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Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer

Author

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  • Jing Sun

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Zhejiang University School of Medicine)

  • Yue Liu

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education
    Zhejiang Provincial Clinical Research Center for CANCER
    Cancer Center of Zhejiang University)

  • Jianhui Zhao

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Zhejiang University School of Medicine)

  • Bin Lu

    (The Second Affiliated Hospital of Zhejiang University School of Medicine)

  • Siyun Zhou

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Zhejiang University School of Medicine)

  • Wei Lu

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education
    Zhejiang Provincial Clinical Research Center for CANCER
    Cancer Center of Zhejiang University)

  • Jingsun Wei

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education
    Zhejiang Provincial Clinical Research Center for CANCER
    Cancer Center of Zhejiang University)

  • Yeting Hu

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education
    Zhejiang Provincial Clinical Research Center for CANCER
    Cancer Center of Zhejiang University)

  • Xiangxing Kong

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education
    Zhejiang Provincial Clinical Research Center for CANCER
    Cancer Center of Zhejiang University)

  • Junshun Gao

    (Cosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School
    Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province)

  • Hong Guan

    (Cosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School
    Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province)

  • Junli Gao

    (Cosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School
    Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province)

  • Qian Xiao

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education
    Zhejiang Provincial Clinical Research Center for CANCER
    Cancer Center of Zhejiang University)

  • Xue Li

    (The Second Affiliated Hospital, Zhejiang University School of Medicine
    Zhejiang University School of Medicine)

Abstract

This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E–03 in training cohort; 0.75 vs 0.69, P = 5.49E–04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening.

Suggested Citation

  • Jing Sun & Yue Liu & Jianhui Zhao & Bin Lu & Siyun Zhou & Wei Lu & Jingsun Wei & Yeting Hu & Xiangxing Kong & Junshun Gao & Hong Guan & Junli Gao & Qian Xiao & Xue Li, 2024. "Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52894-2
    DOI: 10.1038/s41467-024-52894-2
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    References listed on IDEAS

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    1. Qi Zhao & Feng Wang & Yan-Xing Chen & Shifu Chen & Yi-Chen Yao & Zhao-Lei Zeng & Teng-Jia Jiang & Ying-Nan Wang & Chen-Yi Wu & Ying Jing & You-Sheng Huang & Jing Zhang & Zi-Xian Wang & Ming-Ming He & , 2022. "Comprehensive profiling of 1015 patients’ exomes reveals genomic-clinical associations in colorectal cancer," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    3. Minta Thomas & Yu-Ru Su & Elisabeth A. Rosenthal & Lori C. Sakoda & Stephanie L. Schmit & Maria N. Timofeeva & Zhishan Chen & Ceres Fernandez-Rozadilla & Philip J. Law & Neil Murphy & Robert Carreras-, 2023. "Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Linda Kachuri & Rebecca E. Graff & Karl Smith-Byrne & Travis J. Meyers & Sara R. Rashkin & Elad Ziv & John S. Witte & Mattias Johansson, 2020. "Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    5. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    6. Benjamin B. Sun & Joshua Chiou & Matthew Traylor & Christian Benner & Yi-Hsiang Hsu & Tom G. Richardson & Praveen Surendran & Anubha Mahajan & Chloe Robins & Steven G. Vasquez-Grinnell & Liping Hou & , 2023. "Plasma proteomic associations with genetics and health in the UK Biobank," Nature, Nature, vol. 622(7982), pages 329-338, October.
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