Characterization of Ligand-receptor Pair in Acute Myeloid Leukemia: a Scoring Model for Prognosis, Therapeutic Response, and T Cell Dysfunction

Overview

Journal Front Oncol

Specialty Oncology

Date 2024 Nov 1

PMID 39484036

Authors

Chunlan Fu

Di Qiu

Mei Zhou

Shaobo Ni

Xin Jin

Affiliations

Soon will be listed here.

Abstract

Introduction: The significance of ligand-receptor (LR) pair interactions in the progression of acute myeloid leukemia (AML) has been the focus of numerous studies. However, the relationship between LR pairs and the prognosis of AML, as well as their impact on treatment outcomes, is not fully elucidated.

Methods: Leveraging data from the TCGA-LAML cohort, we mapped out the LR pair interactions and distinguished specific molecular subtypes, with each displaying distinct biological characteristics. These subtypes exhibited varying mutation landscapes, pathway characteristics, and immune infiltration levels. Further insight into the immune microenvironment among these subtypes revealed disparities in immune cell abundance.

Results: Notably, one subtype showed a higher prevalence of CD8 T cells and plasma cells, suggesting increased adaptive immune activities. Leveraging a multivariate Lasso regression, we formulated an LR pair-based scoring model, termed "LR.score," to classify patients based on prognostic risk. Our findings underscored the association between elevated LR scores and T-cell dysfunction in AML. This connection highlights the LR score's potential as both a prognostic marker and a guide for personalized therapeutic interventions. Moreover, our LR.score revealed substantial survival prediction capacities in an independent AML cohort. We highlighted CLEC11A, ICAM4, ITGA4, and AVP as notably AML-specific.

Discussion: qRT-PCR analysis on AML versus normal bone marrow samples confirmed the significant downregulation of CLEC11A, ITGA4, ICAM4, and AVP in AML, suggesting their inverse biomarker potential in AML. In summary, this study illuminates the significance of the LR pair network in predicting AML prognosis, offering avenues for more precise treatment strategies tailored to individual patient profiles.

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