Tissue TGF-β expression following conventional radiotherapy and pulsed low-dose-rate radiation.
Joshua E MeyerNiklas K FinnbergLili ChenDusica CvetkovicBin WangLanlan ZhouYanqun DongMark A HallmanChang-Ming C MaWafik S El-DeiryPublished in: Cell cycle (Georgetown, Tex.) (2017)
The release of inflammatory cytokines has been implicated in the toxicity of conventional radiotherapy (CRT). Transforming growth factor β (TGF-β) has been suggested to be a risk marker for pulmonary toxicity following radiotherapy. Pulsed low-dose rate radiotherapy (PLDR) is a technique that involves spreading out a conventional radiotherapy dose into short pulses of dose with breaks in between to reduce toxicities. We hypothesized that the more tolerable toxicity profile of PLDR compared with CRT may be related to differential expression of inflammatory cytokines such as TGF-β in normal tissues. To address this, we analyzed tissues from mice that had been subjected to lethal doses of CRT and PLDR by histology and immunohistochemistry (IHC). Equivalent physical doses of CRT triggered more cellular atrophy in the bone marrow, intestine, and pancreas when compared with PLDR as indicated by hematoxylin and eosin staining. IHC data indicates that TGF-β expression is increased in the bone marrow, intestine, and lungs of mice subjected to CRT as compared with tissues from mice subjected to PLDR. Our in vivo data suggest that differential expression of inflammatory cytokines such as TGF-β may play a role in the more favorable normal tissue late response following treatment with PLDR.
Keyphrases
- transforming growth factor
- early stage
- low dose
- epithelial mesenchymal transition
- bone marrow
- locally advanced
- cardiac resynchronization therapy
- radiation induced
- radiation therapy
- poor prognosis
- gene expression
- high fat diet induced
- oxidative stress
- mesenchymal stem cells
- high dose
- rectal cancer
- big data
- heart failure
- mental health
- left ventricular
- electronic health record
- squamous cell carcinoma
- machine learning
- type diabetes
- combination therapy
- adipose tissue
- deep learning
- wild type
- artificial intelligence
- replacement therapy
- oxide nanoparticles