PINpOINT

• Ramos Alonso, Lucia; Holland, Petter; Le Gras, Stéphanie; Zhao, Xu; Jost, Bernard & Bjørås, Magnar [Show all 9 contributors for this article] (2023). Mitotic chromosome condensation resets chromatin to safeguard transcriptional homeostasis during interphase. Proceedings of the National Academy of Sciences of the United States of America. ISSN 0027-8424. 120(4). doi: 10.1073/pnas.2210593120. Full text in Research Archive
• Ayuda Duran, Pilar; Hermansen, Johanne Uthus; Giliberto, Mariaserena; Yin, Yanping; Hanes, Robert & Gordon, Sandra [Show all 13 contributors for this article] (2023). Standardized assays to monitor drug sensitivity in hematologic cancers. Cell death discovery. ISSN 2058-7716. 9(1), p. 1–13. doi: 10.1038/s41420-023-01722-5. Full text in Research Archive
• Spasevska, Ivana; Sharma, Ankush; Steen, Chloe Beate; Josefsson, Sarah Elisabet; Blaker, Yngvild Nuvin & Kolstad, Arne [Show all 26 contributors for this article] (2023). Diversity of intratumoral regulatory T cells in B-cell non-Hodgkin lymphoma. Blood Advances. ISSN 2473-9529. 7(23), p. 7216–7230. doi: 10.1182/bloodadvances.2023010158. Full text in Research Archive
• Chen, Yingjia; He, Liye; Ianevski, Aleksandr; Ayuda Duran, Pilar; Potdar, Swapnil & Saarela, Jani [Show all 14 contributors for this article] (2023). Robust scoring of selective drug responses for patient-tailored therapy selection. Nature Protocols. ISSN 1754-2189. p. 1–30. doi: 10.1038/s41596-023-00903-x. Full text in Research Archive
• Andersen, Aram Nikolai; Brodersen, Andrea M; Ayuda Duran, Pilar; Piechaczyk, Laure Isabelle; Tadele, Dagim Shiferaw & Baken, Lizet [Show all 12 contributors for this article] (2023). Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling. Cell Reports Methods. 3(12), p. 1–16. doi: 10.1016/j.crmeth.2023.100654. Full text in Research Archive
• Skånland, Sigrid S & Brown, Jennifer R. (2023). PI3K inhibitors in chronic lymphocytic leukemia: where do we go from here? Haematologica. ISSN 0390-6078. 108(1), p. 9–21. doi: 10.3324/haematol.2022.281266. Full text in Research Archive
• al Outa, Amani; Hicks, Steven; Thambawita, Vajira L B; Andresen, Siri; Enserink, Jorrit Martijn & Halvorsen, Pål [Show all 8 contributors for this article] (2023). CELLULAR, A Cell Autophagy Imaging Dataset. Scientific Data. ISSN 2052-4463. doi: 10.1038/s41597-023-02687-x. Full text in Research Archive
• Kohn Luque, Alvaro; Myklebust, Even Moa; Tadele, Dagim Shiferaw; Giliberto, Mariaserena; Schmiester, Leonard & Noory, Jasmine [Show all 15 contributors for this article] (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods. 3(3). doi: 10.1016/j.crmeth.2023.100417. Full text in Research Archive
• Ramos Alonso, Lucia; Garcia Llorente, Ignacio; Enserink, Jorrit & Chymkowitch, Pierre (2022). Analysis of the pheromone signaling pathway by RT-qPCR in the budding yeast Saccharomyces cerevisiae. STAR Protocols. ISSN 2666-1667. 3(1). doi: 10.1016/j.xpro.2022.101210. Full text in Research Archive
• Enserink, Jorrit & Chymkowitch, Pierre (2022). Cell Cycle-Dependent Transcription: The Cyclin Dependent Kinase Cdk1 Is a Direct Regulator of Basal Transcription Machineries. International Journal of Molecular Sciences. ISSN 1661-6596. 23(3), p. 1–23. doi: 10.3390/ijms23031293. Full text in Research Archive
• Aronson, Julia H.; Skånland, Sigrid S; Roeker, Lindsey E.; Thompson, Meghan C. & Mato, Anthony R. (2022). Approach to a patient with “double refractory” chronic lymphocytic leukemia: “Double, double toil and trouble” (Shakespeare). American Journal of Hematology. ISSN 0361-8609. 97(S2), p. S19–S25. doi: 10.1002/ajh.26682.
• Flobak, Åsmund; Skånland, Sigrid S; Hovig, Eivind; Tasken, Kjetil & Russnes, Hege Elisabeth Giercksky (2022). Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models. TIPS - Trends in Pharmacological Sciences. ISSN 0165-6147. 43(11), p. 973–985. doi: 10.1016/j.tips.2022.08.009. Full text in Research Archive
• Giliberto, Mariaserena; Miranda Santana, Leonardo; Holien, Toril; Misund, Kristine; Nakken, Sigve & Vodak, Daniel [Show all 12 contributors for this article] (2022). Mutational analysis and protein profiling predict drug sensitivity in multiple myeloma cell lines. Frontiers in Oncology. ISSN 2234-943X. 12:1040730, p. 1–14. doi: 10.3389/fonc.2022.1040730. Full text in Research Archive
• Hanes, Robert; Ayuda-Duran, Maria del Pilar; Rønneberg, Leiv; Nakken, Sigve; Hovig, Eivind & Zucknick, Manuela [Show all 7 contributors for this article] (2022). screenwerk: a modular tool for the design and analysis of drug combination screens. Bioinformatics. ISSN 1367-4803. 39(1). doi: 10.1093/bioinformatics/btac840. Full text in Research Archive
• Vitelli, Valeria; Fleischer, Thomas; Ankill, Jørgen; Arjas, Elja; Frigessi, Arnoldo & Kristensen, Vessela N. [Show all 7 contributors for this article] (2022). Transcriptomic pan-cancer analysis using rank-based Bayesian inference. Molecular Oncology. ISSN 1574-7891. 00(0), p. 1–16. doi: 10.1002/1878-0261.13354. Full text in Research Archive
• Yin, Yanping; Athanasiadis, Paschalis; Karlsen, Linda; Urban, Aleksandra; Xu, Haifeng & Murali, Ishwarya [Show all 17 contributors for this article] (2022). Functional testing to characterize and stratify PI3K inhibitor responses in chronic lymphocytic leukemia. Clinical Cancer Research. ISSN 1078-0432. 28(20), p. 4444–4455. doi: 10.1158/1078-0432.CCR-22-1221. Full text in Research Archive Show summary

  Purpose: PI3K inhibitors (PI3Ki) are approved for relapsed chronic lymphocytic leukemia (CLL). Although patients may show an initial response to these therapies, development of treatment intolerance or resistance remain clinical challenges. To overcome these, prediction of individual treatment responses based on actionable biomarkers is needed. Here, we characterized the activity and cellular effects of 10 PI3Ki and investigated whether functional analyses can identify treatment vulnerabilities in PI3Ki-refractory/intolerant CLL and stratify responders to PI3Ki. Experimental Design: Peripheral blood mononuclear cell samples (n = 51 in total) from treatment-naïve and PI3Ki-treated patients with CLL were studied. Cells were profiled against 10 PI3Ki and the Bcl-2 antagonist venetoclax. Cell signaling and immune phenotypes were analyzed by flow cytometry. Cell viability was monitored by detection of cleaved caspase-3 and the CellTiter-Glo assay. Results: pan-PI3Kis were most effective at inhibiting PI3K signaling and cell viability, and showed activity in CLL cells from both treatment-naïve and idelalisib-refractory/intolerant patients. CLL cells from idelalisib-refractory/intolerant patients showed overall reduced protein phosphorylation levels. The pan-PI3Ki copanlisib, but not the p110δ inhibitor idelalisib, inhibited PI3K signaling in CD4+ and CD8+ T cells in addition to CD19+ B cells, but did not significantly affect T-cell numbers. Combination treatment with a PI3Ki and venetoclax resulted in synergistic induction of apoptosis. Analysis of drug sensitivities to 73 drug combinations and profiling of 31 proteins stratified responders to idelalisib and umbralisib, respectively. Conclusions: Our findings suggest novel treatment vulnerabilities in idelalisib-refractory/intolerant CLL, and indicate that ex vivo functional profiling may stratify PI3Ki responders.

• Rønneberg, Leiv; Cremaschi, Andrea; Hanes, Robert; Enserink, Jorrit & Zucknick, Manuela (2021). bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments. Briefings in Bioinformatics. ISSN 1467-5463. 22(6), p. 1–12. doi: 10.1093/bib/bbab251. Full text in Research Archive
• Lai, Xiaoran; Taskén, Håkon Austlid; Mo, Torgeir; Funke, Simon Wolfgang; Frigessi, Arnoldo & Rognes, Marie Elisabeth [Show all 7 contributors for this article] (2021). A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy. International Journal for Numerical Methods in Biomedical Engineering. ISSN 2040-7939. 38(1). doi: 10.1002/cnm.3542. Full text in Research Archive Show summary

  Mathematical modeling and simulation is a promising approach to personalizedcancer medicine. Yet, the complexity, heterogeneity and multi-scale nature ofcancer pose significant computational challenges. Coupling discrete cell-basedmodels with continuous models using hybrid cellular automata (CA) is a power-ful approach for mimicking biological complexity and describing the dynamicalexchange of information across different scales. However, when clinically rele-vant cancer portions are taken into account, such models become computation-ally very expensive. While efficient parallelization techniques for continuousmodels exist, their coupling with discrete models, particularly CA, necessitatesmore elaborate solutions. Building upon FEniCS, a popular and powerful scien-tific computing platform for solving partial differential equations, we developedparallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communicationbetween processes that share CA neighborhood values while also allowing forreproducibility during stochastic updates. We demonstrated the potential of oursolution on a complex hybrid cellular automaton model of breast cancer treatedwith combination chemotherapy. On a single-core processor, we obtained nearlylinear scaling with an increasing problem size, whereas weak parallel scalingshowed moderate growth in solving time relative to increase in problem size.Finally, we applied the algorithm to a problem that is 500 times larger than previ-ous work, allowing us to run personalized therapy simulations based on hetero-geneous cell density and tumor perfusion conditions estimated from magneticresonance imaging data on an unprecedented scale

• Bergholtz, Helga; Lien, Tonje Gulbrandsen; Swanson, David; Frigessi Di Rattalma, Arnoldo; Daidone, Maria Grazia & Tost, Jörg [Show all 8 contributors for this article] (2020). Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions. NPJ breast cancer. ISSN 2374-4677. 6. doi: 10.1038/s41523-020-0167-x. Full text in Research Archive
• Skånland, Sigrid Strand; Karlsen, Linda & Tasken, Kjetil (2020). B cell signalling pathways — New targets for precision medicine in chronic lymphocytic leukaemia (SSI 50 YEARS ANNIVERSARY ARTICLE). Scandinavian Journal of Immunology. ISSN 0300-9475. 92:e12931(5), p. 1–15. doi: 10.1111/sji.12931. Full text in Research Archive

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• Frigessi, Arnoldo (2023). From limited patient data, to high frequency synthetic data, to the differential equation of a breast tumour growth.
• Tasken, Kjetil (2023). Keynote Speaker Title: Hvordan forskning bringer helsetjenesten framover - Forskningsstrategi og ledelse.
• Tasken, Kjetil (2023). Drug repurposing in Europe.
• Nilsen, Lars Broch & Tasken, Kjetil (2023). Kjetil Taskén fra OUS får svensk pris for fremragende arbeid på kreftfeltet. [Internet]. Health Talk.
• Tasken, Kjetil (2023). Open innovation system for precision cancer medicine implementation.
• Myklebust, Even Moa (2023). A framework for personalized prognosis of tumor evolution in Multiple Myeloma.
• Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework.
• Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling.
• Myklebust, Even Moa (2023). Personalized treatment recommendations for Multiple Myeloma with a Hierarchical Bayesian model.
• Myklebust, Even Moa (2023). Predicting Progression Free Survival in Multiple Myeloma with a Hierarchical Bayesian model.
• Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework.
• Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling.
• Tasken, Kjetil (2022). Precision Oncology Learning Health Ecosystems.
• McGuigan, Roisin & Tasken, Kjetil (2022). Taking the initiative to advance precision medicine: a perspective from Norway. [Business/trade/industry journal]. Immuno-Oncology Insights (BioInsights).
• Tasken, Kjetil (2022). Exploring the opportunity for large-scale trials across the Nordics: Reviewing InPreD and IMPRESs Norway. A national Ecosystem for implementation of cancer precision medicine. .
• Tasken, Kjetil (2022). IMPRESS-Norway og utfordringer ved dynamisk implementering av diagnostikk og behandling .
• Tasken, Kjetil (2022). OECI Excellent Practices: Translational Research and Innovation in Oslo.
• Tasken, Kjetil (2022). Key learnings from building a precision cancer medicine implementation initiative for Norway.
• Tasken, Kjetil (2022). Moving forward with precision medicine: Present status and challenges ahead.
• Tasken, Kjetil (2022). Kjetil Taskén om persontilpasset kreftmedisin og internasjonal anerkjennelse. [Internet]. Radium Podcast, Episode 225.
• Tasken, Kjetil (2022). Kreftforskning i rivende utvikling med Kjetil Taskén. [Internet]. Abbvie Podcast, Utvid horisonten, episode 17.
• Tasken, Kjetil (2022). A national precision cancer medicine implementation initiative: Prototype learning ecosystem for precision oncology.
• Kalveland, Julie & Tasken, Kjetil (2022). Norsk samarbeid innen kreft løftes frem i Nature Medicine. [Newspaper]. Dagens Medisin.
• Hødnebø, Line; Djukastein, Bjørne Østrem; Tasken, Kjetil & Næss, Gunhild (2022). Nytt prosjekt gir kreftsyke Gunnhild (33) håp om flere år. [Internet]. NRK.
• Stranden, Anne Lise & Tasken, Kjetil (2022). Nå kan flere norske pasienter med alvorlig kreft få presisjons­behandling. [Internet]. Forskning.no.
• Lindén, Sofia; Tasken, Kjetil; Bergli, Eli & Frich, Jan C. (2022). Norwegian cancer initiatives receive international attention Norway is pioneering precision medicine in cancer with three nation-wide initiatives. [Internet]. CONNECT.
• Lindén, Sofia; Tasken, Kjetil; Ross, Ingrid Stenstadvold; Bergli, Eli; Frich, Jan C & Foss, Grethe Synnøve (2022). Norwegian cancer initiatives receive international attention. [Internet]. Oslo Cancer Cluster.
• Nilsen, Lars Brock & Tasken, Kjetil (2022). Impress-studien: Over halvparten har fått tilbud om ny behandling. [Newspaper]. Dagens Medisine.
• Kalveland, Julie; Tasken, Kjetil; Ree, Anne Hansen & Opdalshei, Ole Alexander (2022). Studie: Presisjonsmedisin dobbelt så dyr som annen behandling av kreftpasienter i siste fase. [Newspaper]. Dagens Medisin.
• Tasken, Kjetil; Helland, Åslaug; Russnes, Hege Elisabeth Giercksky; Smeland, Sigbjørn; Frich, Jan C. & Thoresen, Steinar [Show all 13 contributors for this article] (2022). Arendalsuka 2022: Nøkkelen til bedre kreftbehandling er samarbeid. [Internet]. Arendalsuka, Paneldebatt.
• Engsig, Michael; Tasken, Kjetil; Grodt Schmidt, Jannik & Helland, Åslaug (2022). Norway’s precision medicine takes aim at cancer. [Journal]. The Explorer.
• Anderssen, Hans & Tasken, Kjetil (2022). Presenterer norsk kreftinitiativ på ESMO. [Internet]. Health Talk.
• Tasken, Kjetil & Smeland, Sigbjørn (2022). Description of the Norwegian Precision Medicine Initiatives published in Nature Medicine. [Internet]. IMPRESS-Norway.
• Tasken, Kjetil & Curtin, Ciara (2022). Norway's Fledgling Precision Oncology Effort Finding Early Success. [Internet]. GenomeWeb.
• Tasken, Kjetil (2022). NRK Dagsnytt - intervju med Kjetil Taskén. [Radio]. NRK.
• Tasken, Kjetil (2022). P2 Nyhetsmorgen - intervju med Kjetil Taskén. [Radio]. NRK.
• Tasken, Kjetil (2022). Hvor lang er veien til persontilpasset medisin?
• Tasken, Kjetil & Widerberg, Ketil (2022). OECI Excellent Practices: Oslo University Hospital Comprehensive Cancer Centre (OUH-CCC), Institute for Cancer Research (ICR) and Oslo Cancer Cluster (OCC) - Translational research and innovation. [Business/trade/industry journal]. OECI-Magazine.
• Tasken, Kjetil (2022). Den norska modellen (utmaningar och möjligheter?).
• Tasken, Kjetil (2022). Impress: genomikbaserad precisionsmedicinsk cancerbehandling i Norge.
• Tasken, Kjetil (2022). The Norwegian Cancer Precision Medicine Implementation Initiative – a prototype learning ecosystem for precision oncology.
• Tasken, Kjetil (2022). Norwegian Precision Cancer Medicine implementation initiative.
• Tasken, Kjetil (2022). A national precision cancer medicine implementation initiative for Norway.
• Tasken, Kjetil (2022). Experiences with arrangement of a national precision cancer medicine implementation initiative .
• Tasken, Kjetil (2022). DRUP-like trials in the Nordics: Status of the IMPRESS-Norway study, and how to share data and improve recruitment of patients.
• Tasken, Kjetil (2022). Inspiration from Norway - Implementing precision cancer medicine in Norway via interconnected initiatives.
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
• Myklebust, Even Moa (2022). A framework for personalized prognosis of tumor evolution in Multiple Myeloma by multi-output statistical learning.
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
• Myklebust, Even Moa (2021). Phenotypic deconvolution of cancer drug screens.
• Frigessi Di Rattalma, Arnoldo (2020). In silico modelling of breast tumours for personalised therapy.
• Frigessi Di Rattalma, Arnoldo; Köhn-Luque, Alvaro; Kristensen, Vessela N.; Gørbitz, Carl Henrik & Engebråten, Olav (2020). Kan statistikk og matematikk redde like mange liv som nye medisiner? Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116.
• Tasken, Kjetil (2002). Norwegian precision cancer medicine initiative: Ecosystem for implementation.

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