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3. Speed-Up of DNA Melting Algorithm with Complete Nearest Neighbor Properties

Author

Tostesen, E., Liu, F., Jenssen, T. -K., and Hovig, E.

Subjects
Quantitative Biology - Biomolecules
Abstract

We describe a faster and more accurate algorithm for computing the statistical mechanics of DNA denaturation according to the Poland-Scheraga type. Nearest neighbor thermodynamics is included in a complete and general way. The algorithm represents an optimization with respect to algorithmic complexity of the partition function algorithm of Yeramian et al.: We reduce the computation time for a base-pairing probability profile from O(N2) to O(N). This speed-up comes in addition to the speed-up due to a multiexponential approximation of the loop entropy factor as introduced by Fixman and Freire. The speed-up, however, is independent of the multiexponential approximation and reduces time from O(N3) to O(N2) in the exact case. In addition to calculating the standard base-pairing probability profiles, we propose to use the algorithm to calculate various other probabilities (loops, helices, tails) for a more direct view of the melting regions and their positions and sizes., Comment: 20 pages, 4 figures

Published
2004
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5. The population genomic legacy of the second plague pandemic

Author

Gopalakrishnan, S., Ebenesersdóttir, S., Lundstrøm, I., Turner-Walker, G., Moore, K., Luisi, P., Margaryan, A., Martin, M., Ellegaard, M., Magnússon, Ó., Sigurðsson, Á., Snorradóttir, S., Magnúsdóttir, D., Laffoon, J., van Dorp, L., Liu, X., Moltke, I., Ávila-Arcos, M., Schraiber, J., Rasmussen, S., Juan, D., Gelabert, P., de-Dios, T., Fotakis, A., Iraeta-Orbegozo, M., Vågene, Å., Denham, S., Christophersen, A., Stenøien, H., Vieira, F., Liu, S., Günther, T., Kivisild, T., Moseng, O., Skar, B., Cheung, C., Sandoval-Velasco, M., Wales, N., Schroeder, H., Campos, P., Guðmundsdóttir, V., Sicheritz-Ponten, T., Petersen, B., Halgunset, J., Gilbert, E., Cavalleri, G., Hovig, E., Kockum, I., Olsson, T., Alfredsson, L., Hansen, T., Werge, T., Willerslev, E., Balloux, F., Marques-Bonet, T., Lalueza-Fox, C., Nielsen, R., Stefánsson, K., Helgason, A., and Gilbert, M.

Abstract

SummaryHuman populations have been shaped by catastrophes that may have left long-lasting signatures in their genomes. One notable example is the second plague pandemic that entered Europe in ca. 1,347 CE and repeatedly returned for over 300 years, with typical village and town mortality estimated at 10%–40%.1 It is assumed that this high mortality affected the gene pools of these populations. First, local population crashes reduced genetic diversity. Second, a change in frequency is expected for sequence variants that may have affected survival or susceptibility to the etiologic agent (Yersinia pestis).2 Third, mass mortality might alter the local gene pools through its impact on subsequent migration patterns. We explored these factors using the Norwegian city of Trondheim as a model, by sequencing 54 genomes spanning three time periods: (1) prior to the plague striking Trondheim in 1,349 CE, (2) the 17th–19th century, and (3) the present. We find that the pandemic period shaped the gene pool by reducing long distance immigration, in particular from the British Isles, and inducing a bottleneck that reduced genetic diversity. Although we also observe an excess of large FST values at multiple loci in the genome, these are shaped by reference biases introduced by mapping our relatively low genome coverage degraded DNA to the reference genome. This implies that attempts to detect selection using ancient DNA (aDNA) datasets that vary by read length and depth of sequencing coverage may be particularly challenging until methods have been developed to account for the impact of differential reference bias on test statistics. Results and discussion STAR★Methods

Published
2022

6. 1315O Key learnings from building: A precision cancer medicine implementation initiative for Norway

Author

Tasken, K., primary, Russnes, H.G., additional, Aas, E., additional, Bjorge, L., additional, Blix, E.S., additional, Enerly, E., additional, Fagereng, L., additional, Flobak, Å., additional, Gilje, B., additional, Gjertsen, B.T., additional, Guren, T.K.K., additional, Heix, J., additional, Hovig, E., additional, Hovland, R., additional, Lonning, P.E., additional, Mæhle, P.M., additional, Nilsen, H.L., additional, Thoresen, S., additional, Smeland, S., additional, and Helland, A., additional

Published
2022
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7. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio, Ege Üniversitesi, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P., Wang, Dennis, Mason, Mike J., Szalai, Bence, Bulusu, Krishna C., Guan, Yuanfang, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaslavskiy, Mikhail, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric K. Y., Garnett, Mathew J., Di Veroli, Giovanni Y., Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., Saez-Rodriguez, Julio, Abante, Jordi, Abecassis, Barbara Schmitz, Aben, Nanne, Aghamirzaie, Delasa, Aittokallio, Tero, Akhtari, Farida S., Al-lazikani, Bissan, Alam, Tanvir, Allam, Amin, Allen, Chad, de Almeida, Mariana Pelicano, Altarawy, Doaa, Alves, Vinicius, Amadoz, Alicia, Anchang, Benedict, Antolin, Albert A., Ash, Jeremy R., Romeo Aznar, Victoria, Ba-alawi, Wail, Bagheri, Moeen, Bajic, Vladimir, Ball, Gordon, Ballester, Pedro J., Baptista, Delora, Bare, Christopher, Bateson, Mathilde, Bender, Andreas, Bertrand, Denis, Wijayawardena, Bhagya, Boroevich, Keith A., Bosdriesz, Evert, Bougouffa, Salim, Bounova, Gergana, Brouwer, Thomas, Bryant, Barbara, Calaza, Manuel, Calderone, Alberto, Calza, Stefano, Capuzzi, Stephen, Carbonell-Caballero, Jose, Carlin, Daniel, Carter, Hannah, Castagnoli, Luisa, Celebi, Remzi, Cesareni, Gianni, Chang, Hyeokyoon, Chen, Guocai, Chen, Haoran, Chen, Huiyuan, Cheng, Lijun, Chernomoretz, Ariel, Chicco, Davide, Cho, Kwang-Hyun, Cho, Sunghwan, Choi, Daeseon, Choi, Jaejoon, Choi, Kwanghun, Choi, Minsoo, De Cock, Martine, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzo, Miklos, Cubuk, Cankut, Curtis, Christina, Van Daele, Dries, Dang, Cuong C., Dijkstra, Tjeerd, Dopazo, Joaquin, Draghici, Sorin, Drosou, Anastasios, Dumontier, Michel, Ehrhart, Friederike, Eid, Fatma-Elzahraa, ElHefnawi, Mahmoud, Elmarakeby, Haitham, van Engelen, Bo, Engin, Hatice Billur, de Esch, Iwan, Evelo, Chris, Falcao, Andre O., Farag, Sherif, Fernandez-Lozano, Carlos, Fisch, Kathleen, Flobak, Asmund, Fornari, Chiara, Foroushani, Amir B. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. P., Westerman, Bart A., White, Simon Richard, Willighagen, Egon, Wurdinger, Tom, Xie, Lei, Xie, Shuilian, Xu, Hua, Yadav, Bhagwan, Yau, Christopher, Yeerna, Huwate, Yin, Jia Wei, Yu, Michael, Yu, MinHwan, Yun, So Jeong, Zakharov, Alexey, Zamichos, Alexandros, Zanin, Massimiliano, Zeng, Li, Zenil, Hector, Zhang, Frederick, Zhang, Pengyue, Zhang, Wei, Zhao, Hongyu, Zhao, Lan, Zheng, Wenjin, Zoufir, Azedine, Zucknick, Manuela, College of Engineering, Department of Industrial Engineering, Institute of Data Science, RS: FSE DACS IDS, Bioinformatica, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, Promovendi NTM, Tero Aittokallio / Principal Investigator, Bioinformatics, Institute for Molecular Medicine Finland, Hu, Z, Fotso, DC, Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden, Michael P. [0000-0003-0267-5792], Mason, Mike J. [0000-0002-5652-7739], Yu, Thomas [0000-0002-5841-0198], Kang, Jaewoo [0000-0001-6798-9106], Nguyen, Tin [0000-0001-8001-9470], Ahsen, Mehmet Eren [0000-0002-4907-0427], Stolovitzky, Gustavo [0000-0002-9618-2819], Guinney, Justin [0000-0003-1477-1888], Saez-Rodriguez, Julio [0000-0002-8552-8976], Apollo - University of Cambridge Repository, Menden, Michael P [0000-0003-0267-5792], Mason, Mike J [0000-0002-5652-7739], Pathology, CCA - Cancer biology and immunology, Medical oncology laboratory, Neurosurgery, Chemistry and Pharmaceutical Sciences, AIMMS, Medicinal chemistry, Universidade do Minho, Department of Computer Science, Professorship Marttinen P., Aalto-yliopisto, and Aalto University

Subjects
Drug Resistance, 02 engineering and technology, 13, PATHWAY, Antineoplastic Combined Chemotherapy Protocols, Molecular Targeted Therapy, Càncer, lcsh:Science, media_common, Cancer, Tumor, Settore BIO/18, Settore BIO/11, Drug combinations, High-throughput screening, Drug Synergism, purl.org/becyt/ford/1.2 [https], Genomics, Machine Learning, predictions, 3. Good health, ddc, Technologie de l'environnement, contrôle de la pollution, Benchmarking, 5.1 Pharmaceuticals, Cancer treatment, Farmacogenètica, Science & Technology - Other Topics, Development of treatments and therapeutic interventions, 0210 nano-technology, Human, Drug, media_common.quotation_subject, Science, 49/23, ADAM17 Protein, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, SDG 3 - Good Health and Well-being, RESOURCE, Machine learning, Genetics, Chimie, Humans, BREAST-CANCER, CELL, 49/98, Science & Technology, Antineoplastic Combined Chemotherapy Protocol, 45, MUTATIONS, Computational Biology, Androgen receptor, Breast-cancer, Gene, Cell, Inhibition, Resistance, Pathway, Mutations, Landscape, Resource, 631/114/1305, medicine.disease, Drug synergy, 49, 030104 developmental biology, Pharmacogenetics, Mutation, Ciências Médicas::Biotecnologia Médica, lcsh:Q, 631/154/1435/2163, Biomarkers, RESISTANCE, 0301 basic medicine, ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA, Statistical methods, Computer science, General Physics and Astronomy, Datasets as Topic, Drug resistance, purl.org/becyt/ford/1 [https], Phosphatidylinositol 3-Kinases, Biotecnologia Médica [Ciências Médicas], Neoplasms, Science and technology, Phosphoinositide-3 Kinase Inhibitors, Multidisciplinary, Biomarkers, Tumor, Cell Line, Tumor, Drug Antagonism, Drug Resistance, Neoplasm, Treatment Outcome, Pharmacogenetic, article, ANDROGEN RECEPTOR, 49/39, 631/114/2415, 021001 nanoscience & nanotechnology, 692/4028/67, Multidisciplinary Sciences, 317 Pharmacy, Patient Safety, Systems biology, 3122 Cancers, INHIBITION, Computational biology, Cell Line, medicine, LANDSCAPE, Physique, Human Genome, Data Science, General Chemistry, AstraZeneca-Sanger Drug Combination DREAM Consortium, Astronomie, GENE, Good Health and Well Being, Pharmacogenomics, Genomic, Neoplasm, 631/553, Phosphatidylinositol 3-Kinase
Abstract

PubMed: 31209238, The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. © 2019, The Author(s)., National Institute for Health Research, NIHR Wellcome Trust, WT: 102696, 206194 Magyar Tudományos Akadémia, MTA Bayer 668858 PrECISE AstraZeneca, We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)., Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests.

Published
2019

8. Correction: Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the Prospective Lynch Syndrome Database (Genetics in Medicine, (2020), 22, 1, (15-25), 10.1038/s41436-019-0596-9)

Author

Dominguez-Valentin M., Sampson J. R., Seppala T. T., ten Broeke S. W., Plazzer J. -P., Nakken S., Engel C., Aretz S., Jenkins M. A., Sunde L., Bernstein I., Capella G., Balaguer F., Thomas H., Evans D. G., Burn J., Greenblatt M., Hovig E., de Vos tot Nederveen Cappel W. H., Sijmons R. H., Bertario L., Tibiletti M. G., Cavestro G. M., Lindblom A., Della Valle A., Lopez-Kostner F., Gluck N., Katz L. H., Heinimann K., Vaccaro C. A., Buttner R., Gorgens H., Holinski-Feder E., Morak M., Holzapfel S., Huneburg R., Knebel Doeberitz M., Loeffler M., Rahner N., Schackert H. K., Steinke-Lange V., Schmiegel W., Vangala D., Pylvanainen K., Renkonen-Sinisalo L., Hopper J. L., Win A. K., Haile R. W., Lindor N. M., Gallinger S., Le Marchand L., Newcomb P. A., Figueiredo J. C., Thibodeau S. N., Wadt K., Therkildsen C., Okkels H., Ketabi Z., Moreira L., Sanchez A., Serra-Burriel M., Pineda M., Navarro M., Blanco I., Green K., Lalloo F., Crosbie E. J., Hill J., Denton O. G., Frayling I. M., Rodland E. A., Vasen H., Mints M., Neffa F., Esperon P., Alvarez K., Kariv R., Rosner G., Pinero T. A., Gonzalez M. L., Kalfayan P., Tjandra D., Winship I. M., Macrae F., Moslein G., Mecklin J. -P., Nielsen M., Moller P., Dominguez-Valentin, M., Sampson, J. R., Seppala, T. T., ten Broeke, S. W., Plazzer, J. -P., Nakken, S., Engel, C., Aretz, S., Jenkins, M. A., Sunde, L., Bernstein, I., Capella, G., Balaguer, F., Thomas, H., Evans, D. G., Burn, J., Greenblatt, M., Hovig, E., de Vos tot Nederveen Cappel, W. H., Sijmons, R. H., Bertario, L., Tibiletti, M. G., Cavestro, G. M., Lindblom, A., Della Valle, A., Lopez-Kostner, F., Gluck, N., Katz, L. H., Heinimann, K., Vaccaro, C. A., Buttner, R., Gorgens, H., Holinski-Feder, E., Morak, M., Holzapfel, S., Huneburg, R., Knebel Doeberitz, M., Loeffler, M., Rahner, N., Schackert, H. K., Steinke-Lange, V., Schmiegel, W., Vangala, D., Pylvanainen, K., Renkonen-Sinisalo, L., Hopper, J. L., Win, A. K., Haile, R. W., Lindor, N. M., Gallinger, S., Le Marchand, L., Newcomb, P. A., Figueiredo, J. C., Thibodeau, S. N., Wadt, K., Therkildsen, C., Okkels, H., Ketabi, Z., Moreira, L., Sanchez, A., Serra-Burriel, M., Pineda, M., Navarro, M., Blanco, I., Green, K., Lalloo, F., Crosbie, E. J., Hill, J., Denton, O. G., Frayling, I. M., Rodland, E. A., Vasen, H., Mints, M., Neffa, F., Esperon, P., Alvarez, K., Kariv, R., Rosner, G., Pinero, T. A., Gonzalez, M. L., Kalfayan, P., Tjandra, D., Winship, I. M., Macrae, F., Moslein, G., Mecklin, J. -P., Nielsen, M., and Moller, P.

Abstract

The original version of this Article did not contain details of Dutch Cancer Society (DCS) funding (grant number UL 2017-8223) in the Acknowledgements section. This has now been corrected in both the PDF and HTML versions of the Article.

Published
2020

11. Analysis in the Prospective Lynch Syndrome Database identifies sarcoma as part of the Lynch syndrome tumor spectrum

Author

Dominguez-Valentin M., Sampson J. R., Moller P., Seppala T. T., Plazzer J. -P., Nakken S., Engel C., Aretz S., Jenkins M. A., Sunde L., Bernstein I., Capella G., Balaguer F., Thomas H., Evans D. G., Burn J., Greenblatt M., Hovig E., Nielsen M., de Vos tot Nederveen Cappel W. H., Sijmons R. H., Bertario L., Tibiletti M. G., Cavestro G. M., Lindblom A., Valle A. D., Lopez-Kostner F., Gluck N., Katz L. H., Heinimann K., Vaccaro C. A., Buttner R., Gorgens H., Holinski-Feder E., Morak M., Holzapfel S., Huneburg R., von Knebel Doeberitz M., Loeffler M., Rahner N., Weitz J., Steinke-Lange V., ten Broeke S. W., Schmiegel W., Vangala D., Pylvanainen K., Renkonen-Sinisalo L., Hopper J. L., Win A. K., Haile R. W., Lindor N. M., Gallinger S., Le Marchand L., Newcomb P. A., Figueiredo J. C., Thibodeau S. N., Jensen L. H., Madsen M. B., Kroldrup L., Nilbert M., Moreira L., Sanchez A., Serra-Burriel M., Pineda M., Navarro M., Vidal J. B., Blanco I., Green K., Lalloo F., Crosbie E. J., Hill J., Denton O. G., Rodland E. A., Vasen H., Mints M., Neffa F., Esperon P., Alvarez K., Kariv R., Rosner G., Pinero T. A., Gonzalez M. L., Kalfayan P., Tjandra D., Winship I. M., Macrae F., Moslein G., Mecklin J. -P., Dominguez-Valentin, M., Sampson, J. R., Moller, P., Seppala, T. T., Plazzer, J. -P., Nakken, S., Engel, C., Aretz, S., Jenkins, M. A., Sunde, L., Bernstein, I., Capella, G., Balaguer, F., Thomas, H., Evans, D. G., Burn, J., Greenblatt, M., Hovig, E., Nielsen, M., de Vos tot Nederveen Cappel, W. H., Sijmons, R. H., Bertario, L., Tibiletti, M. G., Cavestro, G. M., Lindblom, A., Valle, A. D., Lopez-Kostner, F., Gluck, N., Katz, L. H., Heinimann, K., Vaccaro, C. A., Buttner, R., Gorgens, H., Holinski-Feder, E., Morak, M., Holzapfel, S., Huneburg, R., von Knebel Doeberitz, M., Loeffler, M., Rahner, N., Weitz, J., Steinke-Lange, V., ten Broeke, S. W., Schmiegel, W., Vangala, D., Pylvanainen, K., Renkonen-Sinisalo, L., Hopper, J. L., Win, A. K., Haile, R. W., Lindor, N. M., Gallinger, S., Le Marchand, L., Newcomb, P. A., Figueiredo, J. C., Thibodeau, S. N., Jensen, L. H., Madsen, M. B., Kroldrup, L., Nilbert, M., Moreira, L., Sanchez, A., Serra-Burriel, M., Pineda, M., Navarro, M., Vidal, J. B., Blanco, I., Green, K., Lalloo, F., Crosbie, E. J., Hill, J., Denton, O. G., Rodland, E. A., Vasen, H., Mints, M., Neffa, F., Esperon, P., Alvarez, K., Kariv, R., Rosner, G., Pinero, T. A., Gonzalez, M. L., Kalfayan, P., Tjandra, D., Winship, I. M., Macrae, F., Moslein, G., and Mecklin, J. -P.

Subjects
Male, Adult, Oncology, Cancer Research, 2019-20 coronavirus outbreak, medicine.medical_specialty, sarcoma, Databases, Factual, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Sarcoma/diagnosis, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Humans, Medicine, Aged, business.industry, Sarcoma, Syndrome, Middle Aged, medicine.disease, Colorectal Neoplasms, Hereditary Nonpolyposis, Lynch syndrome, MSH2, 030220 oncology & carcinogenesis, Colorectal Neoplasms, Hereditary Nonpolyposis/diagnosis, Female, 030211 gastroenterology & hepatology, business
Published
2020

12. Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Author

Tolios, A. De Las Rivas, J. Hovig, E. Trouillas, P. Scorilas, A. Mohr, T.

Abstract

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword “bioinformatics”). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature. © 2019

Published
2020

18. High number of kinome‐mutations in non‐small cell lung cancer is associated with reduced immune response and poor relapse‐free survival

Author

Helland, Å., Brustugun, O. T., Nakken, S., Halvorsen, A. R., Dønnem, T., Bremnes, R., Busund, L. T., Sun, J., Lorenz, S., Solberg, S. K., Jørgensen, L. H., Vodak, D., Myklebost, O., Hovig, E., and Meza‐Zepeda, L. A.

Subjects
Adult, Male, Immunity, Cellular, Genome, Human, Phosphotransferases, Short Report, High-Throughput Nucleotide Sequencing, kinome, Tumor Immunology and Microenvironment, sequencing, Middle Aged, mutations, Prognosis, Disease-Free Survival, Neoplasm Proteins, lung cancer, Carcinoma, Non-Small-Cell Lung, Mutation, Humans, Female, Neoplasm Recurrence, Local, Protein Kinase Inhibitors, Aged
Abstract

Lung cancer is the leading cause of cancer related death, and the past years’ improved insight into underlying molecular events has significantly improved outcome for specific subsets of patients. In particular, several new therapies that target protein kinases have been implemented, and many more are becoming available. We have investigated lung cancer specimens for somatic mutations in a targeted panel of 612 human genes, the majority being protein kinases. The somatic mutation profiles were correlated to profiles of immune cell infiltration as well as relapse‐free survival. Targeted deep sequencing was performed on 117 tumour/normal pairs using the SureSelect Human Kinome kit (Agilent Technologies), with capture probes targeting 3.2 Mb of the human genome, including exons and untranslated regions of all known kinases, kinase receptors and selected cancer‐related genes (612 genes in total). CD8 staining was determined using Ventana Benchmark. Survival analyses were performed using SPSS. The number of mutations per sample ranged from 0 to 50 (within the 612 genes tested), with a median of nine. The prognosis was worse for patients with more than the median number of mutations. A significant correlation was found between mutations in one of selected DNA‐repair genes and the total number of mutations in that tumour (p, What's new? Lung carcinomas are among the tumours with highest mutation frequency. Here, the authors performed mutational analyses of 612 genes–including all known kinases and kinase receptors–in 117 non‐small cell lung cancer (NSCLC) tumours. They also investigated the relationship of mutation rate to number of infiltrating lymphocytes and to the clinical course of the disease. The number of mutations per sample varied, and the relapse‐free survival was worse for patients with more than the median number of mutations. Also, there was a significant inverse correlation between the number of infiltrating stromal CD8+ lymphocytes and the presence of EGFR mutations.

Published
2017

22. Patterns of genomic evolution in advanced melanoma

Author

Birkeland, E., primary, Zhang, S., additional, Poduval, D., additional, Geisler, J., additional, Nakken, S., additional, Vodak, D., additional, Meza-Zepeda, L. A., additional, Hovig, E., additional, Myklebost, O., additional, Knappskog, S., additional, and Lønning, P. E., additional

Published
2018
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25. BRAF V600E mutation in early-stage multiple myeloma: good response to broad acting drugs and no relation to prognosis

Author

Rustad, E H, primary, Dai, H Y, additional, Hov, H, additional, Coward, E, additional, Beisvag, V, additional, Myklebost, O, additional, Hovig, E, additional, Nakken, S, additional, Vodák, D, additional, Meza-Zepeda, L A, additional, Sandvik, A K, additional, Wader, K F, additional, Misund, K, additional, Sundan, A, additional, Aarset, H, additional, and Waage, A, additional

Published
2015
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27. Ectopic expression of target genes may represent an inherent limitation of RT-PCR assays used for micrometastasis detection: Studies on the epithelial glycoprotein gene EGP-2

Author

deGraaf, H, Maelandsmo, GM, Ruud, P, Forus, A, Oyjord, T, Fodstad, O, and Hovig, E

Subjects
REVERSE-TRANSCRIPTASE, MOLECULAR-CLONING, BONE-MARROW, BREAST-CANCER, POLYMERASE CHAIN-REACTION, PERIPHERAL-BLOOD, MONOCLONAL-ANTIBODIES, MESSENGER-RNA, CIRCULATING TUMOR-CELLS, ILLEGITIMATE TRANSCRIPTION
Abstract

Our objective was to develop and study the feasibility of a quantitative, nested reverse-transcription polymerase chain reaction (RT-PCR) assay for detection of micrometastatic, epithelial tumor cells using the epithelial glycoprotein EGP-2 gene as a target, Several carcinoma cell lines and peripheral blood samples of 10 healthy volunteers were screened for levels of EGP-2 mRNA, The assay included EGP-2, competitor molecules, carrying an internal deletion, that had been titrated by limiting dilution. Seven carcinoma cell lines showed a wide spectrum of EGP-2 mRNA expression levels, with the highest values (20-100 molecules/cell) seen in 3 breast-cancer cell lines. Unexpectedly, a consistent low level of EGP-2 mRNA expression (0.0004 molecules/cell) was observed in peripheral blood mononuclear cells, probably representing ectopic non-functional expression, Because of this background level, spiking experiments with T47D breast-carcinoma cells added to blood mononuclear cells exhibited a detection limit that was not better than approximately one tumor cell in 2 x 10(4) normal cells, Together with the considerable variation of EGP-2 transcript levels that is observed in different carcinoma cell lines, the extent of expression in normal blood cells would prevent a reliable estimation of low numbers of carcinoma cells in clinical samples. A similar situation might well apply for other target genes, This emphasizes the need for a critical evaluation of the different steps involved in the methods used for RT-PCR detection of micrometastatic tumor cells. (C) 1997 Wiley-Liss, Inc.

Published
1997

39. A BRCA1 founder mutation, identified with haplotype analysis, allowing genotype/phenotype determination and predictive testing

Author

Dørum, A, primary, Møller, P, additional, Kamsteeg, E.J, additional, Scheffer, H, additional, Burton, M, additional, Heimdal, K.R, additional, Mæhle, L.O, additional, Hovig, E, additional, Tropé, C.G, additional, van der Hout, A.H, additional, van der Meulen, M.A, additional, Buys, C.H.C.M, additional, and te Meerman, G.J, additional

Published
1997
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43. Increased expression of IRF4 and ETS1 in CD4+ cells from patients with intermittent allergic rhinitis.

Author

Bruhn, S., Barrenäs, F., Mobini, R., Andersson, B. A., Chavali, S., Egan, B. S., Hovig, E., Sandve, G. K., Langston, M. A., Rogers, G., Wang, H., and Benson, M.

Subjects
RESPIRATORY allergy, ALLERGIC rhinitis, TRANSCRIPTION factors, GENE expression, CYTOKINES, CHROMATIN, PATIENTS
Abstract

To cite this article: Bruhn S, Barrenäs F, Mobini R, Andersson BA, Chavali S, Egan BS, Hovig E, Sandve GK, Langston MA, Rogers G, Wang H, Benson M. Increased expression of IRF4 and ETS1 in CD4+ cells from patients with intermittent allergic rhinitis. Allergy 2012; 67: 33-40. Abstract Background: The transcription factor (TF) IRF4 is involved in the regulation of Th1, Th2, Th9, and Th17 cells, and animal studies have indicated an important role in allergy. However, IRF4 and its target genes have not been examined in human allergy. Methods: IRF4 and its target genes were examined in allergen-challenged CD4+ cells from patients with IAR, using combined gene expression microarrays and chromatin immunoprecipitation chips (ChIP-chips), computational target prediction, and RNAi knockdowns. Results: IRF4 increased in allergen-challenged CD4+ cells from patients with IAR, and functional studies supported its role in Th2 cell activation. IRF4 ChIP-chip showed that IRF4 regulated a large number of genes relevant to Th cell differentiation. However, neither Th1 nor Th2 cytokines were the direct targets of IRF4. To examine whether IRF4 induced Th2 cytokines via one or more downstream TFs, we combined gene expression microarrays, ChIP-chips, and computational target prediction and found a putative intermediary TF, namely ETS1 in allergen-challenged CD4+ cells from allergic patients. ETS1 increased significantly in allergen-challenged CD4+ cells from patients compared to controls. Gene expression microarrays before and after ETS1 RNAi knockdown showed that ETS1 induced Th2 cytokines as well as disease-related pathways. Conclusions: Increased expression of IRF4 in allergen-challenged CD4+ cells from patients with intermittent allergic rhinitis leads to activation of a complex transcriptional program, including Th2 cytokines. [ABSTRACT FROM AUTHOR]

Published
2012
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46. Interferon-? suppresses S100A4 transcription independently of apoptosis or cell cycle arrest.

Author

Andersen, K, Smith-Sorensen, B, Pedersen, K B, Hovig, E, Myklebost, O, Fodstad, O, and Malandsmo, G M

Subjects
MAMMARY gland cancer, COLON cancer, OSTEOSARCOMA
Abstract

The S100A4 protein has been associated with increased metastatic capacity of cancer cells, and recent studies have suggested a correlation between the expression level of S100A4 and the prognostic outcome for patients with various types of cancer. The knowledge about the mechanisms underlying the metastasis-promoting effects is still limited, and the aim of the present study was to elucidate signal transduction pathways involved in the regulation of S100A4. After treatment of human carcinoma cells with interferon-gamma (IFN-γ), we observed downregulation of S100A4 both at mRNA and protein levels. The effect was not dependent on IFN-γ-induced apoptosis or IFN-γ-mediated cell cycle arrest. Moreover, IFN-γ-mediated decrease in mRNA stability could not account for the observed decrease in S100A4 transcript level. Finally, microarray analysis suggests ISGF3G, ETV5, ZNF133 and CEBPG as possible candidate genes involved in IFN-γ-mediated repression of S100A4. [ABSTRACT FROM AUTHOR]

Published
2003
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49. The BRCA1syndrome and other inherited breast or breast–ovarian cancers in a Norwegian prospective series

Author

Møller, P., Borg, A., Heimdal, K., Apold, J., Vallon-Christersson, J., Hovig, E., and Mæhle, L.

Abstract

Inherited breast cancer is a heterogenous group of diseases. We examined this heterogeneity in a prospective series of inherited breast and ovarian cancers, previously demonstrated to include 84% of inherited cancers. Ninety-two tumours (65 breast and 27 ovarian) in 82 patients from 70 kindreds were prospectively diagnosed. Fifteen of the breast cancers were in situ, 50 were infiltrating. 40 (49%) of the 82 women carried a BRCA1mutation, whereas no mutation in BRCA2was found. Approximately, two-thirds of the BRCA1mutation carriers had one of the four most frequent Norwegian founder mutations. Ninety-five per cent of the epithelial ovarian cancers occurred in BRCA1mutation carrying women versus 38% of infiltrating breast cancers and 7% of carcinoma in situof the breast. The BRCA1syndrome was phenotypically distinct with invasive, high grade, oestrogen receptor-negative breast cancers and epithelial ovarian cancers. Non-BRCA1/2inherited breast cancers included carcinoma in situand lobular carcinoma and were frequently bilateral. Non-BRCA1/2inherited breast cancer is not associated with epithelial ovarian cancer and in breast cancers has distinct biological characteristics, indicating that the different subgroups of inherited breast cancer may need different healthcare services.

Published
2001
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50. Intracellular metabolism of a 2'-O-methyl-stabilized ribozyme after uptake by DOTAP transfection or asfree ribozyme. A study by capillary electrophoresis.

Author

Prasmickaite, L, Hogset, A, Maelandsmo, G, Berg, K, Goodchild, J, Perkins, T, Fodstad, O, and Hovig, E

Abstract

The uptake and cellular metabolism of a fluorescein-labelled synthetic ribozyme stabilized by 2'- O -methyl modification and a 3' inverted thymidine have been studied, employing capillary gel electrophoresis as a novel and efficient analytical method. After internalization by DOTAP transfection, electrophoretic peaks of intact ribozyme and different degradation products were easily resolved and the amount of intracellular intact ribozyme was quantified to >10(7) molecules/cell at the peak value after 4 h transfection. On further incubation the amount of intracellular intact ribozyme decreased due to both degradation and efflux from the cell. However, even after 48 h incubation there were still >10(6) intact ribozyme molecules/cell. Clear differences both in uptake and in metabolism were seen when comparing DOTAP transfection with the uptake of free ribozyme. Fluorescence microscopy studies indicated that the ribozyme was mainly localized in intracellular granules, probably not accessible to target mRNA. This implies that agents able to release the intact ribozyme from intracellular vesicles into the cytosol should have a considerable potential for increasing the biological effects of synthetic ribozymes.

Published
1998
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