Introduction
Prostate cancer (PCa) is the second most common solid tumor and the
fifth leading cause of cancer death in men. In 2020, there were over
1,414,000 estimated new cases of PCa worldwide1. It is
well established that significant racial disparities exist regarding PCa
incidence and mortality. African American Men (AAM) are 1.8 times more
likely to be diagnosed with PCa than men of European ancestry and they
also have 2.4 times higher mortality rate2. These
differences in the course of the disease and survival of patients with
PCa are frequently attributed to socioeconomic status and access to
medical care, but the cause of this increased PCa risk for AA men is
unclear3 4. Even if we adjust for
biases attributable to these racial disparities in PCa, incidence and
mortality rates remain significantly different among AAM and EAM;
suggesting an important contribution of molecular and genetic
factors5.
In addition to outcome differences between racial/ethnic groups , PCa
behaves heterogeneously from patient to patient, making the optimal
management strategy for this tumor a subject of ongoing
debate6. This is because the natural history of the
disease is still unknown, as well as what are the characteristics that
make it more aggressive in certain cases. To adequately treat these
patients, risk stratification models have been created to establish
prognosis biomarkers and predict the response to treatments. These
models have traditionally been based on clinical and analytical
parameters such as stage, Gleason differentiation grade and
prostate-specific antigen (PSA) value78 9. While these features are still
useful, their performance in many cases remains
suboptimal10. Advances in DNA sequencing and the study
of the human genome have made it possible to determine a series of
molecular factors that may influence the course of prostate cancer. In
the last decade, genome-wide association studies
(GWAS)11 have been utilized to translate findings of
risk SNPs towards clinical utility, to identify genetic predictors of
prostate cancer risk. For example, the polygenic risk score
(PRS)12 is calculated from the sum of the number of
risk alleles carried by an individual and weighting each one by its
estimated size from GWAS data. This model shows promise in identifying
individuals with much higher or lower lifetime risk than the average
male, and can also improve the predictive value of prostate-specific
antigen (PSA) screening13. For tumor analyses, the
Decipher Prostate Cancer Test is a genomic test that is based on the
expression of 22 RNA markers and serves as a prognostic marker in
patients who have undergone radical prostatectomy. This allows
post-surgical risk stratification and prediction of the probability of
metastasis and cancer-specific mortality to determine the need for
adjuvant treatment14. Furthermore, an increasing
number of somatic and germline tests are performed in patients with
prostate cancer as they determine hereditary risk and guide treatment
decisions in cancer15.
However, the studies behind these genomic applications lack racial
diversity. In this literature review, we outline the currently available
genomic applications to estimate the risk of individuals developing
prostate cancer and to identify precision oncology treatment strategies,
and how disparities have been approached using these applications.