Genomic machine learning model predicts radiation therapy benefit in early-stage breast cancer patients with high accuracy

Abstract Background: Radiation therapy (RT) is frequently recommended for post-surgery treatment of early-stage breast cancer (BC) patients, though not all benefit. Clinical factors currently guide RT treatment decisions. This work presents a high-accuracy genomic machine learning (ML) model to predict RT-benefit in early-stage BC patients and a novel method for selecting genomic features for ML algorithms. Methods: Gene expression data from 477 early-stage BC patients treated with surgery and RT from the METABRIC cohort were obtained. Wilcoxon Rank Sum (Wilcox RS) test and Cox Proportional Hazards (Cox PH) were used to reduce the number of genes used to train 8 ML algorithms. Each ML algorithm was trained on a random subset of 80% of the data using 10-fold cross-validation and tested on the remaining 20% to assess its performance in predicting relapse status within 30 years. Results: The genomic data were reduced using Wilcox RS and Cox PH to a 1,596 gene set and a 977 gene set. These gene sets when used to train the 8 ML algorithms resulted in models that ranged in performance accuracies from 54.01% to 95.6%. The highest accuracies were obtained using Support Vector Machine (SVM977 - 93.41%, SVM1596 - 95.6%) and Neural Networks (NN977 - 92.31%, NN1596 - 93.41%). The accuracy of all models when tested on RT-untreated patients was 30-40% lower compared to RT-treated patients. SVM977 had the highest sensitivity of 91.09%. Members of the 977 gene set were enriched with genes involved in the cell cycle and differentiation as well as radiogenes. Conclusion: We developed an SVM model that used 977 differentially expressed genes as features that predicted RT-benefit in early-stage BC patients with 93.41% accuracy and 91.09% sensitivity. We also developed a novel genomic feature selection approach that used Wilcoxon RS followed by Cox PH that resulted in expression values of only 4% of all genes being used as features in the models. Citation Format: Kimberly Badal, Jerome Foster, Rajini Haraksingh, Melford John. Genomic machine learning model predicts radiation therapy benefit in early-stage breast cancer patients with high accuracy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-19-05.