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.