Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer
Jing Sun, Yue Liu, Jianhui Zhao, Bin Lü, Siyun Zhou, Lu Wei, Jingsun Wei, Yeting Hu, Xiangxing Kong, Junshun Gao, Hong Guan, Junli Gao, Qian Xiao, Xue Li
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. Non-invasive approaches for risk stratification and screening remain crucial for colorectal cancer (CRC). Here, the authors develop a model for CRC risk stratification by integrating plasma proteomics data with polygenic risk score and QCancer-15 risk score, which could guide recommendations for earlier screening of high-risk populations.