Citations 2
Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity
Lyu, Yu-Xuan, Wilczok, Dominika , Antebi, Adam , Vojta, Aleksandar , Stolzing, Alexandra , Moskalev, Alexey , Georgievskaya, Anastasia , Maier, Andrea B. , Olsen, Andrea, Groth, Anja , Brunet, Anne , Jamil, Aisyah, Kulaga, Anton , Bhatti, A.R., Yaden, Benjamin C., Djordjevic, Boris, Chen, Chieh, Huang, Christine Yuan, Chen, Danica , Sołdacki, Dariusz, Meyer, David H. , Chini, Eduardo N. , Morgen, Eric , Vernet, Erik , Pinilla, Estefano, Mercken, Evi M. , Finger, Fabian , Kuipers, Folkert , Gyülvészi, Gábor, Zmudze, Garri, Blander, Gil , Pincus, Harold A., McClure, Joshua, Kirkland, James L. , Peyer, James , McLaughlin, Jerry L., Mannick, Joan B. , Betts-LaCroix, Joe , Sedivy, John M. , Speakman, John R. , Shlain, Jordan, Maltzahn, Julia von , Andreasson, Katrin I. , Moody, Kelsey , Fortney, Kristen , Niedernhofer, Laura J., Veenhoff, Liesbeth M. , Melton, Lisa , Quarta, Marco , Koval, Maria, Marinova, Maria B., Hamalainen, Mark, Unfried, Maximilian , Ringel, Michael S. , Filipović, Miloš R. , Topors, Mourad, Mitin, Natalia , Roy, N. N., Pintar, Nika, Barzilai, Nir , Binetti, Paolo, Kohlhaas, Paul, Rubin, Paul , Kamya, Petrina, Muñoz‐Cánoves, Pura , Конрад, Р. , Ripa, Roberto , Mansukhani, Robin, Büttner, Sabrina , Brunemeier, Sebastian, Jakimov, Sergey, Rosenzweig‐Lipson, Sharon, Tsai, Shih‐Yin , Rando, Thomas A. , Peterson, Timothy R. , Woods, Tina , Wyss‐Coray, Tony , Finkel, Toren , Strauss, Tzipora , Longo, Valter D. , Sorrentino, Vincenzo , Sebastiano, Vittorio , Li, Wenbin , Suh, Yousin , Niedernhofer, Laura J. , Lu, Yuxuan , Passos, Joao, Niedernhofer, Laura J , Wyss-Coray, Tony , Bakula, Daniela , Lyu, Yu-Xuan, Fu, Qiang , Ying, Kejun , King, Aaron, Olsen, Andrea, Simon, Anna Katharina , Jamil, Aisyah, Bhatti, Asif, Pedersen, Bente Klarlund , Schumacher, Björn , Kennedy, Brian Keith , Huang, Christine Yuan, Correll, Christoph Ulrich , Murphy, Coleen Tara , Ewald, Collin Y. , Valenzano, Dario Riccardo , Erritzøe, David , Sinclair, David , Teeling, Emma C. , Verdin, Eric , Pinilla, Estéfano , Fang, Evandro Fei , Bischof, Evelyne Y. , Pun, Frank W. , Civiletto, Gabriele , Justice, Jamie N. , Vijg, Jan , Gruhn, Jennifer R. , McLaughlin, Jerry, Passos, João F. , Baur, Joseph A. , Shlain, Jordan, Palikaras, Konstantinos , Rasmussen, Lene Juel , Ferrucci, Luigi , Koval, Maria, Topors, Mourad, Roy, Nawal, Pintar, Nika , Singh, Parminder Pal , Robbins, Paul D. , Fedichev, Peter O. , Cabo, Rafael de , Faragher, Richard , Konrad, Rob, Wickström, Sara A. , Brunemeier, Sebastian A., Luo, Shan , Dimmeler, Stefanie , Gladyshev, Vadim N. , Dwaraka, Varun B. , Gorbunova, Vera , Acosta-Rodríguez, Victoria A. , Suh, Yousin , Zhavoronkov, Alex , Scheibye‐Knudsen, Morten
Aging | October 16, 2024 | Volume: No volume available | Issue: No issue available | Pages: No page available
Genomic determinants of prognosis in esophageal adenocarcinoma: Using computational methods to account for gene-gene interactions.
Morgen, Eric , Shen, Xiaowei , Vaughan, Thomas L. , Wu, Anna H. , Gammon, Marilie D. , Chow, Wong Ho, Romero, Yvonne , Investigators, on behalf of the BEAGESS, Whiteman, David C. , Stram, Daniel O. , Xu, Wei , Liu, Geoffrey , Investigators, on behalf of the BEAGESS
Journal of Clinical Oncology | February 24, 2017 | Volume: 32 | Issue: 3_suppl | Pages: 42-42
10.1200/jco.2014.32.3_suppl.42
42 Background: Methods of stratifying esophageal adenocarcinoma patients into prognostic groups are needed, as are new insights into genetic determinants of disease behaviour. Prognosis is likely to have non-negligible genetic influences, as mediated by host responses to tumor, resistance to therapeutic side-effects, and/or an influence on tumor development. Prior studies have used candidate-gene approaches. We took an alternative approach, using an unbiased, genome-wide approach, and novel analytic methods that may be better able to detect multi-gene interactions, which may contribute the majority of genetic effects for many clinical phenotypes. Methods: Germline DNA from a Toronto-based cohort of EAC patients (n=270) was analyzed by Omni1 Quad microarray as part of the BEAGESS initiative. Quality control and analysis was performed using PLINK, R, and GenABEL software packages. A Cox proportional hazards (CPH) model for progression-free survival tested each polymorphism for independent effects at a genome-wide significance level of P < 1E-07, adjusting for population stratification. While classical analysis has limited ability to detect gene-gene interactions, a Random Survival Forest algorithm was used to detect effects based on the complex interactions among top 1,000 polymorphisms by p-value ranking. Results: After data cleaning and standard GWAS quality control procedures, there were 735,309 SNPs and 245 patients remaining for analysis. The CPH model, adjusted for population stratification, produced a satisfactory Q-Q plot, and showed one SNP (rs7844673, Chr 8) that was significant at p=7.8E-8. In addition, Random Forest based variable selection produced a set of 20 polymorphisms that (1) reproduced 86% of the predictive ability of the full 1000 variables, and (2) also included the #3 ranked polymorphism by CPH modeling (rs9290822, Chr 3) upstream of the IGF2BP2 gene. Conclusions: A genome-wide approach has discovered two previously undescribed SNPs with a potential influence on EAC prognosis via a combination of independent and interactive effects. Validation in an independent cohort is currently being pursued.