ORIGINAL RESEARCH article
Front. Cell. Infect. Microbiol.
Sec. Clinical Infectious Diseases
Volume 15 - 2025 | doi: 10.3389/fcimb.2025.1629422
This article is part of the Research TopicUnravelling Host-Pathogen Interactions in Bacterial Infection: Insights from Omics and Machine LearningView all 9 articles
Utilization of Machine Learning to Predict Antibiotic Resistant Event Outcomes in Acute Myeloid Leukemia Patients Undergoing Induction Chemotherapy
Provisionally accepted
- Texas A and M University, College Station, United States
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Introduction: Acute myeloid leukemia (AML) patients are highly susceptible to infection. Moreover, prophylactic and empirical antibiotic treatment during chemotherapy disrupts the gut microbiome, raising the risk for antibiotic-resistant (AR) opportunistic pathogens. There is limited data on risk factors for AR infections or colonization events in treated cancer patients, and no predictive models exist. This study aims to combine metagenomic and antibiotic administration data to develop a model predicting AR event outcomes. Methods: Baseline stool microbiome, antibiotic administration, resistome, and clinical metadata from 95 patients were utilized to build a Random Forest model to predict AR infection and colonization events by serious AR threats. Additionally, sparse canonical correlation analysis assessed correlations between microbiome and resistome data, while Spearman correlation networks identified direct associations with AR event outcomes and secondary variables. Results: AR-events were identified in 14 of the 95 included patients, with 8 developing AR infections and 9 identified as AR colonized. A Random Forest model predicted AR event outcomes (AUC = 0.73), identifying bacterial taxa and antibiotic resistance gene (ARG) classes as key variables of importance. Methanobrevibacter smithii, Clostridium leptum, and Bacteroides dorei were identified as key taxa associated with reduced risk of AR events, suggesting the potential roles of commensals in maintaining gut microbial resilience during chemotherapy. ARG classes, particularly those conferring resistance to lincosamides, macrolides, and streptogramins, were negatively associated with AR events. Conclusion: These results underscore the value of integrating microbiome and resistome features to reveal potential protective mechanisms and improve risk prediction for AR outcomes in vulnerable patients.
Keywords: antibiotic resistance, Acute Myeloid Leukemia, Random-Forest, microbiome, Resistome (Min.5-Max. 8)
Received: 15 May 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 McMahon, Franklin and Galloway-Pena. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jessica Galloway-Pena, Texas A and M University, College Station, United States
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