Machine Learning for Predicting Therapeutic Outcomes in Acute Myeloid Leukemia Patients

Background and Objective 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. Methods 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. Results 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 health management 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. Conclusions 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. Highlights ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: the supplementary material of the initial publication, , under the section "Supplementary Tables" and can be found here: [https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-018-0623-z/MediaObjects/41586\_2018\_623\_MOESM3\_ESM.xlsx][1] I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at the supplementary material of the initial publication: , under the section "Supplementary Tables" and can be found here: [https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-018-0623-z/MediaObjects/41586\_2018\_623\_MOESM3\_ESM.xlsx][1] * (AML) : Acute Myeloid Leukemia (AlloSCT) : Allogeneic Stem Cell Transplantation (HMAs) : hypomethylating agents (AUC) : Area Under the dose-response Curve (TCGA) : The Cancer Genome Atlas (TARGET) : Therapeutically Applicable Research to Generate Effective Treatments (LASSO) : regularized regression modelling (DRUML) : Drug Ranking Using ML (CPM) : Counts Per Million (RPKM) : Read Per Kilobase Million (MSE) : the Mean Square Error (auc_hat) : AUC prediction (cvMSE) : cross validated MSE (nestedcvMSE) : nested cross-validation (mean_nestedcvMSE) : mean nestedcvMSE (mean_pearson) : mean Pearson (mean_spearman) : mean Spearman [1]: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-018-0623-z/MediaObjects/41586_2018_623_MOESM3_ESM.xlsx