Person: Armisen, Ricardo
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Armisen
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Ricardo
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Publication SKI regulates rRNA transcription and pericentromeric heterochromatin to ensure centromere integrity and genome stability(2025) Pola, Víctor; Carrero, David; Sagredo, Eduardo; Inostroza, Víctor; Cappelli, Claudio; Rivas, Solange; Bitrán, Mirita; Zambrano, Evelyn; Gonzalez, Evelin; Morales, Fernanda; Manterola, Marcia; Montecino, Martín; Armisen, Ricardo; Marcelain, KatherineAccurate chromosome segregation and ribosomal gene expression silencing are essential for maintaining genome integrity, and disruptions in these processes are key for oncogenesis and cancer progression. Here, we demonstrate a novel role for the transcriptional co-repressor SKI in regulating rDNA and pericentromeric heterochromatin (PCH) silencing in human cells. We found that SKI localizes to the rDNA promoter on acrocentric chromosomes and is crucial for maintaining H3K9 trimethylation (H3K9me3) and repressing 45S rRNA gene expression. SKI is also associated with BSR and HSATII satellites within PCH, where is necessary for H3K9 methylation and recruitment of SUV39H1 and HP1α, key players for heterochromatin silencing and centromere function. Consequently, SKI deficiency disrupted centromere integrity and resulted in aberrant chromosome segregation, micronuclei formation, and chromosome instability. The identification of SKI as a key participant in the epigenetic-mediated silencing of pericentromeric and ribosomal DNA provides a fundamental insight, paving the way for new research into the intricate relationship between transcriptional regulation and genome instability during cancer progression, and opening novel opportunities for therapeutic intervention.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.