Immune signatures of CD4 and CD68 predicts disease progression in cutaneous T cell lymphoma

Sanling Huang; Mengying Liao; Siliang Chen; Ping Zhang; Fangzhou Xu; Hongyu Zhang

Immune signatures of CD4 and CD68 predicts disease progression in cutaneous T cell lymphoma

Am J Transl Res. 2022 May 15;14(5):3037-3051. eCollection 2022.

Authors

Sanling Huang¹, Mengying Liao², Siliang Chen¹, Ping Zhang¹, Fangzhou Xu^{1

3}, Hongyu Zhang^{1

3}

Affiliations

¹ Department of Hematology, Peking University Shenzhen Hospital Shenzhen 518000, Guangdong, P. R. China.
² Department of Pathology, Peking University Shenzhen Hospital Shenzhen 518000, Guangdong, P. R. China.
³ The Clinical Trail Institute, Peking University Shenzhen Hospital Shenzhen 518000, Guangdong, P. R. China.

PMID: 35702080
PMCID: PMC9185059

Abstract

Background: Cutaneous T-cell lymphoma (CTCL) is highly heterogeneous, and its prognosis is closely related to the disease stage. The tumor microenvironment (TME) is an important component of tumor tissue, driving cancer cell growth, progression, and metastasis. However, the diagnostic value of TME in CTCL has not yet been studied in-depth. To date, no study has performed a comprehensive evaluation of the significance of the TME in CTCL.

Methods: Using xCell methods based on bulk RNA sequencing data, we inferred immune cell fraction in the TME in 126 patients and assessed the prognostic importance of immune cells. Consensus clustering was performed to determine the TME subtypes and characterize the transcriptome of each subtype. Based on the TME subtypes, we established the disease progression model using random forest algorithms and logistic regression. The efficacy of the model was examined using an additional 49-patient cohort. Finally, we validated our finding at the protein level using immunochemistry in a 16-patient cohort.

Results: Patients with advanced CTCL presented with a more active immunity overall than those with early stage. Random forest algorithms revealed that the immune cells CD4, macrophages, and dendritic cells (DCs) were the most effective prognosis predictors. Therefore, we constructed a risk model using logistic regression based on these immune cells. The TME score could be used to effectively predict disease conditions in three datasets with the AUC of 0.9414, 0.7912, and 0.7665, respectively. Immunochemistry at the protein level revealed that helper T cells and the macrophage markers CD4 and CD68 could successfully distinguish different CTCL stages in patients, whereas the DC marker langerin showed no change with disease progression.

Conclusion: We found advanced-stage CTCL was associated with an active immune microenvironment, and the immune signatures CD4 and CD68 showed a relatively high accuracy in predicting CTCL disease progression.

Keywords: CD4; CD68; Cutaneous T-cell lymphoma; macrophages; tumor microenvironment.