Search

ITK-SYK and TEL-SYK (also known as ETV6-SYK) are human tumor-causing chimeric proteins containing the kinase region of SYK, and the membrane-targeting, N-terminal, PH-TH domain-doublet of ITK or the dimerizing SAM-PNT domain of TEL, respectively. ITK-SYK causes peripheral T cell lymphoma, while TEL-SYK was reported in myelodysplastic syndrome. BTK is a kinase highly related to ITK and to further delineate the role of the N-terminus, we generated the corresponding fusion-kinase BTK-SYK. By generating and analyzing these fusion kinases, we aim to understand the contribution of N-terminal domains to their distinct cellular behavior and oncogenic properties. The fusion kinases were found to behave differently. TEL-SYK showed stronger oncogenic capacity when compared with ITK-SYK and BTK-SYK. Furthermore, ITK-SYK and BTK-SYK triggered IL-3-independent growth of BAF3 pro-B cells. In contrast to BTK-SYK and TEL-SYK, which predominantly localized in perinuclear region and cytoplasm respectively, ITK-SYK exhibits a more diverse cellular distribution, being present in the nucleus, cytoplasm and membrane-bound compartments. Notably, we observed that ITK-SYK undergoes activation-mediated nuclear translocation, a phenomenon that is uncommon among kinases. This unique feature of ITK-SYK is therefore of particular interest due to its potential connection to its transforming capability., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

The CUSP9 protocol is a polypharmaceutical strategy aiming at addressing the complexity of glioblastoma by targeting multiple pathways. Although the rationale for this 9-drug cocktail is well-supported by theoretical and in vitro data, its effectiveness compared to its 511 possible subsets has not been comprehensively evaluated. Such an analysis could reveal if fewer drugs could achieve similar or better outcomes. We conducted an exhaustive in vitro evaluation of the CUSP9 protocol using COMBImageDL, our specialized framework for testing higher-order drug combinations. This study assessed all 511 subsets of the CUSP9v3 protocol, in combination with temozolomide, on two clonal cultures of glioma-initiating cells derived from patient samples. The drugs were used at fixed, clinically relevant concentrations, and the experiment was performed in quadruplicate with endpoint cell viability and live-cell imaging readouts. Our results showed that several lower-order drug combinations produced effects equivalent to the full CUSP9 cocktail, indicating potential for simplified regimens in personalized therapy. Further validation through in vivo and precision medicine testing is required. Notably, a subset of four drugs (auranofin, disulfiram, itraconazole, sertraline) was particularly effective, reducing cell growth, altering cell morphology, increasing apoptotic-like cells within 4-28 h, and significantly decreasing cell viability after 68 h compared to untreated cells. This study underscores the importance and feasibility of comprehensive in vitro evaluations of complex drug combinations on patient-derived tumor cells, serving as a critical step toward (pre-)clinical development., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest to disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: In patients undergoing resection for pancreatic cancer, adjuvant modified fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) improves overall survival compared with alternative chemotherapy regimens. We aimed to compare the efficacy and safety of neoadjuvant FOLFIRINOX with the standard strategy of upfront surgery in patients with resectable pancreatic ductal adenocarcinoma., Methods: NORPACT-1 was a multicentre, randomised, phase 2 trial done in 12 hospitals in Denmark, Finland, Norway, and Sweden. Eligible patients were aged 18 years or older, with a WHO performance status of 0 or 1, and had a resectable tumour of the pancreatic head radiologically strongly suspected to be pancreatic adenocarcinoma. Participants were randomly assigned (3:2 before October, 2018, and 1:1 after) to the neoadjuvant FOLFIRINOX group or upfront surgery group. Patients in the neoadjuvant FOLFIRINOX group received four neoadjuvant cycles of FOLFIRINOX (oxaliplatin 85 mg/m 2 , irinotecan 180 mg/m 2 , leucovorin 400 mg/m 2 , and fluorouracil 400 mg/m 2 bolus then 2400 mg/m 2 over 46 h on day 1 of each 14-day cycle), followed by surgery and adjuvant chemotherapy. Patients in the upfront surgery group underwent surgery and then received adjuvant chemotherapy. Initially, adjuvant chemotherapy was gemcitabine plus capecitabine (gemcitabine 1000 mg/m 2 over 30 min on days 1, 8, and 15 of each 28-day cycle and capecitabine 830 mg/m 2 twice daily for 3 weeks with 1 week of rest in each 28-day cycle; four cycles in the neoadjuvant FOLFIRINOX group, six cycles in the upfront surgery group). A protocol amendment was subsequently made to permit use of adjuvant modified FOLFIRINOX (oxaliplatin 85 mg/m 2 , irinotecan 150 mg/m 2 , leucovorin 400 mg/m 2 , and fluorouracil 2400 mg/m 2 over 46 h on day 1 of each 14-day cycle; eight cycles in the neoadjuvant FOLFIRINOX group, 12 cycles in the upfront surgery group). Randomisation was performed with a computerised algorithm that stratified for each participating centre and used a concealed block size of two to six. Patients, investigators, and study team members were not masked to treatment allocation. The primary endpoint was overall survival at 18 months. Analyses were done in the intention-to-treat (ITT) and per-protocol populations. Safety was assessed in all patients who were randomly assigned and received at least one cycle of neoadjuvant or adjuvant therapy. This trial is registered with ClinicalTrials.gov, NCT02919787, and EudraCT, 2015-001635-21, and is ongoing., Findings: Between Feb 8, 2017, and April 21, 2021, 77 patients were randomly assigned to receive neoadjuvant FOLFIRINOX and 63 to undergo upfront surgery. All patients were included in the ITT analysis. For the per-protocol analysis, 17 (22%) patients were excluded from the neoadjuvant FOLFIRINOX group (ten did not receive neoadjuvant therapy, four did not have pancreatic ductal adenocarcinoma, and three received another neoadjuvant regimen), and eight (13%) were excluded from the upfront surgery group (seven did not have pancreatic ductal adenocarcinoma and one did not undergo surgical exploration). 61 (79%) of 77 patients in the neoadjuvant FOLFIRINOX group received neoadjuvant therapy. The proportion of patients alive at 18 months by ITT was 60% (95% CI 49-71) in the neoadjuvant FOLFIRINOX group versus 73% (62-84) in the upfront surgery group (p=0·032), and median overall survival by ITT was 25·1 months (95% CI 17·2-34·9) versus 38·5 months (27·6-not reached; hazard ratio [HR] 1·52 [95% CI 1·00-2·33], log-rank p=0·050). The proportion of patients alive at 18 months in per-protocol analysis was 57% (95% CI 46-67) in the neoadjuvant FOLFIRINOX group versus 70% (55-83) in the upfront surgery group (p=0·14), and median overall survival in per-protocol population was 23·0 months (95% CI 16·2-34·9) versus 34·4 months (19·4-not reached; HR 1·46 [95% CI 0·99-2·17], log-rank p=0·058). In the safety population, 42 (58%) of 73 patients in the neoadjuvant FOLFIRINOX group and 19 (40%) of 47 patients in the upfront surgery group had at least one grade 3 or worse adverse event. 63 (82%) of 77 patients in the neoadjuvant group and 56 (89%) of 63 patients in the upfront surgery group had resection (p=0·24). One sudden death of unknown cause and one COVID-19-related death occurred after the first cycle of neoadjuvant FOLFIRINOX. Adjuvant chemotherapy was initiated in 51 (86%) of 59 patients with resected pancreatic ductal adenocarcinoma in the neoadjuvant FOLFIRINOX group and 44 (90%) of 49 patients with resected pancreatic ductal adenocarcinoma in the upfront surgery group (p=0·56). Adjuvant modified FOLFIRINOX was given to 13 (25%) patients in the neoadjuvant FOLFIRINOX group and 19 (43%) patients in the upfront surgery group. During adjuvant chemotherapy, neutropenia (11 [22%] patients in the neoadjuvant FOLFIRINOX group and five [11%] in the upfront surgery group) was the most common grade 3 or worse adverse event., Interpretation: This phase 2 trial did not show a survival benefit from neoadjuvant FOLFIRINOX in resectable pancreatic ductal adenocarcinoma compared with upfront surgery. Implementation of neoadjuvant FOLFIRINOX was challenging. Future trials on treatment sequencing in resectable pancreatic ductal adenocarcinoma should be biomarker driven., Funding: Norwegian Cancer Society, South Eastern Norwegian Health Authority, The Sjöberg Foundation, and Helsinki University Hospital Research Grants., Competing Interests: Declaration of interests HG has received honoraria from Pfizer, Amgen, and Bristol Myers Squibb. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Osteoarthritis (OA) is characterized by irreversible cartilage degradation with very limited therapeutic interventions. Drug candidates targeted at prototypic players had limited success until now and systems based approaches might be necessary. Consequently, drug evaluation platforms should consider the biological complexity looking beyond well-known contributors of OA. In this study an ex vivo model of cartilage degradation, combined with measuring releases of 27 proteins, was utilized to study 9 drug candidates. After an initial single drug evaluation step the 3 most promising compounds were selected and employed in an exhaustive combinatorial experiment. The resulting most and least promising treatment candidates were selected and validated in an independent study. This included estimation of mechanical properties via finite element modelling (FEM) and quantification of cartilage degradation as glycosaminoglycan (GAG) release. The most promising candidate showed increase of Young's modulus, decrease of hydraulic permeability and decrease of GAG release. The least promising candidate exhibited the opposite behaviour. The study shows the potential of a novel drug evaluation platform in identifying treatments that might reduce cartilage degradation. It also demonstrates the promise of exhaustive combination experiments and a connection between chondrocyte responses at the molecular level with changes of biomechanical properties at the tissue level.

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions., Competing Interests: The authors have declared that no competing interests exist.

The pathophysiology of osteoarthritis (OA) involves dysregulation of anabolic and catabolic processes associated with a broad panel of proteins that ultimately lead to cartilage degradation. An increased understanding about these protein interactions with systematic in vitro analyses may give new ideas regarding candidates for treatment of OA related cartilage degradation. Therefore, an ex vivo tissue model of cartilage degradation was established by culturing tissue explants with bacterial collagenase II. Responses of healthy and degrading cartilage were analyzed through protein abundance in tissue supernatant with a 26-multiplex protein profiling assay, after exposing the samples to a panel of 55 protein stimulations present in synovial joints of OA patients. Multivariate data analysis including exhaustive pairwise variable subset selection identified the most outstanding changes in measured protein secretions. MMP9 response to stimulation was outstandingly low in degrading cartilage and there were several protein pairs like IFNG and MMP9 that can be used for successful discrimination between degrading and healthy samples. The discovered changes in protein responses seem promising for accurate detection of degrading cartilage. The ex vivo model seems interesting for drug discovery projects related to cartilage degradation, for example when trying to uncover the unknown interactions between secreted proteins in healthy and degrading tissues., Competing Interests: The authors have declared that no competing interests exist.

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.

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.

We and others have previously reported a correlation between high phosphodiesterase 3A (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 SLFN12 expression. In conclusion, PDE3A seems to be both a promising biomarker and drug target for individualized drug treatment of various cancers., (Copyright © 2017 Elsevier Inc. All rights reserved.)

We analyzed survival effects for 15 different pairs of clinically relevant anti-cancer 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™ 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., Competing Interests: CONFLICTS OF INTEREST The authors do not declare any conflicts of interest.

Disgust plays a crucial role in the avoidance of pathogen threats. In many species, body odors provide important information related to health and disease, and body odors are potent elicitors of disgust in humans. With this background, valid assessments of body odor disgust sensitivity are warranted. In the present article, we report the development and psychometric validation of the Body Odor Disgust Scale (BODS), a measure suited to assess individual differences in disgust reaction to a variety of body odors. Collected data from 3 studies (total n = 528) show that the scale can be used either as a unidimensional scale or as a scale that reflects two hypothesized factors: sensitivity to one's own body odors versus those of others. Guided by our results, we reduced the scale to 12 items that capture the essence of these 2 factors. The final version of the BODS shows an excellent internal consistency (Cronbach's αs > 0.9). The BODS subscales show convergent validity with other general disgust scales, as well as with other olfactory functions measures and with aspects of personality that are related to pathogen avoidance. A fourth study confirmed the construct validity of the BODS and its measurement invariance to gender. Moreover, we found that, compared with other general disgust scales, the BODS is more strongly related to perceived vulnerability to disease. The BODS is a brief and valid assessment of trait body odor disgust sensitivity., (© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Cancer cell lines grown as two-dimensional (2D) cultures have been an essential model for studying cancer biology and anticancer drug discovery. However, 2D cancer cell cultures have major limitations, as they do not closely mimic the heterogeneity and tissue context of in vivo tumors. Developing three-dimensional (3D) cell cultures, such as multicellular tumor spheroids, has the potential to address some of these limitations. Here, we combined a high-throughput gene expression profiling method with a tumor spheroid-based drug-screening assay to identify context-dependent treatment responses. As a proof of concept, we examined drug responses of quiescent cancer cells to oxidative phosphorylation (OXPHOS) inhibitors. Use of multicellular tumor spheroids led to discovery that the mevalonate pathway is upregulated in quiescent cells during OXPHOS inhibition, and that OXPHOS inhibitors and mevalonate pathway inhibitors were synergistically toxic to quiescent spheroids. This work illustrates how 3D cellular models yield functional and mechanistic insights not accessible via 2D cultures., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Cytogenetic lesions often alter kinase signaling in acute myeloid leukemia (AML) and the addition of kinase inhibitors to the treatment arsenal is of interest. We have screened a kinase inhibitor library and performed combination testing to find promising drug-combinations for synergistic killing of AML cells. Cytotoxicity of 160 compounds in the library InhibitorSelect™ 384-Well Protein Kinase Inhibitor I was measured using the fluorometric microculture cytotoxicity assay (FMCA) in three AML cell lines. The 15 most potent substances were evaluated for dose-response. The 6 most cytotoxic compounds underwent combination synergy analysis based on the FMCA readouts after either simultaneous or sequential drug addition in AML cell lines. The 4 combinations showing the highest level of synergy were evaluated in 5 primary AML samples. Synergistic calculations were performed using the combination interaction analysis package COMBIA, written in R, using the Bliss independence model. Based on obtained results, an iterative combination search was performed using the therapeutic algorithmic combinatorial screen (TACS) algorithm. Of 160 substances, cell survival was ⩽50% at <0.5μM for Cdk/Crk inhibitor, KP372-1, synthetic fascaplysin, herbimycin A, PDGF receptor tyrosine kinase inhibitor IV and reference-drug cytarabine. KP372-1, synthetic fascaplysin or herbimycin A obtained synergy when combined with cytarabine in AML cell lines MV4-11 and HL-60. KP372-1 added 24h before cytarabine gave similar results in patient cells. The iterative search gave further improved synergy between cytarabine and KP372-1. In conclusion, our in vitro studies suggest that combining KP372-1 and cytarabine is a potent and synergistic drug combination in AML., (Copyright © 2016 Elsevier Inc. All rights reserved.)

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.

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.

Automated phase-contrast video microscopy now makes it feasible to monitor a high-throughput (HT) screening experiment in a 384-well microtiter plate format by collecting one time-lapse video per well. Being a very cost-effective and label-free monitoring method, its potential as an alternative to cell viability assays was evaluated. Three simple morphology feature extraction and comparison algorithms were developed and implemented for analysis of differentially time-evolving morphologies (DTEMs) monitored in phase-contrast microscopy videos. The most promising layout, pixel histogram hierarchy comparison (PHHC), was able to detect several compounds that did not induce any significant change in cell viability, but made the cell population appear as spheroidal cell aggregates. According to recent reports, all these compounds seem to be involved in inhibition of platelet-derived growth factor receptor (PDGFR) signaling. Thus, automated quantification of DTEM (AQDTEM) holds strong promise as an alternative or complement to viability assays in HT in vitro screening of chemical compounds., (© 2014 Society for Laboratory Automation and Screening.)

Background: We present a method that utilizes DNA methylation profiling for prediction of the cytogenetic subtypes of acute lymphoblastic leukemia (ALL) cells from pediatric ALL patients. The primary aim of our study was to improve risk stratification of ALL patients into treatment groups using DNA methylation as a complement to current diagnostic methods. A secondary aim was to gain insight into the functional role of DNA methylation in ALL., Results: We used the methylation status of ~450,000 CpG sites in 546 well-characterized patients with T-ALL or seven recurrent B-cell precursor ALL subtypes to design and validate sensitive and accurate DNA methylation classifiers. After repeated cross-validation, a final classifier was derived that consisted of only 246 CpG sites. The mean sensitivity and specificity of the classifier across the known subtypes was 0.90 and 0.99, respectively. We then used DNA methylation classification to screen for subtype membership of 210 patients with undefined karyotype (normal or no result) or non-recurrent cytogenetic aberrations ('other' subtype). Nearly half (n = 106) of the patients lacking cytogenetic subgrouping displayed highly similar methylation profiles as the patients in the known recurrent groups. We verified the subtype of 20% of the newly classified patients by examination of diagnostic karyotypes, array-based copy number analysis, and detection of fusion genes by quantitative polymerase chain reaction (PCR) and RNA-sequencing (RNA-seq). Using RNA-seq data from ALL patients where cytogenetic subtype and DNA methylation classification did not agree, we discovered several novel fusion genes involving ETV6, RUNX1, and PAX5., Conclusions: Our findings indicate that DNA methylation profiling contributes to the clarification of the heterogeneity in cytogenetically undefined ALL patient groups and could be implemented as a complementary method for diagnosis of ALL. The results of our study provide clues to the origin and development of leukemic transformation. The methylation status of the CpG sites constituting the classifiers also highlight relevant biological characteristics in otherwise unclassified ALL patients.

Improved understanding of the forces that determine drug specificity to their targets is important for drug design and discovery, as well as for gaining knowledge about molecular recognition. Here, we present a machine learning approach that includes all approved drugs with a known protein target. The drugs were characterized using easily interpretable physico-chemical descriptors. Employing the Random Forest method, we were able to predict whether a drug binds to a soluble or membrane protein with an average accuracy of 84 % and an average area under curve of 0.91. The high average performance suggests that there exist some general physico-chemical differences between drugs that bind to membrane and soluble protein targets. Variable importance measures in combination with permutation tests were used to find the most influential descriptors. This resulted in six outstanding descriptors, that all involve drug flexibility and lipophilicity, suggesting that drugs binding to membrane protein targets are in general more flexible and lipophilic, and conversely, drugs binding to soluble protein targets are more rigid and hydrophilic. With the notion that ligands in general are blueprints of their protein pockets, we may also draw general conclusions about the protein-pocket properties which may add to the understanding of molecular recognition., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Suicidal ideation is more prevalent among physicians, compared to the population in general, but little is known about the factors behind surgeons' suicidal ideation. A surgeon's work environment can be competitive and characterised by degrading experiences, which could contribute to burnout, depression and even thoughts of suicide. Being a surgeon has been reported to be predictor for not seeking help when psychological distressed. The aim of the present study was to investigate to what extent surgeons in Italy and Sweden are affected by suicidal ideation, and how suicidal ideation can be associated with psychosocial work conditions., Methods: A cross-sectional study of surgeons was performed in Italy (N = 149) and Sweden (N = 272), where having suicidal ideation was the outcome variable. Work-related factors, such as harassment, depression and social support, were also measured., Results: Suicidal ideation within the previous twelve months was affirmatively reported by 18% of the Italian surgeons, and by 12% of the Swedish surgeons in the present study. The strongest association with having recent suicidal ideation for both countries was being subjected to degrading experiences/harassment at work by a senior physician. Sickness presenteeism, exhaustion and disengagement were related to recent suicidal ideation among Italian surgeons, while role conflicts and sickness presenteeism were associated with recent suicidal ideation in the Swedish group. For both countries, regular meetings to discuss situations at work were found to be protective., Conclusions: A high percentage of surgeons at two university hospitals in Italy and Sweden reported suicidal ideation during the year before the investigation. This reflects a tough workload, including sickness presenteeism, harassment at work, exhaustion/disengagement and role conflicts. Regular meetings to discuss work situations might be protective.

Drug-induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances, as well as to support drug repurposing, i.e., identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the "Connectivity Map" (CMap). However, the potential of similar exercises at the metabolite level is still largely unexplored. Only recently, the first high-throughput metabolomic assay pilot study was published, which involved screening the metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here, we report results from a separately developed metabolic profiling assay, which leverages (1)H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of the most-tested drugs, according to their respective pharmacological class. A more-advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (3 metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this type of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information-rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.

Label free time-lapse microscopy has opened a new avenue to the study of time evolving events in living cells. When combined with automated image analysis it provides a powerful tool that enables automated large-scale spatiotemporal quantification at the cell population level. Very few attempts, however, have been reported regarding the design of image analysis algorithms dedicated to the detection of apoptotic cells in such time-lapse microscopy images. In particular, none of the reported attempts is based on sufficiently fast signal processing algorithms to enable large-scale detection of apoptosis within hours/days without access to high-end computers. Here we show that it is indeed possible to successfully detect chemically induced apoptosis by applying a two-dimensional linear matched filter tailored to the detection of objects with the typical features of an apoptotic cell in phase-contrast images. First a set of recorded computational detections of apoptosis was validated by comparison with apoptosis specific caspase activity readouts obtained via a fluorescence based assay. Then a large screen encompassing 2,866 drug like compounds was performed using the human colorectal carcinoma cell line HCT116. In addition to many well known inducers (positive controls) the screening resulted in the detection of two compounds here reported for the first time to induce apoptosis.

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., (©2014 American Association for Cancer Research.)

Analysis of vesicle formation and degradation is a central issue in autophagy research and microscopy imaging is revolutionizing the study of such dynamic events inside living cells. A limiting factor is the need for labeling techniques that are labor intensive, expensive, and not always completely reliable. To enable label-free analyses we introduced a generic computational algorithm, the label-free vesicle detector (LFVD), which relies on a matched filter designed to identify circular vesicles within cells using only phase-contrast microscopy images. First, the usefulness of the LFVD is illustrated by presenting successful detections of autophagy modulating drugs found by analyzing the human colorectal carcinoma cell line HCT116 exposed to each substance among 1266 pharmacologically active compounds. Some top hits were characterized with respect to their activity as autophagy modulators using independent in vitro labeling of acidic organelles, detection of LC3-II protein, and analysis of the autophagic flux. Selected detection results for 2 additional cell lines (DLD1 and RKO) demonstrate the generality of the method. In a second experiment, label-free monitoring of dose-dependent vesicle formation kinetics is demonstrated by recorded detection of vesicles over time at different drug concentrations. In conclusion, label-free detection and dynamic monitoring of vesicle formation during autophagy is enabled using the LFVD approach introduced.

Background: Although aberrant DNA methylation has been observed previously in acute lymphoblastic leukemia (ALL), the patterns of differential methylation have not been comprehensively determined in all subtypes of ALL on a genome-wide scale. The relationship between DNA methylation, cytogenetic background, drug resistance and relapse in ALL is poorly understood., Results: We surveyed the DNA methylation levels of 435,941 CpG sites in samples from 764 children at diagnosis of ALL and from 27 children at relapse. This survey uncovered four characteristic methylation signatures. First, compared with control blood cells, the methylomes of ALL cells shared 9,406 predominantly hypermethylated CpG sites, independent of cytogenetic background. Second, each cytogenetic subtype of ALL displayed a unique set of hyper- and hypomethylated CpG sites. The CpG sites that constituted these two signatures differed in their functional genomic enrichment to regions with marks of active or repressed chromatin. Third, we identified subtype-specific differential methylation in promoter and enhancer regions that were strongly correlated with gene expression. Fourth, a set of 6,612 CpG sites was predominantly hypermethylated in ALL cells at relapse, compared with matched samples at diagnosis. Analysis of relapse-free survival identified CpG sites with subtype-specific differential methylation that divided the patients into different risk groups, depending on their methylation status., Conclusions: Our results suggest an important biological role for DNA methylation in the differences between ALL subtypes and in their clinical outcome after treatment.

Summary: The previously disclosed QuantMap method for grouping chemicals by biological activity used online services for much of the data gathering and some of the numerical analysis. The present work attempts to streamline this process by using local copies of the databases and in-house analysis. Using computational methods similar or identical to those used in the previous work, a qualitatively equivalent result was found in just a few seconds on the same dataset (collection of 18 drugs). We use the user-friendly Galaxy framework to enable users to analyze their own datasets. Hopefully, this will make the QuantMap method more practical and accessible and help achieve its goals to provide substantial assistance to drug repositioning, pharmacology evaluation and toxicology risk assessment., Availability: http://galaxy.predpharmtox.org, Contact: mats.gustafsson@medsci.uu.se or ola.spjuth@farmbio.uu.se, Supplementary Information: Supplementary data are available at Bioinformatics online.

Cytosolic compartmentalization through liquid-liquid unmixing, such as the formation of RNA granules, is involved in many cellular processes and might be used to regulate signal transduction. However, specific molecular mechanisms by which liquid-liquid unmixing and signal transduction are coupled remain unknown. Here, we show that during cellular stress the dual specificity kinase DYRK3 regulates the stability of P-granule-like structures and mTORC1 signaling. DYRK3 displays a cyclic partitioning mechanism between stress granules and the cytosol via a low-complexity domain in its N terminus and its kinase activity. When DYRK3 is inactive, it prevents stress granule dissolution and the release of sequestered mTORC1. When DYRK3 is active, it allows stress granule dissolution, releasing mTORC1 for signaling and promoting its activity by directly phosphorylating the mTORC1 inhibitor PRAS40. This mechanism links cytoplasmic compartmentalization via liquid phase transitions with cellular signaling., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Conventional acquisition of three-dimensional (3D) microscopy data requires sequential z scanning and is often too slow to capture biological events. We report an aberration-corrected multifocus microscopy method capable of producing an instant focal stack of nine 2D images. Appended to an epifluorescence microscope, the multifocus system enables high-resolution 3D imaging in multiple colors with single-molecule sensitivity, at speeds limited by the camera readout time of a single image.

In an interferometer-based fluorescence microscope, a beam splitter is often used to combine two emission wavefronts interferometrically. There are two perpendicular paths along which the interference fringes can propagate and normally only one is used for imaging. However, the other path also contains useful information. Here we introduced a second camera to our interferometer-based three-dimensional structured-illumination microscope (I(5)S) to capture the fringes along the normally unused path, which are out of phase by π relative to the fringes along the other path. Based on this complementary phase relationship and the well-defined phase interrelationships among the I(5)S data components, we can deduce and then computationally eliminate the path length errors within the interferometer loop using the simultaneously recorded fringes along the two imaging paths. This self-correction capability can greatly relax the requirement for eliminating the path length differences before and maintaining that status during each imaging session, which are practically challenging tasks. Experimental data is shown to support the theory., (© 2012 The Authors Journal of Microscopy © 2012 Wadsworth Center, New York State Department of Health.)

We determined the genome-wide digital gene expression (DGE) profiles of primary acute lymphoblastic leukemia (ALL) cells from 21 patients taking advantage of 'second-generation' sequencing technology. Patients included in this study represent four cytogenetically distinct subtypes of B-cell precursor (BCP) ALL and T-cell lineage ALL (T-ALL). The robustness of DGE combined with supervised classification by nearest shrunken centroids (NSC) was validated experimentally and by comparison with published expression data for large sets of ALL samples. Genes that were differentially expressed between BCP ALL subtypes were enriched to distinct signaling pathways with dic(9;20) enriched to TP53 signaling, t(9;22) to interferon signaling, as well as high hyperdiploidy and t(12;21) to apoptosis signaling. We also observed antisense tags expressed from the non-coding strand of ~50% of annotated genes, many of which were expressed in a subtype-specific pattern. Antisense tags from 17 gene regions unambiguously discriminated between the BCP ALL and T-ALL subtypes, and antisense tags from 76 gene regions discriminated between the 4 BCP subtypes. We observed a significant overlap of gene regions with alternative polyadenylation and antisense transcription (P<1 × 10(-15)). Our study using DGE profiling provided new insights into the RNA expression patterns in ALL cells.

Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17β-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.

Previous implementations of structured-illumination microscopy (SIM) were slow or designed for one-color excitation, sacrificing two unique and extremely beneficial aspects of light microscopy: live-cell imaging in multiple colors. This is especially unfortunate because, among the resolution-extending techniques, SIM is an attractive choice for live-cell imaging; it requires no special fluorophores or high light intensities to achieve twice diffraction-limited resolution in three dimensions. Furthermore, its wide-field nature makes it light-efficient and decouples the acquisition speed from the size of the lateral field of view, meaning that high frame rates over large volumes are possible. Here, we report a previously undescribed SIM setup that is fast enough to record 3D two-color datasets of living whole cells. Using rapidly programmable liquid crystal devices and a flexible 2D grid pattern algorithm to switch between excitation wavelengths quickly, we show volume rates as high as 4 s in one color and 8.5 s in two colors over tens of time points. To demonstrate the capabilities of our microscope, we image a variety of biological structures, including mitochondria, clathrin-coated vesicles, and the actin cytoskeleton, in either HeLa cells or cultured neurons.

Using ultralow light intensities that are well suited for investigating biological samples, we demonstrate whole-cell superresolution imaging by nonlinear structured-illumination microscopy. Structured-illumination microscopy can increase the spatial resolution of a wide-field light microscope by a factor of two, with greater resolution extension possible if the emission rate of the sample responds nonlinearly to the illumination intensity. Saturating the fluorophore excited state is one such nonlinear response, and a realization of this idea, saturated structured-illumination microscopy, has achieved approximately 50-nm resolution on dye-filled polystyrene beads. Unfortunately, because saturation requires extremely high light intensities that are likely to accelerate photobleaching and damage even fixed tissue, this implementation is of limited use for studying biological samples. Here, reversible photoswitching of a fluorescent protein provides the required nonlinearity at light intensities six orders of magnitude lower than those needed for saturation. We experimentally demonstrate approximately 40-nm resolution on purified microtubules labeled with the fluorescent photoswitchable protein Dronpa, and we visualize cellular structures by imaging the mammalian nuclear pore and actin cytoskeleton. As a result, nonlinear structured-illumination microscopy is now a biologically compatible superresolution imaging method.

Background: High-throughput sequencing is becoming the standard tool for investigating protein-DNA interactions or epigenetic modifications. However, the data generated will always contain noise due to e.g. repetitive regions or non-specific antibody interactions. The noise will appear in the form of a background distribution of reads that must be taken into account in the downstream analysis, for example when detecting enriched regions (peak-calling). Several reported peak-callers can take experimental measurements of background tag distribution into account when analysing a data set. Unfortunately, the background is only used to adjust peak calling and not as a pre-processing step that aims at discerning the signal from the background noise. A normalization procedure that extracts the signal of interest would be of universal use when investigating genomic patterns., Results: We formulated such a normalization method based on linear regression and made a proof-of-concept implementation in R and C++. It was tested on simulated as well as on publicly available ChIP-seq data on binding sites for two transcription factors, MAX and FOXA1 and two control samples, Input and IgG. We applied three different peak-callers to (i) raw (un-normalized) data using statistical background models and (ii) raw data with control samples as background and (iii) normalized data without additional control samples as background. The fraction of called regions containing the expected transcription factor binding motif was largest for the normalized data and evaluation with qPCR data for FOXA1 suggested higher sensitivity and specificity using normalized data over raw data with experimental background., Conclusions: The proposed method can handle several control samples allowing for correction of multiple sources of bias simultaneously. Our evaluation on both synthetic and experimental data suggests that the method is successful in removing background noise.

Rationale: The innate immune system and in particular the pattern-recognition receptors Toll-like receptors have recently been linked to atherosclerosis. Consequently, inhibition of various signaling molecules downstream of the Toll-like receptors has been tested as a strategy to prevent progression of atherosclerosis. Receptor-interacting protein 2 (Rip2) is a serine/threonine kinase that is involved in multiple nuclear factor-κB (NFκB) activation pathways, including Toll-like receptors, and is therefore an interesting potential target for pharmaceutical intervention., Objective: We hypothesized that inhibition of Rip2 would protect against development of atherosclerosis., Methods and Results: Surprisingly, and contrary to our hypothesis, we found that mice transplanted with Rip2(-/-) bone marrow displayed markedly increased atherosclerotic lesions despite impaired local and systemic inflammation. Moreover, lipid uptake was increased whereas immune signaling was reduced in Rip2(-/-) macrophages. Further analysis in Rip2(-/-) macrophages showed that the lipid accumulation was scavenger-receptor independent and mediated by Toll-like receptor 4 (TLR4)-dependent lipid uptake., Conclusions: Our data show that lipid accumulation and inflammation are dissociated in the vessel wall in mice with Rip2(-/-) macrophages. These results for the first time identify Rip2 as a key regulator of cellular lipid metabolism and cardiovascular disease.

Over the last 15 years, an expanding number of databases with information on noxious effects of substances on mammalian organisms and the environment have been made available on the Internet. This set of databases is a key source of information for risk assessment within several areas of toxicology. Here we present features and relationships across a relatively wide set of publicly accessible databases broadly within toxicology, in part by clustering multi-score representations of such repositories, to support risk assessment within food toxicology. For this purpose 36 databases were each scrutinized, using 18 test substances from six different categories as probes. Results have been analyzed by means of various uni- and multi-variate statistical operations. The former included a special index devised to afford context-specific rating of databases across a highly heterogeneous data matrix, whereas the latter involved cluster analysis, enabling the identification of database assemblies with overall shared characteristics. One database - HSDB - was outstanding due to rich and qualified information for most test substances, but an appreciable fraction of the interrogated repositories showed good to decent scoring. Among the six chosen substance groups, Food contact materials had the most comprehensive toxicological information, followed by the Pesticides category., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Three-dimensional (3D) structured-illumination microscopy (SIM) can double the lateral and axial resolution of a wide-field fluorescence microscope but has been too slow for live imaging. Here we apply 3D SIM to living samples and record whole cells at up to 5 s per volume for >50 time points with 120-nm lateral and 360-nm axial resolution. We demonstrate the technique by imaging microtubules in S2 cells and mitochondria in HeLa cells.

The pathological process of allergies generally involves an initial activation of certain immune cells, tied to an ensuing inflammatory reaction on renewed contact with the allergen. In IgE-mediated hypersensitivity, this typically occurs in response to otherwise harmless food- or air-borne proteins. As some members of certain protein families carry special properties that make them allergenic, exploring protein allergens at the molecular level is instrumental to an improved understanding of the disease mechanisms, including the identification of relevant antigen features. For this purpose, we inspected a previously identified set of allergen representative peptides (ARPs) to scrutinize protein intrinsic disorder. The resulting study presented here focused on the association between these ARPs and protein intrinsic disorder. In addition, the connection between the disorder-enriched ARPs and UniProt functional keywords was considered. Our analysis revealed that ∼ 20% of the allergen peptides are highly disordered, and that ∼ 77% of ARPs are either located within disordered regions of corresponding allergenic proteins or show more disorder/flexibility than their neighbor regions. Furthermore, among the subset of allergenic proteins, ∼ 70% of the predicted molecular recognition features (MoRFs that consist of short interactive disordered regions undergoing disorder-to-order transitions at interaction with binding partners) were identified as ARPs. These results suggest that intrinsic disorder and MoRFs may play functional roles in IgE-mediated allergy., (Copyright © 2011 Wiley-Liss, Inc.)

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., (© 2011 Bentham Science Publishers)

Despite intense interest, methods that provide enhanced sensitivity and specificity in parallel measurements of candidate protein biomarkers in numerous samples have been lacking. We present herein a multiplex proximity ligation assay with readout via realtime PCR or DNA sequencing (ProteinSeq). We demonstrate improved sensitivity over conventional sandwich assays for simultaneous analysis of sets of 35 proteins in 5 µl of blood plasma. Importantly, we observe a minimal tendency to increased background with multiplexing, compared to a sandwich assay, suggesting that higher levels of multiplexing are possible. We used ProteinSeq to analyze proteins in plasma samples from cardiovascular disease (CVD) patient cohorts and matched controls. Three proteins, namely P-selectin, Cystatin-B and Kallikrein-6, were identified as putative diagnostic biomarkers for CVD. The latter two have not been previously reported in the literature and their potential roles must be validated in larger patient cohorts. We conclude that ProteinSeq is promising for screening large numbers of proteins and samples while the technology can provide a much-needed platform for validation of diagnostic markers in biobank samples and in clinical use.

Objective: Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (CI) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the CI is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice., Method and Material: It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples., Results: Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets., Conclusions: An empirically derived ME prior seems promising for improving the Bayesian CI for the unknown error rate of a designed classifier., (Copyright 2010 Elsevier B.V. All rights reserved.)

Despite improvements in the prognosis of childhood acute lymphoblastic leukemia (ALL), subgroups of patients would benefit from alternative treatment approaches. Our aim was to identify genes with DNA methylation profiles that could identify such groups. We determined the methylation levels of 1320 CpG sites in regulatory regions of 416 genes in cells from 401 children diagnosed with ALL. Hierarchical clustering of 300 CpG sites distinguished between T-lineage ALL and B-cell precursor (BCP) ALL and between the main cytogenetic subtypes of BCP ALL. It also stratified patients with high hyperdiploidy and t(12;21) ALL into 2 subgroups with different probability of relapse. By using supervised learning, we constructed multivariate classifiers by external cross-validation procedures. We identified 40 genes that consistently contributed to accurate discrimination between the main subtypes of BCP ALL and gene sets that discriminated between subtypes of ALL and between ALL and controls in pairwise classification analyses. We also identified 20 individual genes with DNA methylation levels that predicted relapse of leukemia. Thus, methylation analysis should be explored as a method to improve stratification of ALL patients. The genes highlighted in our study are not enriched to specific pathways, but the gene expression levels are inversely correlated to the methylation levels.