June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Deconvolution of tumor immune microenvironment in uveal melanoma
Author Affiliations & Notes
  • Anthony Cruz
    Ophthalmology, University of Miami School of Medicine, Miami, Florida, United States
  • Stefan Kurtenbach
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
    Sylvester Comprehensive Cancer Center, Miami, Florida, United States
  • J. William Harbour
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
    Sylvester Comprehensive Cancer Center, Miami, Florida, United States
  • Footnotes
    Commercial Relationships   Anthony Cruz, None; Stefan Kurtenbach, None; J. Harbour, Castle Biosciences (C)
  • Footnotes
    Support  NCI Grant 5R01CA125970-14
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2874. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Anthony Cruz, Stefan Kurtenbach, J. William Harbour; Deconvolution of tumor immune microenvironment in uveal melanoma. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2874.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : Uveal melanoma (UM) is the most common primary eye cancer and remains uniformly fatal. In contrast to cutaneous melanoma, UM is an immunologically “cold” tumor that is poorly responsive to immunotherapy. The purpose of this study was to bioinformatically deconvolute the tumor immune microenvironment (TIM) after stratification for key molecular biomarkers associated.

Methods : RNA-seq data were downloaded from The Cancer Genome Atlas Uveal Melanoma (TCGA-UVM) dataset (n=80). Samples were stratified by gene expression profile (GEP) class 1 versus class 2, PRAME expression (negative versus positive), and LAG3 expression (low versus high). TIM deconvolution was performed using Quantiseq from the Immunedeconv R package.

Results : Class 2 UM were inferred to comprise 2.64-fold fewer CD4+ T cells (p =0.0013), 2.3-fold fewer myeloid dendritic cells (p=0.026), 1.74-fold more M2 macrophages (p=4.37E-9), 2.02-fold more NK cells (p=1.2E-6), and 53-fold more CD8+ T cells (p=0.0007) compared to class 1 UM. PRAME-positive UM contained 1.26-fold more M2 macrophages (p=0.022), 1.39-fold more NK cells (p=0.005), and 2.29-fold fewer CD4+ T cells (p=0.0054) than PRAME-negative UM. LAG3 expression was strongly associated with class 2 UM (p=0.0026), increased M1 (p=0.00057) and M2 macrophages (p=5.6E-6), NK cells (p= 0.016), CD8+ T cells (p=1.77E-11), Tregs (p=2.27E-5), and decreased CD4+ T cells (p=0.0045).

Conclusions : The two strongest predictors of metastasis in UM – class 2 GEP and PRAME expression – are strongly associated with an inhibitory TIM. LAG3, which we recently showed by single cell RNA sequencing to be the predominant T cell exhaustion marker in UM, was associated with an increased global inflammatory signature. The data suggest that a subset of Class 2 Tumors are prime candidates for LAG3 Immune Checkpoint Inhibition. Correlation between key molecular biomarkers and TIM will facilitate the development of targeted immune therapy for patients with UM.

This is a 2021 ARVO Annual Meeting abstract.

 

Figure 1. Quantiseq Deconvolution of TIM Composition in Class 1 and 2 UM. *Not shown: Uncharacterized Cell fraction, which comprises the remainder of the cell fractions, totaling 100% of the sample cell population.

Figure 1. Quantiseq Deconvolution of TIM Composition in Class 1 and 2 UM. *Not shown: Uncharacterized Cell fraction, which comprises the remainder of the cell fractions, totaling 100% of the sample cell population.

 

Figure 2. Normalized LAG3 expression level in Class 1 and 2 UM. Class 1 (Average: 40.28; Median: 12; 75th Percentile: 20.5; Max: 701) Class 2 (Average: 648.15; Median: 173; 75th Percentile: 572; Max: 6609). LAG3 expression is strongly associated with Class 2 UM (p=0.0026).

Figure 2. Normalized LAG3 expression level in Class 1 and 2 UM. Class 1 (Average: 40.28; Median: 12; 75th Percentile: 20.5; Max: 701) Class 2 (Average: 648.15; Median: 173; 75th Percentile: 572; Max: 6609). LAG3 expression is strongly associated with Class 2 UM (p=0.0026).

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×