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375 results on '"Aittokallio T"'

1. Noncanonical effector functions of the T-memory–like T-PLL cell are shaped by cooperative TCL1A and TCR signaling

Author

Oberbeck, S., Schrader, A., Warner, K., Jungherz, D., Crispatzu, G., von Jan, J., Chmielewski, M., Ianevski, A., Diebner, H.H., Mayer, P., Kondo Ados, A., Wahnschaffe, L., Braun, T., Müller, T.A., Wagle, P., Bouska, A., Neumann, T., Pützer, S., Varghese, L., Pflug, N., Thelen, M., Makalowski, J., Riet, N., Göx, H.J.M., Rappl, G., Altmüller, J., Kotrová, M., Persigehl, T., Hopfinger, G., Hansmann, M.L., Schlößer, H., Stilgenbauer, S., Dürig, J., Mougiakakos, D., von Bergwelt-Baildon, M., Roeder, I., Hartmann, S., Hallek, M., Moriggl, R., Brüggemann, M., Aittokallio, T., Iqbal, J., Newrzela, S., Abken, H., and Herling, M.

Published
2020
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3. Improving the false nearest neighbors method with graphical analysis

Author

Aittokallio, T., Gyllenberg, M., Hietarinta, J., Kuusela, T., and Multamaki, T.

Subjects
Nonlinear Sciences - Chaotic Dynamics
Abstract

We introduce a graphical presentation for the false nearest neighbors (FNN) method. In the original method only the percentage of false neighbors is computed without regard to the distribution of neighboring points in the time-delay coordinates. With this new presentation it is much easier to distinguish deterministic chaos from noise. The graphical approach also serves as a tool to determine better conditions for detecting low dimensional chaos, and to get a better understanding on the applicability of the FNN method., Comment: 4 pages, with 5 PostScript figures

Published
1998
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5. Glioblastoma and the search for non-hypothesis driven combination therapeutics in academia

Author

Johanssen, T, McVeigh, L, Erridge, S, Higgins, G, Straehla, J, Frame, M, Aittokallio, T, Carragher, NO, Ebner, D, Helsinki Institute for Information Technology, Tero Aittokallio / Principal Investigator, Bioinformatics, Institute for Molecular Medicine Finland, University of Helsinki, Research Programs Unit, and Helsinki Institute of Life Science HiLIFE

Subjects
Cancer Research, Temozolamide, glioblastoma stem cell, Radiotherapy, drug targer combination, hypoxia, high throughput screening (HTS), 3122 Cancers, glioblastoma, radiothereapy, Oncology, temozolamide, Glioblastoma stem cell, Glioblastoma, Hypoxia, Drug target combination
Abstract

Glioblastoma (GBM) remains a cancer of high unmet clinical need. Currentstandard of care for GBM, consisting of maximal surgical resection, followed byionisation radiation (IR) plus concomitant and adjuvant temozolomide (TMZ),provides less than 15-month survival benefit. Efforts by conventional drugdiscovery to improve overall survival have failed to overcome challengespresented by inherent tumor heterogeneity, therapeutic resistance attributed toGBM stem cells, and tumor niches supporting self-renewal. In this review wedescribe the steps academic researchers are taking to address these limitations in high throughput screening programs to identify novel GBM combinatorial targets.We detail how they are implementing more physiologically relevant phenotypicassays which better recapitulate key areas of disease biology coupled with morefocussed libraries of small compounds, such as drug repurposing, target discovery, pharmacologically active and novel, more comprehensive anti-cancer targetannotated compound libraries. Herein, we discuss the rationale for current GBM combination trials and the need for more systematic and transparent strategies for identification, validation and prioritisation of combinations that lead to clinical trials. Finally, we make specific recommendations to the preclinical, small compound screening paradigm that could increase the likelihood of identifying tractable, combinatorial, small molecule inhibitors and better drug targets specific to GBM.

Published
2023
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7. P626: FUNCTIONAL SCREENING OF PI3K INHIBITORS STRATIFIES RESPONDERS TO IDELALISIB AND INDICATES DRUG CLASS ACTIVITY IN IDELALISIB-REFRACTORY CLL

Author

Skånland, S., primary, Yanping, Y., additional, Athanasiadis, P., additional, Karlsen, L., additional, Urban, A., additional, Murali, I., additional, Fernandes, S., additional, Hilli, A., additional, Taskén, K., additional, Bertoni, F., additional, Mato, A., additional, Normant, E., additional, Brown, J., additional, Tjønnfjord, G., additional, and Aittokallio, T., additional

Published
2022
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9. Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer:real-time therapy tailoring for a patient with low-grade serous carcinoma

Author

Murumägi, A. (Astrid), Ungureanu, D. (Daniela), Khan, S. (Suleiman), Arjama, M. (Mariliina), Välimäki, K. (Katja), Ianevski, A. (Aleksandr), Ianevski, P. (Philipp), Bergström, R. (Rebecka), Dini, A. (Alice), Kanerva, A. (Anna), Koivisto-Korander, R. (Riitta), Tapper, J. (Johanna), Lassus, H. (Heini), Loukovaara, M. (Mikko), Mägi, A. (Andrus), Hirasawa, A. (Akira), Aoki, D. (Daisuke), Pietiäinen, V. (Vilja), Pellinen, T. (Teijo), Bützow, R. (Ralf), Aittokallio, T. (Tero), Kallioniemi, O. (Olli), Murumägi, A. (Astrid), Ungureanu, D. (Daniela), Khan, S. (Suleiman), Arjama, M. (Mariliina), Välimäki, K. (Katja), Ianevski, A. (Aleksandr), Ianevski, P. (Philipp), Bergström, R. (Rebecka), Dini, A. (Alice), Kanerva, A. (Anna), Koivisto-Korander, R. (Riitta), Tapper, J. (Johanna), Lassus, H. (Heini), Loukovaara, M. (Mikko), Mägi, A. (Andrus), Hirasawa, A. (Akira), Aoki, D. (Daisuke), Pietiäinen, V. (Vilja), Pellinen, T. (Teijo), Bützow, R. (Ralf), Aittokallio, T. (Tero), and Kallioniemi, O. (Olli)

Abstract

Many efforts are underway to develop novel therapies against the aggressive high-grade serous ovarian cancers (HGSOCs), while our understanding of treatment options for low-grade (LGSOC) or mucinous (MUCOC) of ovarian malignancies is not developing as well. We describe here a functional precision oncology (fPO) strategy in epithelial ovarian cancers (EOC), which involves high-throughput drug testing of patient-derived ovarian cancer cells (PDCs) with a library of 526 oncology drugs, combined with genomic and transcriptomic profiling. HGSOC, LGSOC and MUCOC PDCs had statistically different overall drug response profiles, with LGSOCs responding better to targeted inhibitors than HGSOCs. We identified several subtype-specific drug responses, such as LGSOC PDCs showing high sensitivity to MDM2, ERBB2/EGFR inhibitors, MUCOC PDCs to MEK inhibitors, whereas HGSOCs showed strongest effects with CHK1 inhibitors and SMAC mimetics. We also explored several drug combinations and found that the dual inhibition of MEK and SHP2 was synergistic in MAPK-driven EOCs. We describe a clinical case study, where real-time fPO analysis of samples from a patient with metastatic, chemorefractory LGSOC with a CLU-NRG1 fusion guided clinical therapy selection. fPO-tailored therapy with afatinib, followed by trastuzumab and pertuzumab, successfully reduced tumour burden and blocked disease progression over a five-year period. In summary, fPO is a powerful approach for the identification of systematic drug response differences across EOC subtypes, as well as to highlight patient-specific drug regimens that could help to optimise therapies to individual patients in the future.

Published
2022

10. Tumor microenvironment as a metapopulation model: the effects of angiogenesis, emigration and treatment modalities

Author

Halkola, A.S., Aittokallio, T., Parvinen, K., Halkola, A.S., Aittokallio, T., and Parvinen, K.

Abstract

Tumors consist of heterogeneous cell subpopulations that may develop differing phenotypes, such as increased cell growth, metastatic potential and treatment sensitivity or resistance. To study the dynamics of cancer development at a single-cell level, we model the tumor microenvironment as a metapopulation, in which habitat patches correspond to possible sites for cell subpopulations. Cancer cells may emigrate into dispersal pool (e.g. circulation system) and spread to new sites (i.e. metastatic disease). In the patches, cells divide and new variants may arise, possibly leading into an invasion provided the aberration promotes the cell growth. To study such adaptive landscape of cancer ecosystem, we consider various evolutionary strategies (phenotypes), such as emigration and angiogenesis, which are important determinants during early stages of tumor development. We use the metapopulation fitness of new variants to investigate how these strategies evolve through natural selection and disease progression. We further study various treatment effects and investigate how different therapy regimens affect the evolution of the cell populations. These aspects are relevant, for example, when examining the dynamic process of a benign tumor becoming cancerous, and what is the best treatment strategy during the early stages of cancer development. It is shown that positive angiogenesis promotes cancer cell growth in the absence of anti-angiogenic treatment, and that the anti-angiogenic treatment reduces the need of cytotoxic treatment when used in a combination. Interestingly, the model predicts that treatment resistance might become a favorable quality to cancer cells when the anti-angiogenic treatment is intensive enough. Thus, the optimal treatment dosage should remain below a patient-specific level to avoid treatment resistance.

Published
2022

11. 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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12. 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, 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

13. Crowdsourced mapping of unexplored target space of kinase inhibitors

Author

Cichońska, A., Ravikumar, B., Allaway, R., Wan, F., Park, S., Isayev, O., Li, S., Mason, M., Lamb, A., Tanoli, Z., Jeon, M., Kim, S., Popova, M., Capuzzi, S., Zeng, J., Dang, K., Koytiger, G., Kang, J., Wells, C., Willson, T., Lienhard, M., Prasse, P., Bachmann, I., Ganzlin, J., Barel, G., Herwig, R., Oprea, T., Schlessinger, A., Drewry, D., Stolovitzky, G., Wennerberg, K., Guinney, J., and Aittokallio, T.

Abstract

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Published
2021

14. Crowdsourced mapping of unexplored target space of kinase inhibitors

Author

Cichonska, A, Ravikumar, B, Allaway, RJ, Wan, F, Park, S, Isayev, O, Li, S, Mason, M, Lamb, A, Tanoli, Z, Jeon, M, Kim, S, Popova, M, Capuzzi, S, Zeng, J, Dang, K, Koytiger, G, Kang, J, Wells, CI, Willson, TM, Oprea, TI, Schlessinger, A, Drewry, DH, Stolovitzky, G, Wennerberg, K, Guinney, J, Aittokallio, T, Tan, M, Huang, C-H, Shih, ESC, Chen, T-M, Wu, C-H, Fang, W-Q, Chen, J-Y, Hwang, M-J, Wang, X, Ben Guebila, M, Shamsaei, B, Singh, S, Nguyen, Thin, Karimi, M, Wu, D, Wang, Z, Shen, Y, Ozturk, H, Ozkirimli, E, Ozgur, A, Lim, H, Xie, L, Kanev, GK, Kooistra, AJ, Westerman, BA, Terzopoulos, P, Ntagiantas, K, Fotis, C, Alexopoulos, L, Boeckaerts, D, Stock, M, De Baets, B, Briers, Y, Luo, Y, Hu, H, Peng, J, Dogan, T, Rifaioglu, AS, Atas, H, Atalay, RC, Atalay, V, Martin, MJ, Lee, J, Yun, S, Kim, B, Chang, B, Turu, G, Misak, A, Szalai, B, Hunyady, L, Lienhard, M, Prasse, P, Bachmann, I, Ganzlin, J, Barel, G, Herwig, R, Orsolic, D, Lucic, B, Stepanic, V, Smuc, T, Cichonska, A, Ravikumar, B, Allaway, RJ, Wan, F, Park, S, Isayev, O, Li, S, Mason, M, Lamb, A, Tanoli, Z, Jeon, M, Kim, S, Popova, M, Capuzzi, S, Zeng, J, Dang, K, Koytiger, G, Kang, J, Wells, CI, Willson, TM, Oprea, TI, Schlessinger, A, Drewry, DH, Stolovitzky, G, Wennerberg, K, Guinney, J, Aittokallio, T, Tan, M, Huang, C-H, Shih, ESC, Chen, T-M, Wu, C-H, Fang, W-Q, Chen, J-Y, Hwang, M-J, Wang, X, Ben Guebila, M, Shamsaei, B, Singh, S, Nguyen, Thin, Karimi, M, Wu, D, Wang, Z, Shen, Y, Ozturk, H, Ozkirimli, E, Ozgur, A, Lim, H, Xie, L, Kanev, GK, Kooistra, AJ, Westerman, BA, Terzopoulos, P, Ntagiantas, K, Fotis, C, Alexopoulos, L, Boeckaerts, D, Stock, M, De Baets, B, Briers, Y, Luo, Y, Hu, H, Peng, J, Dogan, T, Rifaioglu, AS, Atas, H, Atalay, RC, Atalay, V, Martin, MJ, Lee, J, Yun, S, Kim, B, Chang, B, Turu, G, Misak, A, Szalai, B, Hunyady, L, Lienhard, M, Prasse, P, Bachmann, I, Ganzlin, J, Barel, G, Herwig, R, Orsolic, D, Lucic, B, Stepanic, V, and Smuc, T

Abstract

AbstractDespite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Published
2021

15. Modelling of killer T-cell and cancer cell subpopulation dynamics under immuno- and chemotherapies

Author

Halkola, A.S., Parvinen, K., Kasanen, H., Mustjoki, S., Aittokallio, T., Department of Clinical Chemistry and Hematology, University of Helsinki, HUS Comprehensive Cancer Center, Department of Oncology, Hematologian yksikkö, TRIMM - Translational Immunology Research Program, Research Programs Unit, Helsinki Institute for Information Technology, Institute for Molecular Medicine Finland, and Bioinformatics

Subjects
3122 Cancers, PROLIFERATION, DRUG-COMBINATIONS, Killer T-cells, Personalized medicine, THERAPY, EVOLUTION, Side-effects, METASTATIC MELANOMA, HETEROGENEITY, 3111 Biomedicine, Immunotherapy, Combination therapy, LEUKEMIA, RESISTANCE
Abstract

Each patient’s cancer has a unique molecular makeup, often comprised of distinct cancer cell subpopulations. Improved understanding of dynamic processes between cancer cell populations is therefore critical for making treatment more effective and personalized. It has been shown that immunotherapy increases the survival of melanoma patients. However, there remain critical open questions, such as timing and duration of immunotherapy and its added benefits when combined with other types of treatments. We introduce a model for the dynamics of active killer T-cells and cancer cell subpopulations. Rather than defining the cancer cell populations based on their genetic makeup alone, we consider also other, non-genetic differences that make the cell populations either sensitive or resistant to a therapy. Using the model, we make predictions of possible outcomes of the various treatment strategies in virtual melanoma patients, providing hypotheses regarding therapeutic efficacy and side-effects. It is shown, for instance, that starting immunotherapy with a denser treatment schedule may enable changing to a sparser schedule later during the treatment. Furthermore, combination of targeted and immunotherapy results in a better treatment effect, compared to mono-immunotherapy, and a stable disease can be reached with a patient-tailored combination. These results offer better understanding of the competition between T-cells and cancer cells, toward personalized immunotherapy regimens.

Published
2020

17. Reinstated p53 response and high anti-T-cell leukemia activity by the novel alkylating deacetylase inhibitor tinostamustine

Author

Puetzer, S., Varghese, L., von Jan, J., Braun, T., Giri, A. K., Mayer, P., Riet, N., Timonen, S., Oberbeck, S., Kuusanmaki, H., Mustjoki, S., Stern, M. -H., Aittokallio, T., Newrzela, S., Schrader, A., Herling, M., Puetzer, S., Varghese, L., von Jan, J., Braun, T., Giri, A. K., Mayer, P., Riet, N., Timonen, S., Oberbeck, S., Kuusanmaki, H., Mustjoki, S., Stern, M. -H., Aittokallio, T., Newrzela, S., Schrader, A., and Herling, M.

Published
2020

19. Corrections: Reinstated p53 response and high anti-T-cell leukemia activity by the novel alkylating deacetylase inhibitor tinostamustine

Author

Pützer, S., primary, Varghese, L., additional, von Jan, J., additional, Braun, T., additional, Giri, A. K., additional, Mayer, P., additional, Riet, N., additional, Timonen, S., additional, Oberbeck, S., additional, Kuusanmäki, H., additional, Mustjoki, S., additional, Stern, M.-H., additional, Aittokallio, T., additional, Newrzela, S., additional, Schrader, A., additional, and Herling, M., additional

Published
2020
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22. Novel screening method identifies PI3Kα, mTOR, and IGF1R as key kinases regulating cardiomyocyte survival

Author

Elmadani, M. (Manar), Khan, S. (Suleiman), Tenhunen, O. (Olli), Magga, J. (Johanna), Aittokallio, T. (Tero), Wennerberg, K. (Krister), Kerkelä, R. (Risto), Elmadani, M. (Manar), Khan, S. (Suleiman), Tenhunen, O. (Olli), Magga, J. (Johanna), Aittokallio, T. (Tero), Wennerberg, K. (Krister), and Kerkelä, R. (Risto)

Abstract

Background: Small molecule kinase inhibitors (KIs) are a class of agents currently used for treatment of various cancers. Unfortunately, treatment of cancer patients with some of the KIs is associated with cardiotoxicity, and there is an unmet need for methods to predict their cardiotoxicity. Here, we utilized a novel computational method to identify protein kinases crucial for cardiomyocyte viability. Methods and Results: One hundred forty KIs were screened for their toxicity in cultured neonatal cardiomyocytes. The kinase targets of KIs were determined based on integrated data from binding assays. The key kinases mediating the toxicity of KIs to cardiomyocytes were identified by using a novel machine learning method for target deconvolution that combines the information from the toxicity screen and from the kinase profiling assays. The top kinases identified by the model were phosphoinositide 3‐kinase catalytic subunit alpha, mammalian target of rapamycin, and insulin‐like growth factor 1 receptor. Knockdown of the individual kinases in cardiomyocytes confirmed their role in regulating cardiomyocyte viability. Conclusions: Combining the data from analysis of KI toxicity on cardiomyocytes and KI target profiling provides a novel method to predict cardiomyocyte toxicity of KIs.

Published
2019

27. Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma

Author

Huvila, J., Laajala, T. D., Edqvist, P. -H, Mardinoglu, Adil, Talve, L., Pontén, F., Grénman, S., Carpén, O., Aittokallio, T., Auranen, A., Huvila, J., Laajala, T. D., Edqvist, P. -H, Mardinoglu, Adil, Talve, L., Pontén, F., Grénman, S., Carpén, O., Aittokallio, T., and Auranen, A.

Abstract

Objective: In clinical practise, prognostication of endometrial cancer is based on clinicopathological risk factors. The use of immunohistochemistry-based markers as prognostic tools is generally not recommended and a systematic analysis of their utility as a panel is lacking. We evaluated whether an immunohistochemical marker panel could reliably assess endometrioid endometrial cancer (EEC) outcome independent of clinicopathological information. Methods: A cohort of 306 EEC specimens was profiled using tissue microarray (TMA). Cost- and time-efficient immunohistochemical analysis of well-established tissue biomarkers (ER, PR, HER2, Ki-67, MLH1 and p53) and two new biomarkers (L1CAM and ASRGL1) was carried out. Statistical modelling with embedded variable selection was applied on the staining results to identify minimal prognostic panels with maximal prognostic accuracy without compromising generalizability. Results: A panel including p53 and ASRGL1 immunohistochemistry was identified as the most accurate predictor of relapse-free and disease-specific survival. Within this panel, patients were allocated into high- (5.9%), intermediate- (29.5%) and low- (64.6%) risk groups where high-risk patients had a 30-fold risk (P < 0.001) of dying of EEC compared to the low-risk group. Conclusions: P53 and ASRGL1 immunoprofiling stratifies EEC patients into three risk groups with significantly different outcomes. This simple and easily applicable panel could provide a useful tool in EEC risk stratification and guiding the allocation of treatment modalities., Export Date: 9 May 2018; Article; CODEN: GYNOA; Correspondence Address: Huvila, J.; Department of Pathology, University of Turku, Kiinamyllynkatu 8-10, Finland; email: jutta.huvila@utu.fi; Funding details: 16X064, NCI, National Cancer Institute; Funding details: UH, University Hospitals; Funding details: Swedish Cancer Foundation; Funding details: HMRF, Health and Medical Research Fund; Funding details: Knut och Alice Wallenbergs Stiftelse; Funding details: 272437, Academy of Finland; Funding details: 295504, Academy of Finland; Funding details: 269862, Academy of Finland; Funding details: 292611, Academy of Finland; Funding details: 279163, Academy of Finland; Funding details: 85121904; Funding details: Syöpäjärjestöt; Funding details: TA, Tennis Australia; Funding details: Evonik Stiftung; Funding details: PHE, Public Health England; Funding details: FPInnovations; Funding text: This work was supported by University of Turku Doctoral Program of Clinical Investigations, Regional Fund of Finland Proper of the Finnish Cultural Foundation (grant 85121904 ), Medical Research Fund (EVO) of Turku University Hospital [JH]; Academy of Finland (grants 292611 , 269862 , 272437 , 279163 , 295504 ), National Cancer Institute ( 16X064 ), Cancer Society of Finland [TA] and by grants from the Knut and Alice Wallenberg Foundation (FP, AM, PHE) and the Swedish Cancer foundation (FP). QC 20180604

Published
2018
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28. Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling

Author

Andersson, E. I., Puetzer, S., Yadav, B., Dufva, O., Khan, S., He, L., Sellner, L., Schrader, A., Crispatzu, G., Oles, M., Zhang, H., Adnan-Awad, S., Lagstrom, S., Bellanger, D., Mpindi, J. P., Eldfors, S., Pemovska, T., Pietarinen, P., Lauhio, A., Tomska, K., Cuesta-Mateos, C., Faber, E., Koschmieder, S., Bruemmendorf, T. H., Kytola, S., Savolainen, E-R, Siitonen, T., Ellonen, P., Kallioniemi, O., Wennerberg, K., Ding, W., Stern, M-H, Huber, W., Anders, S., Tang, J., Aittokallio, T., Zenz, T., Herling, M., Mustjoki, S., Andersson, E. I., Puetzer, S., Yadav, B., Dufva, O., Khan, S., He, L., Sellner, L., Schrader, A., Crispatzu, G., Oles, M., Zhang, H., Adnan-Awad, S., Lagstrom, S., Bellanger, D., Mpindi, J. P., Eldfors, S., Pemovska, T., Pietarinen, P., Lauhio, A., Tomska, K., Cuesta-Mateos, C., Faber, E., Koschmieder, S., Bruemmendorf, T. H., Kytola, S., Savolainen, E-R, Siitonen, T., Ellonen, P., Kallioniemi, O., Wennerberg, K., Ding, W., Stern, M-H, Huber, W., Anders, S., Tang, J., Aittokallio, T., Zenz, T., Herling, M., and Mustjoki, S.

Abstract

T-cell prolymphocytic leukemia (T-PLL) is a rare and aggressive neoplasm of mature T-cells with an urgent need for rationally designed therapies to address its notoriously chemo-refractory behavior. The median survival of T-PLL patients is <2 years and clinical trials are difficult to execute. Here we systematically explored the diversity of drug responses in T-PLL patient samples using an ex vivo drug sensitivity and resistance testing platform and correlated the findings with somatic mutations and gene expression profiles. Intriguingly, all T-PLL samples were sensitive to the cyclin-dependent kinase inhibitor SNS-032, which overcame stromal-cell-mediated protection and elicited robust p53-activation and apoptosis. Across all patients, the most effective classes of compounds were histone deacetylase, phosphoinositide-3 kinase/ AKT/mammalian target of rapamycin, heat-shock protein 90 and BH3-family protein inhibitors as well as p53 activators, indicating previously unexplored, novel targeted approaches for treating T-PLL. Although Janus-activated kinase-signal transducer and activator of transcription factor (JAK-STAT) pathway mutations were common in T-PLL (71% of patients), JAK-STAT inhibitor responses were not directly linked to those or other T-PLL-specific lesions. Overall, we found that genetic markers do not readily translate into novel effective therapeutic vulnerabilities. In conclusion, novel classes of compounds with high efficacy in T-PLL were discovered with the comprehensive ex vivo drug screening platform warranting further studies of synergisms and clinical testing.

Published
2018

29. Susceptibility of low-density lipoprotein particles to aggregate depends on particle lipidome, is modifiable, and associates with future cardiovascular deaths

Author

Ruuth, M. (Maija), Duy Nguyen, S. (Su), Vihervaara, T. (Terhi), Hilvo, M. (Mika), Laajala, T. D. (Teemu D), Kumar Kondadi, P. (Pradeep), Gisterå, A. (Anton), Lähteenmäki, H. (Hanna), Kittilä, T. (Tiia), Huusko, J. (Jenni), Uusitupa, M. (Matti), Schwab, U. (Ursula), Savolainen, M. J. (Markku J), Sinisalo, J. (Juha), Lokki, M.-L. (Marja-Liisa), Nieminen, M. S. (Markku S), Jula, A. (Antti), Perola, M. (Markus), Ylä-Herttula, S. (Seppo), Rudel, L. (Lawrence), Öörni, A. (Anssi), Baumann, M. (Marc), Baruch, A. (Amos), Laaksonen, R. (Reijo), Ketelhuth, D. F. (Daniel F J), Aittokallio, T. (Tero), Jauhiainen, M. (Matti), Käkelä, R. (Reijo), Borén, J. (Jan), Williams, K. J. (Kevin Jon), Kovanen, P. T. (Petri T), Öörni, K. (Katariina), Ruuth, M. (Maija), Duy Nguyen, S. (Su), Vihervaara, T. (Terhi), Hilvo, M. (Mika), Laajala, T. D. (Teemu D), Kumar Kondadi, P. (Pradeep), Gisterå, A. (Anton), Lähteenmäki, H. (Hanna), Kittilä, T. (Tiia), Huusko, J. (Jenni), Uusitupa, M. (Matti), Schwab, U. (Ursula), Savolainen, M. J. (Markku J), Sinisalo, J. (Juha), Lokki, M.-L. (Marja-Liisa), Nieminen, M. S. (Markku S), Jula, A. (Antti), Perola, M. (Markus), Ylä-Herttula, S. (Seppo), Rudel, L. (Lawrence), Öörni, A. (Anssi), Baumann, M. (Marc), Baruch, A. (Amos), Laaksonen, R. (Reijo), Ketelhuth, D. F. (Daniel F J), Aittokallio, T. (Tero), Jauhiainen, M. (Matti), Käkelä, R. (Reijo), Borén, J. (Jan), Williams, K. J. (Kevin Jon), Kovanen, P. T. (Petri T), and Öörni, K. (Katariina)

Abstract

Aims: Low-density lipoprotein (LDL) particles cause atherosclerotic cardiovascular disease (ASCVD) through their retention, modification, and accumulation within the arterial intima. High plasma concentrations of LDL drive this disease, but LDL quality may also contribute. Here, we focused on the intrinsic propensity of LDL to aggregate upon modification. We examined whether inter-individual differences in this quality are linked with LDL lipid composition and coronary artery disease (CAD) death, and basic mechanisms for plaque growth and destabilization. Methods and results: We developed a novel, reproducible method to assess the susceptibility of LDL particles to aggregate during lipolysis induced ex vivo by human recombinant secretory sphingomyelinase. Among patients with an established CAD, we found that the presence of aggregation-prone LDL was predictive of future cardiovascular deaths, independently of conventional risk factors. Aggregation-prone LDL contained more sphingolipids and less phosphatidylcholines than did aggregation-resistant LDL. Three interventions in animal models to rationally alter LDL composition lowered its susceptibility to aggregate and slowed atherosclerosis. Similar compositional changes induced in humans by PCSK9 inhibition or healthy diet also lowered LDL aggregation susceptibility. Aggregated LDL in vitro activated macrophages and T cells, two key cell types involved in plaque progression and rupture. Conclusion: Our results identify the susceptibility of LDL to aggregate as a novel measurable and modifiable factor in the progression of human ASCVD.

Published
2018

32. Secreted frizzled-related protein 2 (SFRP2) expression promotes lesion proliferation via canonical WNT signaling and indicates lesion borders in extraovarian endometriosis

Author

Heinosalo, T, primary, Gabriel, M, additional, Kallio, L, additional, Adhikari, P, additional, Huhtinen, K, additional, Laajala, T D, additional, Kaikkonen, E, additional, Mehmood, A, additional, Suvitie, P, additional, Kujari, H, additional, Aittokallio, T, additional, Perheentupa, A, additional, and Poutanen, M, additional

Published
2018
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34. rapalogs can promote cancer cell stemness in vitro in a Galectin-1 and H-ras-dependent manner

Author

Posada, IMD, Lectez, B, Sharma, M, Oetken-Lindholm, C, Yetukuri, L, Zhou, Y, Aittokallio, T, Abankwa, Daniel, Posada, IMD, Lectez, B, Sharma, M, Oetken-Lindholm, C, Yetukuri, L, Zhou, Y, Aittokallio, T, and Abankwa, Daniel

Abstract

Currently several combination treatments of mTor- and Ras-pathway inhibitors are being tested in cancer therapy. While multiple feedback loops render these central signaling pathways robust, they complicate drug targeting. Here, we describe a novel H-ras specific feedback, which leads to an inadvertent rapalog induced activation of tumorigenicity in Ras transformed cells. We find that rapalogs specifically increase nanoscale clustering (nanoclustering) of oncogenic H-ras but not K-ras on the plasma membrane. This increases H-ras signaling output, promotes mammosphere numbers in a H-ras-dependent manner and tumor growth in ovo. Surprisingly, also other FKBP12 binders, but not mTor-inhibitors, robustly decrease FKBP12 levels after prolonged (>2 days) exposure. This leads to an upregulation of the nanocluster scaffold galectin-1 (Gal-1), which is responsible for the rapamycin-induced increase in H-ras nanoclustering and signaling output. We provide evidence that Gal-1 promotes stemness features in tumorigenic cells. Therefore, it may be necessary to block inadvertent induction of stemness traits in H-ras transformed cells by specific Gal-1 inhibitors that abrogate its effect on H-ras nanocluster. On a more general level, our findings may add an important mechanistic explanation to the pleiotropic physiological effects that are observed with rapalogs.

Published
2017
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35. Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling

Author

Andersson, E I, primary, Pützer, S, additional, Yadav, B, additional, Dufva, O, additional, Khan, S, additional, He, L, additional, Sellner, L, additional, Schrader, A, additional, Crispatzu, G, additional, Oleś, M, additional, Zhang, H, additional, Adnan-Awad, S, additional, Lagström, S, additional, Bellanger, D, additional, Mpindi, J P, additional, Eldfors, S, additional, Pemovska, T, additional, Pietarinen, P, additional, Lauhio, A, additional, Tomska, K, additional, Cuesta-Mateos, C, additional, Faber, E, additional, Koschmieder, S, additional, Brümmendorf, T H, additional, Kytölä, S, additional, Savolainen, E-R, additional, Siitonen, T, additional, Ellonen, P, additional, Kallioniemi, O, additional, Wennerberg, K, additional, Ding, W, additional, Stern, M-H, additional, Huber, W, additional, Anders, S, additional, Tang, J, additional, Aittokallio, T, additional, Zenz, T, additional, Herling, M, additional, and Mustjoki, S, additional

Published
2017
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36. Cancer stem cell drugs target K-ras signaling in a stemness context

Author

University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Najumudeen, A. K., Jaiswal, A., Lectez, B., Oetken-Lindholm, C., Guzman, C., Siljamaki, E., Posada, I. M. D., Lacey, E., Aittokallio, T., Abankwa, D., University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Najumudeen, A. K., Jaiswal, A., Lectez, B., Oetken-Lindholm, C., Guzman, C., Siljamaki, E., Posada, I. M. D., Lacey, E., Aittokallio, T., and Abankwa, D.

Abstract

Cancer stem cells (CSCs) are considered to be responsible for treatment relapse and have therefore become a major target in cancer research. Salinomycin is the most established CSC inhibitor. However, its primary mechanistic target is still unclear, impeding the discovery of compounds with similar anti-CSC activity. Here, we show that salinomycin very specifically interferes with the activity of K-ras4B, but not H-ras, by disrupting its nanoscale membrane organization. We found that caveolae negatively regulate the sensitivity to this drug. On the basis of this novel mechanistic insight, we defined a K-ras-associated and stem cell-derived gene expression signature that predicts the drug response of cancer cells to salinomycin. Consistent with therapy resistance of CSC, 8% of tumor samples in the TCGA-database displayed our signature and were associated with a significantly higher mortality. Using our K-ras-specific screening platform, we identified several new candidate CSC drugs. Two of these, ophiobolin A and conglobatin A, possessed a similar or higher potency than salinomycin. Finally, we established that the most potent compound, ophiobolin A, exerts its K-ras4B-specific activity through inactivation of calmodulin. Our data suggest that specific interference with the K-ras4B/calmodulin interaction selectively inhibits CSC.

Published
2016

37. Enhanced sensitivity to glucocorticoids in cytarabine-resistant AML

Author

Malani, D, primary, Murumägi, A, additional, Yadav, B, additional, Kontro, M, additional, Eldfors, S, additional, Kumar, A, additional, Karjalainen, R, additional, Majumder, M M, additional, Ojamies, P, additional, Pemovska, T, additional, Wennerberg, K, additional, Heckman, C, additional, Porkka, K, additional, Wolf, M, additional, Aittokallio, T, additional, and Kallioniemi, O, additional

Published
2016
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42. Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling

Author

Andersson, E I, Pützer, S, Yadav, B, Dufva, O, Khan, S, He, L, Sellner, L, Schrader, A, Crispatzu, G, Oles, M, Zhang, H, Adnan-Awad, S, Lagström, S, Bellanger, D, Mpindi, J P, Eldfors, S, Pemovska, T, Pietarinen, P, Lauhio, A, Tomska, K, Cuesta-Mateos, C, Faber, E, Koschmieder, S, Brümmendorf, T H, Kytölä, S, Savolainen, E-R, Siitonen, T, Ellonen, P, Kallioniemi, O, Wennerberg, K, Ding, W, Stern, M-H, Huber, W, Anders, S, Tang, J, Aittokallio, T, Zenz, T, Herling, M, and Mustjoki, S

Abstract

T-cell prolymphocytic leukemia (T-PLL) is a rare and aggressive neoplasm of mature T-cells with an urgent need for rationally designed therapies to address its notoriously chemo-refractory behavior. The median survival of T-PLL patients is <2 years and clinical trials are difficult to execute. Here we systematically explored the diversity of drug responses in T-PLL patient samples using an ex vivo drug sensitivity and resistance testing platform and correlated the findings with somatic mutations and gene expression profiles. Intriguingly, all T-PLL samples were sensitive to the cyclin-dependent kinase inhibitor SNS-032, which overcame stromal-cell-mediated protection and elicited robust p53-activation and apoptosis. Across all patients, the most effective classes of compounds were histone deacetylase, phosphoinositide-3 kinase/AKT/mammalian target of rapamycin, heat-shock protein 90 and BH3-family protein inhibitors as well as p53 activators, indicating previously unexplored, novel targeted approaches for treating T-PLL. Although Janus-activated kinase–signal transducer and activator of transcription factor (JAK-STAT) pathway mutations were common in T-PLL (71% of patients), JAK-STAT inhibitor responses were not directly linked to those or other T-PLL-specific lesions. Overall, we found that genetic markers do not readily translate into novel effective therapeutic vulnerabilities. In conclusion, novel classes of compounds with high efficacy in T-PLL were discovered with the comprehensive ex vivo drug screening platform warranting further studies of synergisms and clinical testing.

Published
2018
Full Text
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43. Anticancer compound ABT-263 accelerates apoptosis in virus-infected cells and imbalances cytokine production and lowers survival rates of infected mice

Author

Kakkola, L, primary, Denisova, O V, additional, Tynell, J, additional, Viiliäinen, J, additional, Ysenbaert, T, additional, Matos, R C, additional, Nagaraj, A, additional, Öhman, T, additional, Kuivanen, S, additional, Paavilainen, H, additional, Feng, L, additional, Yadav, B, additional, Julkunen, I, additional, Vapalahti, O, additional, Hukkanen, V, additional, Stenman, J, additional, Aittokallio, T, additional, Verschuren, E W, additional, Ojala, P M, additional, Nyman, T, additional, Saelens, X, additional, Dzeyk, K, additional, and Kainov, D E, additional

Published
2013
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45. Enhanced sensitivity to glucocorticoids in cytarabine-resistant AML

Author

Malani, D, Murumägi, A, Yadav, B, Kontro, M, Eldfors, S, Kumar, A, Karjalainen, R, Majumder, M M, Ojamies, P, Pemovska, T, Wennerberg, K, Heckman, C, Porkka, K, Wolf, M, Aittokallio, T, and Kallioniemi, O

Abstract

We sought to identify drugs that could counteract cytarabine resistance in acute myeloid leukemia (AML) by generating eight resistant variants from MOLM-13 and SHI-1 AML cell lines by long-term drug treatment. These cells were compared with 66 ex vivo chemorefractory samples from cytarabine-treated AML patients. The models and patient cells were subjected to genomic and transcriptomic profiling and high-throughput testing with 250 emerging and clinical oncology compounds. Genomic profiling uncovered deletion of the deoxycytidine kinase (DCK) gene in both MOLM-13- and SHI-1-derived cytarabine-resistant variants and in an AML patient sample. Cytarabine-resistant SHI-1 variants and a subset of chemorefractory AML patient samples showed increased sensitivity to glucocorticoids that are often used in treatment of lymphoid leukemia but not AML. Paired samples taken from AML patients before treatment and at relapse also showed acquisition of glucocorticoid sensitivity. Enhanced glucocorticoid sensitivity was only seen in AML patient samples that were negative for the FLT3 mutation (P=0.0006). Our study shows that development of cytarabine resistance is associated with increased sensitivity to glucocorticoids in a subset of AML, suggesting a new therapeutic strategy that should be explored in a clinical trial of chemorefractory AML patients carrying wild-type FLT3.

Published
2017
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