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2. Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk

Author

Tian, Yu, Lin, Yi, Qu, Conghui, Arndt, Volker, Baurley, James W., Berndt, Sonja I., Bien, Stephanie A., Bishop, D. Timothy, Brenner, Hermann, Buchanan, Daniel D., Budiarto, Arif, Campbell, Peter T., Carreras-Torres, Robert, Casey, Graham, Chan, Andrew T., Chen, Rui, Chen, Xuechen, Conti, David V., Díez-Obrero, Virginia, Dimou, Niki, Drew, David A., Figueiredo, Jane C., Gallinger, Steven, Giles, Graham G., Gruber, Stephen B., Gunter, Marc J., Harlid, Sophia, Harrison, Tabitha A., Hidaka, Akihisa, Hoffmeister, Michael, Huyghe, Jeroen R., Jenkins, Mark A., Jordahl, Kristina M., Joshi, Amit D., Keku, Temitope O., Kawaguchi, Eric, Kim, Andre E., Kundaje, Anshul, Larsson, Susanna C., Marchand, Loic Le, Lewinger, Juan Pablo, Li, Li, Moreno, Victor, Morrison, John, Murphy, Neil, Nan, Hongmei, Nassir, Rami, Newcomb, Polly A., Obón-Santacana, Mireia, Ogino, Shuji, Ose, Jennifer, Pardamean, Bens, Pellatt, Andrew J., Peoples, Anita R., Platz, Elizabeth A., Potter, John D., Prentice, Ross L., Rennert, Gad, Ruiz-Narvaez, Edward A., Sakoda, Lori C., Schoen, Robert E., Shcherbina, Anna, Stern, Mariana C., Su, Yu-Ru, Thibodeau, Stephen N., Thomas, Duncan C., Tsilidis, Konstantinos K., van Duijnhoven, Franzel J. B., Van Guelpen, Bethany, Visvanathan, Kala, White, Emily, Wolk, Alicja, Woods, Michael O., Wu, Anna H., Peters, Ulrike, Gauderman, W. James, Hsu, Li, and Chang-Claude, Jenny

Published
2024
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3. Genetic Susceptibility to Nonalcoholic Fatty Liver Disease and Risk for Pancreatic Cancer: Mendelian Randomization.

Author

Canzian, Federico, Du, Mengmeng, Gallinger, Steven, Giles, Graham, Goodman, Phyllis, Haiman, Christopher, Kogevinas, Manolis, Kooperberg, Charles, LeMarchand, Loic, Neale, Rachel, Visvanathan, Kala, White, Emily, Albanes, Demetrius, Andreotti, Gabriella, Babic, Ana, Berndt, Sonja, Brais, Lauren, Brennan, Paul, Buring, Julie, Rabe, Kari, Bamlet, William, Chanock, Stephen, Fuchs, Charles, Gaziano, J, Giovannucci, Edward, Hackert, Thilo, Hassan, Manal, Katzke, Verena, Kurtz, Robert, Lee, I-Min, Malats, Núria, Murphy, Neil, Oberg, Ann, Orlow, Irene, Porta, Miquel, Real, Francisco, Rothman, Nathaniel, Sesso, Howard, Silverman, Debra, Thompson, Ian, Wactawski-Wende, Jean, Wang, Xiaoliang, Wentzensen, Nicolas, Yu, Herbert, Zeleniuch-Jacquotte, Anne, Yu, Kai, Wolpin, Brian, Duell, Eric, Li, Donghui, Hung, Rayjean, Perdomo, Sandra, McCullough, Marjorie, Freedman, Neal, Patel, Alpa, Peters, Ulrike, Riboli, Elio, Sund, Malin, Tjønneland, Anne, Zhong, Jun, Van Den Eeden, Stephen, Kraft, Peter, Risch, Harvey, Amundadottir, Laufey, Klein, Alison, Stolzenberg-Solomon, Rachael, Antwi, Samuel, King, Sontoria, Veliginti, Swathi, Brouwers, Martijn, Ren, Zhewen, Zheng, Wei, Setiawan, Veronica, Wilkens, Lynne, Shu, Xiao-Ou, Arslan, Alan, Beane Freeman, Laura, and Bracci, Paige

Subjects
Humans, Non-alcoholic Fatty Liver Disease, Genetic Predisposition to Disease, Genome-Wide Association Study, Mendelian Randomization Analysis, Pancreatic Neoplasms, Obesity, Polymorphism, Single Nucleotide
Abstract

BACKGROUND: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. METHODS: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. RESULTS: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88-1.22; MR-Egger OR, 0.89; 95% CI, 0.65-1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90-1.27; MR-Egger OR, 0.93; 95% CI, 0.67-1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. CONCLUSIONS: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. IMPACT: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.

Published
2023

5. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Author

Chen, Zhishan, Guo, Xingyi, Tao, Ran, Huyghe, Jeroen R., Law, Philip J., Fernandez-Rozadilla, Ceres, Ping, Jie, Jia, Guochong, Long, Jirong, Li, Chao, Shen, Quanhu, Xie, Yuhan, Timofeeva, Maria N., Thomas, Minta, Schmit, Stephanie L., Díez-Obrero, Virginia, Devall, Matthew, Moratalla-Navarro, Ferran, Fernandez-Tajes, Juan, Palles, Claire, Sherwood, Kitty, Briggs, Sarah E. W., Svinti, Victoria, Donnelly, Kevin, Farrington, Susan M., Blackmur, James, Vaughan-Shaw, Peter G., Shu, Xiao-Ou, Lu, Yingchang, Broderick, Peter, Studd, James, Harrison, Tabitha A., Conti, David V., Schumacher, Fredrick R., Melas, Marilena, Rennert, Gad, Obón-Santacana, Mireia, Martín-Sánchez, Vicente, Oh, Jae Hwan, Kim, Jeongseon, Jee, Sun Ha, Jung, Keum Ji, Kweon, Sun-Seog, Shin, Min-Ho, Shin, Aesun, Ahn, Yoon-Ok, Kim, Dong-Hyun, Oze, Isao, Wen, Wanqing, Matsuo, Keitaro, Matsuda, Koichi, Tanikawa, Chizu, Ren, Zefang, Gao, Yu-Tang, Jia, Wei-Hua, Hopper, John L., Jenkins, Mark A., Win, Aung Ko, Pai, Rish K., Figueiredo, Jane C., Haile, Robert W., Gallinger, Steven, Woods, Michael O., Newcomb, Polly A., Duggan, David, Cheadle, Jeremy P., Kaplan, Richard, Kerr, Rachel, Kerr, David, Kirac, Iva, Böhm, Jan, Mecklin, Jukka-Pekka, Jousilahti, Pekka, Knekt, Paul, Aaltonen, Lauri A., Rissanen, Harri, Pukkala, Eero, Eriksson, Johan G., Cajuso, Tatiana, Hänninen, Ulrika, Kondelin, Johanna, Palin, Kimmo, Tanskanen, Tomas, Renkonen-Sinisalo, Laura, Männistö, Satu, Albanes, Demetrius, Weinstein, Stephanie J., Ruiz-Narvaez, Edward, Palmer, Julie R., Buchanan, Daniel D., Platz, Elizabeth A., Visvanathan, Kala, Ulrich, Cornelia M., Siegel, Erin, Brezina, Stefanie, Gsur, Andrea, Campbell, Peter T., Chang-Claude, Jenny, Hoffmeister, Michael, Brenner, Hermann, Slattery, Martha L., Potter, John D., Tsilidis, Kostas K., Schulze, Matthias B., Gunter, Marc J., Murphy, Neil, Castells, Antoni, Castellví-Bel, Sergi, Moreira, Leticia, Arndt, Volker, Shcherbina, Anna, Bishop, D. Timothy, Giles, Graham G., Southey, Melissa C., Idos, Gregory E., McDonnell, Kevin J., Abu-Ful, Zomoroda, Greenson, Joel K., Shulman, Katerina, Lejbkowicz, Flavio, Offit, Kenneth, Su, Yu-Ru, Steinfelder, Robert, Keku, Temitope O., van Guelpen, Bethany, Hudson, Thomas J., Hampel, Heather, Pearlman, Rachel, Berndt, Sonja I., Hayes, Richard B., Martinez, Marie Elena, Thomas, Sushma S., Pharoah, Paul D. P., Larsson, Susanna C., Yen, Yun, Lenz, Heinz-Josef, White, Emily, Li, Li, Doheny, Kimberly F., Pugh, Elizabeth, Shelford, Tameka, Chan, Andrew T., Cruz-Correa, Marcia, Lindblom, Annika, Hunter, David J., Joshi, Amit D., Schafmayer, Clemens, Scacheri, Peter C., Kundaje, Anshul, Schoen, Robert E., Hampe, Jochen, Stadler, Zsofia K., Vodicka, Pavel, Vodickova, Ludmila, Vymetalkova, Veronika, Edlund, Christopher K., Gauderman, W. James, Shibata, David, Toland, Amanda, Markowitz, Sanford, Kim, Andre, Chanock, Stephen J., van Duijnhoven, Franzel, Feskens, Edith J. M., Sakoda, Lori C., Gago-Dominguez, Manuela, Wolk, Alicja, Pardini, Barbara, FitzGerald, Liesel M., Lee, Soo Chin, Ogino, Shuji, Bien, Stephanie A., Kooperberg, Charles, Li, Christopher I., Lin, Yi, Prentice, Ross, Qu, Conghui, Bézieau, Stéphane, Yamaji, Taiki, Sawada, Norie, Iwasaki, Motoki, Le Marchand, Loic, Wu, Anna H., Qu, Chenxu, McNeil, Caroline E., Coetzee, Gerhard, Hayward, Caroline, Deary, Ian J., Harris, Sarah E., Theodoratou, Evropi, Reid, Stuart, Walker, Marion, Ooi, Li Yin, Lau, Ken S., Zhao, Hongyu, Hsu, Li, Cai, Qiuyin, Dunlop, Malcolm G., Gruber, Stephen B., Houlston, Richard S., Moreno, Victor, Casey, Graham, Peters, Ulrike, Tomlinson, Ian, and Zheng, Wei

Published
2024
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6. Common Genetic Variation and Age of Onset of Anorexia Nervosa.

Author

Watson, Hunna J, Thornton, Laura M, Yilmaz, Zeynep, Baker, Jessica H, Coleman, Jonathan RI, Adan, Roger AH, Alfredsson, Lars, Andreassen, Ole A, Ask, Helga, Berrettini, Wade H, Boehnke, Michael, Boehm, Ilka, Boni, Claudette, Buehren, Katharina, Bulant, Josef, Burghardt, Roland, Chang, Xiao, Cichon, Sven, Cone, Roger D, Courtet, Philippe, Crow, Scott, Crowley, James J, Danner, Unna N, de Zwaan, Martina, Dedoussis, George, DeSocio, Janiece E, Dick, Danielle M, Dikeos, Dimitris, Dina, Christian, Djurovic, Srdjan, Dmitrzak-Weglarz, Monika, Docampo-Martinez, Elisa, Duriez, Philibert, Egberts, Karin, Ehrlich, Stefan, Eriksson, Johan G, Escaramís, Geòrgia, Esko, Tõnu, Estivill, Xavier, Farmer, Anne, Fernández-Aranda, Fernando, Fichter, Manfred M, Föcker, Manuel, Foretova, Lenka, Forstner, Andreas J, Frei, Oleksandr, Gallinger, Steven, Giegling, Ina, Giuranna, Johanna, Gonidakis, Fragiskos, Gorwood, Philip, Gratacòs, Mònica, Guillaume, Sébastien, Guo, Yiran, Hakonarson, Hakon, Hauser, Joanna, Havdahl, Alexandra, Hebebrand, Johannes, Helder, Sietske G, Herms, Stefan, Herpertz-Dahlmann, Beate, Herzog, Wolfgang, Hinney, Anke, Hübel, Christopher, Hudson, James I, Imgart, Hartmut, Jamain, Stephanie, Janout, Vladimir, Jiménez-Murcia, Susana, Jones, Ian R, Julià, Antonio, Kalsi, Gursharan, Kaminská, Deborah, Kaprio, Jaakko, Karhunen, Leila, Kas, Martien JH, Keel, Pamela K, Kennedy, James L, Keski-Rahkonen, Anna, Kiezebrink, Kirsty, Klareskog, Lars, Klump, Kelly L, Knudsen, Gun Peggy S, La Via, Maria C, Le Hellard, Stephanie, Leboyer, Marion, Li, Dong, Lilenfeld, Lisa, Lin, Bochao, Lissowska, Jolanta, Luykx, Jurjen, Magistretti, Pierre, Maj, Mario, Marsal, Sara, Marshall, Christian R, Mattingsdal, Morten, Meulenbelt, Ingrid, Micali, Nadia, Mitchell, Karen S, and Monteleone, Alessio Maria

Subjects
Age of onset, Anorexia nervosa, Early-onset, GWAS, Genetic risk score, Genetics, Menarche, Mendelian randomization, Puberty, Mental Health, Human Genome, Eating Disorders, Anorexia, Brain Disorders, Pediatric, Prevention, Aetiology, 2.1 Biological and endogenous factors
Abstract

BackgroundGenetics and biology may influence the age of onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to age of onset of AN and to investigate the genetic associations between age of onset of AN and age at menarche.MethodsA secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed, which included 9335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age of onset, early-onset AN (

Published
2022

7. Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk.

Author

Tian, Yu, Kim, Andre E, Bien, Stephanie A, Lin, Yi, Qu, Conghui, Harrison, Tabitha A, Carreras-Torres, Robert, Díez-Obrero, Virginia, Dimou, Niki, Drew, David A, Hidaka, Akihisa, Huyghe, Jeroen R, Jordahl, Kristina M, Morrison, John, Murphy, Neil, Obón-Santacana, Mireia, Ulrich, Cornelia M, Ose, Jennifer, Peoples, Anita R, Ruiz-Narvaez, Edward A, Shcherbina, Anna, Stern, Mariana C, Su, Yu-Ru, van Duijnhoven, Franzel JB, Arndt, Volker, Baurley, James W, Berndt, Sonja I, Bishop, D Timothy, Brenner, Hermann, Buchanan, Daniel D, Chan, Andrew T, Figueiredo, Jane C, Gallinger, Steven, Gruber, Stephen B, Harlid, Sophia, Hoffmeister, Michael, Jenkins, Mark A, Joshi, Amit D, Keku, Temitope O, Larsson, Susanna C, Le Marchand, Loic, Li, Li, Giles, Graham G, Milne, Roger L, Nan, Hongmei, Nassir, Rami, Ogino, Shuji, Budiarto, Arif, Platz, Elizabeth A, Potter, John D, Prentice, Ross L, Rennert, Gad, Sakoda, Lori C, Schoen, Robert E, Slattery, Martha L, Thibodeau, Stephen N, Van Guelpen, Bethany, Visvanathan, Kala, White, Emily, Wolk, Alicja, Woods, Michael O, Wu, Anna H, Campbell, Peter T, Casey, Graham, Conti, David V, Gunter, Marc J, Kundaje, Anshul, Lewinger, Juan Pablo, Moreno, Victor, Newcomb, Polly A, Pardamean, Bens, Thomas, Duncan C, Tsilidis, Konstantinos K, Peters, Ulrike, Gauderman, W James, Hsu, Li, and Chang-Claude, Jenny

Subjects
Humans, Colorectal Neoplasms, Estrogens, Progestins, Risk Factors, Case-Control Studies, Menopause, Polymorphism, Single Nucleotide, Female, Aging, Digestive Diseases, Estrogen, Colo-Rectal Cancer, Genetics, Prevention, Human Genome, Clinical Research, Cancer, 2.1 Biological and endogenous factors, Aetiology, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

BackgroundThe use of menopausal hormone therapy (MHT) may interact with genetic variants to influence colorectal cancer (CRC) risk.MethodsWe conducted a genome-wide, gene-environment interaction between single nucleotide polymorphisms and the use of any MHT, estrogen only, and combined estrogen-progestogen therapy with CRC risk, among 28 486 postmenopausal women (11 519 CRC patients and 16 967 participants without CRC) from 38 studies, using logistic regression, 2-step method, and 2- or 3-degree-of-freedom joint test. A set-based score test was applied for rare genetic variants.ResultsThe use of any MHT, estrogen only and estrogen-progestogen were associated with a reduced CRC risk (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.64 to 0.78; OR = 0.65, 95% CI = 0.53 to 0.79; and OR = 0.73, 95% CI = 0.59 to 0.90, respectively). The 2-step method identified a statistically significant interaction between a GRIN2B variant rs117868593 and MHT use, whereby MHT-associated CRC risk was statistically significantly reduced in women with the GG genotype (OR = 0.68, 95% CI = 0.64 to 0.72) but not within strata of GC or CC genotypes. A statistically significant interaction between a DCBLD1 intronic variant at 6q22.1 (rs10782186) and MHT use was identified by the 2-degree-of-freedom joint test. The MHT-associated CRC risk was reduced with increasing number of rs10782186-C alleles, showing odds ratios of 0.78 (95% CI = 0.70 to 0.87) for TT, 0.68 (95% CI = 0.63 to 0.73) for TC, and 0.66 (95% CI = 0.60 to 0.74) for CC genotypes. In addition, 5 genes in rare variant analysis showed suggestive interactions with MHT (2-sided P

Published
2022

8. Folate intake and colorectal cancer risk according to genetic subtypes defined by targeted tumor sequencing

Author

Aglago, Elom K, Qu, Conghui, Harlid, Sophia, Phipps, Amanda I, Steinfelder, Robert S, Ogino, Shuji, Thomas, Claire E, Hsu, Li, Toland, Amanda E, Brenner, Hermann, Berndt, Sonja I, Buchanan, Daniel D, Campbell, Peter T, Cao, Yin, Chan, Andrew T, Drew, David A, Figueiredo, Jane C, French, Amy J, Gallinger, Steven, Georgeson, Peter, Giannakis, Marios, Goode, Ellen L, Gruber, Stephen B, Gunter, Marc J, Harrison, Tabitha A, Hoffmeister, Michael, Huang, Wen-Yi, Hullar, Meredith AJ, Huyghe, Jeroen R, Jenkins, Mark A, Lynch, Brigid M, Moreno, Victor, Murphy, Neil, Newton, Christina C, Nowak, Jonathan A, Obón-Santacana, Mireia, Sun, Wei, Ugai, Tomotaka, Um, Caroline Y, Zaidi, Syed H, Tsilidis, Konstantinos K, van Guelpen, Bethany, and Peters, Ulrike

Published
2024
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10. Probing the diabetes and colorectal cancer relationship using gene – environment interaction analyses

Author

Dimou, Niki, Kim, Andre E., Flanagan, Orlagh, Murphy, Neil, Diez-Obrero, Virginia, Shcherbina, Anna, Aglago, Elom K., Bouras, Emmanouil, Campbell, Peter T., Casey, Graham, Gallinger, Steven, Gruber, Stephen B., Jenkins, Mark A., Lin, Yi, Moreno, Victor, Ruiz-Narvaez, Edward, Stern, Mariana C., Tian, Yu, Tsilidis, Kostas K., Arndt, Volker, Barry, Elizabeth L., Baurley, James W., Berndt, Sonja I., Bézieau, Stéphane, Bien, Stephanie A., Bishop, D. Timothy, Brenner, Hermann, Budiarto, Arif, Carreras-Torres, Robert, Cenggoro, Tjeng Wawan, Chan, Andrew T., Chang-Claude, Jenny, Chanock, Stephen J., Chen, Xuechen, Conti, David V., Dampier, Christopher H., Devall, Matthew, Drew, David A., Figueiredo, Jane C., Giles, Graham G., Gsur, Andrea, Harrison, Tabitha A., Hidaka, Akihisa, Hoffmeister, Michael, Huyghe, Jeroen R., Jordahl, Kristina, Kawaguchi, Eric, Keku, Temitope O., Larsson, Susanna C., Le Marchand, Loic, Lewinger, Juan Pablo, Li, Li, Mahesworo, Bharuno, Morrison, John, Newcomb, Polly A., Newton, Christina C., Obon-Santacana, Mireia, Ose, Jennifer, Pai, Rish K., Palmer, Julie R., Papadimitriou, Nikos, Pardamean, Bens, Peoples, Anita R., Pharoah, Paul D. P., Platz, Elizabeth A., Potter, John D., Rennert, Gad, Scacheri, Peter C., Schoen, Robert E., Su, Yu-Ru, Tangen, Catherine M., Thibodeau, Stephen N., Thomas, Duncan C., Ulrich, Cornelia M., Um, Caroline Y., van Duijnhoven, Franzel J. B., Visvanathan, Kala, Vodicka, Pavel, Vodickova, Ludmila, White, Emily, Wolk, Alicja, Woods, Michael O., Qu, Conghui, Kundaje, Anshul, Hsu, Li, Gauderman, W. James, Gunter, Marc J., and Peters, Ulrike

Published
2023
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11. Genetically proxied glucose-lowering drug target perturbation and risk of cancer: a Mendelian randomisation analysis

Author

Yarmolinsky, James, Bouras, Emmanouil, Constantinescu, Andrei, Burrows, Kimberley, Bull, Caroline J., Vincent, Emma E., Martin, Richard M., Dimopoulou, Olympia, Lewis, Sarah J., Moreno, Victor, Vujkovic, Marijana, Chang, Kyong-Mi, Voight, Benjamin F., Tsao, Philip S., Gunter, Marc J., Hampe, Jochen, Pellatt, Andrew J., Pharoah, Paul D. P., Schoen, Robert E., Gallinger, Steven, Jenkins, Mark A., Pai, Rish K., Gill, Dipender, and Tsilidis, Kostas K.

Published
2023
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12. Simulated dose painting of hypoxic sub-volumes in pancreatic cancer stereotactic body radiotherapy

Author

Elamir, Ahmed M., Stanescu, Teodor, Shessel, Andrea, Tadic, Tony, Yeung, Ivan, Letourneau, Daniel, Kim, John, Lukovic, Jelena, Dawson, Laura A., Wong, Rebecca, Barry, Aisling, Brierley, James, Gallinger, Steven, Knox, Jennifer, O'Kane, Grainne, Dhani, Neesha, Hosni, Ali, and Taylor, Edward

Subjects
Physics - Medical Physics
Abstract

Dose painting of hypoxic tumour sub-volumes using positron-emission tomography (PET) has been shown to improve tumour control in silico in several sites. Pancreatic cancer presents a more stringent challenge, given its proximity to critical organs-at-risk (OARs) and anatomic motion. A radiobiological model was developed to estimate clonogen survival fraction (SF), using 18F-fluoroazomycin arabinoside PET (FAZA PET) images from ten patients with pancreatic cancer to quantify oxygen enhancement effects. For each patient, four simulated five-fraction stereotactic body radiotherapy (SBRT) plans were generated: 1) a standard SBRT plan aiming to cover the planning target volume with 40 Gy, 2) dose painting plans delivering escalated doses to FAZA-avid hypoxic sub-volumes, 3) dose painting plans with simulated spacer separating the duodenum and pancreatic head, and 4), plans with integrated boosts to geometric contractions of the tumour (GTV). All plans saturated at least one OAR dose limit. SF was calculated for each plan and sensitivity of SF to simulated hypoxia quantification errors was evaluated. Dose painting resulted in a 55% reduction in SF as compared to standard SBRT; 78% with spacer. Integrated boosts to hypoxia-blind geometric contractions resulted in a 41% reduction in SF. The reduction in SF for dose-painting plans persisted for all hypoxia quantification parameters studied, including registration and rigid motion errors that resulted in shifts and rotations of the GTV and hypoxic sub-volumes by as much as 1 cm and 10 degrees. Although proximity to OARs ultimately limited dose escalation, with estimated SFs (~10^-5) well above levels required to completely ablate a ~10 cm^3 tumour, dose painting robustly reduced clonogen survival when accounting for expected treatment and imaging uncertainties and thus, may improve local response and associated morbidity.

Published
2021
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14. Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors.

Author

Mullins, Niamh, Kang, JooEun, Campos, Adrian I, Coleman, Jonathan RI, Edwards, Alexis C, Galfalvy, Hanga, Levey, Daniel F, Lori, Adriana, Shabalin, Andrey, Starnawska, Anna, Su, Mei-Hsin, Watson, Hunna J, Adams, Mark, Awasthi, Swapnil, Gandal, Michael, Hafferty, Jonathan D, Hishimoto, Akitoyo, Kim, Minsoo, Okazaki, Satoshi, Otsuka, Ikuo, Ripke, Stephan, Ware, Erin B, Bergen, Andrew W, Berrettini, Wade H, Bohus, Martin, Brandt, Harry, Chang, Xiao, Chen, Wei J, Chen, Hsi-Chung, Crawford, Steven, Crow, Scott, DiBlasi, Emily, Duriez, Philibert, Fernández-Aranda, Fernando, Fichter, Manfred M, Gallinger, Steven, Glatt, Stephen J, Gorwood, Philip, Guo, Yiran, Hakonarson, Hakon, Halmi, Katherine A, Hwu, Hai-Gwo, Jain, Sonia, Jamain, Stéphane, Jiménez-Murcia, Susana, Johnson, Craig, Kaplan, Allan S, Kaye, Walter H, Keel, Pamela K, Kennedy, James L, Klump, Kelly L, Li, Dong, Liao, Shih-Cheng, Lieb, Klaus, Lilenfeld, Lisa, Liu, Chih-Min, Magistretti, Pierre J, Marshall, Christian R, Mitchell, James E, Monson, Eric T, Myers, Richard M, Pinto, Dalila, Powers, Abigail, Ramoz, Nicolas, Roepke, Stefan, Rozanov, Vsevolod, Scherer, Stephen W, Schmahl, Christian, Sokolowski, Marcus, Strober, Michael, Thornton, Laura M, Treasure, Janet, Tsuang, Ming T, Witt, Stephanie H, Woodside, D Blake, Yilmaz, Zeynep, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Air, Tracy M, Alda, Martin, Alfredsson, Lars, Andreassen, Ole A, Anjorin, Adebayo, Appadurai, Vivek, Soler Artigas, María, Van der Auwera, Sandra, Azevedo, M Helena, Bass, Nicholas, Bau, Claiton HD, Baune, Bernhard T, Bellivier, Frank, Berger, Klaus, Biernacka, Joanna M, Bigdeli, Tim B, Binder, Elisabeth B, Boehnke, Michael, Boks, Marco P, Bosch, Rosa, and Braff, David L

Subjects
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Eating Disorders Working Group of the Psychiatric Genomics Consortium, German Borderline Genomics Consortium, MVP Suicide Exemplar Workgroup, VA Million Veteran Program, Humans, Risk Factors, Suicide, Attempted, Mental Disorders, Depressive Disorder, Major, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Genetic correlation, Genome-wide association study, Pleiotropy, Polygenicity, Suicide, Suicide attempt, Human Genome, Behavioral and Social Science, Mental Health, Prevention, Genetics, Brain Disorders, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundSuicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders.MethodsWe conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors.ResultsTwo loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged.ConclusionsOur results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.

Published
2022

15. Genome-wide association study identifies tumor anatomical site-specific risk variants for colorectal cancer survival

Author

Labadie, Julia D, Savas, Sevtap, Harrison, Tabitha A, Banbury, Barb, Huang, Yuhan, Buchanan, Daniel D, Campbell, Peter T, Gallinger, Steven J, Giles, Graham G, Gunter, Marc J, Hoffmeister, Michael, Hsu, Li, Jenkins, Mark A, Lin, Yi, Ogino, Shuji, Phipps, Amanda I, Slattery, Martha L, Steinfelder, Robert S, Sun, Wei, Van Guelpen, Bethany, Hua, Xinwei, Figuieredo, Jane C, Pai, Rish K, Nassir, Rami, Qi, Lihong, Chan, Andrew T, Peters, Ulrike, and Newcomb, Polly A

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Oncology and Carcinogenesis, Human Genome, Cancer, Colo-Rectal Cancer, Prevention, Digestive Diseases, Aetiology, 2.1 Biological and endogenous factors, Adult, Aged, Aged, 80 and over, Biomarkers, Tumor, Colorectal Neoplasms, Databases, Genetic, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Middle Aged, Neoplasm Staging, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Young Adult
Abstract

Identification of new genetic markers may improve the prediction of colorectal cancer prognosis. Our objective was to examine genome-wide associations of germline genetic variants with disease-specific survival in an analysis of 16,964 cases of colorectal cancer. We analyzed genotype and colorectal cancer-specific survival data from a consortium of 15 studies. Approximately 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, adjusting for clinical factors and principal components. Additionally, we ran secondary analyses stratifying by tumor site and disease stage. We used a genome-wide p-value threshold of 5 × 10-8 to assess statistical significance. No variants were statistically significantly associated with disease-specific survival in the full case analysis or in the stage-stratified analyses. Three SNPs were statistically significantly associated with disease-specific survival for cases with tumors located in the distal colon (rs698022, HR = 1.48, CI 1.30-1.69, p = 8.47 × 10-9) and the proximal colon (rs189655236, HR = 2.14, 95% CI 1.65-2.77, p = 9.19 × 10-9 and rs144717887, HR = 2.01, 95% CI 1.57-2.58, p = 3.14 × 10-8), whereas no associations were detected for rectal tumors. Findings from this large genome-wide association study highlight the potential for anatomical-site-stratified genome-wide studies to identify germline genetic risk variants associated with colorectal cancer-specific survival. Larger sample sizes and further replication efforts are needed to more fully interpret these findings.

Published
2022

17. Systemic inflammatory prognostic scores in advanced pancreatic adenocarcinoma

Author

Ma, Lucy X., Wang, Yifan, Espin-Garcia, Osvaldo, Allen, Michael J., Jang, Gun Ho, Zhang, Amy, Dodd, Anna, Ramotar, Stephanie, Hutchinson, Shawn, Tehfe, Mustapha, Ramjeesingh, Ravi, Biagi, James, Wilson, Julie M., Notta, Faiyaz, Fischer, Sandra E., Zogopoulos, George, Gallinger, Steven, Grant, Robert C., Khokha, Rama, Chan, Nathan, Grünwald, Barbara T., Knox, Jennifer J., and O’Kane, Grainne M.

Published
2023
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19. The Mutographs biorepository: A unique genomic resource to study cancer around the world

Author

Perdomo, Sandra, Abedi-Ardekani, Behnoush, de Carvalho, Ana Carolina, Ferreiro-Iglesias, Aida, Gaborieau, Valérie, Cattiaux, Thomas, Renard, Hélène, Chopard, Priscilia, Carreira, Christine, Spanu, Andreea, Nikmanesh, Arash, Cardoso Penha, Ricardo Cortez, Antwi, Samuel O., Ashton-Prolla, Patricia, Canova, Cristina, Chitapanarux, Taned, Cox, Riley, Curado, Maria Paula, de Oliveira, José Carlos, Dzamalala, Charles, Fabianova, Elenora, Ferri, Lorenzo, Fitzgerald, Rebecca, Foretova, Lenka, Gallinger, Steven, Goldstein, Alisa M., Holcatova, Ivana, Huertas, Antonio, Janout, Vladimir, Jarmalaite, Sonata, Kaneva, Radka, Kowalski, Luiz Paulo, Kulis, Tomislav, Lagiou, Pagona, Lissowska, Jolanta, Malekzadeh, Reza, Mates, Dana, McCorrmack, Valerie, Menya, Diana, Mhatre, Sharayu, Mmbaga, Blandina Theophil, de Moricz, André, Nyirády, Péter, Ognjanovic, Miodrag, Papadopoulou, Kyriaki, Polesel, Jerry, Purdue, Mark P., Rascu, Stefan, Rebolho Batista, Lidia Maria, Reis, Rui Manuel, Ribeiro Pinto, Luis Felipe, Rodríguez-Urrego, Paula A., Sangkhathat, Surasak, Sangrajrang, Suleeporn, Shibata, Tatsuhiro, Stakhovsky, Eduard, Świątkowska, Beata, Vaccaro, Carlos, Vasconcelos de Podesta, Jose Roberto, Vasudev, Naveen S., Vilensky, Marta, Yeung, Jonathan, Zaridze, David, Zendehdel, Kazem, Scelo, Ghislaine, Chanudet, Estelle, Wang, Jingwei, Fitzgerald, Stephen, Latimer, Calli, Moody, Sarah, Humphreys, Laura, Alexandrov, Ludmil B., Stratton, Michael R., and Brennan, Paul

Published
2024
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20. Body size and risk of colorectal cancer molecular defined subtypes and pathways: Mendelian randomization analyses

Author

Papadimitriou, Nikos, Qu, Conghui, Harrison, Tabitha A., Bever, Alaina M., Martin, Richard M., Tsilidis, Konstantinos K., Newcomb, Polly A., Thibodeau, Stephen N., Newton, Christina C., Um, Caroline Y., Obón-Santacana, Mireia, Moreno, Victor, Brenner, Hermann, Mandic, Marko, Chang-Claude, Jenny, Hoffmeister, Michael, Pellatt, Andrew J., Schoen, Robert E., Harlid, Sophia, Ogino, Shuji, Ugai, Tomotaka, Buchanan, Daniel D., Lynch, Brigid M., Gruber, Stephen B., Cao, Yin, Hsu, Li, Huyghe, Jeroen R., Lin, Yi, Steinfelder, Robert S., Sun, Wei, Van Guelpen, Bethany, Zaidi, Syed H., Toland, Amanda E., Berndt, Sonja I., Huang, Wen-Yi, Aglago, Elom K., Drew, David A., French, Amy J., Georgeson, Peter, Giannakis, Marios, Hullar, Meredith, Nowak, Johnathan A., Thomas, Claire E., Le Marchand, Loic, Cheng, Iona, Gallinger, Steven, Jenkins, Mark A., Gunter, Marc J., Campbell, Peter T., Peters, Ulrike, Song, Mingyang, Phipps, Amanda I., and Murphy, Neil

Published
2024
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21. Prognostic role of detailed colorectal location and tumor molecular features: analyses of 13,101 colorectal cancer patients including 2994 early-onset cases

Author

Ugai, Tomotaka, Akimoto, Naohiko, Haruki, Koichiro, Harrison, Tabitha A., Cao, Yin, Qu, Conghui, Chan, Andrew T., Campbell, Peter T., Berndt, Sonja I., Buchanan, Daniel D., Cross, Amanda J., Diergaarde, Brenda, Gallinger, Steven J., Gunter, Marc J., Harlid, Sophia, Hidaka, Akihisa, Hoffmeister, Michael, Brenner, Hermann, Chang-Claude, Jenny, Hsu, Li, Jenkins, Mark A., Lin, Yi, Milne, Roger L., Moreno, Victor, Newcomb, Polly A., Nishihara, Reiko, Obon-Santacana, Mireia, Pai, Rish K., Sakoda, Lori C., Schoen, Robert E., Slattery, Martha L., Sun, Wei, Amitay, Efrat L., Alwers, Elizabeth, Thibodeau, Stephen N., Toland, Amanda E., Van Guelpen, Bethany, Zaidi, Syed H., Potter, John D., Meyerhardt, Jeffrey A., Giannakis, Marios, Song, Mingyang, Nowak, Jonathan A., Peters, Ulrike, Phipps, Amanda I., and Ogino, Shuji

Published
2023
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22. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

Author

Fernandez-Rozadilla, Ceres, Timofeeva, Maria, Chen, Zhishan, Law, Philip, Thomas, Minta, Schmit, Stephanie, Díez-Obrero, Virginia, Hsu, Li, Fernandez-Tajes, Juan, Palles, Claire, Sherwood, Kitty, Briggs, Sarah, Svinti, Victoria, Donnelly, Kevin, Farrington, Susan, Blackmur, James, Vaughan-Shaw, Peter, Shu, Xiao-ou, Long, Jirong, Cai, Qiuyin, Guo, Xingyi, Lu, Yingchang, Broderick, Peter, Studd, James, Huyghe, Jeroen, Harrison, Tabitha, Conti, David, Dampier, Christopher, Devall, Mathew, Schumacher, Fredrick, Melas, Marilena, Rennert, Gad, Obón-Santacana, Mireia, Martín-Sánchez, Vicente, Moratalla-Navarro, Ferran, Oh, Jae Hwan, Kim, Jeongseon, Jee, Sun Ha, Jung, Keum Ji, Kweon, Sun-Seog, Shin, Min-Ho, Shin, Aesun, Ahn, Yoon-Ok, Kim, Dong-Hyun, Oze, Isao, Wen, Wanqing, Matsuo, Keitaro, Matsuda, Koichi, Tanikawa, Chizu, Ren, Zefang, Gao, Yu-Tang, Jia, Wei-Hua, Hopper, John, Jenkins, Mark, Win, Aung Ko, Pai, Rish, Figueiredo, Jane, Haile, Robert, Gallinger, Steven, Woods, Michael, Newcomb, Polly, Duggan, David, Cheadle, Jeremy, Kaplan, Richard, Maughan, Timothy, Kerr, Rachel, Kerr, David, Kirac, Iva, Böhm, Jan, Mecklin, Lukka-Pekka, Jousilahti, Pekka, Knekt, Paul, Aaltonen, Lauri, Rissanen, Harri, Pukkala, Eero, Eriksson, Johan, Cajuso, Tatiana, Hänninen, Ulrika, Kondelin, Johanna, Palin, Kimmo, Tanskanen, Tomas, Renkonen-Sinisalo, Laura, Zanke, Brent, Männistö, Satu, Albanes, Demetrius, Weinstein, Stephanie, Ruiz-Narvaez, Edward, Palmer, Julie, Buchanan, Daniel, Platz, Elizabeth, Visvanathan, Kala, Ulrich, Cornelia, Siegel, Erin, Brezina, Stefanie, Gsur, Andrea, Campbell, Peter, Chang-Claude, Jenny, Hoffmeister, Michael, Brenner, Hermann, Slattery, Martha, Potter, John, Tsilidis, Konstantinos, Schulze, Matthias, Gunter, Marc, Murphy, Neil, Castells, Antoni, Castellví-Bel, Sergi, Moreira, Leticia, Arndt, Volker, Shcherbina, Anna, Stern, Mariana, Pardamean, Bens, Bishop, Timothy, Giles, Graham, Southey, Melissa, Idos, Gregory, McDonnell, Kevin, Abu-Ful, Zomoroda, Greenson, Joel, Shulman, Katerina, Lejbkowicz, Flavio, Offit, Kenneth, Su, Yu-Ru, Steinfelder, Robert, Keku, Temitope, van Guelpen, Bethany, Hudson, Thomas, Hampel, Heather, Pearlman, Rachel, Berndt, Sonja, Hayes, Richard, Martinez, Marie Elena, Thomas, Sushma, Corley, Douglas, Pharoah, Paul, Larsson, Susanna, Yen, Yun, Lenz, Heinz-Josef, White, Emily, Li, Li, Doheny, Kimberly, Pugh, Elizabeth, Shelford, Tameka, Chan, Andrew, Cruz-Correa, Marcia, Lindblom, Annika, Hunter, David, Joshi, Amit, Schafmayer, Clemens, Scacheri, Peter, Kundaje, Anshul, Nickerson, Deborah, Schoen, Robert, Hampe, Jochen, Stadler, Zsofia, Vodicka, Pavel, Vodickova, Ludmila, Vymetalkova, Veronika, Papadopoulos, Nickolas, Edlund, Chistopher, Gauderman, William, Thomas, Duncan, Shibata, David, Toland, Amanda, Markowitz, Sanford, Kim, Andre, Chanock, Stephen, van Duijnhoven, Franzel, Feskens, Edith, Sakoda, Lori, Gago-Dominguez, Manuela, Wolk, Alicja, Naccarati, Alessio, Pardini, Barbara, FitzGerald, Liesel, Lee, Soo Chin, Ogino, Shuji, Bien, Stephanie, Kooperberg, Charles, Li, Christopher, Lin, Yi, Prentice, Ross, Qu, Conghui, Bézieau, Stéphane, Tangen, Catherine, Mardis, Elaine, Yamaji, Taiki, Sawada, Norie, Iwasaki, Motoki, Haiman, Christopher, Le Marchand, Loic, Wu, Anna, Qu, Chenxu, McNeil, Caroline, Coetzee, Gerhard, Hayward, Caroline, Deary, Ian, Harris, Sarah, Theodoratou, Evropi, Reid, Stuart, Walker, Marion, Ooi, Li Yin, Moreno, Victor, Casey, Graham, Gruber, Stephen, Tomlinson, Ian, Zheng, Wei, Dunlop, Malcolm, Houlston, Richard, and Peters, Ulrike

Published
2023
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23. Genetically predicted circulating concentrations of micronutrients and risk of colorectal cancer among individuals of European descent: a Mendelian randomization study

Author

Tsilidis, Konstantinos K, Papadimitriou, Nikos, Dimou, Niki, Gill, Dipender, Lewis, Sarah J, Martin, Richard M, Murphy, Neil, Markozannes, Georgios, Zuber, Verena, Cross, Amanda J, Burrows, Kimberley, Lopez, David S, Key, Timothy J, Travis, Ruth C, Perez-Cornago, Aurora, Hunter, David J, van Duijnhoven, Fränzel JB, Albanes, Demetrius, Arndt, Volker, Berndt, Sonja I, Bézieau, Stéphane, Bishop, D Timothy, Boehm, Juergen, Brenner, Hermann, Burnett-Hartman, Andrea, Campbell, Peter T, Casey, Graham, Castellví-Bel, Sergi, Chan, Andrew T, Chang-Claude, Jenny, de la Chapelle, Albert, Figueiredo, Jane C, Gallinger, Steven J, Giles, Graham G, Goodman, Phyllis J, Gsur, Andrea, Hampe, Jochen, Hampel, Heather, Hoffmeister, Michael, Jenkins, Mark A, Keku, Temitope O, Kweon, Sun-Seog, Larsson, Susanna C, Le Marchand, Loic, Li, Christopher I, Li, Li, Lindblom, Annika, Martín, Vicente, Milne, Roger L, Moreno, Victor, Nan, Hongmei, Nassir, Rami, Newcomb, Polly A, Offit, Kenneth, Pharoah, Paul DP, Platz, Elizabeth A, Potter, John D, Qi, Lihong, Rennert, Gad, Sakoda, Lori C, Schafmayer, Clemens, Slattery, Martha L, Snetselaar, Linda, Schenk, Jeanette, Thibodeau, Stephen N, Ulrich, Cornelia M, Van Guelpen, Bethany, Harlid, Sophia, Visvanathan, Kala, Vodickova, Ludmila, Wang, Hansong, White, Emily, Wolk, Alicja, Woods, Michael O, Wu, Anna H, Zheng, Wei, Bueno-de-Mesquita, Bas, Boutron-Ruault, Marie-Christine, Hughes, David J, Jakszyn, Paula, Kühn, Tilman, Palli, Domenico, Riboli, Elio, Giovannucci, Edward L, Banbury, Barbara L, Gruber, Stephen B, Peters, Ulrike, Gunter, Marc J, and on behalf of GECCO, CORECT

Subjects
Complementary and Integrative Health, Digestive Diseases, Clinical Research, Clinical Trials and Supportive Activities, Cancer, Colo-Rectal Cancer, Prevention, Nutrition, Prevention of disease and conditions, and promotion of well-being, 3.3 Nutrition and chemoprevention, Case-Control Studies, Colorectal Neoplasms, Dietary Supplements, Genetic Predisposition to Disease, Humans, Mendelian Randomization Analysis, Micronutrients, Risk Factors, Selenium, Vitamin B 12, White People, Mendelian randomization, genes, nutrition, supplements, colorectal cancer, Engineering, Medical and Health Sciences, Nutrition & Dietetics
Abstract

BackgroundThe literature on associations of circulating concentrations of minerals and vitamins with risk of colorectal cancer is limited and inconsistent. Evidence from randomized controlled trials (RCTs) to support the efficacy of dietary modification or nutrient supplementation for colorectal cancer prevention is also limited.ObjectivesTo complement observational and RCT findings, we investigated associations of genetically predicted concentrations of 11 micronutrients (β-carotene, calcium, copper, folate, iron, magnesium, phosphorus, selenium, vitamin B-6, vitamin B-12, and zinc) with colorectal cancer risk using Mendelian randomization (MR).MethodsTwo-sample MR was conducted using 58,221 individuals with colorectal cancer and 67,694 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Cancer Transdisciplinary Study, and Colon Cancer Family Registry. Inverse variance-weighted MR analyses were performed with sensitivity analyses to assess the impact of potential violations of MR assumptions.ResultsNominally significant associations were noted for genetically predicted iron concentration and higher risk of colon cancer [ORs per SD (ORSD): 1.08; 95% CI: 1.00, 1.17; P value = 0.05] and similarly for proximal colon cancer, and for vitamin B-12 concentration and higher risk of colorectal cancer (ORSD: 1.12; 95% CI: 1.03, 1.21; P value = 0.01) and similarly for colon cancer. A nominally significant association was also noted for genetically predicted selenium concentration and lower risk of colon cancer (ORSD: 0.98; 95% CI: 0.96, 1.00; P value = 0.05) and similarly for distal colon cancer. These associations were robust to sensitivity analyses. Nominally significant inverse associations were observed for zinc and risk of colorectal and distal colon cancers, but sensitivity analyses could not be performed. None of these findings survived correction for multiple testing. Genetically predicted concentrations of β-carotene, calcium, copper, folate, magnesium, phosphorus, and vitamin B-6 were not associated with disease risk.ConclusionsThese results suggest possible causal associations of circulating iron and vitamin B-12 (positively) and selenium (inversely) with risk of colon cancer.

Published
2021

25. Elucidating the Risk of Colorectal Cancer for Variants in Hereditary Colorectal Cancer Genes

Author

Wang, Xiaoliang, Huyghe, Jeroen R., Joo, Jihoon E., Georgeson, Peter, Arndt, Volker, Berndt, Sonja I., Bézieau, Stéphane, Bien, Stephanie A., Bishop, D. Timothy, Brenner, Hermann, Brezina, Stefanie, Burnett-Hartman, Andrea, Campbell, Peter T., Casey, Graham, Castellví-Bel, Sergi, Chan, Andrew T., Chang-Claude, Jenny, Chen, Xuechen, Conti, David V., Cremolini, Chiara, Diergaarde, Brenda, Figueiredo, Jane C., FitzGerald, Liesel M., Gago-Dominguez, Manuela, Gallinger, Steven, Giles, Graham G., Gsu, Andrea, Gunter, Marc J., Hampe, Jochen, Hampel, Heather, Harrison, Tabitha A., Hoffmeister, Michael, Keku, Temitope O., Kundaje, Anshul, Le Marchand, Loic, Lenz, Heinz-Josef, Li, Christopher I., Li, Li, Lin, Yi, Lindblom, Annika, Moreno, Victor, Murphy, Neil, Newcomb, Polly A., Newton, Christina C., Obón-Santacana, Mireia, Ogino, Shuji, Pai, Rish K., Palmer, Julie R., Pearlman, Rachel, Pharoah, Paul D.P., Phipps, Amanda I., Platz, Elizabeth A., Potter, John D., Rennert, Gad, Sakoda, Lori C., Schafmayer, Clemens, Schmit, Stephanie L., Schoen, Robert E., Slattery, Martha L., Stadler, Zsofia K., Steinfelder, Robert S., Thibodeau, Stephen N., Ulrich, Cornelia M., Um, Caroline Y., van Duijnhoven, Franzel J.B., Van Guelpen, Bethany, Visvanathan, Kala, Vodicka, Pavel, Vodickova, Ludmila, Vymetalkova, Veronika, Weinstein, Stephanie J., White, Emily, Winship, Ingrid M., Wolk, Alicja, Gruber, Stephen B., Jenkins, Mark A., Mahmood, Khalid, Thomas, Minta, Qu, Conghui, Hsu, Li, Buchanan, Daniel D., and Peters, Ulrike

Published
2023
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26. Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies

Author

Munn‐Chernoff, Melissa A, Johnson, Emma C, Chou, Yi‐Ling, Coleman, Jonathan RI, Thornton, Laura M, Walters, Raymond K, Yilmaz, Zeynep, Baker, Jessica H, Hübel, Christopher, Gordon, Scott, Medland, Sarah E, Watson, Hunna J, Gaspar, Héléna A, Bryois, Julien, Hinney, Anke, Leppä, Virpi M, Mattheisen, Manuel, Ripke, Stephan, Yao, Shuyang, Giusti‐Rodríguez, Paola, Hanscombe, Ken B, Adan, Roger AH, Alfredsson, Lars, Ando, Tetsuya, Andreassen, Ole A, Berrettini, Wade H, Boehm, Ilka, Boni, Claudette, Perica, Vesna Boraska, Buehren, Katharina, Burghardt, Roland, Cassina, Matteo, Cichon, Sven, Clementi, Maurizio, Cone, Roger D, Courtet, Philippe, Crow, Scott, Crowley, James J, Danner, Unna N, Davis, Oliver SP, de Zwaan, Martina, Dedoussis, George, Degortes, Daniela, DeSocio, Janiece E, Dick, Danielle M, Dikeos, Dimitris, Dina, Christian, Dmitrzak‐Weglarz, Monika, Docampo, Elisa, Duncan, Laramie E, Egberts, Karin, Ehrlich, Stefan, Escaramís, Geòrgia, Esko, Tõnu, Estivill, Xavier, Farmer, Anne, Favaro, Angela, Fernández‐Aranda, Fernando, Fichter, Manfred M, Fischer, Krista, Föcker, Manuel, Foretova, Lenka, Forstner, Andreas J, Forzan, Monica, Franklin, Christopher S, Gallinger, Steven, Giegling, Ina, Giuranna, Johanna, Gonidakis, Fragiskos, Gorwood, Philip, Mayora, Monica Gratacos, Guillaume, Sébastien, Guo, Yiran, Hakonarson, Hakon, Hatzikotoulas, Konstantinos, Hauser, Joanna, Hebebrand, Johannes, Helder, Sietske G, Herms, Stefan, Herpertz‐Dahlmann, Beate, Herzog, Wolfgang, Huckins, Laura M, Hudson, James I, Imgart, Hartmut, Inoko, Hidetoshi, Janout, Vladimir, Jiménez‐Murcia, Susana, Julià, Antonio, Kalsi, Gursharan, Kaminská, Deborah, Karhunen, Leila, Karwautz, Andreas, Kas, Martien JH, Kennedy, James L, Keski‐Rahkonen, Anna, Kiezebrink, Kirsty, Kim, Youl‐Ri, Klump, Kelly L, Knudsen, Gun Peggy S, and La Via, Maria C

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Epidemiology, Health Sciences, Psychology, Pharmacology and Pharmaceutical Sciences, Nutrition, Brain Disorders, Alcoholism, Alcohol Use and Health, Mental Health, Eating Disorders, Substance Misuse, Genetics, Tobacco Smoke and Health, Tobacco, Human Genome, Drug Abuse (NIDA only), Prevention, Clinical Research, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Good Health and Well Being, Alcoholism, Depressive Disorder, Major, Feeding and Eating Disorders, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Phenotype, Polymorphism, Single Nucleotide, Risk Factors, Schizophrenia, Substance-Related Disorders, Tobacco Use Disorder, eating disorders, genetic correlation, substance use, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Biomedical and clinical sciences, Health sciences
Abstract

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [rg ], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (rg = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (rg = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (rgs = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.

Published
2021

27. Intraductal Transplantation Models of Human Pancreatic Ductal Adenocarcinoma Reveal Progressive Transition of Molecular Subtypes

Author

Miyabayashi, Koji, Baker, Lindsey A, Deschênes, Astrid, Traub, Benno, Caligiuri, Giuseppina, Plenker, Dennis, Alagesan, Brinda, Belleau, Pascal, Li, Siran, Kendall, Jude, Jang, Gun Ho, Kawaguchi, Risa Karakida, Somerville, Tim DD, Tiriac, Hervé, Hwang, Chang-Il, Burkhart, Richard A, Roberts, Nicholas J, Wood, Laura D, Hruban, Ralph H, Gillis, Jesse, Krasnitz, Alexander, Vakoc, Christopher R, Wigler, Michael, Notta, Faiyaz, Gallinger, Steven, Park, Youngkyu, and Tuveson, David A

Subjects
Rare Diseases, Clinical Research, Cancer, Pancreatic Cancer, Digestive Diseases, 2.1 Biological and endogenous factors, Aetiology, Adenocarcinoma, Animals, Carcinoma, Pancreatic Ductal, Disease Models, Animal, Gene Expression Regulation, Neoplastic, Humans, Mice, Pancreatic Ducts, Prognosis, Oncology and Carcinogenesis
Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC. SIGNIFICANCE: Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification.See related commentary by Pickering and Morton, p. 1448.This article is highlighted in the In This Issue feature, p. 1426.

Published
2020

29. Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images

Author

Zhang, Yucheng, Lobo-Mueller, Edrise M., Karanicolas, Paul, Gallinger, Steven, Haider, Masoom A., and Khalvati, Farzad

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past a few years. Recent studies in radiomics aim to investigate the relationship between tumors imaging features and clinical outcomes. Open source radiomics feature banks enable the extraction and analysis of thousands of predefined features. On the other hand, recent advances in deep learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether predefined radiomics features have predictive information in addition to deep learning features. In this study, we propose a feature fusion method and investigate whether a combined feature bank of deep learning and predefined radiomics features can improve the prognostics performance. CT images from resectable Pancreatic Adenocarcinoma (PDAC) patients were used to compare the prognosis performance of common feature reduction and fusion methods and the proposed risk-score based feature fusion method for overall survival. It was shown that the proposed feature fusion method significantly improves the prognosis performance for overall survival in resectable PDAC cohorts, elevating the area under ROC curve by 51% compared to predefined radiomics features alone, by 16% compared to deep learning features alone, and by 32% compared to existing feature fusion and reduction methods for a combination of deep learning and predefined radiomics features.

Published
2019

30. CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging

Author

Zhang, Yucheng, Lobo-Mueller, Edrise M., Karanicolas, Paul, Gallinger, Steven, Haider, Masoom A., and Khalvati, Farzad

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In addition, the multicollinearity of radiomic features and multiple testing problem further impedes the CPH models performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients. The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index by 22%, providing a better fit for patients' survival patterns. The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.

Published
2019

31. Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma

Author

Zhang, Yucheng, Lobo-Mueller, Edrise M., Karanicolas, Paul, Gallinger, Steven, Haider, Masoom A., and Khalvati, Farzad

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited. Convolutional neural networks (CNNs) have been shown to outperform these feature-based models in computer vision tasks. However, training a CNN from scratch needs a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning has shown potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning approach for prognostication of PDAC patients for overall survival using two independent resectable PDAC cohorts. The proposed deep transfer learning model for prognostication of PDAC achieved the area under the receiver operating characteristic curve of 0.74, which was significantly higher than that of the traditional radiomics model (0.56) as well as a CNN model trained from scratch (0.50). These results suggest that deep transfer learning may significantly improve prognosis performance using small datasets in medical imaging.

Published
2019

32. Mortality by age, gene and gender in carriers of pathogenic mismatch repair gene variants receiving surveillance for early cancer diagnosis and treatment: a report from the prospective Lynch syndrome database

Author

Dominguez-Valentin, Mev, Haupt, Saskia, Seppälä, Toni T., Sampson, Julian R., Sunde, Lone, Bernstein, Inge, Jenkins, Mark A., Engel, Christoph, Aretz, Stefan, Nielsen, Maartje, Capella, Gabriel, Balaguer, Francesc, Evans, Dafydd Gareth, Burn, John, Holinski-Feder, Elke, Bertario, Lucio, Bonanni, Bernardo, Lindblom, Annika, Levi, Zohar, Macrae, Finlay, Winship, Ingrid, Plazzer, John-Paul, Sijmons, Rolf, Laghi, Luigi, Della Valle, Adriana, Heinimann, Karl, Dębniak, Tadeusz, Fruscio, Robert, Lopez-Koestner, Francisco, Alvarez-Valenzuela, Karin, Katz, Lior H., Laish, Ido, Vainer, Elez, Vaccaro, Carlos, Carraro, Dirce Maria, Monahan, Kevin, Half, Elizabeth, Stakelum, Aine, Winter, Des, Kennelly, Rory, Gluck, Nathan, Sheth, Harsh, Abu-Freha, Naim, Greenblatt, Marc, Rossi, Benedito Mauro, Bohorquez, Mabel, Cavestro, Giulia Martina, Lino-Silva, Leonardo S., Horisberger, Karoline, Tibiletti, Maria Grazia, Nascimento, Ivana do, Thomas, Huw, Rossi, Norma Teresa, Apolinário da Silva, Leandro, Zaránd, Attila, Ruiz-Bañobre, Juan, Heuveline, Vincent, Mecklin, Jukka-Pekka, Pylvänäinen, Kirsi, Renkonen-Sinisalo, Laura, Lepistö, Anna, Peltomäki, Päivi, Therkildsen, Christina, Madsen, Mia Gebauer, Burgdorf, Stefan Kobbelgaard, Hopper, John L., Win, Aung Ko, Haile, Robert W., Lindor, Noralane, Gallinger, Steven, Le Marchand, Loïc, Newcomb, Polly A., Figueiredo, Jane, Buchanan, Daniel D., Thibodeau, Stephen N., von Knebel Doeberitz, Magnus, Loeffler, Markus, Rahner, Nils, Schröck, Evelin, Steinke-Lange, Verena, Schmiegel, Wolff, Vangala, Deepak, Perne, Claudia, Hüneburg, Robert, Redler, Silke, Büttner, Reinhard, Weitz, Jürgen, Pineda, Marta, Duenas, Nuria, Vidal, Joan Brunet, Moreira, Leticia, Sánchez, Ariadna, Hovig, Eivind, Nakken, Sigve, Green, Kate, Lalloo, Fiona, Hill, James, Crosbie, Emma, Mints, Miriam, Goldberg, Yael, Tjandra, Douglas, ten Broeke, Sanne W., Kariv, Revital, Rosner, Guy, Advani, Suresh H., Thomas, Lidiya, Shah, Pankaj, Shah, Mithun, Neffa, Florencia, Esperon, Patricia, Pavicic, Walter, Torrezan, Giovana Tardin, Bassaneze, Thiago, Martin, Claudia Alejandra, Moslein, Gabriela, and Moller, Pål

Published
2023
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33. Landscape of somatic single nucleotide variants and indels in colorectal cancer and impact on survival.

Author

Zaidi, Syed H, Harrison, Tabitha A, Phipps, Amanda I, Steinfelder, Robert, Trinh, Quang M, Qu, Conghui, Banbury, Barbara L, Georgeson, Peter, Grasso, Catherine S, Giannakis, Marios, Adams, Jeremy B, Alwers, Elizabeth, Amitay, Efrat L, Barfield, Richard T, Berndt, Sonja I, Borozan, Ivan, Brenner, Hermann, Brezina, Stefanie, Buchanan, Daniel D, Cao, Yin, Chan, Andrew T, Chang-Claude, Jenny, Connolly, Charles M, Drew, David A, Farris, Alton Brad, Figueiredo, Jane C, French, Amy J, Fuchs, Charles S, Garraway, Levi A, Gruber, Steve, Guinter, Mark A, Hamilton, Stanley R, Harlid, Sophia, Heisler, Lawrence E, Hidaka, Akihisa, Hopper, John L, Huang, Wen-Yi, Huyghe, Jeroen R, Jenkins, Mark A, Krzyzanowski, Paul M, Lemire, Mathieu, Lin, Yi, Luo, Xuemei, Mardis, Elaine R, McPherson, John D, Miller, Jessica K, Moreno, Victor, Mu, Xinmeng Jasmine, Nishihara, Reiko, Papadopoulos, Nickolas, Pasternack, Danielle, Quist, Michael J, Rafikova, Adilya, Reid, Emma EG, Shinbrot, Eve, Shirts, Brian H, Stein, Lincoln D, Teney, Cherie D, Timms, Lee, Um, Caroline Y, Van Guelpen, Bethany, Van Tassel, Megan, Wang, Xiaolong, Wheeler, David A, Yung, Christina K, Hsu, Li, Ogino, Shuji, Gsur, Andrea, Newcomb, Polly A, Gallinger, Steven, Hoffmeister, Michael, Campbell, Peter T, Thibodeau, Stephen N, Sun, Wei, Hudson, Thomas J, and Peters, Ulrike

Abstract

Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge of CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional genes significantly mutated in CRC. We find that among hypermutated tumors, an increased mutation burden is associated with improved CRC-specific survival (HR = 0.42, 95% CI: 0.21-0.82). Mutations in TP53 are associated with poorer CRC-specific survival, which is most pronounced in cases carrying TP53 mutations with predicted 0% transcriptional activity (HR = 1.53, 95% CI: 1.21-1.94). Furthermore, we observe differences in mutational frequency of several genes and pathways by tumor location, stage, and sex. Overall, this large study provides deep insights into somatic mutations in CRC, and their potential relationships with survival and tumor features.

Published
2020

34. Potential impact of family history–based screening guidelines on the detection of early‐onset colorectal cancer

Author

Gupta, Samir, Bharti, Balambal, Ahnen, Dennis J, Buchanan, Daniel D, Cheng, Iona C, Cotterchio, Michelle, Figueiredo, Jane C, Gallinger, Steven J, Haile, Robert W, Jenkins, Mark A, Lindor, Noralane M, Macrae, Finlay A, Le Marchand, Loïc, Newcomb, Polly A, Thibodeau, Stephen N, Win, Aung Ko, and Martinez, Maria Elena

Subjects
Prevention, Cancer, Health Services, Digestive Diseases, Aging, Clinical Research, Colo-Rectal Cancer, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 4.4 Population screening, Adult, Age Factors, Age of Onset, Case-Control Studies, Colorectal Neoplasms, Early Detection of Cancer, Family Health, Female, Humans, Male, Middle Aged, Practice Guidelines as Topic, Retrospective Studies, Sensitivity and Specificity, case-control study, family history, guidelines, sensitivity, specificity, young-onset colorectal cancer, Oncology and Carcinogenesis, Public Health and Health Services, Oncology & Carcinogenesis
Abstract

BackgroundInitiating screening at an earlier age based on cancer family history is one of the primary recommended strategies for the prevention and detection of early-onset colorectal cancer (EOCRC), but data supporting the effectiveness of this approach are limited. The authors assessed the performance of family history-based guidelines for identifying individuals with EOCRC.MethodsThe authors conducted a population-based, case-control study of individuals aged 40 to 49 years with (2473 individuals) and without (772 individuals) incident CRC in the Colon Cancer Family Registry from 1998 through 2007. They estimated the sensitivity and specificity of family history-based criteria jointly recommended by the American Cancer Society, the US Multi-Society Task Force on CRC, and the American College of Radiology in 2008 for early screening, and the age at which each participant could have been recommended screening initiation if these criteria had been applied.ResultsFamily history-based early screening criteria were met by approximately 25% of cases (614 of 2473 cases) and 10% of controls (74 of 772 controls), with a sensitivity of 25% and a specificity of 90% for identifying EOCRC cases aged 40 to 49 years. Among 614 individuals meeting early screening criteria, 98.4% could have been recommended screening initiation at an age younger than the observed age of diagnosis.ConclusionsOf CRC cases aged 40 to 49 years, 1 in 4 met family history-based early screening criteria, and nearly all cases who met these criteria could have had CRC diagnosed earlier (or possibly even prevented) if earlier screening had been implemented as per family history-based guidelines. Additional strategies are needed to improve the detection and prevention of EOCRC for individuals not meeting family history criteria for early screening.

Published
2020

35. Functional informed genome‐wide interaction analysis of body mass index, diabetes and colorectal cancer risk

Author

Xia, Zhiyu, Su, Yu‐Ru, Petersen, Paneen, Qi, Lihong, Kim, Andre E, Figueiredo, Jane C, Lin, Yi, Nan, Hongmei, Sakoda, Lori C, Albanes, Demetrius, Berndt, Sonja I, Bézieau, Stéphane, Bien, Stephanie, Buchanan, Daniel D, Casey, Graham, Chan, Andrew T, Conti, David V, Drew, David A, Gallinger, Steven J, Gauderman, W James, Giles, Graham G, Gruber, Stephen B, Gunter, Marc J, Hoffmeister, Michael, Jenkins, Mark A, Joshi, Amit D, Le Marchand, Loic, Lewinger, Juan P, Li, Li, Lindor, Noralane M, Moreno, Victor, Murphy, Neil, Nassir, Rami, Newcomb, Polly A, Ogino, Shuji, Rennert, Gad, Song, Mingyang, Wang, Xiaoliang, Wolk, Alicja, Woods, Michael O, Brenner, Hermann, White, Emily, Slattery, Martha L, Giovannucci, Edward L, Chang‐Claude, Jenny, Pharoah, Paul DP, Hsu, Li, Campbell, Peter T, and Peters, Ulrike

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Digestive Diseases, Human Genome, Genetics, Cancer, Diabetes, Prevention, Nutrition, Colo-Rectal Cancer, Metabolic and endocrine, ATPases Associated with Diverse Cellular Activities, Aged, Body Mass Index, Colorectal Neoplasms, Databases, Genetic, Diabetes Mellitus, Type 2, Female, Gene Expression, Genotype, Hepatocyte Nuclear Factor 3-alpha, Humans, Male, Microtubule-Associated Proteins, Middle Aged, Obesity, Phenotype, Proteasome Endopeptidase Complex, Protein Tyrosine Phosphatase, Non-Receptor Type 2, Sex Factors, Sialic Acid Binding Ig-like Lectin 3, Voltage-Gated Sodium Channel beta-1 Subunit, BMI, colorectal cancer, diabetes, gene expression, gene-environmental interaction, Biochemistry and Cell Biology, Oncology and carcinogenesis
Abstract

BackgroundBody mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology.MethodsTo improve statistical power and interpretation for gene-environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2 ) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype-Tissue Expression Project for all genes with heritability ≥1%. We used a mixed-effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI-CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR

Published
2020

36. Combined burden and functional impact tests for cancer driver discovery using DriverPower

Author

Shuai, Shimin, Gallinger, Steven, and Stein, Lincoln D

Subjects
Human Genome, Genetics, Cancer, Good Health and Well Being, Algorithms, Genome, Human, Genomics, Humans, MEF2 Transcription Factors, Mutation, Mutation Rate, Neoplasms, Peptide Elongation Factor 1, Receptors, G-Protein-Coupled, Software, Whole Genome Sequencing, PCAWG Drivers and Functional Interpretation Working Group, PCAWG Consortium
Abstract

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

Published
2020

40. Early results of the PASS-01 trial: Pancreatic adenocarcinoma signature stratification for treatment-01.

Author

Knox, Jennifer J., primary, Jaffee, Elizabeth M., additional, O'Kane, Grainne M., additional, King, Daniel, additional, Laheru, Dan, additional, Yu, Kenneth H., additional, Perez, Kimberly, additional, Habowski, Amber N, additional, Grant, Robert C, additional, Fischer, Sandra, additional, Aguirre, Andrew, additional, Jang, Raymond Woo-Jun, additional, Devoe, Craig E., additional, O'Reilly, Eileen M., additional, Dodd, Anna, additional, Wolpin, Brian M., additional, Ye, Xiang Y, additional, Notta, Faiyaz, additional, Gallinger, Steven, additional, and Tuveson, David A., additional

Published
2024
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41. Supplementary Table S1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Author

Sinicrope, Frank A., primary, Nelson, Garth D., primary, Saberzadeh-Ardestani, Bahar, primary, Segovia, Diana I., primary, Graham, Rondell P., primary, Wu, Christina, primary, Hagen, Catherine E., primary, Shivji, Sameer, primary, Savage, Paul, primary, Buchanan, Dan D., primary, Jenkins, Mark A., primary, Phipps, Amanda I., primary, Swallow, Carol, primary, LeMarchand, Loic, primary, Gallinger, Steven, primary, Grant, Robert C., primary, Pai, Reetesh K., primary, Sinicrope, Stephen N., primary, Yan, Dongyao, primary, Shanmugam, Kandavel, primary, Conner, James, primary, Cyr, David P., primary, Kirsch, Richard, primary, Banerjee, Imon, primary, Alberts, Steve R., primary, Shi, Qian, primary, and Pai, Rish K., primary

Published
2024
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42. TABLE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Author

Sinicrope, Frank A., primary, Nelson, Garth D., primary, Saberzadeh-Ardestani, Bahar, primary, Segovia, Diana I., primary, Graham, Rondell P., primary, Wu, Christina, primary, Hagen, Catherine E., primary, Shivji, Sameer, primary, Savage, Paul, primary, Buchanan, Dan D., primary, Jenkins, Mark A., primary, Phipps, Amanda I., primary, Swallow, Carol, primary, LeMarchand, Loic, primary, Gallinger, Steven, primary, Grant, Robert C., primary, Pai, Reetesh K., primary, Sinicrope, Stephen N., primary, Yan, Dongyao, primary, Shanmugam, Kandavel, primary, Conner, James, primary, Cyr, David P., primary, Kirsch, Richard, primary, Banerjee, Imon, primary, Alberts, Steve R., primary, Shi, Qian, primary, and Pai, Rish K., primary

Published
2024
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43. FIGURE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Author

Sinicrope, Frank A., primary, Nelson, Garth D., primary, Saberzadeh-Ardestani, Bahar, primary, Segovia, Diana I., primary, Graham, Rondell P., primary, Wu, Christina, primary, Hagen, Catherine E., primary, Shivji, Sameer, primary, Savage, Paul, primary, Buchanan, Dan D., primary, Jenkins, Mark A., primary, Phipps, Amanda I., primary, Swallow, Carol, primary, LeMarchand, Loic, primary, Gallinger, Steven, primary, Grant, Robert C., primary, Pai, Reetesh K., primary, Sinicrope, Stephen N., primary, Yan, Dongyao, primary, Shanmugam, Kandavel, primary, Conner, James, primary, Cyr, David P., primary, Kirsch, Richard, primary, Banerjee, Imon, primary, Alberts, Steve R., primary, Shi, Qian, primary, and Pai, Rish K., primary

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2024
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44. FIGURE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Author

Sinicrope, Frank A., primary, Nelson, Garth D., primary, Saberzadeh-Ardestani, Bahar, primary, Segovia, Diana I., primary, Graham, Rondell P., primary, Wu, Christina, primary, Hagen, Catherine E., primary, Shivji, Sameer, primary, Savage, Paul, primary, Buchanan, Dan D., primary, Jenkins, Mark A., primary, Phipps, Amanda I., primary, Swallow, Carol, primary, LeMarchand, Loic, primary, Gallinger, Steven, primary, Grant, Robert C., primary, Pai, Reetesh K., primary, Sinicrope, Stephen N., primary, Yan, Dongyao, primary, Shanmugam, Kandavel, primary, Conner, James, primary, Cyr, David P., primary, Kirsch, Richard, primary, Banerjee, Imon, primary, Alberts, Steve R., primary, Shi, Qian, primary, and Pai, Rish K., primary

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2024
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45. TABLE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Author

Sinicrope, Frank A., primary, Nelson, Garth D., primary, Saberzadeh-Ardestani, Bahar, primary, Segovia, Diana I., primary, Graham, Rondell P., primary, Wu, Christina, primary, Hagen, Catherine E., primary, Shivji, Sameer, primary, Savage, Paul, primary, Buchanan, Dan D., primary, Jenkins, Mark A., primary, Phipps, Amanda I., primary, Swallow, Carol, primary, LeMarchand, Loic, primary, Gallinger, Steven, primary, Grant, Robert C., primary, Pai, Reetesh K., primary, Sinicrope, Stephen N., primary, Yan, Dongyao, primary, Shanmugam, Kandavel, primary, Conner, James, primary, Cyr, David P., primary, Kirsch, Richard, primary, Banerjee, Imon, primary, Alberts, Steve R., primary, Shi, Qian, primary, and Pai, Rish K., primary

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2024
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46. TABLE 3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Author

Sinicrope, Frank A., primary, Nelson, Garth D., primary, Saberzadeh-Ardestani, Bahar, primary, Segovia, Diana I., primary, Graham, Rondell P., primary, Wu, Christina, primary, Hagen, Catherine E., primary, Shivji, Sameer, primary, Savage, Paul, primary, Buchanan, Dan D., primary, Jenkins, Mark A., primary, Phipps, Amanda I., primary, Swallow, Carol, primary, LeMarchand, Loic, primary, Gallinger, Steven, primary, Grant, Robert C., primary, Pai, Reetesh K., primary, Sinicrope, Stephen N., primary, Yan, Dongyao, primary, Shanmugam, Kandavel, primary, Conner, James, primary, Cyr, David P., primary, Kirsch, Richard, primary, Banerjee, Imon, primary, Alberts, Steve R., primary, Shi, Qian, primary, and Pai, Rish K., primary

Published
2024
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47. Data from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Author

Sinicrope, Frank A., primary, Nelson, Garth D., primary, Saberzadeh-Ardestani, Bahar, primary, Segovia, Diana I., primary, Graham, Rondell P., primary, Wu, Christina, primary, Hagen, Catherine E., primary, Shivji, Sameer, primary, Savage, Paul, primary, Buchanan, Dan D., primary, Jenkins, Mark A., primary, Phipps, Amanda I., primary, Swallow, Carol, primary, LeMarchand, Loic, primary, Gallinger, Steven, primary, Grant, Robert C., primary, Pai, Reetesh K., primary, Sinicrope, Stephen N., primary, Yan, Dongyao, primary, Shanmugam, Kandavel, primary, Conner, James, primary, Cyr, David P., primary, Kirsch, Richard, primary, Banerjee, Imon, primary, Alberts, Steve R., primary, Shi, Qian, primary, and Pai, Rish K., primary

Published
2024
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