Browsing by Author "Wennerberg, Krister"

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  • Breeze: An integrated quality control and data analysis application for high-throughput drug screening
    (2020-06-01) Potdar, Swapnil; Ianevski, Aleksandr; Mpindi, John Patrick; Bychkov, Dmitrii; Fiere, Clément; Ianevski, Philipp; Yadav, Bhagwan; Wennerberg, Krister; Aittokallio, Tero; Kallioniemi, Olli; Saarela, Jani; Östling, Päivi
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose-response curve fitting and quantification of the drug responses along with interactive visualization of the results. Contact: swapnil.potdar@helsinki.fi
  • Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs
    (2012) Khan, Suleiman A.; Faisal, Ali; Mpindi, John Patrick; Parkkinen, Juuso A.; Kalliokoski, Tuomo; Poso, Antti; Kallioniemi, Olli P.; Wennerberg, Krister; Kaski, Samuel
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
  • Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
    (2017-08-01) Cichonska, Anna; Ravikumar, Balaguru; Parri, Elina; Timonen, Sanna; Pahikkala, Tapio; Airola, Antti; Wennerberg, Krister; Rousu, Juho; Aittokallio, Tero
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.
  • Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
    (2016-08-23) Sieberts, Solveig K.; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O.; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R.; Marttinen, Pekka; Mezlini, Aziz M.; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E.; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; De Vries, Niek; Tak, Paul P.; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F.; Louis Bridges, S.; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K.; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in 1/4one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA-Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h 2 =0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.
  • Discovery of MINC1, a GTPase-Activating Protein Small Molecule Inhibitor, Targeting MgcRacGAP
    (2015) van Adrichem, Arjan J.; Fagerholm, Annika; Turunen, Laura; Lehto, Anna; Saarela, Jani; Koskinen, Ari; Repasky, Gretchen A.; Wennerberg, Krister
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The Rho family of Ras superfamily small GTPases regulates a broad range of biological processes such as migration, differentiation, cell growth and cell survival. Therefore, the availability of small molecule modulators as tool compounds could greatly enhance research on these proteins and their biological function. To this end, we designed a biochemical, high throughput screening assay with complementary follow-up assays to identify small molecule compounds inhibiting MgcRacGAP, a Rho family GTPase activating protein involved in cytokinesis and transcriptionally upregulated in many cancers. We first performed an in-house screen of 20,480 compounds, and later we tested the assay against 342,046 compounds from the NIH Molecular Libraries Small Molecule Repository. Primary screening hit rates were about 1% with the majority of those affecting the primary readout, an enzyme-coupled GDP detection assay. After orthogonal and counter screens, we identified two hits with high selectivity towards MgcRacGAP, compared with other RhoGAPs, and potencies in the low micromolar range. The most promising hit, termed MINC1, was then examined with cell-based testing where it was observed to induce an increased rate of cytokinetic failure and multinucleation in addition to other cell division defects, suggesting that it may act as an MgcRacGAP inhibitor also in cells.
  • Erythroid/megakaryocytic differentiation confers BCL-XL dependency and venetoclax resistance in acute myeloid leukemia
    (2023-03-30) Kuusanmäki, Heikki; Dufva, Olli; Vähä-Koskela, Markus; Leppä, Aino Maija; Huuhtanen, Jani; Vänttinen, Ida; Nygren, Petra; Klievink, Jay; Bouhlal, Jonas; Pölönen, Petri; Zhang, Qi; Adnan-Awad, Shady; Mancebo-Pérez, Cristina; Saad, Joseph; Miettinen, Juho; Javarappa, Komal K.; Aakko, Sofia; Ruokoranta, Tanja; Eldfors, Samuli; Heinäniemi, Merja; Theilgaard-Mönch, Kim; Wartiovaara-Kautto, Ulla; Keränen, Mikko; Porkka, Kimmo; Konopleva, Marina; Wennerberg, Krister; Kontro, Mika; Heckman, Caroline A.; Mustjoki, Satu
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Myeloid neoplasms with erythroid or megakaryocytic differentiation include pure erythroid leukemia, myelodysplastic syndrome with erythroid features, and acute megakaryoblastic leukemia (FAB M7) and are characterized by poor prognosis and limited treatment options. Here, we investigate the drug sensitivity landscape of these rare malignancies. We show that acute myeloid leukemia (AML) cells with erythroid or megakaryocytic differentiation depend on the antiapoptotic protein B-cell lymphoma (BCL)-XL, rather than BCL-2, using combined ex vivo drug sensitivity testing, genetic perturbation, and transcriptomic profiling. High-throughput screening of >500 compounds identified the BCL-XL–selective inhibitor A-1331852 and navitoclax as highly effective against erythroid/megakaryoblastic leukemia cell lines. In contrast, these AML subtypes were resistant to the BCL-2 inhibitor venetoclax, which is used clinically in the treatment of AML. Consistently, genome-scale CRISPR-Cas9 and RNAi screening data demonstrated the striking essentiality of BCL-XL-encoding BCL2L1 but not BCL2 or MCL1, for the survival of erythroid/megakaryoblastic leukemia cell lines. Single-cell and bulk transcriptomics of patient samples with erythroid and megakaryoblastic leukemias identified high BCL2L1 expression compared with other subtypes of AML and other hematological malignancies, where BCL2 and MCL1 were more prominent. BCL-XL inhibition effectively killed blasts in samples from patients with AML with erythroid or megakaryocytic differentiation ex vivo and reduced tumor burden in a mouse erythroleukemia xenograft model. Combining the BCL-XL inhibitor with the JAK inhibitor ruxolitinib showed synergistic and durable responses in cell lines. Our results suggest targeting BCL-XL as a potential therapy option in erythroid/megakaryoblastic leukemias and highlight an AML subgroup with potentially reduced sensitivity to venetoclax-based treatments.
  • Global proteomics profiling improves drug sensitivity prediction : results from a multi-omics, pan-cancer modeling approach
    (2018-04-15) Ali, Mehreen; Khan, Suleiman A.; Wennerberg, Krister; Aittokallio, Tero
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Motivation: Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Results: Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients.
  • Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression
    (2017-07-15) Ammad-Ud-Din, Muhammad; Khan, Suleiman A.; Wennerberg, Krister; Aittokallio, Tero
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Motivation: A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations. Results: We present a novel approach that leverages on systematic integration of data sources to identify response predictive features of multiple drugs. To solve the modeling task we implement a Bayesian linear regression method. To further improve the usefulness of the proposed model, we exploit the known human cancer kinome for identifying biologically relevant feature combinations. In case studies with a synthetic dataset and two publicly available cancer cell line datasets, we demonstrate the improved accuracy of our method compared to the widely used approaches in drug response analysis. As key examples, our model identifies meaningful combinations of features for the well known EGFR, ALK, PLK and PDGFR inhibitors.
  • A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury
    (2017-07-03) Kohonen, Pekka; Parkkinen, Juuso A.; Willighagen, Egon L.; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafström, Roland C.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion' - concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.