A machine learning (ML) model incorporating both clinical and genomic factors outperformed models based solely on either clinical or genomic data in predicting which patients with hormone receptor (HR)-positive, HER2-negative metastatic breast cancer would have better outcomes from adding CDK4/6 inhibitors to endocrine therapy as first-line...
American Association for Cancer Research Dec 14 2024 A machine learning model incorporating both clinical and genomic factors outperformed models based solely on either clinical or genomic data in predicting which patients with hormone receptor -positive, HER2-negative metastatic breast cancer would have better outcomes from adding CDK4/6 inhibitors to endocrine therapy as first-line treatment, according to results presented at the San Antonio Breast Cancer Symposium , held December 10-13, 2024.
Pedram Razavi, MD, PhD, Scientific Director, Global Research Program at Memorial Sloan Kettering Cancer Center The models trained on CF and GF each identified three risk groups: high, intermediate, and low, with median PFS of 6.3, 15.2, and 24.5 months for CF, and 9.9, 18.1, and 23.1 months for GF, respectively.
The clinical and genomic factors that were selected by the ML model were mainly those known to be associated with outcomes or resistance to either CDK4/6 inhibitors or endocrine therapy. Key genomic predictors of poor outcomes were biologically plausible alterations such as TP53 loss, MYC amplifications, PTEN loss, RTK-MAPK pathway alterations, RB1 loss, whole genome doubling, and high proportion of loss of heterozygosity.
Cancer Machine Learning Diagnostics Endocrine Genetics Genomic Hormone Metastasis Oncology Receptor Research Therapeutics Tumor
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