PINpOINT

• Andresen, Siri; Outa, Amani al; Formica, Miriam; Enserink, Jorrit & Knævelsrud, Helene (2025). Improved detection of lipidated Atg8a by immunoblotting in Drosophila melanogaster cells and tissues enables precise investigation of Atg8a flux and its termination. Autophagy. ISSN 1554-8627. 21(11), p. 2497–2517. doi: 10.1080/15548627.2025.2508551. Full text in Research Archive Show summary

  Macroautophagy/autophagy is an essential intracellular catabolic process for maintaining cellularhomeostasis. In Drosophila melanogaster, Atg8a lipidation serves as a key marker for autophagy, yettraditional methods often fail to effectively detect its lipidated state. To overcome this limitation, wedeveloped a refined approach that employs N-ethylmaleimide (NEM) to inhibit Atg4, thereby pre-serving Atg8a lipidation during sample preparation both in vitro and in vivo. We determined theoptimal concentration of the autophagic inhibitors bafilomycin A1 (BafA1) and chloroquine (CQ)required for inhibition of autolysosomal degradation. Furthermore, we investigated the effects ofprolonged nutrient deprivation on autophagic flux and TORC1 signaling. Our findings not onlyvalidate the effectiveness of this new approach to monitor lipidation of Atg8a but also provideinsights into selection of autolysosomal inhibitors and nutrient-dependent regulatory roles of TORC1in Drosophila.

• Wu, Chenyu; Gunnarsson, Einar Bjarki; Myklebust, Even Moa; Luque, Alvaro Kohn; Tadele, Dagim Shiferaw & Enserink, Jorrit [Show all 9 contributors for this article] (2024). Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data. PLoS Computational Biology. ISSN 1553-734X. 20(3). doi: 10.1371/journal.pcbi.1011888. Full text in Research Archive Show summary

  Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

• Fuentes-Martín, Raúl; Duran, Pilar Ayuda; Hanes, Robert; Gallego-Yerga, Laura; Wolterinck, Lisanne & Enserink, Jorrit [Show all 8 contributors for this article] (2024). Promising anti-proliferative indolic benzenesulfonamides alter mechanisms with sulfonamide nitrogen substituents. European Journal of Medicinal Chemistry. ISSN 0223-5234. 275, p. 1–20. doi: 10.1016/j.ejmech.2024.116617. Full text in Research Archive Show summary

  Agents that cause apoptotic cell death by interfering with tubulin dynamics, such as vinblastine and paclitaxel, are an important class of chemotherapeutics. Unfortunately, these compounds are substrates for multidrug resistance (MDR) pumps, allowing cancer cells to gain resistance to these chemotherapeutics. The indolesulfonamide family of tubulin inhibitors are not excluded by MDR pumps and have a promising activity profile, although their high lipophilicity is a pharmacokinetic limitation for their clinical use. Here we present a new family of N-indolyl-3,4,5-trimethoxybenzenesulfonamide derivatives with modifications on the indole system at positions 1 and 3 and on the sulfonamide nitrogen. We synthesized and screened against HeLa cells 34 novel indolic benzenesulfonamides. The most potent derivatives (1.7–109 nM) were tested against a broad panel of cancer cell lines, which revealed that substituted benzenesulfonamides analogs had highest potency. Importantly, these compounds were only moderately toxic to non-tumorigenic cells, suggesting the presence of a therapeutic index. Consistent with known clinical anti-tubulin agents, these compounds arrested the cell cycle at G2/M phase. Mechanistically, they induced apoptosis via caspase 3/7 activation, which occurred during M arrest. The substituents on the sulfonamide nitrogen appeared to determine different mechanistic results and cell fates. These results suggest that the compounds act differently depending on the bridge substituents, thus making them very interesting as mechanistic probes as well as potential drugs for further development.

• Alonso, Lucia Ramos; Holland, Petter; Gras, Stéphanie Le; 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 (PNAS). ISSN 0027-8424. 120(4). doi: 10.1073/pnas.2210593120. Full text in Research Archive Show summary

  Mitotic entry correlates with the condensation of the chromosomes, changes in histone modifications, exclusion of transcription factors from DNA, and the broad downregulation of transcription. However, whether mitotic condensation influences transcription in the subsequent interphase is unknown. Here, we show that preventing one chromosome to condense during mitosis causes it to fail resetting of transcription. Rather, in the following interphase, the affected chromosome contains unusually high levels of the transcription machinery, resulting in abnormally high expression levels of genes in cis, including various transcription factors. This subsequently causes the activation of inducible transcriptional programs in trans, such as the GAL genes, even in the absence of the relevant stimuli. Thus, mitotic chromosome condensation exerts stringent control on interphase gene expression to ensure the maintenance of basic cellular functions and cell identity across cell divisions. Together, our study identifies the maintenance of transcriptional homeostasis during interphase as an unexpected function of mitosis and mitotic chromosome condensation.

• Outa, Amani al; 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. doi: 10.1038/s41597-023-02687-x. Full text in Research Archive Show summary

  Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target. Image analysis to follow autophagy in cells, especially on high-content screens, has proven to be a bottleneck. Machine learning (ML) algorithms have recently emerged as crucial in analyzing images to efficiently extract information, thus contributing to a better understanding of the questions at hand. This paper presents CELLULAR, an open dataset consisting of images of cells expressing the autophagy reporter mRFP-EGFP-Atg8a with cell-specific segmentation masks. Each cell is annotated into either basal autophagy, activated autophagy, or unknown. Furthermore, we introduce some preliminary experiments using the dataset that can be used as a baseline for future research.

• Chen, Yingjia; He, Liye; Ianevski, Aleksandr; Duran, Pilar Ayuda; 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 Show summary

  Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient’s responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

• Andersen, Aram Nikolai; Brodersen, Andrea M; Duran, Pilar Ayuda; 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 Show summary

  Current treatment selection for acute myeloid leukemia (AML) patients depends on risk stratification based on cytogenetic and genomic markers. However, the forecasting accuracy of treatment response remains modest, with most patients receiving intensive chemotherapy. Recently, ex vivo drug screening has gained traction in personalized treatment selection and as a tool for mapping patient groups based on relevant cancer dependencies. Here, we systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can assess risk for individual AML patients and may guide clinical decision-making.

• 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 Show summary

  Phosphatidylinositol 3-kinase (PI3K) inhibitors are effective in chronic lymphocytic leukemia (CLL). However, the severe toxicity profile associated with the first-generation inhibitors idelalisib and duvelisib, combined with the availability of other more tolerable agents, have limited their use. CLL is still considered incurable, and relapse after treatment, development of resistance, and treatment intolerance are common. It is therefore of interest to optimize the administration of currently approved PI3K inhibitors and to develop next-generation agents to improve tolerability, so that this class of agents will be considered an effective and safe treatment option when needed. These efforts are reflected in the large number of emerging clinical trials with PI3K inhibitors in CLL. Current strategies to overcome treatment limitations include intermittent dosing, which is established for copanlisib and zandelisib and under investigation for duvelisib and parsaclisib. A second strategy is to combine the PI3K inhibitor with another novel agent, either as a continuous regimen or a fixedduration regimen, to deepen responses. In addition to these approaches, it is of interest to identify higher-resolution actionable biomarkers that can predict treatment responses and toxicity, and inform personalized treatment decisions. Here, we discuss the current status of PI3K inhibitors in CLL, factors limiting the use of currently approved PI3K inhibitors in CLL, current strategies to overcome these limitations, and where to go next.

• 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 Show summary

  Abstract Tumor-infiltrating regulatory T cells (Tregs) contribute to an immunosuppressive tumor microenvironment. Despite extensive studies, the prognostic impact of tumor-infiltrating Tregs in B-cell non-Hodgkin lymphomas (B-NHLs) remains unclear. Emerging studies suggest substantial heterogeneity in the phenotypes and suppressive capacities of Tregs, emphasizing the importance of understanding Treg diversity and the need for additional markers to identify highly suppressive Tregs. Here, we applied single-cell RNA sequencing and T-cell receptor sequencing combined with high-dimensional cytometry to decipher the heterogeneity of intratumoral Tregs in diffuse large B-cell lymphoma and follicular lymphoma (FL), compared with that in nonmalignant tonsillar tissue. We identified 3 distinct transcriptional states of Tregs: resting, activated, and unconventional LAG3+FOXP3− Tregs. Activated Tregs were enriched in B-NHL tumors, coexpressed several checkpoint receptors, and had stronger immunosuppressive activity compared with resting Tregs. In FL, activated Tregs were found in closer proximity to CD4+ and CD8+ T cells than other cell types. Furthermore, we used a computational approach to develop unique gene signature matrices, which were used to enumerate each Treg subset in cohorts with bulk gene expression data. In 2 independent FL cohorts, activated Tregs was the major subset, and high abundance was associated with adverse outcome. This study demonstrates that Tregs infiltrating B-NHL tumors are transcriptionally and functionally diverse. Highly immunosuppressive activated Tregs were enriched in tumor tissue but absent in the peripheral blood. Our data suggest that a deeper understanding of Treg heterogeneity in B-NHL could open new paths for rational drug design, facilitating selective targeting to improve antitumor immunity.

• Duran, Pilar Ayuda; 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. 9(1), p. 1–13. doi: 10.1038/s41420-023-01722-5. Full text in Research Archive Show summary

  Abstract The principle of drug sensitivity testing is to expose cancer cells to a library of different drugs and measure its effects on cell viability. Recent technological advances, continuous approval of targeted therapies, and improved cell culture protocols have enhanced the precision and clinical relevance of such screens. Indeed, drug sensitivity testing has proven diagnostically valuable for patients with advanced hematologic cancers. However, different cell types behave differently in culture and therefore require optimized drug screening protocols to ensure that their ex vivo drug sensitivity accurately reflects in vivo drug responses. For example, primary chronic lymphocytic leukemia (CLL) and multiple myeloma (MM) cells require unique microenvironmental stimuli to survive in culture, while this is less the case for acute myeloid leukemia (AML) cells. Here, we present our optimized and validated protocols for culturing and drug screening of primary cells from AML, CLL, and MM patients, and a generic protocol for cell line models. We also discuss drug library designs, reproducibility, and quality controls. We envision that these protocols may serve as community guidelines for the use and interpretation of assays to monitor drug sensitivity in hematologic cancers and thus contribute to standardization. The read-outs may provide insight into tumor biology, identify or confirm treatment resistance and sensitivity in real time, and ultimately guide clinical decision-making.

• Luque, Alvaro Kohn; 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 Show summary

  Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

• 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 Show summary

  Functional precision medicine is a new, emerging area that can guide cancer treatment by capturing information from direct perturbations of tumor-derived, living cells, such as by drug sensitivity screening. Precision cancer medicine as currently implemented in clinical practice has been driven by genomics, and current molecular tumor boards rely extensively on genomic characterization to advise on therapeutic interventions. However, genomic biomarkers can only guide treatment decisions for a fraction of the patients. In this review we provide an overview of the current state of functional precision medicine, highlight advances for drug-sensitivity screening enabled by cell culture models, and discuss how artificial intelligence (AI) can be coupled to functional precision medicine to guide patient stratification.

• 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. 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.

• 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 Show summary

  The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

• 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 Show summary

  Abstract Motivation There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications. Results screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens. Availability and implementation The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no Supplementary information Supplementary data are available at Bioinformatics online.

• 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 Show summary

  The cyclin-dependent kinase Cdk1 is best known for its function as master regulator of the cell cycle. It phosphorylates several key proteins to control progression through the different phases of the cell cycle. However, studies conducted several decades ago with mammalian cells revealed that Cdk1 also directly regulates the basal transcription machinery, most notably RNA polymerase II. More recent studies in the budding yeast Saccharomyces cerevisiae have revisited this function of Cdk1 and also revealed that Cdk1 directly controls RNA polymerase III activity. These studies have also provided novel insight into the physiological relevance of this process. For instance, cell cycle-stage-dependent activity of these complexes may be important for meeting the increased demand for various proteins involved in housekeeping, metabolism, and protein synthesis. Recent work also indicates that direct regulation of the RNA polymerase II machinery promotes cell cycle entry. Here, we provide an overview of the regulation of basal transcription by Cdk1, and we hypothesize that the original function of the primordial cell-cycle CDK was to regulate RNAPII and that it later evolved into specialized kinases that govern various aspects of the transcription machinery and the cell cycle.

• Giliberto, Mariaserena; Santana, Leonardo Miranda; 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. 12:1040730, p. 1–14. doi: 10.3389/fonc.2022.1040730. Full text in Research Archive Show summary

  Introduction: Multiple myeloma (MM) is a heterogeneous disease where cancer-driver mutations and aberrant signaling may lead to disease progression and drug resistance. Drug responses vary greatly, and there is an unmet need for biomarkers that can guide precision cancer medicine in this disease. Methods: To identify potential predictors of drug sensitivity, we applied integrated data from drug sensitivity screening, mutational analysis and functional signaling pathway profiling in 9 cell line models of MM. We studied the sensitivity to 33 targeted drugs and their association with the mutational status of cancer-driver genes and activity level of signaling proteins. Results: We found that sensitivity to mitogen-activated protein kinase kinase 1 (MEK1) and phosphatidylinositol-3 kinase (PI3K) inhibitors correlated with mutations in NRAS/KRAS, and PI3K family genes, respectively. Phosphorylation status of MEK1 and protein kinase B (AKT) correlated with sensitivity to MEK and PI3K inhibition, respectively. In addition, we found that enhanced phosphorylation of proteins, including Tank-binding kinase 1 (TBK1), as well as high expression of B cell lymphoma 2 (Bcl-2), correlated with low sensitivity to MEK inhibitors. Discussion: Taken together, this study shows that mutational status and signaling protein profiling might be used in further studies to predict drug sensitivities and identify resistance markers in MM.

• Alonso, Lucia Ramos; Llorente, Ignacio Garcia; Enserink, Jorrit & Chymkowitch, Pierre (2022). Analysis of the pheromone signaling pathway by RT-qPCR in the budding yeast Saccharomyces cerevisiae. STAR Protocols. 3(1). doi: 10.1016/j.xpro.2022.101210. Full text in Research Archive Show summary

  FUS3 and STE2 expression levels can be used as reporters for signaling through the pheromone pathway in the budding yeast Saccharomyces cerevisiae. Here, we describe an optimized protocol to measure the expression levels of FUS3 and STE2 using quantitative reverse transcription PCR (RT-qPCR). We describe the steps for comparing untreated and pheromone-treated yeast cells and how to quantify the changes in various deletion strains. The protocol can be applied to determine potential regulators of the pheromone pathway. For complete details on the use and execution of this protocol, please refer to Garcia et al. (2021).

• 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

• 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 Show summary

  Abstract The effect of cancer therapies is often tested pre-clinically via in vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). Although the model formulation makes use of the Bliss independence assumption, we note that the posterior estimates of the dose–response surface can also be used to extract synergy scores based on other reference models, which we illustrate for the Highest Single Agent model. The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the open-source Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.

• Bergholtz, Helga; Lien, Tonje Gulbrandsen; Swanson, David; Rattalma, Arnoldo Frigessi Di; 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. 6. doi: 10.1038/s41523-020-0167-x. Full text in Research Archive Show summary

  Ductal carcinoma in situ (DCIS) is a non-invasive type of breast cancer with highly variable potential of becoming invasive and affecting mortality. Currently, many patients with DCIS are overtreated due to the lack of specific biomarkers that distinguish low risk lesions from those with a higher risk of progression. In this study, we analyzed 57 pure DCIS and 313 invasive breast cancers (IBC) from different patients. Three levels of genomic data were obtained; gene expression, DNA methylation, and DNA copy number. We performed subtype stratified analyses and identified key differences between DCIS and IBC that suggest subtype specific progression. Prominent differences were found in tumors of the basal-like subtype: Basal-like DCIS were less proliferative and showed a higher degree of differentiation than basal-like IBC. Also, core basal tumors (characterized by high correlation to the basal-like centroid) were not identified amongst DCIS as opposed to IBC. At the copy number level, basal-like DCIS exhibited fewer copy number aberrations compared with basal-like IBC. An intriguing finding through analysis of the methylome was hypermethylation of multiple protocadherin genes in basal-like IBC compared with basal-like DCIS and normal tissue, possibly caused by long range epigenetic silencing. This points to silencing of cell adhesion-related genes specifically in IBC of the basal-like subtype. Our work confirms that subtype stratification is essential when studying progression from DCIS to IBC, and we provide evidence that basal-like DCIS show less aggressive characteristics and question the assumption that basal-like DCIS is a direct precursor of basal-like invasive breast cancer.

• 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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• Tasken, Kjetil (2023). Keynote Speaker Title: Hvordan forskning bringer helsetjenesten framover - Forskningsstrategi og ledelse. Full text in Research Archive
• Tasken, Kjetil (2023). Open innovation system for precision cancer medicine implementation. Full text in Research Archive
• Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling. Full text in Research Archive
• Tasken, Kjetil (2023). Drug repurposing in Europe. Full text in Research Archive
• Myklebust, Even Moa (2023). Personalized treatment recommendations for Multiple Myeloma with a Hierarchical Bayesian model. Full text in Research Archive
• Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework. Full text in Research Archive
• Myklebust, Even Moa (2023). A framework for personalized prognosis of tumor evolution in Multiple Myeloma. Full text in Research Archive
• Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework. Full text in Research Archive
• Myklebust, Even Moa (2023). Predicting Progression Free Survival in Multiple Myeloma with a Hierarchical Bayesian model. Full text in Research Archive
• Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling. Full text in Research Archive
• Frigessi, Arnoldo (2023). From limited patient data, to high frequency synthetic data, to the differential equation of a breast tumour growth. Full text in Research Archive
• Nilsen, Lars Broch & Tasken, Kjetil (2023). Kjetil Taskén fra OUS får svensk pris for fremragende arbeid på kreftfeltet. [Internet]. Health Talk. Full text in Research Archive
• 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. Full text in Research Archive
• Myklebust, Even Moa (2022). A framework for personalized prognosis of tumor evolution in Multiple Myeloma by multi-output statistical learning. Full text in Research Archive
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
• Tasken, Kjetil (2022). Inspiration from Norway - Implementing precision cancer medicine in Norway via interconnected initiatives. Full text in Research Archive
• Tasken, Kjetil (2022). Norwegian Precision Cancer Medicine implementation initiative. Full text in Research Archive
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
• Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
• Tasken, Kjetil (2022). A national precision cancer medicine implementation initiative for Norway. Full text in Research Archive
• Tasken, Kjetil (2022). Experiences with arrangement of a national precision cancer medicine implementation initiative. Full text in Research Archive
• Tasken, Kjetil (2022). Impress: genomikbaserad precisionsmedicinsk cancerbehandling i Norge. Full text in Research Archive
• Tasken, Kjetil (2022). Den norska modellen (utmaningar och möjligheter?). Full text in Research Archive
• Tasken, Kjetil (2022). Hvor lang er veien til persontilpasset medisin? Full text in Research Archive
• Tasken, Kjetil (2022). The Norwegian Cancer Precision Medicine Implementation Initiative – a prototype learning ecosystem for precision oncology. Full text in Research Archive
• Engsig, Michael; Tasken, Kjetil; Schmidt, Jannik Grodt & Helland, Åslaug (2022). Norway’s precision medicine takes aim at cancer. [Journal]. The Explorer. Full text in Research Archive
• Tasken, Kjetil (2022). Kjetil Taskén om persontilpasset kreftmedisin og internasjonal anerkjennelse. [Internet]. Radium Podcast, Episode 225. Full text in Research Archive
• Tasken, Kjetil (2022). P2 Nyhetsmorgen - intervju med Kjetil Taskén. [Radio]. NRK. Full text in Research Archive
• Tasken, Kjetil (2022). NRK Dagsnytt - intervju med Kjetil Taskén. [Radio]. NRK. Full text in Research Archive
• Stranden, Anne Lise & Tasken, Kjetil (2022). Nå kan flere norske pasienter med alvorlig kreft få presisjons­behandling. [Internet]. Forskning.no. Full text in Research Archive
• Kalveland, Julie & Tasken, Kjetil (2022). Norsk samarbeid innen kreft løftes frem i Nature Medicine. [Journal]. Dagens Medisin. Full text in Research Archive
• Tasken, Kjetil (2022). Moving forward with precision medicine: Present status and challenges ahead. Full text in Research Archive
• Tasken, Kjetil (2022). A national precision cancer medicine implementation initiative: Prototype learning ecosystem for precision oncology. Full text in Research Archive
• Anderssen, Hans & Tasken, Kjetil (2022). Presenterer norsk kreftinitiativ på ESMO. [Internet]. Health Talk. Full text in Research Archive
• 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. [Journal]. OECI-Magazine. Full text in Research Archive
• Nilsen, Lars Brock & Tasken, Kjetil (2022). Impress-studien: Over halvparten har fått tilbud om ny behandling. [Journal]. Dagens Medisine. Full text in Research Archive
• 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. Full text in Research Archive
• Tasken, Kjetil (2022). Kreftforskning i rivende utvikling med Kjetil Taskén. [Internet]. Abbvie Podcast, Utvid horisonten, episode 17. Full text in Research Archive
• Kalveland, Julie; Tasken, Kjetil; Ree, Anne Hansen & Opdalshei, Ole Alexander (2022). Studie: Presisjonsmedisin dobbelt så dyr som annen behandling av kreftpasienter i siste fase. [Journal]. Dagens Medisin. Full text in Research Archive
• Tasken, Kjetil & Smeland, Sigbjørn (2022). Description of the Norwegian Precision Medicine Initiatives published in Nature Medicine. [Internet]. IMPRESS-Norway. Full text in Research Archive
• Tasken, Kjetil & Curtin, Ciara (2022). Norway's Fledgling Precision Oncology Effort Finding Early Success. [Internet]. GenomeWeb. Full text in Research Archive
• McGuigan, Roisin & Tasken, Kjetil (2022). Taking the initiative to advance precision medicine: a perspective from Norway. [Journal]. Immuno-Oncology Insights (BioInsights). Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Hødnebø, Line; Djukastein, Bjørne Østrem; Tasken, Kjetil & Næss, Gunhild (2022). Nytt prosjekt gir kreftsyke Gunnhild (33) håp om flere år. [Internet]. NRK. Full text in Research Archive
• Tasken, Kjetil (2022). Precision Oncology Learning Health Ecosystems. Full text in Research Archive
• Tasken, Kjetil (2022). OECI Excellent Practices: Translational Research and Innovation in Oslo. Full text in Research Archive
• 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. Full text in Research Archive
• Tasken, Kjetil (2022). Key learnings from building a precision cancer medicine implementation initiative for Norway. Full text in Research Archive
• Tasken, Kjetil (2022). IMPRESS-Norway og utfordringer ved dynamisk implementering av diagnostikk og behandling. Full text in Research Archive
• Myklebust, Even Moa (2021). Phenotypic deconvolution of cancer drug screens. Full text in Research Archive
• Rattalma, Arnoldo Frigessi Di (2020). In silico modelling of breast tumours for personalised therapy. Full text in Research Archive
• Rattalma, Arnoldo Frigessi Di; 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. Full text in Research Archive
• Tasken, Kjetil (2002). Norwegian precision cancer medicine initiative: Ecosystem for implementation. Full text in Research Archive

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