Browsing by Author "Oróstica, Karen"
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Publication A Comprehensive Analysis of the Effect of A>I(G) RNA-Editing Sites on Genotoxic Drug Response and Progression in Breast Cancer(2024) Bernal, Yanara; Blanco, Alejandro; Sagredo, Eduardo; Oróstica, Karen; Alfaro, Ivan; Marcelain, Katherine; Armisén, RicardoDysregulated A>I(G) RNA editing, which is mainly catalyzed by ADAR1 and is a type of post-transcriptional modification, has been linked to cancer. A low response to therapy in breast cancer (BC) is a significant contributor to mortality. However, it remains unclear if there is an association between A>I(G) RNA-edited sites and sensitivity to genotoxic drugs. To address this issue, we employed a stringent bioinformatics approach to identify differentially RNA-edited sites (DESs) associated with low or high sensitivity (FDR 0.1, log2 fold change 2.5) according to the IC50 of PARP inhibitors, anthracyclines, and alkylating agents using WGS/RNA-seq data in BC cell lines. We then validated these findings in patients with basal subtype BC. These DESs are mainly located in non-coding regions, but a lesser proportion in coding regions showed predicted deleterious consequences. Notably, some of these DESs are previously reported as oncogenic variants, and in genes related to DNA damage repair, drug metabolism, gene regulation, the cell cycle, and immune response. In patients with BC, we uncovered DESs predominantly in immune response genes, and a subset with a significant association (log-rank test p < 0.05) between RNA editing level in LSR, SMPDL3B, HTRA4, and LL22NC03-80A10.6 genes, and progression-free survival. Our findings provide a landscape of RNA-edited sites that may be involved in drug response mechanisms, highlighting the value of A>I(G) RNA editing in clinical outcomes for BC.Publication Beyond tobacco: genomic disparities in lung cancer between smokers and never-smokers(2024) Garrido, Javiera; Bernal, Yanara; González, Evelin; Blanco, Alejandro; Sepúlveda, Gonzalo; Freire, Matías; Oróstica, Karen; Rivas, Solange; Marcelain, Katherine; Owen, Gareth; Ibañez, Carolina; Corvalan, Alejandro; Garrido, Marcelo; Assar, Rodrigo; Lizana, Rodrigo; Cáceres, Javier; Ampuero, Diego; Ramos, Liliana; Pérez, Paola; Aren, Osvaldo; Chernilo, Sara; Fernández, Cristina; Spencer, María; Flores, Jacqueline; Bernal, Giuliano; Ahumada, Mónica; Rasse, Germán; Sánchez, Carolina; De Amorim, Maria; Bartelli, Thais; Noronha, Diana; Dias, Emmanuel; Freitas, Helano; Armisén, RicardoBackground: Tobacco use is one of the main risk factors for Lung Cancer (LC) development. However, about 10-20% of those diagnosed with the disease are never-smokers. For Non-Small Cell Lung Cancer (NSCLC) there are clear differences in both the clinical presentation and the tumor genomic profiles between smokers and never-smokers. For example, the Lung Adenocarcinoma (LUAD) histological subtype in never-smokers is predominately found in young women of European, North American, and Asian descent. While the clinical presentation and tumor genomic profiles of smokers have been widely examined, never-smokers are usually underrepresented, especially those of a Latin American (LA) background. In this work, we characterize, for the first time, the difference in the genomic profiles between smokers and never-smokers LC patients from Chile. Methods: We conduct a comparison by smoking status in the frequencies of genomic alterations (GAs) including somatic mutations and structural variants (fusions) in a total of 10 clinically relevant genes, including the eight most common actionable genes for LC (EGFR, KRAS, ALK, MET, BRAF, RET, ERBB2, and ROS1) and two established driver genes for malignancies other than LC (PIK3CA and MAP2K1). Study participants were grouped as either smokers (current and former, n = 473) or never-smokers (n = 200) according to self-report tobacco use at enrollment. Results: Our findings indicate a higher overall GA frequency for never-smokers compared to smokers (58 vs. 45.7, p-value < 0.01) with the genes EGFR, KRAS, and PIK3CA displaying the highest prevalence while ERBB2, RET, and ROS1 the lowest. Never-smokers present higher frequencies in seven out of the 10 genes; however, smokers harbor a more complex genomic profile. The clearest differences between groups are seen for EGFR (15.6 vs. 21.5, p-value: < 0.01), PIK3CA (6.8 vs 9.5) and ALK (3.2 vs 7.5) in favor of never-smokers, and KRAS (16.3 vs. 11.5) and MAP2K1 (6.6 vs. 3.5) in favor of smokers. Alterations in these genes are comprised almost exclusively by somatic mutations in EGFR and mainly by fusions in ALK, and only by mutations in PIK3CA, KRAS and MAP2K1. Conclusions: We found clear differences in the genomic landscape by smoking status in LUAD patients from Chile, with potential implications for clinical management in these limited-resource settings.Item Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients(2022) Oróstica, Karen; Saez, Juan; De Santiago, Pamela; Rivas, Solange; Contreras, Sebastián; Navarro, Gonzalo; Asenjo, Juan; Olivera, Álvaro; Armisén, RicardoAbstract Background: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohortsof cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), sum‑marises the mutational and clinical profles of diferent subtypes of Lung Cancer (LC). Mutational and clinical signa‑ tures have been used independently for tumour typifcation and prediction of metastasis in LC patients. Is it then possible to achieve better typifcations and predictions when combining both data streams? Methods: In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for diferent classes of patients. Using the TML and diferent sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classifcation models that calculate the likelihood of developing metastasis. Results: We found that LC patients diferent in age, smoking status, and tumour type had signifcantly diferent mean TMLs. Although TML was an informative feature, its efect was secondary to the "tumour stage" feature. However, its contribution to the classifcation is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest perfor‑mance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]). Conclusions: Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evi‑ dence of the need for comprehensive diagnostic tools for metastasis.