A recent study published in Nature Communications highlights the potential of combining plasma proteomic and polygenic profiling to improve risk stratification and personalize screening strategies for colorectal cancer (CRC). The research, involving a two-stage design with a discovery cohort from China and a validation cohort from the UK Biobank (UKBB), demonstrates that integrating protein biomarkers with genetic risk scores enhances the accuracy of CRC risk prediction.
Enhanced Risk Prediction Model
The study developed a combined model incorporating a protein risk score (ProS) based on 15 CRC-related proteins, a polygenic risk score (PRS) derived from genome-wide association studies, and the QCancer-15, a clinical risk assessment tool. This integrated approach demonstrated superior performance in predicting CRC risk compared to using QCancer-15 alone. The area under the receiver operating characteristic curve (AUC) was used to assess model discrimination.
Identification of Key Protein Biomarkers
The researchers identified a panel of 15 proteins that were significantly associated with CRC risk. These proteins were selected through a rigorous two-stage process involving proteome-wide differential expression analysis and Cox proportional hazards modeling. The levels of these proteins in plasma samples were used to construct the ProS, which was then integrated with the PRS and QCancer-15.
Potential for Personalized Screening
One of the key findings of the study is the potential for risk-adapted screening strategies. By stratifying individuals into different risk groups based on their combined proteomic and polygenic risk scores, it may be possible to tailor the starting age of screening. For example, individuals in the high-risk group could begin screening earlier than the currently recommended age of 50, while those in the low-risk group could potentially delay screening.
The study estimated risk-adapted starting ages of screening based on the 10-year cumulative risk of CRC. The researchers defined the risk-adapted starting age as the age at which individuals with a particular risk level reached a similar 10-year cumulative risk as the general population at age 50.
Study Design and Participants
The discovery stage of the study included 150 newly diagnosed CRC cases and 50 age- and sex-matched controls from the Second Affiliated Hospital of Zhejiang University School of Medicine. The validation cohort comprised 52,231 individuals (731 CRC incident cases, 51,500 controls) aged 39–70 years from the UKBB.
Plasma proteomics measurements were performed using iodo Tandem Mass Tags (TMT)-6plex quantitative proteomics in the discovery cohort and Olink Proximity Extension Assay in the UKBB cohort. Statistical analyses included differential expression analysis, Cox proportional hazards modeling, and decision curve analysis.
Clinical Utility and Implications
The decision curve analysis revealed that the combined model (QCancer-S + PRS + ProS) offered a greater net benefit in selecting individuals for screening colonoscopy compared to using QCancer-S alone. This suggests that incorporating proteomic and polygenic information into risk assessment could lead to more effective and personalized CRC screening strategies.
"The integration of proteomic and polygenic data holds promise for improving CRC risk stratification and tailoring screening approaches to individual risk profiles," the authors stated. "Further research is needed to validate these findings in diverse populations and to assess the clinical impact of implementing risk-adapted screening strategies based on combined proteomic and polygenic risk scores."
