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. 2021 Aug;9(8):e002812.
doi: 10.1136/jitc-2021-002812.

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

Brett A Schroeder  1   2 , Natalie A LaFranzo  3 , Bonnie J LaFleur  4 , Rachel M Gittelman  5 , Marissa Vignali  5 , Shihong Zhang  2 , Kevin C Flanagan  3 , Julie Rytlewski  6 , Laura Riolobos  7 , Brian C Schulte  8 , Teresa S Kim  9 , Eleanor Chen  10 , Kimberly S Smythe  2 , Michael J Wagner  2   11 , Jose G Mantilla  12 , Jean S Campbell  13 , Robert H Pierce  2 , Robin L Jones  14 , Lee D Cranmer  15 , Seth M Pollack  16   17
Affiliations

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

Brett A Schroeder et al. J Immunother Cancer. 2021 Aug.

Abstract

Background: Dedifferentiated 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.

Methods: We 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.

Results: Although 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.

Conclusions: Immune 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.

Keywords: CD4-positive T-lymphocytes; immunity; macrophages; sarcoma; tumor microenvironment.

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Conflict of interest statement

Competing interests: BAS, SZ, LRS, BCS, KSS, JGM, EC, JSC, and RHP declare no potential conflicts of interest. SMP reported research funding from Merck during the conduct of the study; research funding from EMD Serono, Incyte, Presage, Janssen, OncoSec, and Juno and consulting, honoraria, and advisory activity with GlaxoSmithKline, Eli Lilly and Company, Seattle Genetics, Bayer, Tempus, Daiichi Sankyo, and Blueprint Medicine, outside the submitted work. NAL and KCF are employed by Cofactor Genomics, Inc, the company that developed and produces the ImmunoPrism® reagent kit and informatics tools used in this article. BJL is a paid consultant for Cofactor Genomics, Inc. RMG and MV have a financial interest in Adaptive Biotechnologies. JR has equity in Adaptive Biotechnologies, equity and employment with Bristol Myers Squibb. MJW reported research funding from Athenex, Deciphera, Incyte, Tempus, Adaptimmune, and GlaxoSmithKline and consulting, honoraria, and advisory activity with Tempus, Deciphera, and Adaptimmune, outside the submitted work. RLJ reported grants from Merck during the conduct of the study; research support from Merck Sharp & Dohme and GlaxoSmithKline and consultation fees from Adaptimmune, Athenex, Blueprint Medicine, Clinigen, Eisai, Epizyme, Daiichi Sankyo, Deciphera, Immune Design, Eli Lilly and Company, Merck, Pharma Mar, and UpToDate, outside the submitted work; in addition, RLJ had a patent to biomarker, issued. LDC receives research funding, paid to institution, from Eli Lilly, AADi, BluePrint Medicine, Iterion, Gradalis, Philogen, Advenchen Laboratories, and CBA Pharma. LDC institution has received funding from Eli Lilly for conduct of clinical trials. LDC has received honoraria or has served on advisory boards for BluePrint Medicines and Regeneron.

Figures

Figure 1
Figure 1
T-cell fraction and clonality in relation to 3-year overall survival (OS). (A) Combined T-cell repertoire clonality and T-cell fraction subdivided into quadrants based on mean values for each metric. Relative 3-year OS is reported for each quadrant. (B) Cox regression for tumor T-cell fraction and repertoire clonality in relation to OS in patients with dedifferentiated liposarcoma. HR p<0.001. Likelihood ratio test p<0.001. ULQ, upper left quadrant.
Figure 2
Figure 2
Dedifferentiated liposarcoma multiplex immunohistochemistry of representative patients. Rows 1, 2, and 3 (left to right): Core at ×5 magnification with all colors, ×40 with all colors, ×40 with CD68/CD163 and DAPI, ×40 with CD68/CD163 and CD3. CD11c, green; CD16, light blue; HLA-DR, yellow; CD14, magenta; CD3, red; CD68/CD163, white; DAPI, dark blue.
Figure 3
Figure 3
Pairwise correlation for multiplex immunohistochemistry (mIHC). The heat map demonstrates pairwise correlation between mIHC markers where red represents a strong positive correlation, and blue represents a strong negative correlation. All boxes are statistically significant with a p≤0.05, except for boxes marked with an ‘X’.
Figure 4
Figure 4
Hierarchical clustering using the Hoeffding D statistic as a pairwise distance measure and biplots based on 3-year overall survival (OS). Biplots based on patient 3-year OS using immune cells (A) and escape genes (B). Example of inverse relationships include between CD8+ T cells, M1 macrophage and M2 macrophages, and additionally between CD19+ B cells, CD14+ monocytes and regulatory T-cells. Example of multicolinear relationship between CD4+ T cells and CD19+ B cells. Hierarchical clustering with immune cells (C), escape genes (D), and combined (E). Tregs, regulatory T cells. ARG1, arginase 1; BTLA, B and T lymphocyte attenuator; CD, cluster of differentiation; CTLA-4, cytotoxic T-lymphocyte associated protein 4; ICOS, inducible costimulator; PD-1, programmed cell death protein 1; TIM3, T cell immunoglobulin and mucin-domain containing 3.
Figure 5
Figure 5
Three immune components were selected from the elastic net algorithm to create an immune score (top) for predicting patient outcomes in which weights greater than zero are more highly expressed in patients with greater than 3-year recurrence-free survival. The adjusted area under the receiver operating characteristic curve (AUC) (bottom) used to measure strength of predictions increased when immune markers were combined with genes demonstrating improved statistical prediction.
See this image and copyright information in PMC

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