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Aggressive variant prostate cancer (AVPC) is characterized by a molecular signature involving combined defects in TP53 , RB1 , and/or PTEN (AVPC-TSGs), identifiable through immunohistochemistry or genomic analysis. The reported prevalence of AVPC-TSG alterations varies widely, reflecting differences in assay sensitivity, treatment pressure, and disease stage evolution. Although robust clinical evidence is still emerging, the study of AVPC-TSG alterations in prostate cancer (PCa) is promising. Alterations in TP53 , RB1 , and PTEN , as well as the combined loss of AVPC-TSGs, may have significant implications for prognosis and treatment. These biomarkers might help predict responses to various therapies, including hormonal treatments, cytotoxic agents, radiotherapy, and targeted therapies. Understanding the impact of these molecular alterations in patients with PCa is crucial for personalized management. In this review, we provide a comprehensive overview of the emerging prognostic and predictive roles of AVPC-TSG alterations across PCa stages. Moreover, we discuss the implications of different methods used for detecting AVPC-TSG alterations and summarize factors influencing their prevalence. As our comprehension of the genomic landscape of PCa disease deepens, incorporating genomic profiling into clinical decision making will become increasingly important for improving patient outcomes.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.