Back

Radiation-induced persistent DNA damage response and late toxicity in cardiac tissue

Shigemori, K.; Jiang, Y.; Martin, J. C.; Hawkins, M. A.; Ryan, A. J.; Parkes, E. E.

2023-02-28 cancer biology
10.1101/2023.02.27.530210 bioRxiv
Show abstract

BackgroundRadiotherapy treatment is a mainstay of cancer treatments including for thoracic malignancies such as lung or breast cancer. Cardiac toxicity is a recognised long-term complication of thoracic radiotherapy which persists despite improvements in therapeutic modalities. The mechanisms and potential therapeutic targets that could provide cardiac protection in the context of radiation therapy remain incompletely understood. Here we investigated early and late cardiac toxicity following irradiation using the A/J mouse model to identify potential molecular drivers. MethodsSingle doses of irradiation of either 13 or 15 Gy were delivered to female A/J mice aged 6 - 8 weeks using a SmART-PLAN system. ECG traces and analysis were performed on anaesthetised mice. Cardiac tissue was harvested up to 32 weeks following irradiation for histological analysis and second-harmonic imaging microscopy. ResultsCardiac RT resulted in cardiac conductivity abnormalities including prolonged QTc interval. Additionally, an increase in pericardial and perivascular fibrosis was noted with a marked increase in pericardial fibrosis at 15 Gy compared to 13 Gy. Persistent DNA damage response was identified in cardiomyocytes at 7 days post irradiation and polarisation of cardiac-infiltrating macrophages towards a CD206+ M2-like phenotype at the same timepoint. ConclusionsOur data introduce the A/J mouse as a novel model for the study of physiologically relevant early and late cardiac toxicities following irradiation. Early events that could contribute to long term toxicities include persistent DNA damage response and repolarisation of macrophages, further investigation of which could identify potential future cardioprotective therapeutic strategies.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.