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17 results on '"Bounova G"'

1. Proteogenomic analysis of the autoreactive B cell repertoire in blood and tissues of patients with Sjögren's syndrome

Author

Broeren, M.G.A., Wang, J.J., Balzaretti, G., Groenen, P.J.T.A., Schaik, B.D. van, Chataway, T., Kaffa, C., Bervoets, S., Hebeda, K.M., Bounova, G., Pruijn, G.J.M., Gordon, T.P., Vries, N. De, Thurlings, R.M., Broeren, M.G.A., Wang, J.J., Balzaretti, G., Groenen, P.J.T.A., Schaik, B.D. van, Chataway, T., Kaffa, C., Bervoets, S., Hebeda, K.M., Bounova, G., Pruijn, G.J.M., Gordon, T.P., Vries, N. De, and Thurlings, R.M.

Abstract

Contains fulltext : 249151.pdf (Publisher’s version ) (Open Access), OBJECTIVE: To comparatively analyse the aberrant affinity maturation of the antinuclear and rheumatoid factor (RF) B cell repertoires in blood and tissues of patients with Sjögren's syndrome (SjS) using an integrated omics workflow. METHODS: Peptide sequencing of anti-Ro60, anti-Ro52, anti-La and RF was combined with B cell repertoire analysis at the DNA, RNA and single cell level in blood B cell subsets, affected salivary gland and extranodal marginal zone lymphomas of mucosa-associated lymphoid tissue (MALT) of patients with SjS. RESULTS: Affected tissues contained anti-Ro60, anti-Ro52, anti-La and RF clones as a small part of a polyclonal infiltrate. Anti-Ro60, anti-La and anti-Ro52 clones outnumbered RF clones. MALT lymphoma tissues contained monoclonal RF expansions. Autoreactive clones were not selected from a restricted repertoire in a circulating B cell subset. The antinuclear antibody (ANA) repertoires displayed similar antigen-dependent and immunoglobulin (Ig) G1-directed affinity maturation. RF clones displayed antigen-dependent, IgM-directed and more B cell receptor integrity-dependent affinity maturation. This coincided with extensive intra-clonal diversification in RF-derived lymphomas. Regeneration of clinical disease manifestations after rituximab coincided with large RF clones, which not necessarily belonged to the lymphoma clone, that displayed continuous affinity maturation and intra-clonal diversification. CONCLUSION: The ANA and RF repertoires in patients with SjS display tissue-restricted, antigen-dependent and divergent affinity maturation. Affinity maturation of RF clones deviates further during RF clone derived lymphomagenesis and during regeneration of the autoreactive repertoire after temporary disruption by rituximab. These data give insight into the molecular mechanisms of autoreactive inflammation in SjS, assist MALT lymphoma diagnosis and allow tracking its response to rituximab.

Published
2022

2. 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
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3. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

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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, Hu, Z, 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 M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., 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. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., 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. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., 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. D., 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. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., 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. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., Saez-Rodriguez J., 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, Hu, Z, 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 M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., 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. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., 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. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., 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. D., 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. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., 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. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., and Saez-Rodriguez J.

Abstract

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.

Published
2019

4. Identifying biomarkers of anti-cancer drug synergy using multi-task learning

Author

Evert Bosdriesz, Bounova G, Magali Michaut, de Ruiter, Daniel J. Vis, Nanne Aben, Yongsoo Kim, and L. F. A. Wessels

Subjects
Drug, Elastic net regularization, 0303 health sciences, business.industry, media_common.quotation_subject, Multi-task learning, 02 engineering and technology, Computational biology, medicine.disease, Regression, 3. Good health, Random forest, 03 medical and health sciences, Breast cancer, Text mining, Sample size determination, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, medicine, business, 030304 developmental biology, media_common
Abstract

Combining anti-cancer drugs has the potential to increase treatment efficacy. Because patient responses to drug combinations are highly variable, predictive biomarkers of synergy are required to identify which patients are likely to benefit from a drug combination. To aid biomarker identification, the DREAM challenge consortium has recently released data from a screen containing 85 cell lines and 167 drug combinations. The main challenge of these data is the low sample size: per drug combination, a median of 14 cell lines have been screened. We found that widely used methods in single drug response prediction, such as Elastic Net regression per drug, are not predictive in this setting. Instead, we propose to use multi-task learning: training a single model simultaneously on all drug combinations, which we show results in increased predictive performance. In contrast to other multi-task learning approaches, our approach allows for the identification of biomarkers, by using a modified random forest variable importance score, which we illustrate using artificial data and the DREAM challenge data. Notably, we find that mutations in MYO15A are associated with synergy between ALK / IGFR dual inhibitors and PI3K pathway inhibitors in triple-negative breast cancer.Author summaryCombining drugs is a promising strategy for cancer treatment. However, it is often not known which patients will benefit from a particular drug combination. To identify patients that are likely to benefit, we need to identify biomarkers, such as mutations in the tumor’s DNA, that are associated with favorable response to the drug combination. In this work, we identified such biomarkers using the drug combination data released by the DREAM challenge consortium, which contain 85 tumor cell lines and 167 drug combinations. The main challenge of these data is the extremely low sample size: a median of 14 cell lines have been screened per drug combination. We found that traditional methods to identify biomarkers for monotherapy response, which analyze each drug separately, are not suitable in this low sample size setting. Instead, we used a technique called multi-task learning to jointly analyze all drug combinations in a single statistical model. In contrast to existing multi-task learning algorithms, which are black-box methods, our method allows for the identification of biomarkers. Notably, we find that, in a subset of breast cancer cell lines, MYO15A mutations associate with response to the combination of ALK / IGFR dual inhibitors and PI3K pathway inhibitors.

Published
2018
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5. Consensus molecular subtype classification of colorectal adenomas

Author

Komor, MA, Bosch, L J W, Bounova, G, Bolijn, AS, Delis-van Diemen, PM, Rausch, C, Hoogstrate, Youri, Stubbs, Andrew, Jong, M, Jenster, Guido, van Grieken, NCT, Carvalho, B, Wessels, LFA, Jimenez, CR, Fijneman, RJA, Meijer, GA, Dits, NFJ, Böttcher, René, Hiemstra, AC, Ylstra, B, Sie, D, Broek, E, Meer, D, Pepers, F, Caldenhoven, E, Janssen, B, van Workum, W, van Lieshout, S, Bangma, CH, van Leenders, Arno, de Werken, HV, Urology, and Pathology

Published
2018

6. Consensus molecular subtype classification of colorectal adenomas

Author

Komor, M.A. (Malgorzata A), Bosch, L.J.W. (Linda), Bounova, G. (Gergana), Bolijn, A.S. (Anne S), Delis-van Diemen, P.M. (Pien M.), Rausch, C. (Christian), Hoogstrate, Y. (Youri), Stubbs, A.P. (Andrew), de Jong, M. (Mark), Jenster, G.W. (Guido), Grieken, N.C.T. (Nicole), Carvalho, B. (Beatriz), Wessels, L. (Lodewyk), Jimenez, C.R. (Connie), Fijneman, R.J.A. (Remond J. A.), Meijer, C.J.L.M. (Chris), Dits, N.F. (Natasja), Böttcher, R. (René), Hiemstra, A.C. (Annemieke C), Ylstra, B. (Bauke), Sie, D.L.S. (Daoud), van den Broek, E. (Evert), van der Meer, D. (David), Pepers, F. (Floor), Caldenhoven, E. (Eric), Janssen, B. (Bart), Workum, W. (W) van, Lieshout, S. (Stef) van, Bangma, C.H. (Chris), Leenders, G.J.H.L. (Geert), Werken, H.J.G. (Harmen) van de, Komor, M.A. (Malgorzata A), Bosch, L.J.W. (Linda), Bounova, G. (Gergana), Bolijn, A.S. (Anne S), Delis-van Diemen, P.M. (Pien M.), Rausch, C. (Christian), Hoogstrate, Y. (Youri), Stubbs, A.P. (Andrew), de Jong, M. (Mark), Jenster, G.W. (Guido), Grieken, N.C.T. (Nicole), Carvalho, B. (Beatriz), Wessels, L. (Lodewyk), Jimenez, C.R. (Connie), Fijneman, R.J.A. (Remond J. A.), Meijer, C.J.L.M. (Chris), Dits, N.F. (Natasja), Böttcher, R. (René), Hiemstra, A.C. (Annemieke C), Ylstra, B. (Bauke), Sie, D.L.S. (Daoud), van den Broek, E. (Evert), van der Meer, D. (David), Pepers, F. (Floor), Caldenhoven, E. (Eric), Janssen, B. (Bart), Workum, W. (W) van, Lieshout, S. (Stef) van, Bangma, C.H. (Chris), Leenders, G.J.H.L. (Geert), and Werken, H.J.G. (Harmen) van de

Abstract

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS class

Published
2018
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7. Differential regulation of RNA polymerase III genes during liver regeneration

Author

Meghdad Yeganeh, Cristian Carmeli, Dominic Villeneuve, Mauro Delorenzi, Winship Herr, Leonor Rib, Nouria Hernandez, Viviane Praz, Nicolas Guex, CycliX consortium, Hernandez, N., Delorenzi, M., Deplancke, B., Desvergne, B., Guex, N., Herr, W., Naef, F., Rougemont, J., Schibler, U., Andersin, T., Cousin, P., Gilardi, F., Gos, P., Lammers, F., Lopes, M., Mange, F., Minocha, S., Raghav, S., Villeneuve, D., Fabbretti, R., Vlegel, V., Xenarios, I., Migliavacca, E., Praz, V., David, F., Jarosz, Y., Kuznetsov, D., Liechti, R., Martin, O., Delafontaine, J., Cajan, J., Carmeli, C., Gustafson, K., Krier, I., Leleu, M., Molina, N., Naldi, A., Rib, L., Sobel, J., Symul, L., Bounova, G., and Jacquet, P.

Subjects
Chromatin Immunoprecipitation, Transcription, Genetic, DNA polymerase, DNA polymerase II, viruses, RNA polymerase II, RNA polymerase III, Histones, Mice, 03 medical and health sciences, 0302 clinical medicine, Transcription (biology), Genetics, Animals, Hepatectomy, Humans, Gene, 030304 developmental biology, 0303 health sciences, biology, Cell Cycle, Gene regulation, Chromatin and Epigenetics, Gene Expression Regulation, Developmental, RNA Polymerase III, Histone-Lysine N-Methyltransferase, Liver regeneration, Liver Regeneration, Housekeeping gene, Cell biology, Liver, biology.protein, RNA Polymerase II, Cell Division, 030217 neurology & neurosurgery, Protein Binding
Abstract

Mouse liver regeneration after partial hepatectomy involves cells in the remaining tissue synchronously entering the cell division cycle. We have used this system and H3K4me3, Pol II and Pol III profiling to characterize adaptations in Pol III transcription. Our results broadly define a class of genes close to H3K4me3 and Pol II peaks, whose Pol III occupancy is high and stable, and another class, distant from Pol II peaks, whose Pol III occupancy strongly increases after partial hepatectomy. Pol III regulation in the liver thus entails both highly expressed housekeeping genes and genes whose expression can adapt to increased demand.

Published
2019

8. Predictive gene expression profile for adjuvant taxane benefit in breast cancer in the MATADOR trial.

Author

Opdam M, van Rossum AGJ, Hoogstraat M, Bounova G, Horlings HM, van Werkhoven E, Mandjes IAM, van Leeuwen-Stok AE, Canisius S, van Tinteren H, Imholz ALT, Portielje JEA, Bos MEMM, Bakker S, Wesseling J, Kester L, van Rheenen J, Rutgers EJ, de Menezes RX, Wessels LFA, Kok M, Oosterkamp HM, and Linn SC

Abstract

The primary objective of the prospective, randomized, multicenter, phase 3 biomarker Microarray Analysis in breast cancer to Taylor Adjuvant Drugs Or Regimens trial (MATADOR: ISRCTN61893718) is to generate a gene expression profile that can predict benefit from either docetaxel, doxorubicin, and cyclophosphamide (TAC) or dose-dense scheduled doxorubicin and cyclophosphamide (ddAC). Patients with a pT1-3, pN0-3 tumor were randomized 1:1 between ddAC and TAC. The primary endpoint was a gene profile-treatment interaction for recurrence-free survival (RFS). We observed 117 RFS events in 664 patients with a median follow-up of 7 years. Hallmark gene set analyses showed significant association between enrichment in immune-related gene expression and favorable outcome after TAC in hormone receptor-negative, human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC) (triple-negative breast cancer [TNBC]). We validated this association in TNBC patients treated with TAC on H&E slides; stromal tumor-infiltrating lymphocytes (sTILs) ≥20% was associated with longer RFS (hazard ratio 0.18, p  = 0.01), while in patients treated with ddAC no difference in RFS was seen (hazard ratio 0.92, p  = 0.86, p interaction  = 0.02)., Competing Interests: S.C.L. and H.M.O. received an institutional unrestricted research grant from Sanofi and Amgen to conduct the MATADOR study (ISRCTN61893718); H.M.O. received institutional research support funding from Roche; M.K. received institutional research support funding from Roche, BMS, and AZ; S.C.L. received institutional research support funding from Agendia, Amgen, AstraZeneca, Eurocept, Genentech, Immunomedics (now Gilead), Roche, Sanofi, and TESARO (now GSK). S.C.L. is an advisory board member for AstraZeneca for which the institute receives compensation, and for Cergentis (pro bono). S.C.L. receives funding for educational activities from Daiichi Sankyo, paid to the institute. H.M.O. is an advisory board member for Roche, Pfizer, and Novartis. M.K. is an advisory board member for AZ, BMS, Roche, MSD, and Daiichi for which the institute receives compensation., (© 2024 The Authors.)

Published
2024
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9. Characterization of the T cell receptor repertoire and melanoma tumor microenvironment upon combined treatment with ipilimumab and hTERT vaccination.

Author

Ellingsen EB, Bounova G, Kerzeli I, Anzar I, Simnica D, Aamdal E, Guren T, Clancy T, Mezheyeuski A, Inderberg EM, Mangsbo SM, Binder M, Hovig E, and Gaudernack G

Subjects
Humans, Ipilimumab pharmacology, Ipilimumab therapeutic use, Tumor Microenvironment, Vaccination, Melanoma pathology, Telomerase
Abstract

Background: This clinical trial evaluated a novel telomerase-targeting therapeutic cancer vaccine, UV1, in combination with ipilimumab, in patients with metastatic melanoma. Translational research was conducted on patient-derived blood and tissue samples with the goal of elucidating the effects of treatment on the T cell receptor repertoire and tumor microenvironment., Methods: The trial was an open-label, single-center phase I/IIa study. Eligible patients had unresectable metastatic melanoma. Patients received up to 9 UV1 vaccinations and four ipilimumab infusions. Clinical responses were assessed according to RECIST 1.1. Patients were followed up for progression-free survival (PFS) and overall survival (OS). Whole-exome and RNA sequencing, and multiplex immunofluorescence were performed on the biopsies. T cell receptor (TCR) sequencing was performed on the peripheral blood and tumor tissues., Results: Twelve patients were enrolled in the study. Vaccine-specific immune responses were detected in 91% of evaluable patients. Clinical responses were observed in four patients. The mPFS was 6.7 months, and the mOS was 66.3 months. There was no association between baseline tumor mutational burden, neoantigen load, IFN-γ gene signature, tumor-infiltrating lymphocytes, and response to therapy. Tumor telomerase expression was confirmed in all available biopsies. Vaccine-enriched TCR clones were detected in blood and biopsy, and an increase in the tumor IFN-γ gene signature was detected in clinically responding patients., Conclusion: Clinical responses were observed irrespective of established predictive biomarkers for checkpoint inhibitor efficacy, indicating an added benefit of the vaccine-induced T cells. The clinical and immunological read-out warrants further investigation of UV1 in combination with checkpoint inhibitors. Trial registration Clinicaltrials.gov identifier: NCT02275416. Registered October 27, 2014. https://clinicaltrials.gov/ct2/show/NCT02275416?term=uv1&draw=2&rank=6., (© 2022. The Author(s).)

Published
2022
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10. Proteogenomic analysis of the autoreactive B cell repertoire in blood and tissues of patients with Sjögren's syndrome.

Author

Broeren MGA, Wang JJ, Balzaretti G, Groenen PJTA, van Schaik BDC, Chataway T, Kaffa C, Bervoets S, Hebeda KM, Bounova G, Pruijn GJM, Gordon TP, De Vries N, and Thurlings RM

Subjects
B-Lymphocytes immunology, B-Lymphocytes metabolism, Humans, Immunoglobulin G immunology, Rheumatoid Factor metabolism, Rituximab therapeutic use, Lymphoma, B-Cell, Marginal Zone, Proteogenomics, Sjogren's Syndrome immunology
Abstract

Objective: To comparatively analyse the aberrant affinity maturation of the antinuclear and rheumatoid factor (RF) B cell repertoires in blood and tissues of patients with Sjögren's syndrome (SjS) using an integrated omics workflow., Methods: Peptide sequencing of anti-Ro60, anti-Ro52, anti-La and RF was combined with B cell repertoire analysis at the DNA, RNA and single cell level in blood B cell subsets, affected salivary gland and extranodal marginal zone lymphomas of mucosa-associated lymphoid tissue (MALT) of patients with SjS., Results: Affected tissues contained anti-Ro60, anti-Ro52, anti-La and RF clones as a small part of a polyclonal infiltrate. Anti-Ro60, anti-La and anti-Ro52 clones outnumbered RF clones. MALT lymphoma tissues contained monoclonal RF expansions. Autoreactive clones were not selected from a restricted repertoire in a circulating B cell subset. The antinuclear antibody (ANA) repertoires displayed similar antigen-dependent and immunoglobulin (Ig) G1-directed affinity maturation. RF clones displayed antigen-dependent, IgM-directed and more B cell receptor integrity-dependent affinity maturation. This coincided with extensive intra-clonal diversification in RF-derived lymphomas. Regeneration of clinical disease manifestations after rituximab coincided with large RF clones, which not necessarily belonged to the lymphoma clone, that displayed continuous affinity maturation and intra-clonal diversification., Conclusion: The ANA and RF repertoires in patients with SjS display tissue-restricted, antigen-dependent and divergent affinity maturation. Affinity maturation of RF clones deviates further during RF clone derived lymphomagenesis and during regeneration of the autoreactive repertoire after temporary disruption by rituximab. These data give insight into the molecular mechanisms of autoreactive inflammation in SjS, assist MALT lymphoma diagnosis and allow tracking its response to rituximab., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Published
2022
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11. Neoadjuvant immunotherapy leads to pathological responses in MMR-proficient and MMR-deficient early-stage colon cancers.

Author

Chalabi M, Fanchi LF, Dijkstra KK, Van den Berg JG, Aalbers AG, Sikorska K, Lopez-Yurda M, Grootscholten C, Beets GL, Snaebjornsson P, Maas M, Mertz M, Veninga V, Bounova G, Broeks A, Beets-Tan RG, de Wijkerslooth TR, van Lent AU, Marsman HA, Nuijten E, Kok NF, Kuiper M, Verbeek WH, Kok M, Van Leerdam ME, Schumacher TN, Voest EE, and Haanen JB

Subjects
Adenocarcinoma genetics, Adenocarcinoma pathology, Adult, Aged, Aged, 80 and over, Antineoplastic Agents, Immunological administration & dosage, Cells, Cultured, Colonic Neoplasms genetics, Colonic Neoplasms pathology, Combined Modality Therapy, DNA Mismatch Repair drug effects, Digestive System Surgical Procedures, Drug Administration Schedule, Feasibility Studies, Female, Humans, Immunotherapy methods, Ipilimumab administration & dosage, Ipilimumab adverse effects, Male, Middle Aged, Neoadjuvant Therapy adverse effects, Neoadjuvant Therapy methods, Neoplasm Staging, Nivolumab administration & dosage, Nivolumab adverse effects, Treatment Failure, Adenocarcinoma therapy, Antineoplastic Agents, Immunological adverse effects, Colonic Neoplasms therapy, DNA Mismatch Repair genetics, Immunotherapy adverse effects
Abstract

PD-1 plus CTLA-4 blockade is highly effective in advanced-stage, mismatch repair (MMR)-deficient (dMMR) colorectal cancers, yet not in MMR-proficient (pMMR) tumors. We postulated a higher efficacy of neoadjuvant immunotherapy in early-stage colon cancers. In the exploratory NICHE study (ClinicalTrials.gov: NCT03026140), patients with dMMR or pMMR tumors received a single dose of ipilimumab and two doses of nivolumab before surgery, the pMMR group with or without celecoxib. The primary objective was safety and feasibility; 40 patients with 21 dMMR and 20 pMMR tumors were treated, and 3 patients received nivolumab monotherapy in the safety run-in. Treatment was well tolerated and all patients underwent radical resections without delays, meeting the primary endpoint. Of the patients who received ipilimumab + nivolumab (20 dMMR and 15 pMMR tumors), 35 were evaluable for efficacy and translational endpoints. Pathological response was observed in 20/20 (100%; 95% exact confidence interval (CI): 86-100%) dMMR tumors, with 19 major pathological responses (MPRs, ≤10% residual viable tumor) and 12 pathological complete responses. In pMMR tumors, 4/15 (27%; 95% exact CI: 8-55%) showed pathological responses, with 3 MPRs and 1 partial response. CD8 + PD-1 + T cell infiltration was predictive of response in pMMR tumors. These data indicate that neoadjuvant immunotherapy may have the potential to become the standard of care for a defined group of colon cancer patients when validated in larger studies with at least 3 years of disease-free survival data.

Published
2020
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12. Pancreatic cancer organoids recapitulate disease and allow personalized drug screening.

Author

Driehuis E, van Hoeck A, Moore K, Kolders S, Francies HE, Gulersonmez MC, Stigter ECA, Burgering B, Geurts V, Gracanin A, Bounova G, Morsink FH, Vries R, Boj S, van Es J, Offerhaus GJA, Kranenburg O, Garnett MJ, Wessels L, Cuppen E, Brosens LAA, and Clevers H

Abstract

We report the derivation of 30 patient-derived organoid lines (PDOs) from tumors arising in the pancreas and distal bile duct. PDOs recapitulate tumor histology and contain genetic alterations typical of pancreatic cancer. In vitro testing of a panel of 76 therapeutic agents revealed sensitivities currently not exploited in the clinic, and underscores the importance of personalized approaches for effective cancer treatment. The PRMT5 inhibitor EZP015556, shown to target MTAP (a gene commonly lost in pancreatic cancer)-negative tumors, was validated as such, but also appeared to constitute an effective therapy for a subset of MTAP-positive tumors. Taken together, the work presented here provides a platform to identify novel therapeutics to target pancreatic tumor cells using PDOs.

Published
2019
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13. Consensus molecular subtype classification of colorectal adenomas.

Author

Komor MA, Bosch LJ, Bounova G, Bolijn AS, Delis-van Diemen PM, Rausch C, Hoogstrate Y, Stubbs AP, de Jong M, Jenster G, van Grieken NC, Carvalho B, Wessels LF, Jimenez CR, Fijneman RJ, and Meijer GA

Subjects
Adenoma classification, Adenoma metabolism, Carcinoma classification, Carcinoma metabolism, Cell Differentiation, Colorectal Neoplasms classification, Colorectal Neoplasms pathology, Consensus, Disease Progression, Genetic Predisposition to Disease, Humans, Neoplasm Staging, Phenotype, Predictive Value of Tests, Reproducibility of Results, Transcriptome, Adenoma genetics, Biomarkers, Tumor genetics, Carcinoma genetics, Colorectal Neoplasms genetics, DNA Copy Number Variations, Gene Dosage, Gene Expression Profiling methods, Microsatellite Instability
Abstract

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 'metabolic subtype', which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the 'MSI immune' subtype. Eight adenomas (13%) were classified as the 'canonical' CMS2. No adenomas were classified as the 'mesenchymal' CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland., (© 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.)

Published
2018
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14. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity.

Author

Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, Balgobind AV, Wind K, Gracanin A, Begthel H, Korving J, van Boxtel R, Duarte AA, Lelieveld D, van Hoeck A, Ernst RF, Blokzijl F, Nijman IJ, Hoogstraat M, van de Ven M, Egan DA, Zinzalla V, Moll J, Boj SF, Voest EE, Wessels L, van Diest PJ, Rottenberg S, Vries RGJ, Cuppen E, and Clevers H

Subjects
Animals, Antineoplastic Agents pharmacology, Breast Neoplasms genetics, Cells, Cultured, Drug Screening Assays, Antitumor methods, Female, Humans, Mice, Mice, Nude, Organoids drug effects, Precision Medicine methods, Breast Neoplasms pathology, Genetic Heterogeneity, Organoids pathology, Tissue Banks
Abstract

Breast cancer (BC) comprises multiple distinct subtypes that differ genetically, pathologically, and clinically. Here, we describe a robust protocol for long-term culturing of human mammary epithelial organoids. Using this protocol, >100 primary and metastatic BC organoid lines were generated, broadly recapitulating the diversity of the disease. BC organoid morphologies typically matched the histopathology, hormone receptor status, and HER2 status of the original tumor. DNA copy number variations as well as sequence changes were consistent within tumor-organoid pairs and largely retained even after extended passaging. BC organoids furthermore populated all major gene-expression-based classification groups and allowed in vitro drug screens that were consistent with in vivo xeno-transplantations and patient response. This study describes a representative collection of well-characterized BC organoids available for cancer research and drug development, as well as a strategy to assess in vitro drug response in a personalized fashion., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Published
2018
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15. Large variety in a panel of human colon cancer organoids in response to EZH2 inhibition.

Author

Koppens MA, Bounova G, Cornelissen-Steijger P, de Vries N, Sansom OJ, Wessels LF, and van Lohuizen M

Subjects
Animals, Apoptosis Regulatory Proteins analysis, Cell Line, Tumor, Colonic Neoplasms genetics, Colonic Neoplasms pathology, Enhancer of Zeste Homolog 2 Protein analysis, Humans, Membrane Proteins analysis, Mice, Mitochondrial Proteins, Mutation, Organoids, PAX2 Transcription Factor genetics, Tumor Suppressor Protein p53 physiology, X-linked Nuclear Protein genetics, Colonic Neoplasms drug therapy, Enhancer of Zeste Homolog 2 Protein antagonists & inhibitors, Indoles pharmacology, Pyridones pharmacology
Abstract

EZH2 inhibitors have gained great interest for their use as anti-cancer therapeutics. However, most research has focused on EZH2 mutant cancers and recently adverse effects of EZH2 inactivation have come to light. To determine whether colorectal cancer cells respond to EZH2 inhibition and to explore which factors influence the degree of response, we treated a panel of 20 organoid lines derived from human colon tumors with different concentrations of the EZH2 inhibitor GSK126. The resulting responses were associated with mutation status, gene expression and responses to other drugs. We found that the response to GSK126 treatment greatly varied between organoid lines. Response associated with the mutation status of ATRX and PAX2, and correlated with BIK expression. It also correlated well with response to Nutlin-3a which inhibits MDM2-p53 interaction thereby activating p53 signaling. Sensitivity to EZH2 ablation depended on the presence of wild type p53, as tumor organoids became resistant when p53 was mutated or knocked down. Our exploratory study provides insight into which genetic factors predict sensitivity to EZH2 inhibition. In addition, we show that the response to EZH2 inhibition requires wild type p53. We conclude that a subset of colorectal cancer patients may benefit from EZH2-targeting therapies.

Published
2016
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16. Deletion of Polycomb Repressive Complex 2 From Mouse Intestine Causes Loss of Stem Cells.

Author

Koppens MA, Bounova G, Gargiulo G, Tanger E, Janssen H, Cornelissen-Steijger P, Blom M, Song JY, Wessels LF, and van Lohuizen M

Subjects
Animals, Base Sequence, Cell Differentiation, Cell Lineage, Cell Proliferation, Chromatin Immunoprecipitation, Enhancer of Zeste Homolog 2 Protein deficiency, Intestinal Mucosa metabolism, Mice, Mice, Knockout, Polycomb Repressive Complex 2 genetics, Wnt Signaling Pathway, Adult Stem Cells physiology, Intestines cytology, Polycomb Repressive Complex 2 deficiency
Abstract

Background & Aims: The polycomb repressive complex 2 (PRC2) regulates differentiation by contributing to repression of gene expression and thereby stabilizing the fate of stem cells and their progeny. PRC2 helps to maintain adult stem cell populations, but little is known about its functions in intestinal stem cells. We studied phenotypes of mice with intestine-specific deletion of the PRC2 proteins embryonic ectoderm development (EED) (a subunit required for PRC2 function) and enhancer of zeste homolog 2 (EZH2) (a histone methyltransferase)., Methods: We performed studies of AhCre;EedLoxP/LoxP (EED knockout) mice and AhCre;Ezh2LoxP/LoxP (EZH2 knockout) mice, which have intestine-specific disruption in EED and EZH2, respectively. Small intestinal crypts were isolated and subsequently cultured to grow organoids. Intestines and organoids were analyzed by immunohistochemical, in situ hybridization, RNA sequence, and chromatin immunoprecipitation methods., Results: Intestines of EED knockout mice had massive crypt degeneration and lower numbers of proliferating cells compared with wild-type control mice. Cdkn2a became derepressed and we detected increased levels of P21. We did not observe any differences between EZH2 knockout and control mice. Intestinal crypts from EED knockout mice had signs of aberrant differentiation of uncommitted crypt cells-these differentiated toward the secretory cell lineage. Furthermore, crypts from EED-knockout mice had impaired Wnt signaling and concomitant loss of intestinal stem cells, this phenotype was not reversed upon ectopic stimulation of Wnt and Notch signaling in organoids. Analysis of gene expression patterns from intestinal tissues of EED knockout mice showed dysregulation of several genes involved in Wnt signaling. Wnt signaling was regulated directly by PRC2., Conclusions: In intestinal tissues of mice, PRC2 maintains small intestinal stem cells by promoting proliferation and preventing differentiation in the intestinal stem cell compartment. PRC2 controls gene expression in multiple signaling pathways that regulate intestinal homeostasis. Sequencing data are available in the genomics data repository GEO under reference series GSE81578; RNA sequencing data are available under subseries GSE81576; and ChIP sequencing data are available under subseries GSE81577., (Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Published
2016
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17. Prospective derivation of a living organoid biobank of colorectal cancer patients.

Author

van de Wetering M, Francies HE, Francis JM, Bounova G, Iorio F, Pronk A, van Houdt W, van Gorp J, Taylor-Weiner A, Kester L, McLaren-Douglas A, Blokker J, Jaksani S, Bartfeld S, Volckman R, van Sluis P, Li VS, Seepo S, Sekhar Pedamallu C, Cibulskis K, Carter SL, McKenna A, Lawrence MS, Lichtenstein L, Stewart C, Koster J, Versteeg R, van Oudenaarden A, Saez-Rodriguez J, Vries RG, Getz G, Wessels L, Stratton MR, McDermott U, Meyerson M, Garnett MJ, and Clevers H

Subjects
Colorectal Neoplasms drug therapy, DNA-Binding Proteins metabolism, Humans, Oncogene Proteins metabolism, Organ Culture Techniques, Precision Medicine, Ubiquitin-Protein Ligases, Biological Specimen Banks, Colorectal Neoplasms pathology, Drug Screening Assays, Antitumor methods, Organoids drug effects
Abstract

In Rspondin-based 3D cultures, Lgr5 stem cells from multiple organs form ever-expanding epithelial organoids that retain their tissue identity. We report the establishment of tumor organoid cultures from 20 consecutive colorectal carcinoma (CRC) patients. For most, organoids were also generated from adjacent normal tissue. Organoids closely recapitulate several properties of the original tumor. The spectrum of genetic changes within the "living biobank" agrees well with previous large-scale mutational analyses of CRC. Gene expression analysis indicates that the major CRC molecular subtypes are represented. Tumor organoids are amenable to high-throughput drug screens allowing detection of gene-drug associations. As an example, a single organoid culture was exquisitely sensitive to Wnt secretion (porcupine) inhibitors and carried a mutation in the negative Wnt feedback regulator RNF43, rather than in APC. Organoid technology may fill the gap between cancer genetics and patient trials, complement cell-line- and xenograft-based drug studies, and allow personalized therapy design. PAPERCLIP., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Published
2015
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