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. 2020 Oct 31;12(11):3208.
doi: 10.3390/cancers12113208.

Impact of Genetic Ancestry on Prognostic Biomarkers in Uveal Melanoma

Daniel A Rodriguez  1   2   3 , Margaret I Sanchez  1   2   3 , Christina L Decatur  1   2   3 , Zelia M Correa  1   2   3 , Eden R Martin  4   5 , J William Harbour  1   2   3
Affiliations

Impact of Genetic Ancestry on Prognostic Biomarkers in Uveal Melanoma

Daniel A Rodriguez et al. Cancers (Basel). .

Abstract

Uveal melanoma (UM) is the most common cancer of the eye and leads to metastatic death in up to half of patients. Genomic prognostic biomarkers play an important role in clinical management in UM. However, research has been conducted almost exclusively in patients of European descent, such that the association between genetic admixture and prognostic biomarkers is unknown. In this study, we compiled 1381 control genomes from West African, European, East Asian, and Native American individuals, assembled a bioinformatic pipeline for assessing global and local ancestry, and performed an initial pilot study of 141 UM patients from our international referral center that manages many admixed individuals. Global and local estimates were associated with genomic prognostic determinants. Expression quantitative trait loci (eQTL) analysis was performed on variants found in segments. Globally, after correction for multiple testing, no prognostic variable was significantly enriched in a given ancestral group. However, there was a trend suggesting an increased proportion of European ancestry associated with expression of the PRAME oncogene (q = 0.06). Locally enriched European haplotypes were associated with the poor prognosis class 2 gene expression profile and with genes involved in immune regulation (q = 4.7 × 10-11). These findings reveal potential influences of genetic ancestry on prognostic variables, implicate immune genes in prognostic differences based on ancestry, and provide a basis for future studies of admixed patients with UM using rigorous genetic ancestry methodology.

Keywords: admixture; ancestry; biomarkers; genetics; uveal melanoma.

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Conflict of interest statement

Harbour is an inventor of intellectual property related to gene expression profiling in uveal melanoma. He is a paid consultant for Castle Biosciences, licensee of this intellectual property, and he receives royalties from its commercialization. All remaining authors declare no competing financial interests. The following authors declare no conflict of interest: Daniel A. Rodriguez, Margaret I. Sanchez, Christina L. Decatur, Eden R. Martin, and Zelia M. Correa. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Population structure of studied patients with uveal melanoma (UM). (A) The global catchment area of our patient cohort. The smaller pie chart indicates patients born outside of the United States. (B) Principal component analysis (PCA) based on approximately 100 K variant loci in common across 141 UM patients (colored circles) and the global ancestry reference panel populations (grey circles). (C) Unsupervised clustering of ADMIXTURE algorithm analysis of our cohort assuming K = 4 ancestral clusters. Each stacked column represents an individual patient, and the height of each stacked column represents the contribution of each indicated ancestry to the overall genetic makeup of each patient.
Figure 2
Figure 2
Global ancestral enrichment with prognostic biomarkers in UM. (A) Box plots of ancestral percentages of individuals grouped by their self-reported ethnicity/race. (B) Violin plots of ancestral percentages of patients stratified by their respective PRAME status. The q values were calculated using Wilcoxon rank sum test and adjusted for multiple testing.
Figure 3
Figure 3
Local ancestry and admixture mapping. (A) Local ancestry karyograms for representative Latino patients with UM demonstrating the complexity of genetic ancestry in this population. EU, European; AFR, West African; EA, East Asian; NAT, Native American; UNK, unknown. (B) Manhattan plot of ADMIXTURE algorithm by Gene Expression Profile (GEP) class assignment (class 1 versus 2 status). (C) Manhattan plot of ADMIXTURE algorithm by PRAME status (negative versus positive). X-axis, chromosome position; Y-axis, −log10 (p value) for the association between biomarker (GEP Class or PRAME status) and local ancestry at each variant, correcting for sex, age, and global European and Native American ancestry. Each dot represents a single nucleotide polymorphism tested in the association test. Horizontal dashed lines represent enrichment significance threshold, p < 0.005.
Figure 4
Figure 4
Expression quantitative trait loci (eQTL) analysis. Circos plots showing links between eQTL variants and the cis and trans genes they are affecting. Chromosomes are arranged in a circle as indicated. Lines of the same color are associated with the same set of variants (represented by the root of each group of lines). (A) genes affected by the five variant loci associated with GEP assignment (class 1 versus class 2). (B) genes affected by the two variant loci associated with PRAME status (negative versus positive).
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