Person: Carvajal-Hausdorf, Daniel
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Carvajal-Hausdorf
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Daniel
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Publication Development and internal validation of a multifactorial riskprediction model for gallbladder cancer in a high-incidencecountry(2023) Boekstegers, Felix; Scherer, Dominique; Barahona, Carol; Marcelain, Katherine; Gárate, Valentina; Waldenberge, Melanie; Morales, Erik; Rojas, Armando; Munoz, César; Retamales, Javier; De Toro, Gonzalo; Barajas, Olga; Rivera, María; Cortés, Analía; Loader, Denisse; Saavedra, Javiera; Gutiérrez, Lorena; Ortega, Alejandro; Bertrán, Maria; Bartolotti, Leonardo; Gabler, Fernando; Campos, Monica; Alvarado, Juan; Moisán, Fabricio; Spencer, Loreto; Nervi, Bruno; Carvajal-Hausdorf, Daniel; Losada, Héctor; Almau, Mauricio; Fernández, Plinio; Olloquequi, Jordi; Fuentes, Macarena; Gonzalez, Rolando; Bortolin, Maria; Acuña, Victor; Gallo, Carla; Ruiz, Andres; Rothhamme, Francisco; Bermejo, JustoSince 2006, Chile has been implementing a gallbladder cancer (GBC) prevention program based on prophylactic cholecystectomy for gallstone patients aged 35 to 49 years. The effectiveness of this prevention program has not yet been comprehensively evaluated. We conducted a retrospective study of 473 Chilean GBC patients and 2137 population-based controls to develop and internally validate three GBC risk prediction models. The Baseline Model accounted for gallstones while adjusting for sex and birth year. Enhanced Model I also included the non-genetic risk factors: body mass index, educational level, Mapuche surnames, number of children and family history of GBC. Enhanced Model II further included Mapuche ancestry and the genotype for rs17209837. Multiple Cox regression was applied to assess the predictive performance, quantified by the area under the precision-recall curve (AUC-PRC) and the number of cholecystectomies needed (NCN) to prevent one case of GBC at age 70 years. The AUC-PRC for the Baseline Model (0.44%, 95%CI 0.42-0.46) increased by 0.22 (95%CI 0.15-0.29) when non-genetic factors were included, and by 0.25 (95%CI 0.20-0.30) when incorporating non-genetic and genetic factors. The overall NCN for Chileans with gallstones (115, 95%CI 104-131) decreased to 92 (95%CI 60-128) for Chileans with a higher risk than the median according to Enhanced Model I, and to 80 (95%CI 59-110) according to Enhanced Model II. In conclusion, age, sex and gallstones are strong risk factors for GBC, but consideration of other non-genetic factors and individual genotype data improves risk prediction and may optimize allocation of financial resources and surgical capacity.Publication Gallbladder Cancer Risk and Indigenous South American Mapuche Ancestry: Instrumental Variable Analysis Using Ancestry-Informative Markers(2023) Zollner, Linda; Boekstegers, Felix; Barahona, Carol; Scherer, Dominique; Marcelain, Katherine; Gárate, Valentina; Waldenberger, Melanie; Morales, Erik; Rojas, Armando; Munoz, César; Retamales, Javier; De Toro, Gonzalo; Vera, Allan; Barajas, Olga; Rivera, María; Cortés, Analía; Loader, Denisse; Saavedra, Javiera; Gutiérrez, Lorena; Ortega, Alejandro; Bertrán, Maria; Bartolotti, Leonardo; Gabler, Fernando; Campos, Mónica; Alvarado, Juan; Moisán, Fabricio; Spencer, Loreto; Nervi, Bruno; Carvajal-Hausdorf, Daniel; Losada, Héctor; Almau, Mauricio; Fernández, Plinio; Olloquequi, Jordi; Carter, Alice; Miquel, Juan; Bustos, Bernabe; Fuentes, Macarena; Gonzalez, Rolando; Bortolini, Maria; Acuña, Victor; Gallo, Carla; Ruiz, Andres; Rothhammer, Francisco; Bermejo, JustoA strong association between the proportion of indigenous South American Mapuche ancestry and the risk of gallbladder cancer (GBC) has been reported in observational studies. Chileans show the highest incidence of GBC worldwide, and the Mapuche are the largest indigenous people in Chile. We set out to assess the confounding-free effect of the individual proportion of Mapuche ancestry on GBC risk and to investigate the mediating effects of gallstone disease and body mass index (BMI) on this association. Genetic markers of Mapuche ancestry were selected based on the informativeness for assignment measure, and then used as instrumental variables in two-sample Mendelian randomization analyses and complementary sensitivity analyses. Results suggested a putatively causal effect of Mapuche ancestry on GBC risk (inverse variance-weighted (IVW) risk increase of 0.8% per 1% increase in Mapuche ancestry proportion, 95% CI 0.4% to 1.2%, p = 6.7 × 10-5) and also on gallstone disease (3.6% IVW risk increase, 95% CI 3.1% to 4.0%), pointing to a mediating effect of gallstones on the association between Mapuche ancestry and GBC. In contrast, the proportion of Mapuche ancestry showed a negative effect on BMI (IVW estimate -0.006 kg/m2, 95% CI -0.009 to -0.003). The results presented here may have significant implications for GBC prevention and are important for future admixture mapping studies. Given that the association between the individual proportion of Mapuche ancestry and GBC risk previously noted in observational studies appears to be free of confounding, primary and secondary prevention strategies that consider genetic ancestry could be particularly efficient.Publication Identification of Circulating lncRNAs Associated with Gallbladder Cancer Risk by Tissue-Based Preselection, Cis-eQTL Validation, and Analysis of Association with Genotype-Based Expression(2022) Blandino, Alice; Scherer, Dominique; Rounge, Trine B.; Umu, Sinan U.; Boekstegers, Felix; Barahona Ponce, Carol; Marcelain, Katherine; Gárate-Calderón, Valentina; Waldenberger, Melanie; Morales, Erik; Rojas, Armando; Muñoz, César; Retamales, Javier; Toro, Gonzalo de; Barajas, Olga; Rivera, María Teresa; Cortés, Analía; Loader, Denisse; Saavedra, Javiera; Gutiérrez, Lorena; Ortega, Alejandro; Bertrán, María Enriqueta; Gabler, Fernando; Campos, Mónica; Alvarado, Juan; Fabrizio Moisán 18, Loreto Spencer 18, Bruno Nervi 19, Daniel E Carvajal-Hausdorf; Spencer, Loreto; Nervi, Bruno; Carvajal-Hausdorf, Daniel; Losada, Héctor; Almau, Mauricio; Fernández, Plinio; Gallegos, Iván; Olloquequi, Jordi; Fuentes-Guajardo, Macarena; González -Jose, Rolando; Bortolini, María Cátira; Gallo, Carla; Ruíz Linares, Andrés; Rothhammer, Francisco; Bermejo, Justo LorenzoLong noncoding RNAs (lncRNAs) play key roles in cell processes and are good candidates for cancer risk prediction. Few studies have investigated the association between individual genotypes and lncRNA expression. Here we integrate three separate datasets with information on lncRNA expression only, both lncRNA expression and genotype, and genotype information only to identify circulating lncRNAs associated with the risk of gallbladder cancer (GBC) using robust linear and logistic regression techniques. In the first dataset, we preselect lncRNAs based on expression changes along the sequence "gallstones → dysplasia → GBC". In the second dataset, we validate associations between genetic variants and serum expression levels of the preselected lncRNAs (cis-lncRNA-eQTLs) and build lncRNA expression prediction models. In the third dataset, we predict serum lncRNA expression based on individual genotypes and assess the association between genotype-based expression and GBC risk. AC084082.3 and LINC00662 showed increasing expression levels (p-value = 0.009), while C22orf34 expression decreased in the sequence from gallstones to GBC (p-value = 0.04). We identified and validated two cis-LINC00662-eQTLs (r2 = 0.26) and three cis-C22orf34-eQTLs (r2 = 0.24). Only LINC00662 showed a genotyped-based serum expression associated with GBC risk (OR = 1.25 per log2 expression unit, 95% CI 1.04-1.52, p-value = 0.02). Our results suggest that preselection of lncRNAs based on tissue samples and exploitation of cis-lncRNA-eQTLs may facilitate the identification of circulating noncoding RNAs linked to cancer risk.Publication Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review(2024) Oróstica, Karen; Mardones, Felipe; Bernal, Yanara; Molina, Samuel; Orchard, Marcos; Verdugo, Ricardo; Carvajal-Hausdorf, Daniel; Marcelain, Katherine; Contreras, Seba; Armisen, RicardoCancers of unknown primary (CUP) are a heterogeneous group of aggressive metastatic cancers where standardised diagnostic techniques fail to identify the organ where it originated, resulting in a poor prognosis and resistance to treatment. Recent advances in large-scale sequencing techniques have enabled the identification of mutational signatures specific to particular tumour subtypes, even from liquid biopsy samples such as blood. This breakthrough paves the way for the development of new cost-effective diagnostic strategies. This mini-review explores recent advancements in Machine Learning (ML) and its application to tumour classification methods for CUP patients, identifying its weaknesses and strengths when classifying the tumour type. In the era of multi-omics, integrating several sources of information (e.g., imaging, molecular biomarkers, and family history) requires important theoretical advancements: increasing the dimensionality of the problem can result in lowering the predictive accuracy and robustness when data is scarce. Here, we review and discuss different architectures and strategies for incorporating cutting-edge machine learning into CUP diagnosis, aiming to bridge the gap between theory and clinical practice.