DNA-Based Deep Learning and Association Studies for Drug Response Prediction in Leiomyosarcoma

Abstract Leiomyosarcoma (LMS) is a rare and aggressive soft tissue sarcoma with limited treatment options and poor prognosis. Standard therapies, including doxorubicin, gemcitabine, trabectedin, and pazopanib, demonstrate variable efficacy across patients, underscoring the need for predictive biomarkers and computational models to inform personalized therapy. We developed a deep learning framework using DNA mutation and expression data to predict multi-task binary drug responses in LMS. Feedforward neural networks (FNNs) and transformer-based models were trained with binary cross-entropy (BCE) and weighted BCE (WBCE) loss functions to address class imbalance. In addition to predictive modeling, we conducted statistical association studies to identify links between genomic alterations and drug sensitivity, and performed Kaplan–Meier survival analyses to assess the prognostic relevance. Transformer models outperformed FNN baselines, achieving an overall F1-score of 0.87. Association studies revealed biologically meaningful links: TP53 mutations correlated with doxorubicin resistance, RB1 deletions with gemcitabine non-response, ATRX mutations with poor pazopanib outcomes, and MDM2 amplification with trabectedin resistance. This study demonstrates the utility of DNA-driven deep learning combined with association studies for predicting drug responses in LMS. Our framework not only provides multi-task binary predictions but also yields biologically interpretable associations for the targeted DNAs, highlighting key genomic drivers of therapy resistance. These findings support the development of precision oncology strategies for this rare and challenging cancer. 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 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 Data Availability All data produced in the present study are available upon reasonable request to the authors