Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention

Qing Li¹, Qingyuan Song², Zhishan Chen³, Jungyoon Choi⁴, Victor Moreno⁵, Jie Ping³, Wanqing Wen³, Chao Li³, Xiang Shu⁶, Jun Yan⁷, Xiao-Ou Shu³, Qiuyin Cai³, Jirong Long³, Jeroen R Huyghe⁸, Rish Pai⁹, Stephen B Gruber¹⁰, Yaohua Yang¹¹, Graham Casey¹¹, Xusheng Wang¹², Adetunji T Toriola¹³, Li Li¹⁴, Bhuminder Singh¹⁵, Ken S Lau¹⁵, Li Zhou¹⁶, Zichen Zhang¹⁷, Chong Wu¹⁷, Ulrike Peters¹⁸, Wei Zheng³, Quan Long¹⁹, Zhijun Yin²⁰, Xingyi Guo²¹

Affiliations

¹ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada.
² Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
³ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴ Division of Oncology and Hematology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Ansan 15355, Korea.
⁵ Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, ONCOBELL Program, Institut de Recerca Biomedica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
⁶ Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁷ Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada.
⁸ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
⁹ Mayo Clinic Arizona, Scottsdale, AZ, USA.
¹⁰ Department of Preventive Medicine & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
¹¹ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
¹² Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
¹³ Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine and Siteman Cancer Center, St. Louis, MO, USA.
¹⁴ Department of Family Medicine, School of Medicine, University of Virginia, Charlottesville, VA, USA.
¹⁵ Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
¹⁶ Harvard Medical School, Boston, MA, USA; Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹⁷ Department of Biostatistics, the University of Texas MD Anderson, Houston, TX, USA.
¹⁸ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
¹⁹ Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada; Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada.
²⁰ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA. Electronic address: zhijun.yin.1@vumc.org.
²¹ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address: xingyi.guo@vumc.org.

PMID: 41338217
PMCID: PMC12824608 (available on 2026-07-08)
DOI: 10.1016/j.ajhg.2025.11.008

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention

Qing Li et al. Am J Hum Genet. 2026.

. 2026 Jan 8;113(1):41-56.

doi: 10.1016/j.ajhg.2025.11.008. Epub 2025 Dec 2.

Authors

Affiliations

¹ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada.
² Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
³ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴ Division of Oncology and Hematology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Ansan 15355, Korea.
⁵ Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, ONCOBELL Program, Institut de Recerca Biomedica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
⁶ Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁷ Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada.
⁸ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
⁹ Mayo Clinic Arizona, Scottsdale, AZ, USA.
¹⁰ Department of Preventive Medicine & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
¹¹ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
¹² Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
¹³ Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine and Siteman Cancer Center, St. Louis, MO, USA.
¹⁴ Department of Family Medicine, School of Medicine, University of Virginia, Charlottesville, VA, USA.
¹⁵ Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
¹⁶ Harvard Medical School, Boston, MA, USA; Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹⁷ Department of Biostatistics, the University of Texas MD Anderson, Houston, TX, USA.
¹⁸ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
¹⁹ Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada; Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada.
²⁰ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA. Electronic address: zhijun.yin.1@vumc.org.
²¹ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address: xingyi.guo@vumc.org.

PMID: 41338217
PMCID: PMC12824608 (available on 2026-07-08)
DOI: 10.1016/j.ajhg.2025.11.008

Abstract

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 (pQTLs) 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 conducted analyses of emulated trials for 11 drugs across 290 comparisons and identified three drugs significantly associated with reduced colorectal cancer risk: caffeine vs. paroxetine (hazard ratio [HR], 0.51; 95% confidence interval [CI], 0.41-0.64), haloperidol vs. prochlorperazine (HR, 0.47; 95% CI, 0.33-0.68), and trazodone hydrochloride vs. paroxetine (HR, 0.49; 95% CI, 0.38-0.63). Conversely, caffeine was associated with increased cancer risk in comparison with finasteride (colorectal cancer) and fluoxetine (breast cancer). Meta-analysis identified six drugs significantly associated with cancer risk, including acetazolamide, which was associated with reduced colorectal cancer risk (HR, 0.79; 95% CI, 0.72-0.87). This study identifies previously unreported protein biomarkers and candidate drug targets across six major cancer types and highlights several approved drugs with potential chemopreventive effects.

Keywords: EHR; GWAS; acetazolamide; caffeine; drug trial emulation; human cancers; pQTL; proteomics; risk variants; therapeutic drug.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Update of

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention.
Li Q, Song Q, Chen Z, Choi J, Moreno V, Ping J, Wen W, Li C, Shu X, Yan J, Shu XO, Cai Q, Long J, Huyghe JR, Pai R, Gruber SB, Yang Y, Casey G, Wang X, Toriola AT, Li L, Singh B, Lau KS, Zhou L, Zhang Z, Wu C, Peters U, Zheng W, Long Q, Yin Z, Guo X. Li Q, et al. medRxiv [Preprint]. 2025 Jul 11:2024.05.29.24308170. doi: 10.1101/2024.05.29.24308170. medRxiv. 2025. Update in: Am J Hum Genet. 2026 Jan 8;113(1):41-56. doi: 10.1016/j.ajhg.2025.11.008. PMID: 38853880 Free PMC article. Updated. Preprint.

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Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention

Affiliations

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention

Authors

Affiliations

Abstract

Conflict of interest statement

Update of

References

MeSH terms

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LinkOut - more resources

Full Text Sources

Medical