by immunohistochemistry and confocal immunofluorescence microscopy.
Results
Our analysis revealed a large inter- and intratumor gene expression heterogeneity in the T lymphocyte subset, as well as a common gene expression signature in highly proliferating lymphocytes that was validated in multiple advanced-stage skin tumors. In addition, we established the immunologic state of reactive lymphocytes and found heterogeneity in effector and exhaustion programs across patient samples. Conclusions: Single-cell analysis of CTCL skin tumor samples reveals patient-specific landscapes of malignant and reactive lymphocytes within the local microenvironment of each tumor, giving an unprecedented view of lymphocyte heterogeneity and identifying tumor-specific molecular signatures, with important implications for diagnosis and personalized disease treatment.