光动力疗法
阿霉素
联合疗法
结直肠癌
医学
癌症研究
癌症
药理学
化疗
肿瘤科
放射治疗
转移
免疫系统
内科学
免疫学
化学
有机化学
作者
Ushasri Chilakamarthi,Namita S. Mahadik,Koteshwar Devulapally,Narra Vamsi Krishna,Lingamallu Giribabu,Rajkumar Banerjee
标识
DOI:10.1016/j.jphotobiol.2022.112625
摘要
Photodynamic therapy (PDT) is a promising non-invasive treatment modality for cancer and can be potentiated by combination with chemotherapy. Here, we combined PDT of novel porphyrin-based photosensitizers with low dose doxorubicin (Dox) to get maximum outcome. Dox potentiated and showed synergism with PDT under in vitro conditions on CT26.WT cells. The current colon cancer treatment strategies assure partial or even complete tumour regression but loco-regional relapse or distant metastasis is the major cause of death despite combination therapy. The spared cells after the treatment contribute to relapse and it is important to study their behaviour in host environment. Hence, we developed relapse models for PDT, Dox and combination treatments by transplanting respectively treated equal number of live cells to mice (n = 5) for tumour formation. Most of the treated cells lost tumour forming ability, but some treatment resistant cells developed tumours in few mice. These tumours served as relapse models and Western blot analysis of tumour samples provided clinically relevant information to delineate resistance strategies of individual as well as combination therapies at molecular level. Our results showed that low dose Dox helped in increasing the tumour inhibiting effect of PDT in combination therapy, but still there are indeed possibilities of relapse at later stages due to chemoresistance and immune suppression that may occur post-treatment. We observed that the combination therapy may also lead to the development of multidrug resistant (MDR) phenotype during relapse. Thus, this study provided clinically relevant information to further strengthen and improve PDT-drug combination therapy in order to avoid relapse and to treat cancer more effectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI