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Colorectal cancer risk prediction using a simple multivariable model.

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New models accurately stratify colorectal cancer risk using polygenic risk scores and family history, enabling personalized screening for high-risk individuals. This approach improves early detection and risk-reduction strategies for better patient outcomes.

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Area of Science:

  • Genetics and Genomics
  • Epidemiology
  • Oncology

Background:

  • Accurate population stratification for colorectal cancer (CRC) risk is crucial for targeted screening and risk-reducing interventions.
  • Existing risk prediction models may not fully capture individual CRC susceptibility.
  • The UK Biobank provides a large cohort for developing and validating robust risk prediction models.

Purpose of the Study:

  • To develop and validate novel risk prediction models for 10-year CRC risk in an unaffected UK population.
  • To compare the performance of multivariable and simplified models incorporating polygenic risk scores (PRS) and family history.
  • To identify individuals who could benefit from personalized screening and risk-reduction strategies.

Main Methods:

  • A population-based cohort study of nearly 400,000 UK Biobank participants (aged 40-69) with genetically determined UK ancestry.
  • Development of two models: (i) multivariable (family history, PRS, clinical factors) and (ii) simple (family history, PRS), separately for women and men.
  • Cox regression models were used for development (70% training data) and performance assessment (30% testing data) including discrimination (Harrell's C-index) and calibration.

Main Results:

  • The new multivariable models demonstrated superior discrimination compared to the simple models in the testing dataset (e.g., Harrell's C-index of 0.690 for women and 0.699 for men).
  • Both new models showed improved discrimination over existing models, with statistically significant changes (P=0.02 for women, P=0.01 for men).
  • The study identified specific subgroups of individuals at increased risk for colorectal cancer.

Conclusions:

  • The developed multivariable and simple risk prediction models effectively stratify individuals by their 10-year risk of colorectal cancer.
  • These models, incorporating polygenic risk scores and family history, offer improved discrimination for identifying at-risk individuals.
  • The findings support the implementation of personalized screening and risk-reduction strategies based on genetic and familial risk factors.