Androgen Receptor Inhibition Enhances Immune Response in Prostate Cancer

A preclinical study has revealed that standard prostate cancer treatments may have an unexpected benefit: making tumors more visible to the immune system. Research published in Cancer Discovery suggests that inhibiting the androgen receptor (AR), a key driver of prostate cancer growth, could enhance the body’s ability to recognize and attack cancer cells. This finding could pave the way for new treatment strategies combining hormonal therapy with immunotherapy.

Prostate cancer is typically treated with androgen deprivation therapy (ADT) and androgen receptor signaling inhibitors (ARSi), which limit the cancer’s ability to use testosterone for growth. However, scientists have now discovered another effect of AR inhibition—it increases the presence of Major Histocompatibility Complex Class I (MHC-I) molecules on cancer cells. These molecules act as a kind of “flag” that allows immune cells, particularly T cells, to recognize and attack cancerous tissue. When MHC-I expression is low, tumors remain hidden from the immune system. But when AR is blocked, these cancer cells become easier for immune cells to detect and destroy.

This discovery is significant because prostate cancer has long been considered a “cold” tumor—one that does not provoke a strong immune response. Immunotherapies, such as checkpoint inhibitors that help T cells fight cancer, have had limited success in prostate cancer compared to other types of malignancies. However, if AR inhibition increases antigen presentation through MHC-I, it could help transform a cold tumor into a “hot” one, making immunotherapy much more effective.

For patients already undergoing ADT and ARSi, these findings suggest a potential advantage: their current treatment may be priming their cancer cells for an improved immune response. If further studies confirm this effect, it could lead to a shift in how advanced prostate cancer is treated, with combination therapies that integrate both hormonal and immune-based approaches.

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