Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach

• Published in: Computer methods and programs in biomedicine Vol. 257; p. 108432
• Main Authors: Karathanasis, Nestoras, Papasavva, Panayiota L., Oulas, Anastasis, Spyrou, George M
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.12.2024
• Subjects: Acute Myeloid Leukemia; Drug response; Machine Learning; AML; TARGET; Drug response; CPM; DRUML; cvMSE; RPKM; auc_hat; Machine Learning; TCGA; AUC; AlloSCT; Acute Myeloid Leukemia; HMAs; LASSO; mean_spearman; mean_pearson; MSE; outercvMSE; mean_outercvMSE
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2024.108432

•Machine learning can predict response to treatment in acute myeloid leukemia patients.•Our method uses molecular and clinical data to predict ex vivo drug sensitivity for 122 drugs in the BeatAML dataset.•We combined clinical and RNA sequencing data to report a pearson correlation of 0.36 across all drugs, with our best-performing model achieving a pearson correlation of 0.67.•We utilized our models’ predictions to generate a drug sensitivity score, which ranks an individual's anticipated response to treatment.•We identified a more potent drug than the administered one for 88 % (78 out of 89) of the patients examined. The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents for most mutational events, implementing individualized treatment for AML has proven difficult. We reanalysed the BeatAML dataset employing Machine Learning algorithms. The BeatAML project entails patients extensively characterized at the molecular and clinical levels and linked to drug sensitivity outputs. Our approach capitalizes on the molecular and clinical data provided by the BeatAML dataset to predict the ex vivo drug sensitivity for the 122 drugs evaluated by the project. We utilized ElasticNet, which produces fully interpretable models, in combination with a two-step training protocol that allowed us to narrow down computations. We automated the genes’ filtering step by employing two metrics, and we evaluated all possible data combinations to identify the best training configuration settings per drug. We report a Pearson correlation across all drugs of 0.36 when clinical and RNA sequencing data were combined, with the best-performing models reaching a Pearson correlation of 0.67. When we trained using the datasets in isolation, we noted that RNA Sequencing data (Pearson: 0.36) attained three times the predictive power of whole exome sequencing data (Pearson: 0.11), with clinical data falling somewhere in between (Pearson 0.26). Lastly, we present a paradigm of clinical significance. We used our models’ prediction as a drug sensitivity score to rank an individual's expected response to treatment. We identified 78 patients out of 89 (88 %) that the proposed drug was more potent than the administered one based on their ex vivo drug sensitivity data. In conclusion, our reanalysis of the BeatAML dataset using Machine Learning algorithms demonstrates the potential for individualized treatment prediction in Acute Myeloid Leukemia patients, addressing the longstanding challenge of treatment personalization in this disease. By leveraging molecular and clinical data, our approach yields promising correlations between predicted drug sensitivity and actual responses, highlighting a significant step forward in improving therapeutic outcomes for AML patients.