Claes Andersson

Multiplexed Homogeneous Proximity Ligation Assays for High-throughput Protein Biomarker Research in Serological Material

A high throughput protein biomarker discovery tool has been developed based on multiplexed proximity ligation assays in a homogeneous format in the sense of no washing steps. The platform consists of four 24-plex panels profiling 74 putative biomarkers with sub-pm sensitivity each consuming only 1 μl of human plasma sample. The system uses either matched monoclonal antibody pairs or the more readily available single batches of affinity purified polyclonal antibodies to generate the target specific reagents by covalently linking with unique nucleic acid sequences. These paired sequences are united by DNA ligation upon simultaneous target binding forming a PCR amplicon. Multiplex proximity ligation assays thereby converts multiple target analytes into real-time PCR amplicons that are individually quantified using microfluidic high capacity qPCR in nano liter volumes. The assay shows excellent specificity, even in multiplex, by its dual recognition feature, its proximity requirement, and most importantly by using unique sequence specific reporter fragments on both antibody-based probes. To illustrate the potential of this protein detection technology, a pilot biomarker research project was performed using biobanked plasma samples for the detection of colorectal cancer using a multivariate signature.

Bayesian detection of periodic mRNA time profiles without use of training examples

BackgroundDetection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time.ResultsA learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented.ConclusionIn comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.

Benchmarking study of parameter variation when using signature fingerprints together with support vector machines.

QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, γ, and signature height. C is a penalty parameter that limits overfitting, γ controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and γ in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.

Quantitative Chemogenomics: Machine-Learning Models of Protein-Ligand Interaction

Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed.

Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy.

Although medical cancer treatment has improved during the past decades, it is difficult to choose between several first-line treatments supposed to be equally active in the diagnostic group. It is even more difficult to select a treatment after the standard protocols have failed. Any guidance for selection of the most effective treatment is valuable at these critical stages. We describe the principles and procedures for ex vivo assessment of drug activity in tumor cells from patients as a basis for tailored cancer treatment. Patient tumor cells are assayed for cytotoxicity with a panel of drugs. Acoustic drug dispensing provides great flexibility in the selection of drugs for testing; currently, up to 80 compounds and/or combinations thereof may be tested for each patient. Drug response predictions are obtained by classification using an empirical model based on historical responses for the diagnosis. The laboratory workflow is supported by an integrated system that enables rapid analysis and automatic generation of the clinical referral response.

A Pragmatic Definition of Therapeutic Synergy Suitable for Clinically Relevant In Vitro Multicompound Analyses

For decades, the standard procedure when screening for candidate anticancer drug combinations has been to search for synergy, defined as any positive deviation from trivial cases like when the drugs are regarded as diluted versions of each other (Loewe additivity), independent actions (Bliss independence), or no interaction terms in a response surface model (no interaction). Here, we show that this kind of conventional synergy analysis may be completely misleading when the goal is to detect if there is a promising in vitro therapeutic window. Motivated by this result, and the fact that a drug combination offering a promising therapeutic window seldom is interesting if one of its constituent drugs can provide the same window alone, the largely overlooked concept of therapeutic synergy (TS) is reintroduced. In vitro TS is said to occur when the largest therapeutic window obtained by the best drug combination cannot be achieved by any single drug within the concentration range studied. Using this definition of TS, we introduce a procedure that enables its use in modern massively parallel experiments supported by a statistical omnibus test for TS designed to avoid the multiple testing problem. Finally, we suggest how one may perform TS analysis, via computational predictions of the reference cell responses, when only the target cell responses are available. In conclusion, the conventional error-prone search for promising drug combinations may be improved by replacing conventional (toxicology-rooted) synergy analysis with an analysis focused on (clinically motivated) TS. Mol Cancer Ther; 13(7); 1964–76. ©2014 AACR.

In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index

In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in terms of appropriate model systems. Here, such a pipeline is presented allowing the search for optimal combinations among an arbitrary number of drugs while also taking experimental variability into account. The TI optimized is the cytotoxicity difference (in vitro) between a target model and an adverse side effect model. Focusing on colorectal carcinoma (CRC), the pipeline provided several combinations that are effective in six different CRC models with limited cytotoxicity in normal cell models. Herein we describe the identification of the combination (Trichostatin A, Afungin, 17-AAG) and present results from subsequent characterisations, including efficacy in primary cultures of tumour cells from CRC patients. We hypothesize that its effect derives from potentiation of the proteotoxic action of 17-AAG by Trichostatin A and Afungin. The discovered drug combinations against CRC are significant findings themselves and also indicate that the proposed strategy has great potential for suggesting drug combination treatments suitable for other cancer types as well as for other complex diseases.

Synergistic interactions between camptothecin and EGFR or RAC1 inhibitors and between imatinib and Notch signaling or RAC1 inhibitors in glioblastoma cell lines

PurposeThe current treatment strategies for glioblastoma have limited health and survival benefits for the patients. A common obstacle in the treatment is chemoresistance. A possible strategy to evade this problem may be to combine chemotherapeutic drugs with agents inhibiting resistance mechanisms. The aim with this study was to identify molecular pathways influencing drug resistance in glioblastoma-derived cells and to evaluate the potential of pharmacological interference with these pathways to identify synergistic drug combinations.MethodsGlobal gene expressions and drug sensitivities to three chemotherapeutic drugs (imatinib, camptothecin and temozolomide) were measured in six human glioblastoma-derived cell lines. Gene expressions that correlated to drug sensitivity or resistance were identified and mapped to specific pathways. Selective inhibitors of these pathways were identified. The effects of six combinations of inhibitors and chemotherapeutic drugs were evaluated in glioblastoma-derived cell lines. Drug combinations with synergistic effects were also evaluated in non-cancerous epithelial cells.ResultsFour drug combinations had synergistic effects in at least one of the tested glioblastoma-derived cell lines; camptothecin combined with gefitinib (epidermal growth factor receptor inhibitor) or NSC 23766 (ras-related C3 botulinum toxin substrate 1 inhibitor) and imatinib combined with DAPT (Notch signaling inhibitor) or NSC 23766. Of these, imatinib combined with DAPT or NSC 23766 did not have synergistic effects in non-cancerous epithelial cells. Two drug combinations had at least additive effects in one of the tested glioblastoma-derived cell lines; temozolomide combined with gefitinib or PF-573228 (focal adhesion kinase inhibitor).ConclusionFour synergistic and two at least additive drug combinations were identified in glioblastoma-derived cells. Pathways targeted by these drug combinations may serve as targets for future drug development with the potential to increase efficacy of currently used/evaluated chemotherapy.

Screening for phenotype selective activity in multidrug resistant cells identifies a novel tubulin active agent insensitive to common forms of cancer drug resistance.

BackgroundDrug resistance is a common cause of treatment failure in cancer patients and encompasses a multitude of different mechanisms. The aim of the present study was to identify drugs effective on multidrug resistant cells.MethodsThe RPMI 8226 myeloma cell line and its multidrug resistant subline 8226/Dox40 was screened for cytotoxicity in response to 3,000 chemically diverse compounds using a fluorometric cytotoxicity assay (FMCA). Follow-up profiling was subsequently performed using various cellular and biochemical assays.ResultsOne compound, designated VLX40, demonstrated a higher activity against 8226/Dox40 cells compared to its parental counterpart. VLX40 induced delayed cell death with apoptotic features. Mechanistic exploration was performed using gene expression analysis of drug exposed tumor cells to generate a drug-specific signature. Strong connections to tubulin inhibitors and microtubule cytoskeleton were retrieved. The mechanistic hypothesis of VLX40 acting as a tubulin inhibitor was confirmed by direct measurements of interaction with tubulin polymerization using a biochemical assay and supported by demonstration of G2/M cell cycle arrest. When tested against a broad panel of primary cultures of patient tumor cells (PCPTC) representing different forms of leukemia and solid tumors, VLX40 displayed high activity against both myeloid and lymphoid leukemias in contrast to the reference compound vincristine to which myeloid blast cells are often insensitive. Significant in vivo activity was confirmed in myeloid U-937 cells implanted subcutaneously in mice using the hollow fiber model.ConclusionsThe results indicate that VLX40 may be a useful prototype for development of novel tubulin active agents that are insensitive to common mechanisms of cancer drug resistance.

Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors

BackgroundWe address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.ResultsWe explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.ConclusionThe results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.