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Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer.

E Amiri Souri1, A Chenoweth2,3,4, A Cheung2,3,4

  • 1Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London, UK.

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Machine learning accurately classifies breast cancer prognostic types using gene expression, aiding clinical decisions. This Cancer Grade Model (CGM) improves risk stratification for better treatment, reducing under- and over-treatment.

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Area of Science:

  • Genomics
  • Computational Biology
  • Oncology

Background:

  • Accurate prognostic stratification of breast cancers is crucial for effective clinical decision-making.
  • Current methods face challenges in precisely categorizing tumor aggressiveness.
  • Identifying specific gene expression patterns linked to prognosis is an unmet need.

Purpose of the Study:

  • To develop a machine learning model for prognostic stratification of breast tumors.
  • To link specific gene expression profiles to histological grade and tumor aggressiveness.
  • To classify tumors into distinct prognostic types for improved clinical management.

Main Methods:

  • Integrated microarray data from 5031 untreated breast tumors across 33 datasets.
  • Trained a gradient boosted trees model (Cancer Grade Model, CGM) on histological grade-1 and grade-3 samples.
  • Applied the CGM to grade-2 and unknown-grade samples for prognostic risk classification.

Main Results:

  • Identified a 70-gene signature with 90% accuracy in assessing clinical risk on known histological-grade samples.
  • Validated the predictive framework through survival analysis, demonstrating robust prognostic performance.
  • CGM showed competitive predictive power compared to existing genomic tests.

Conclusions:

  • CGM effectively classifies tumors into well-defined prognostic categories without relying on tumor size, stage, or subgroups.
  • The model enhances prognosis and supports clinical decisions for precision treatments.
  • CGM has the potential to prevent underdiagnosis of high-risk tumors and minimize overtreatment of low-risk disease.