From Anti-PD-1/PD-L1 to CTLA-4 and to MUC1-Is the Better Response to Treatment in Smokers of Cancer Patients Drug Specific?
Lishi WangFengxia LiuJing LiLi MaHelin FengQingyi LiuWilliam Chi Shing ChoHai-Yong ChenHong ChenHua GuoZhujun LiScott C HowardMinghui LiBaoen ShanWeikuan GuJiafu JiPublished in: Journal of personalized medicine (2021)
Whether smokers respond to anti-cancer drugs differently than non-smokers remains controversial. The objective of this study is to explore whether the better response of the smokers is specific to therapy of anti-PD-1/PD-L1, anti-checkpoint inhibitor, individual drugs on the cell surface, or lung cancer. Our results showed that among all non-small cell lung cancer (NSCLC) patients, when the data from anti-PD-1/PD-L1, anti-CTLA-4, and anti-MUC1 drugs are combined, the mean hazard ratios (HR) of smokers and non-smokers were 0.751 and 1.016, respectively. A meta-analysis with a fixed effect (FE) model indicated that the smokers have an HR value of 0.023 lower than that of the non-smokers. A stratified subgroup meta-analysis indicated that when treated with anti-CTLA-4 drugs, smokers had reduced HR values of 0.152 and 0.165 on average and FE model meta-analysis, respectively. When treated with an anti-MUC1 drug, smokers had reduced HR values of 1.563 and 0.645, on average and FE model meta-analysis, respectively. When treated with a combination of nivolumab and ipilimumab drugs, smokers had, on average, reduced HR and FE model meta-analysis values (0.257 and 0.141), respectively. Smoking is a clinical response predictor for anti-PD/PD-L1 monotherapy or first-line treatment in lung, urothelial carcinoma, and head and neck cancer. Smokers treated with other drugs have shown worse responses in comparison to non-smokers. These data suggest that, along with the progress in the development of new drugs for cancer, drugs acting on specific genotypes of smokers likely will arise.
Keyphrases
- smoking cessation
- systematic review
- replacement therapy
- small cell lung cancer
- newly diagnosed
- stem cells
- randomized controlled trial
- emergency department
- electronic health record
- squamous cell carcinoma
- clinical trial
- drug induced
- dna damage
- young adults
- machine learning
- deep learning
- cell proliferation
- cell surface
- bone marrow
- end stage renal disease
- artificial intelligence
- lymph node metastasis
- patient reported outcomes
- papillary thyroid
- cell cycle
- metal organic framework
- brain metastases