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Gustafsson, Mats G
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Publications (10 of 60) Show all publications
Chantzi, E., Jarvius, M., Enarsson, M., Segerman, A. & Gustafsson, M. G. (2019). COMBImage2: a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining. BMC Bioinformatics, 20, Article ID 304.
Open this publication in new window or tab >>COMBImage2: a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining
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2019 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 20, article id 304Article in journal (Refereed) Published
Abstract [en]

Background: Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated.

Results: Motivated by this, we developed COMBImage2, a parallel computational framework for higher-order drug combination analysis. COMBImage2 goes far beyond its predecessor COMBImage in many different ways. In particular, it offers automated 384-well plate design, as well as quality control that involves resampling statistics and inter-plate analyses. Moreover, it is equipped with a generic matched filter based object counting method that is currently designed for apoptotic-like cells. Furthermore, apart from higher-order synergy analyses, COMBImage2 introduces a novel data mining approach for identifying interesting temporal response patterns and disentangling higher- from lower- and single-drug effects.COMBImage2 was employed in the context of a small pilot study focused on the CUSP9v4 protocol, which is currently used in the clinic for treatment of recurrent glioblastoma. For the first time, all 246 possible combinations of order 4 or lower of the 9 single drugs consisting the CUSP9v4 cocktail, were evaluated on an in vitro clonal culture of glioma initiating cells.

Conclusions: COMBImage2 is able to automatically design and robustly analyze exhaustive and in general higher-order drug combination experiments. Such a versatile video microscopy oriented framework is likely to enable, guide and accelerate systematic large-scale drug combination studies not only for cancer but also other diseases.

Place, publisher, year, edition, pages
BMC, 2019
Keywords
Label-free time-lapse video microscopy, Automated plate design, Higher-order drug combination analysis, Matched filter, Resampling, Data mining, MapReduce, CUSP9v4, Glioblastoma
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:uu:diva-387728 (URN)10.1186/s12859-019-2908-0 (DOI)000470281400002 ()31164078 (PubMedID)
Funder
Swedish Research Council, 2017-04655Knut and Alice Wallenberg Foundation, 2013.0280
Available from: 2019-06-25 Created: 2019-06-25 Last updated: 2019-06-25Bibliographically approved
Chantzi, E., Jarvius, M., Niklasson, M., Segerman, A. & Gustafsson, M. G. (2018). COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. BMC Bioinformatics, 19, Article ID 453.
Open this publication in new window or tab >>COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies
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2018 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 19, article id 453Article in journal (Refereed) Published
Abstract [en]

Background: Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements.

Results: COMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer.This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use.

Conclusions: COMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument.

Keywords
Time-lapse video microscopy, Label-free, Drug combination analysis, Therapeutic synergy, MapReduce, Parallel image processing, Systematic parameter optimization, Glioblastoma multiforme
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:uu:diva-372449 (URN)10.1186/s12859-018-2458-x (DOI)000451261300004 ()30477419 (PubMedID)
Funder
Knut and Alice Wallenberg Foundation, 2013.0280Swedish Research Council, 2017-04655
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Bäcklin, C. L. & Gustafsson, M. G. (2018). Developer-Friendly and Computationally Efficient Predictive Modeling without Information Leakage: The emil Package for R. Journal of Statistical Software, 85(13), 1-30
Open this publication in new window or tab >>Developer-Friendly and Computationally Efficient Predictive Modeling without Information Leakage: The emil Package for R
2018 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 85, no 13, p. 1-30Article in journal (Refereed) Published
Abstract [en]

Data driven machine learning for predictive modeling problems (classification, regression, or survival analysis) typically involves a number of steps beginning with data preprocessing and ending with performance evaluation. A large number of packages providing tools for the individual steps are available for R, but there is a lack of tools for facilitating rigorous performance evaluation of the complete procedures assembled from them by means of cross-validation, bootstrap, or similar methods. Such a tool should strictly prevent test set observations from influencing model training and meta- parameter tuning, so- called information leakage, in order to not produce overly optimistic performance estimates. Here we present a new package for R denoted emil (evaluation of modeling without information leakage) that offers this form of performance evaluation. It provides a transparent and highly customizable framework for facilitating the assembly, execution, performance evaluation, and interpretation of complete procedures for classification, regression, and survival analysis. The components of package emil have been designed to be as modular and general as possible to allow users to combine, replace, and extend them if needed. Package emil was also developed with scalability in mind and has a small computational overhead, which is a key requirement for analyzing the very big data sets now available in fields like medicine, physics, and finance. First package emil's functionality and usage is explained. Then three specific application examples are presented to show its potential in terms of parallelization, customization for survival analysis, and development of ensemble models. Finally a brief comparison to similar software is provided.

Place, publisher, year, edition, pages
JOURNAL STATISTICAL SOFTWARE, 2018
Keywords
predictive modeling, machine learning, performance evaluation, resampling, high performance computing
National Category
Computer Sciences Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-362159 (URN)10.18637/jss.v085.i13 (DOI)000440230100001 ()
Funder
Swedish Foundation for Strategic Research , RBc08-008Swedish Research Council, 621-2008-5854
Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2018-10-19Bibliographically approved
Neidlin, M., Chantzi, E., Macheras, G., Gustafsson, M. G. & Alexopoulos, L. (2018). Investigations Of Cytokine Interplay With An In Vitro Model Of Osteoarthritis. Paper presented at OARSI World Congress on Osteoarthritis - Promoting Clinical and Basic Research in Osteoarthritis, APR 26-29, 2018, Liverpool, ENGLAND. Osteoarthritis and Cartilage, 26(S1), S111-S111
Open this publication in new window or tab >>Investigations Of Cytokine Interplay With An In Vitro Model Of Osteoarthritis
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2018 (English)In: Osteoarthritis and Cartilage, ISSN 1063-4584, E-ISSN 1522-9653, Vol. 26, no S1, p. S111-S111Article in journal, Meeting abstract (Other academic) Published
National Category
Rheumatology and Autoimmunity
Identifiers
urn:nbn:se:uu:diva-358135 (URN)000432189700235 ()
Conference
OARSI World Congress on Osteoarthritis - Promoting Clinical and Basic Research in Osteoarthritis, APR 26-29, 2018, Liverpool, ENGLAND
Available from: 2018-08-24 Created: 2018-08-24 Last updated: 2018-08-31Bibliographically approved
Bäcklin, C., Andersson, C. & Gustafsson, M. G. (2018). Self-tuning density estimation based on Bayesian averaging of adaptive kernel density estimations yields state-of-the-art performance. Pattern Recognition, 78, 133-143
Open this publication in new window or tab >>Self-tuning density estimation based on Bayesian averaging of adaptive kernel density estimations yields state-of-the-art performance
2018 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 78, p. 133-143Article in journal (Refereed) Published
Abstract [en]

Non-parametric probability density function (pdf) estimation is a general problem encountered in many fields. A promising alternative to the dominating solutions, kernel density estimation (KDE) and Gaussian mixture modeling, is adaptive KDE where kernels are given individual bandwidths adjusted to the local data density. Traditionally the bandwidths are selected by a non-linear transformation of a pilot pdf estimate, containing parameters controlling the scaling, but identifying parameters values yielding competitive performance has turned out to be non-trivial. We present a new self-tuning (parameter free) pdf estimation method called adaptive density estimation by Bayesian averaging (ADEBA) that approximates pdf estimates in the form of weighted model averages across all possible parameter values, weighted by their Bayesian posterior calculated from the data. ADEBA is shown to be simple, robust, competitive in comparison to the current practice, and easily generalize to multivariate distributions. An implementation of the method for R is publicly available.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2018
Keywords
Adaptive density estimation, Variable bandwidth, Bayesian model averaging, Square root law, Multivariate, Univariate
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-353097 (URN)10.1016/j.patcog.2018.01.008 (DOI)000428490900010 ()
Funder
Swedish Foundation for Strategic Research , RBc08-008]EU, FP7, Seventh Framework ProgrammeSwedish Research Council, 621-2008-5854]
Available from: 2018-06-11 Created: 2018-06-11 Last updated: 2018-06-11Bibliographically approved
Kashif, M., Andersson, C., Mansoori, S., Larsson, R., Nygren, P. & Gustafsson, M. G. (2017). Bliss and Loewe interaction analyses of clinically relevant drug combinations in human colon cancer cell lines reveal complex patterns of synergy and antagonism. OncoTarget, 8(61), 103952-103967
Open this publication in new window or tab >>Bliss and Loewe interaction analyses of clinically relevant drug combinations in human colon cancer cell lines reveal complex patterns of synergy and antagonism
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2017 (English)In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 8, no 61, p. 103952-103967Article in journal (Refereed) Published
Abstract [en]

We analyzed survival effects for 15 different pairs of clinically relevant anticancer drugs in three iso-genic pairs of human colorectal cancer carcinoma cell lines, by applying for the first time our novel software (R package) called COMBIA. In our experiments iso-genic pairs of cell lines were used, differing only with respect to a single clinically important KRAS or BRAF mutation. Frequently, concentration dependent but mutation independent joint Bliss and Loewe synergy/antagonism was found statistically significant. Four combinations were found synergistic/antagonistic specifically to the parental (harboring KRAS or BRAF mutation) cell line of the corresponding iso-genic cell lines pair. COMBIA offers considerable improvements over established software for synergy analysis such as MacSynergy (TM) II as it includes both Bliss (independence) and Loewe (additivity) analyses, together with a tailored non-parametric statistical analysis employing heteroscedasticity, controlled resampling, and global (omnibus) testing. In many cases Loewe analyses found significant synergistic as well as antagonistic effects in a cell line at different concentrations of a tested drug combination. By contrast, Bliss analysis found only one type of significant effect per cell line. In conclusion, the integrated Bliss and Loewe interaction analysis based on non-parametric statistics may provide more robust interaction analyses and reveal complex patterns of synergy and antagonism.

Place, publisher, year, edition, pages
IMPACT JOURNALS LLC, 2017
Keywords
synergy analysis, combinations, iso-genic, COMBIA, R package
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:uu:diva-345218 (URN)10.18632/oncotarget.21895 (DOI)000419562500095 ()29262612 (PubMedID)
Available from: 2018-03-09 Created: 2018-03-09 Last updated: 2018-03-09Bibliographically approved
Herman, S., Emami Khoonsari, P., Aftab, O., Krishnan, S., Strömbom, E., Larsson, R., . . . Gustafsson, M. G. (2017). Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.. Metabolomics, 13(7), Article ID 79.
Open this publication in new window or tab >>Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.
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2017 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 7, article id 79Article in journal (Refereed) Published
Abstract [en]

INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols.

OBJECTIVES: This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them.

METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling.

RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information.

CONCLUSION: LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.

Keywords
Batch effects, Data handling, Drug metabolism, Mass spectrometry, Metabolomics
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-323946 (URN)10.1007/s11306-017-1213-z (DOI)000403779800002 ()28596718 (PubMedID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish Research Council
Available from: 2017-06-11 Created: 2017-06-11 Last updated: 2019-04-29Bibliographically approved
Nazir, M., Senkowski, W., Nyberg, F., Blom, K., Edqvist, P.-H. D., Jarvius, M., . . . Fryknäs, M. (2017). Targeting tumor cells based on Phosphodiesterase 3A expression. Experimental Cell Research, 361(2), 308-315
Open this publication in new window or tab >>Targeting tumor cells based on Phosphodiesterase 3A expression
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2017 (English)In: Experimental Cell Research, ISSN 0014-4827, E-ISSN 1090-2422, Vol. 361, no 2, p. 308-315Article in journal (Refereed) Published
Abstract [en]

We and others have previously reported a correlation between high phosphodiesterase 3 A (PDE3A) expression and selective sensitivity to phosphodiesterase (PDE) inhibitors. This indicates that PDE3A could serve both as a drug target and a biomarker of sensitivity to PDE3 inhibition. In this report, we explored publicly available mRNA gene expression data to identify cell lines with different PDE3A expression. Cell lines with high PDE3A expression showed marked in vitro sensitivity to PDE inhibitors zardaverine and quazinone, when compared with those having low PDE3A expression. Immunofluorescence and immunohistochemical stainings were in agreement with PDE3A mRNA expression, providing suitable alternatives for biomarker analysis of clinical tissue specimens. Moreover, we here demonstrate that tumor cells from patients with ovarian carcinoma show great variability in PDE3A protein expression and that level of PDE3A expression is correlated with sensitivity to PDE inhibition. Finally, we demonstrate that PDE3A is highly expressed in subsets of patient tumor cell samples from different solid cancer diagnoses and expressed at exceptional levels in gastrointestinal stromal tumor (GIST) specimens. Importantly, vulnerability to PDE3 inhibitors has recently been associated with co-expression of PDE3A and Schlafen family member 12 (SLFN12). We here demonstrate that high expression of PDE3A in clinical specimens, at least on the mRNA level, seems to be frequently associated with high SLFIV12 expression. In conclusion, PDE3A seems to be both a promising biomarker and drug target for individualized drug treatment of various cancers.

Keywords
Repositioning, Cancer, Therapy, PDE3A, Biomarker
National Category
Cancer and Oncology Cell Biology
Identifiers
urn:nbn:se:uu:diva-339786 (URN)10.1016/j.yexcr.2017.10.032 (DOI)000417774300013 ()29107068 (PubMedID)
Funder
Swedish Cancer Society, 2016/335Swedish Research Council, 2016-01112
Available from: 2018-02-16 Created: 2018-02-16 Last updated: 2018-04-04Bibliographically approved
Eriksson, A., Chantzi, E., Fryknäs, M., Gullbo, J., Nygren, P., Gustafsson, M. G., . . . Larsson, R. (2017). Towards repositioning of quinacrine for treatment of acute myeloid leukemia - Promising synergies and in vivo effects.. Leukemia research: a Forum for Studies on Leukemia and Normal Hemopoiesis, 63, 41-46
Open this publication in new window or tab >>Towards repositioning of quinacrine for treatment of acute myeloid leukemia - Promising synergies and in vivo effects.
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2017 (English)In: Leukemia research: a Forum for Studies on Leukemia and Normal Hemopoiesis, ISSN 0145-2126, E-ISSN 1873-5835, Vol. 63, p. 41-46Article in journal (Refereed) Published
Abstract [en]

We previously reported that the anti-malarial drug quinacrine has potential to be repositioned for treatment of acute myeloid leukemia (AML). As a next step towards clinical use, we assessed the efficacy of quinacrine in an AML-PS mouse model and investigated possible synergistic effects when combining quinacrine with nine other antileukemic compounds in two AML cell lines. Furthermore, we explored the in vivo activity of quinacrine in combination with the widely used AML agent cytarabine. The in vivo use of quinacrine (100mg/kg three times per week for two consecutive weeks) significantly suppressed circulating blast cells at days 30/31 and increased the median survival time (MST). The in vitro drug combination analysis yielded promising synergistic interactions when combining quinacrine with cytarabine, azacitidine and geldanamycin. Finally, combining quinacrine with cytarabine in vivo showed a significant decrease in circulating leukemic blast cells and increased MST compared to the effect of either drug used alone, thus supporting the findings from the in vitro combination experiments. Taken together, the repositioning potential of quinacrine for treatment of AML is reinforced by demonstrating significant in vivo activity and promising synergies when quinacrine is combined with different agents, including cytarabine, the hypomethylating agent azacitidine and HSP-90 inhibitor geldanamycin.

Keywords
Acute myeloid leukemia, Drug combinations, Quinacrine, Repositioning
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:uu:diva-342993 (URN)10.1016/j.leukres.2017.10.012 (DOI)000416744700007 ()29100024 (PubMedID)
Available from: 2018-02-24 Created: 2018-02-24 Last updated: 2018-03-01Bibliographically approved
Lind, A.-L., Yu, D., Freyhult, E., Bodolea, C., Ekegren, T., Larsson, A., . . . Kamali-Moghaddam, M. (2016). A Multiplex Protein Panel Applied to Cerebrospinal Fluid Reveals Three New Biomarker Candidates in ALS but None in Neuropathic Pain Patients. PLoS ONE, 11(2), Article ID e0149821.
Open this publication in new window or tab >>A Multiplex Protein Panel Applied to Cerebrospinal Fluid Reveals Three New Biomarker Candidates in ALS but None in Neuropathic Pain Patients
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 2, article id e0149821Article in journal (Refereed) Published
Abstract [en]

The objective of this study was to develop and apply a novel multiplex panel of solid-phase proximity ligation assays (SP-PLA) requiring only 20 μL of samples, as a tool for discovering protein biomarkers for neurological disease and treatment thereof in cerebrospinal fluid (CSF). We applied the SP-PLA to samples from two sets of patients with poorly understood nervous system pathologies amyotrophic lateral sclerosis (ALS) and neuropathic pain, where patients were treated with spinal cord stimulation (SCS). Forty-seven inflammatory and neurotrophic proteins were measured in samples from 20 ALS patients and 15 neuropathic pain patients, and compared to normal concentrations in CSF from control individuals. Nineteen of the 47 proteins were detectable in more than 95% of the 72 controls. None of the 21 proteins detectable in CSF from neuropathic pain patients were significantly altered by SCS. The levels of the three proteins, follistatin, interleukin-1 alpha, and kallikrein-5 were all significantly reduced in the ALS group compared to age-matched controls. These results demonstrate the utility of purpose designed multiplex SP-PLA panels in CSF biomarker research for understanding neuropathological and neurotherapeutic mechanisms. The protein changes found in the CSF of ALS patients may be of diagnostic interest.

National Category
Neurology Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-281233 (URN)10.1371/journal.pone.0149821 (DOI)000371175700030 ()26914813 (PubMedID)
Funder
Swedish Research CouncilKnut and Alice Wallenberg FoundationEU, European Research Council, 316929EU, European Research Council, 294409
Available from: 2016-03-21 Created: 2016-03-21 Last updated: 2017-11-30Bibliographically approved
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