Browsing by Author "Armisén, Ricardo"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Beyond tobacco: genomic disparities in lung cancer between smokers and never-smokers
    (Springer Nature, 2024) Garrido, Javiera; Bernal, Yanara; González, Evelin; Blanco, Alejandro; Sepúlveda-Hermosilla, Gonzalo; Freire, Matías; Oróstica, Karen; Rivas, Solange; Marcelain, Katherine; Owen, Gareth Ivor; Ibáñez Cáceres, Carolina; Corvalán Rodríguez, Alejandro; Garrido, Marcelo; Assar, Rodrigo; Lizana, Rodrigo; Cáceres-Molina, Javier; Ampuero, Diego; Ramos, Liliana; Pérez, Paola; Aren, Osvaldo; Chernilo, Sara; Fernández, Cristina; Spencer, María L.; Aguila, Jacqueline F.; Dossetto, Giuliano B.; Olea, Mónica A.; Rasse, Germán; Sánchez, Carolina; Amorim, Maria Galli de; Bartelli, Thais F.; Nunes, Diana N.; Dias-Neto, Emmanuel; Freitas, Helano C.; Armisén, Ricardo
    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.
  • Thumbnail Image
    Item
    Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients
    (2022) Oróstica, Karen Y.; Saez-Hidalgo, Juan; Rojas de Santiago, Pamela Roxana; Rivas, Solange; Contreras, Sebastian; Navarro, Gonzalo; Asenjo, Juan A.; Olivera-Nappa, Álvaro; Armisén, Ricardo
    Background: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohorts of cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), summarises the mutational and clinical profiles of different subtypes of Lung Cancer (LC). Mutational and clinical signatures have been used independently for tumour typification and prediction of metastasis in LC patients. Is it then possible to achieve better typifications 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 different classes of patients. Using the TML and different sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classification models that calculate the likelihood of developing metastasis. Results: We found that LC patients different in age, smoking status, and tumour type had significantly different mean TMLs. Although TML was an informative feature, its effect was secondary to the "tumour stage" feature. However, its contribution to the classification 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 performance, 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 evidence of the need for comprehensive diagnostic tools for metastasis.