Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.
Qing LiQingyuan SongZhishan ChenJungyoon ChoiVictor MorenoJie PingWanqing WenChao LiXiang ShuJun YanXiao-Ou ShuQiuyin CaiJirong LongJeroen R HuygheRish PaiStephen B GruberGraham CaseyXusheng WangAdetunji T ToriolaLi LiBhuminder SinghKen S LauLi ZhouChong WuUlrike PetersWei ZhengQuan LongZhijun YinXingyi GuoPublished in: medRxiv : the preprint server for health sciences (2024)
Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.
Keyphrases
- electronic health record
- papillary thyroid
- clinical decision support
- squamous cell
- high resolution
- randomized controlled trial
- genome wide association
- genome wide
- prostate cancer
- adverse drug
- protein protein
- childhood cancer
- lymph node metastasis
- mass spectrometry
- squamous cell carcinoma
- air pollution
- risk assessment
- drug induced
- amino acid
- data analysis
- young adults
- gene expression
- dna methylation
- deep learning
- small molecule
- cancer therapy
- genome wide association study