Polygenic risk scores in assessing lung cancer susceptibility in non-Hispanic White and Black populations

Document Type

Conference Proceeding

Publication Date

6-1-2023

Publication Title

J Clin Oncol

Abstract

Background: Polygenic risk scores (PRS) have become an increasingly popular approach to evaluate cancer susceptibility, but have not adequately represented Black patients in model development. We used previously identified single nucleotide polymorphisms (SNPs) and annotated SNPs in associated gene regions to develop PRS in non-Hispanic Whites and Blacks using the INHALE dataset. Methods: Using the Multi-Ethnic Genotype Array, 1,204 SNPs for non-Hispanic Whites and 1,515 SNPs for Blacks were evaluated for their association with lung cancer risk adjusting for age, sex, total pack-years, family history of lung cancer, history of COPD and the top five PCs for genetic ancestry. Gene regionspecific significant SNPs (p<0.05) were used to develop race-specific PRS. Results: The race-specific PRS included different sets of significant SNPs and were highly associated with lung cancer risk in both non-Hispanic Whites (OR = 1.07, 95% CI: 1.05-1.09, p = 3.44x10-9) and Blacks (OR = 1.12, 95% CI: 1.08-1.17, p = 9.14x10-8). These models remained significant for both Whites (OR = 1.05, 95% CI: 1.03-1.09, p = 0.0004) and Blacks (OR = 1.08, 95% CI: 1.01-1.15, p = 0.01) who currently do not meet USPSTF screening guidelines. AUC analysis demonstrated the Black-specific model (AUC = 0.68) outperformed the White-specific model (AUC = 0.57) (p = 0.03) when examined exclusively in the Black cohort. Conclusions: Using previously validated SNPs and gene regions, we developed racespecific PRS in non-Hispanic White and Black INHALE participants. Further validation of PRS could enable the incorporation of genetic risk modeling into lung cancer screening to identify patients who do not have traditional risk factors for lung cancer, as well as stratify patients into different levels of risk based on their genetic profile. Through the development of a reliable genetic risk factor prediction model, clinicians will have another method by which to evaluate lung cancer susceptibility, potentially leading to earlier diagnoses that portend more favorable treatment outcomes.

Medical Subject Headings

Hematology

PubMed ID

Not assigned.

Volume

41

Issue

16

First Page

10548

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