We aim to determine whether incremental changes in genetic ancestry percentages influence molecular and clinical outcome characteristics of breast cancer in an admixed population.
Breast cancer patients are predominantly characterized as "Black" or "White" based on self-identified race/ethnicity or arbitrary genetic ancestry cutoffs. This limits scientific discovery in populations that are admixed or of mixed race/ethnicity as they cannot be classified based on historical race/ethnicity boxes or genetic ancestry cutoffs.
We used the TCGA cohort and focused on genetically admixed patients that had less than 90% European, African, Asian, or Native American ancestry.
Genetically admixed breast cancer patients exhibited improved 10-year overall survival relative to those with>90% European ancestry. Within the luminal A subtype, patients with lower African ancestry had longer 10-year overall survival compared to those with higher African ancestry. Correlation of genetic ancestry with gene expression and DNA methylation in the admixed cohort revealed novel ancestry-specific intrinsic PAM50 subtype patterns. In luminal A tumors, genetic ancestry was correlated with both the expression and methylation of signaling genes, while in basal-like tumors, genetic ancestry was correlated with stemness genes. In addition, we took a machine-learning approach to estimate genetic ancestry from gene expression or DNA methylation, and were able to accurately calculate ancestry values from a reduced set of 10 genes or 50 methylation sites that were specific for each molecular subtype.
Our results suggest that incremental changes in genetic ancestry percentages result in ancestry-specific molecular differences even between well-established PAM50 subtypes which may influence disparities in breast cancer survival outcomes. Accounting for incremental changes in ancestry will be important in future research, prognostication, and risk-stratification, particularly in ancestrally diverse populations.
Pharmacology, Toxicology and Pharmaceutical Science
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8%
Agricultural and Biological Sciences
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8%
Medicine and Dentistry
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8%
Neuroscience
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Other
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Unknown
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Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 December 2023.
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