Potentiating Salvage Radiotherapy in Radiorecurrent Prostate Cancer Through Anti-CTLA4 Therapy: Implications from a Syngeneic Model.
Hanzhi WangLinsey GongXiaoyong HuangStephanie D WhiteHans T ChungDanny VespriniTera N PetchinyEmmanouil FokasHansen HeRobert S KerbelStanley K LiuPublished in: Cancers (2024)
High-risk prostate cancer (PCa) is a leading cause in cancer death and can elicit significant morbidity and mortality. Currently, the salvage of local disease recurrence after radiation therapy (RT) is a major clinical problem. Immune checkpoint inhibitors (ICIs), which enhance immune activation, have demonstrated clinical therapeutic promise in combination with ionizing radiation (IR) in certain advanced cancers. We generated the TRAMP-C2 HF radiorecurrent syngeneic mouse model to evaluate the therapeutic efficacy of ICIs in combination with RT. The administration of anti-PDL1 and/or anti-CTLA4 did not achieve a significant tumor growth delay compared to the control. The combination of IR and anti-PDL1 did not yield additional a growth delay compared to IR and the isotype control. Strikingly, a significant tumor growth delay and complete cure in one-third of the mice were seen with the combination of IR and anti-CTLA4. Immune cells in tumor-draining lymph nodes and tumor-infiltrating lymphocytes from mice treated with IR and anti-CTLA4 demonstrated an upregulation of genes in T-cell functions and enrichment in both CD4+ and CD8+ T-cell populations compared to mice given IR and the isotype control. Taken together, these results indicate enhancement of T-cell response in radiorecurrent PCa by IR and anti-CTLA4.
Keyphrases
- prostate cancer
- radiation therapy
- lymph node
- mouse model
- stem cells
- early stage
- radical prostatectomy
- signaling pathway
- squamous cell carcinoma
- heart failure
- mesenchymal stem cells
- adipose tissue
- poor prognosis
- high fat diet induced
- bone marrow
- insulin resistance
- atrial fibrillation
- deep learning
- newly diagnosed
- artificial intelligence
- replacement therapy
- childhood cancer