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BMJ Publishing Group, Journal for ImmunoTherapy of Cancer, 8(9), p. e002812, 2021

DOI: 10.1136/jitc-2021-002812

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CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

BackgroundDedifferentiated liposarcoma (DDLPS) is one of the most common soft tissue sarcoma subtypes and is devastating in the advanced/metastatic stage. Despite the observation of clinical responses to PD-1 inhibitors, little is known about the immune microenvironment in relation to patient prognosis.MethodsWe performed a retrospective study of 61 patients with DDLPS. We completed deep sequencing of the T-cell receptor (TCR) β-chain and RNA sequencing for predictive modeling, evaluating both immune markers and tumor escape genes. Hierarchical clustering and recursive partitioning were employed to elucidate relationships of cellular infiltrates within the tumor microenvironment, while an immune score for single markers was created as a predictive tool.ResultsAlthough many DDLPS samples had low TCR clonality, high TCR clonality combined with low T-cell fraction predicted lower 3-year overall survival (p=0.05). Higher levels of CD14+ monocytes (p=0.02) inversely correlated with 3-year recurrence-free survival (RFS), while CD4+ T-cell infiltration (p=0.05) was associated with a higher RFS. Genes associated with longer RFS included PD-1 (p=0.003), ICOS (p=0.006), BTLA (p=0.033), and CTLA4 (p=0.02). In a composite immune score, CD4+ T cells had the strongest positive predictive value, while CD14+ monocytes and M2 macrophages had the strongest negative predictive values.ConclusionsImmune cell infiltration predicts clinical outcome in DDLPS, with CD4+ cells associated with better outcomes; CD14+ cells and M2 macrophages are associated with worse outcomes. Future checkpoint inhibitor studies in DDLPS should incorporate immunosequencing and gene expression profiling techniques that can generate immune landscape profiles.