Person: Armisen, Ricardo
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Armisen
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Ricardo
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Publication Multimorbidity profile among cancer-related hospitalization events in younger and older patients: a large-scale nationwide cross-sectional study(2025) Bernal, Yanara; Campaña, Carla; Sanhueza, Cristobal; Apablaza, Mauricio; Armisen, Ricardo; Delgado, IrisBackground Multimorbidity, the coexistence of two or more chronic diseases, among cancer patients offers critical insights into shared risk factors, while posing increasing challenges for healthcare systems due to the complexity of care required. Despite its relevance, research in multimorbidity across different age groups is limited in middle income countries. Methods We analyzed cancer-related hospitalizations between 2019 and 2023, using a nationwide Diagnosis-Related Groups database covering 68 Chilean health institutions. We examined the distribution of 40 chronic conditions, multimorbidity prevalence, comorbidity profile, and their distribution across age group, sex, and cancer diagnosis. Findings We identified 4,722,723 hospitalization events, including 149,270 unique adult patients hospitalized with cancer (mean of 63 ± 15.17 years old). Multimorbidity was present in 47.9% of all cancer-related hospitalizations, increasing steeply with age: 14% in patients aged 18–35, 24.9% in those 36–50, and 55.5% in patients >50 years. Obesity and diabetes were among the most common comorbid conditions across age groups, with significant variations by sex. Notably, obesity was more prevalent in younger patients, particularly those aged 18–35, whereas hypertension showed an inverse profile, increasing markedly with age. Interpretation Multimorbidity profile reflect both the clinical complexity of cancer care and potential shared biological and environmental pathways in carcinogenesis. These findings highlight the need to transition from diseasecentered to person-centered care models. In Chile, understanding multimorbidity in younger and middle-aged adults may inform precision prevention, integrated service delivery, and equitable planning for both oncologic and non-oncologic care. Funding This study was conducted without external funding.Publication Integration of RNA Editing into Multiomics Machine Learning Models for Predicting Drug Responses in Breast Cancer Patients(2026) Bernal, Yanara; Blanco, Alejandro; Oróstica, Karen; Delgado, Iris; Armisen, RicardoBackground: The integration of multi-omics data, such as genomics and transcriptomics, into artificial intelligence models has advanced precision medicine. However, their clinical applicability remains limited due to model complexity. We integrated DNA mutation, RNA expression, and A>I(G) RNA editing data to develop a predictive model for drug response in breast cancer. Methods: We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (ClinicalTrials.gov: NCT02022202). Clinical variables, gene expression, tumor and germline DNA variants, and RNA editing features were integrated into machine learning models to predict therapy response. Generalized linear models (GLM), random forest (RF), and support vector machines (SVM) were trained and evaluated across multiple random 70/30 train-test splits. Feature selection was performed exclusively within the training set using LASSO regularization. Model performance was assessed using the F1-score on independent test sets. The additive effect of RNA editing was evaluated using paired comparisons across identical train/test splits. Results: We characterized the cohort using clinical, mutational, transcriptomic, and RNA editing profiles in 69 non-responders and 35 responders. Across repeated splits, adding RNA editing frequently maintained or modestly improved predictive performance, particularly in expression-based models, with paired analyses showing a statistically significant increase in F1-score. Conclusions: RNA editing represents a complementary molecular layer that can enhance multi-omic models for therapy response prediction in breast cancer, supporting further investigation of epitranscriptomic features in precision oncology.