Standardized assays to monitor drug sensitivity in hematologic cancers.

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

Protein Interaction Network Analysis to Investigate Stress Response, Virulence, and Antibiotic Resistance Mechanisms in Listeria monocytogenes.

Listeria monocytogenes is a deadly and costly foodborne pathogen that has a high fatality rate in the elderly, pregnant women, and people with weakened immunity. It can survive under various stress conditions and is a significant concern for the food industry. In this work, a data analysis approach was developed with existing tools and databases and used to create individual and combined protein interaction networks to study stress response, virulence, and antimicrobial resistance and their interaction with L. monocytogenes. The networks were analyzed, and 28 key proteins were identified that may serve as potential targets for new strategies to combat L. monocytogenes. Five of the twenty-eight proteins (i.e., sigB, flaA, cheA, cheY, and lmo0693) represent the most promising targets because they are highly interconnected within the combined network. The results of this study provide a new set of targets for future work to identify new strategies to improve food preservation methods and treatments for L. monocytogenes.

screenwerk: a modular tool for the design and analysis of drug combination screens.

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

Investigation of Antimicrobial Resistance Genes in Listeria monocytogenes from 2010 through to 2021

Antimicrobial resistance (AMR) is a serious public health issue. Due to resistance to current antibiotics and a low rate of development of new classes of antimicrobials, AMR is a leading cause of death worldwide. Listeria monocytogenes is a deadly foodborne pathogen that causes listeriosis for the immunocompromised, the elderly, and pregnant women. Unfortunately, antimicrobial resistance has been reported in L. monocytogenes. This study conducted the first comprehensive statistical analysis of L. monocytogenes isolate data from the National Pathogen Detection Isolate Browser (NPDIB) to identify the trends for AMR genes in L. monocytogenes. Principal component analysis was firstly used to project the multi-dimensional data into two dimensions. Hierarchical clustering was then used to identify the significant AMR genes found in L. monocytogenes samples and to assess changes during the period from 2010 through to 2021. Statistical analysis of the data identified fosX, lin, abc-f, and tet(M) as the four most common AMR genes found in L. monocytogenes. It was determined that there was no increase in AMR genes during the studied time period. It was also observed that the number of isolates decreased from 2016 to 2020. This study establishes a baseline for the ongoing monitoring of L. monocytogenes for AMR genes.

Mcl-1 and Bcl-xL levels predict responsiveness to dual MEK/Bcl-2 inhibition in B-cell malignancies.

Most patients with chronic lymphocytic leukemia (CLL) initially respond to targeted therapies, but eventually relapse and develop resistance. Novel treatment strategies are therefore needed to improve patient outcomes. Here, we performed direct drug testing on primary CLL cells and identified synergy between eight different mitogen-activated protein kinase kinase (MEK) inhibitors and the B-cell lymphoma 2 (Bcl-2) antagonist venetoclax. Drug sensitivity was independent of immunoglobulin heavy-chain gene variable region (IGVH) and tumor protein p53 (TP53) mutational status, and CLL cells from idelalisib-resistant patients remained sensitive to the treatment. This suggests that combined MEK/Bcl-2 inhibition may be an option for high-risk CLL. To test whether sensitivity could be detected in other B-cell malignancies, we performed drug testing on cell line models of CLL (n = 4), multiple myeloma (MM; n = 8), and mantle cell lymphoma (MCL; n = 7). Like CLL, MM cells were sensitive to the MEK inhibitor trametinib, and synergy was observed with venetoclax. In contrast, MCL cells were unresponsive to MEK inhibition. To investigate the underlying mechanisms of the disease-specific drug sensitivities, we performed flow cytometry-based high-throughput profiling of 31 signaling proteins and regulators of apoptosis in the 19 cell lines. We found that high expression of the antiapoptotic proteins myeloid cell leukemia-1 (Mcl-1) or B-cell lymphoma-extra large (Bcl-xL) predicted low sensitivity to trametinib + venetoclax. The low sensitivity could be overcome by combined treatment with an Mcl-1 or Bcl-xL inhibitor. Our findings suggest that MEK/Bcl-2 inhibition has therapeutic potential in leukemia and myeloma, and demonstrate that protein expression levels can serve as predictive biomarkers for treatment sensitivities.

bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments.

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

Discovery of novel candidates for anti-liposarcoma therapies by medium-scale high-throughput drug screening.

Sarcomas are a heterogeneous group of mesenchymal orphan cancers and new treatment alternatives beyond traditional chemotherapeutic regimes are much needed. So far, tumor mutation analysis has not led to significant treatment advances, and we have attempted to bypass this limitation by performing direct drug testing of a library of 353 anti-cancer compounds that are either FDA-approved, in clinical trial, or in advanced stages of preclinical development on a panel of 13 liposarcoma cell lines. We identified and validated six drugs, targeting different mechanisms and with good efficiency across the cell lines: MLN2238 -a proteasome inhibitor, GSK2126458 -a PI3K/mTOR inhibitor, JNJ-26481585 -a histone deacetylase inhibitor, triptolide-a multi-target drug, YM155 -a survivin inhibitor, and APO866 (FK866)-a nicotinamide phosphoribosyl transferase inhibitor. GR50s for those drugs were mostly in the nanomolar range, and in many cases below 10 nM. These drugs had long-lasting effect upon drug withdrawal, limited toxicity to normal cells and good efficacy also against tumor explants. Finally, we identified potential genomic biomarkers of their efficacy. Being approved or in clinical trials, these drugs are promising candidates for liposarcoma treatment.

Preclinical Evaluation of the Pan-FGFR Inhibitor LY2874455 in FRS2-Amplified Liposarcoma.

Background: FGFR inhibition has been proposed as treatment for dedifferentiated liposarcoma (DDLPS) with amplified FRS2, but we previously only demonstrated transient cytostatic effects when treating FRS2-amplified DDLPS cells with NVP-BGJ398. Methods: Effects of the more potent FGFR inhibitor LY2874455 were investigated in three DDLPS cell lines by measuring effects on cell growth and apoptosis in vitro and also testing efficacy in vivo. Genome, transcriptome and protein analyses were performed to characterize the signaling components in the FGFR pathway. Results: LY2874455 induced a stronger, longer-lasting growth inhibitory effect and moderate level of apoptosis for two cell lines. The third cell line, did not respond to FGFR inhibition, suggesting that FRS2 amplification alone is not sufficient to predict response. Importantly, efficacy of LY2874455 was confirmed in vivo, using an independent FRS2-amplified DDLPS xenograft model. Expression of FRS2 was similar in the responding and non-responding cell lines and we could not find any major difference in downstream FGFR signaling. The only FGF expressed by unstimulated non-responding cells was the intracellular ligand FGF11, whereas the responding cell lines expressed extracellular ligand FGF2. Conclusion: Our study supports LY2874455 as a better therapy than NVP-BGJ398 for FRS2-amplified liposarcoma, and a clinical trial is warranted.

PP2A Regulatory Subunit B55γ is a Gatekeeper of Osteoblast Maturation and Lineage Maintenance.

Bone marrow mesenchymal stem cells (BM-MSCs) mediate skeletal remodeling by differentiating into osteoblasts. However, this remodeling is impaired with aging as well as following long-term glucocorticoid treatment, resulting in osteoporosis. In this study, we report a novel factor of osteoblast differentiation-PP2A regulatory subunit B55γ. We show that B55γ is induced by glucocorticoid receptor (GR) in human primary BM-MSCs during differentiation to osteoblast, but not to adipocytes, and is required for osteoblast morphogenesis and mineralization. Moreover, B55γ knockdown under osteogenic conditions leads to the accumulation of lipid droplets in a subset of BM-MSCs. Treatment of BM-MSCs with only GR ligand dexamethasone induces B55γ transcript, but not protein, and causes widespread generation of lipid droplets. These data indicate that B55γ is an essential factor of osteoblast lineage, acting as a gatekeeper downstream of master regulators. This opens a new direction of research for understanding mechanisms of glucocorticoid-induced osteoporosis and bone marrow adiposity.

Preclinical evaluation of potential therapeutic targets in dedifferentiated liposarcoma.

Sarcomas are rare cancers with limited treatment options. Patients are generally treated by chemotherapy and/or radiotherapy in combination with surgery, and would benefit from new personalized approaches. In this study we demonstrate the potential of combining personal genomic characterization of patient tumors to identify targetable mutations with in vitro testing of specific drugs in patient-derived cell lines. We have analyzed three metastases from a patient with high-grade metastatic dedifferentiated liposarcoma (DDLPS) by exome and transcriptome sequencing as well as DNA copy number analysis. Genomic aberrations of several potentially targetable genes, including amplification of KITLG and FRS2, in addition to amplification of CDK4 and MDM2, characteristic of this disease, were identified. We evaluated the efficacy of drugs targeting these aberrations or the corresponding signaling pathways in a cell line derived from the patient. Interestingly, the pan-FGFR inhibitor NVP-BGJ398, which targets FGFR upstream of FRS2, strongly inhibited cell proliferation in vitro and induced an accumulation of cells into the G0 phase of the cell cycle. This study indicates that FGFR inhibitors have therapeutic potential in the treatment of DDLPS with amplified FRS2.

Tumour microenvironments induce expression of urokinase plasminogen activator receptor (uPAR) and concomitant activation of gelatinolytic enzymes.

BACKGROUND: The urokinase plasminogen activator receptor (uPAR) is associated with poor prognosis in oral squamous cell carcinoma (OSCC), and increased expression of uPAR is often found at the invasive tumour front. The aim of the current study was to elucidate the role of uPAR in invasion and metastasis of OSCC, and the effects of various tumour microenvironments in these processes. Furthermore, we wanted to study whether the cells' expression level of uPAR affected the activity of gelatinolytic enzymes. METHODS: The Plaur gene was both overexpressed and knocked-down in the murine OSCC cell line AT84. Tongue and skin tumours were established in syngeneic mice, and cells were also studied in an ex vivo leiomyoma invasion model. Soluble factors derived from leiomyoma tissue, as well as purified extracellular matrix (ECM) proteins, were assessed for their ability to affect uPAR expression, glycosylation and cleavage. Activity of gelatinolytic enzymes in the tissues were assessed by in situ zymography. RESULTS: We found that increased levels of uPAR did not induce tumour invasion or metastasis. However, cells expressing low endogenous levels of uPAR in vitro up-regulated uPAR expression both in tongue, skin and leiomyoma tissue. Various ECM proteins had no effect on uPAR expression, while soluble factors originating from the leiomyoma tissue increased both the expression and glycosylation of uPAR, and possibly also affected the proteolytic processing of uPAR. Tumours with high levels of uPAR, as well as cells invading leiomyoma tissue with up-regulated uPAR expression, all displayed enhanced activity of gelatinolytic enzymes. CONCLUSIONS: Although high levels of uPAR are not sufficient to induce invasion and metastasis, the activity of gelatinolytic enzymes was increased. Furthermore, several tumour microenvironments have the capacity to induce up-regulation of uPAR expression, and soluble factors in the tumour microenvironment may have an important role in the regulation of posttranslational modification of uPAR.

Aminated ß-1,3-D-glucan has a dose-dependent effect on wound healing in diabetic db/db mice.

Inflammatory responses are common in diabetes and are operative in angiopathy, neuropathy, and wound healing. There are indications of incomplete macrophage activation in diabetes and reduced expression of growth factors. We have previously found that up to 15 topical applications of the macrophage-stimulant, aminated ß-1,3-D-glucan (AG), improved wound healing in db/db mice. The present open-label study was undertaken to examine dose-dependent effects of AG over 40 days in db/db mice. AG was given as a single dose (group 1), one dose every 10th day (group 2), five initial doses on consecutive days (group 3), and ≥15 doses (group 4). Controls were db/db mice receiving platelet-derived growth factor + insulin-like growth factor-1 (group 5), topical placebo (NaCl 9 mg/mL) and insulin (group 6), placebo (group 7), and a nondiabetic group receiving placebo (group 8). Seven to 14 animals were allocated to each group. Percentage wound closure 17 days after surgery in groups 1 and 2 were (mean ± standard error of the mean) 25.5 ± 5.3 and 32.2 ± 6.3, respectively. Corresponding closure in groups 3, 4, and 5 was 55.7 ± 5.0, 57.3 ± 5.0, and 55.6 ± 4.8, respectively (p < 0.05 vs. groups 1 and 2). Groups 6, 7, and 8 closed 32.0 ± 4.5, 38.2 ± 5.3, and 98.5 ± 0.4%, respectively. Significant association between the number of AG-dosages and wound closure indicates dose-related effects in db/db mice.

Large pKa shifts of alpha-carbon acids induced by copper(II) complexes.

A series of synthetic receptors (4-6) incorporating metal ions, specifically copper(II), were examined for their ability to enhance the acidity of active methylene compounds. The copper(II) complexes were observed to reduce the pKa of 1,3-diketone carbon acids in acetonitrile by as much as 12 pKa units. The relatively large pKa reduction achieved by the complex is attributed to the electrostatic interaction between the anionic pi system of the enolate and the copper(II) ions. The cage structure and hydrogen bonding sites in receptors 4 and 5 lead to a very modest further enhancement of the acidity relative to that with 6. This study provides insight into the way in which metalloenzymes stabilize an enolate intermediate.

Toward a stable hydroxyphosphorane.

[reaction: see text] Hydroxy-pentaoxy-phosphoranes are transient intermediates formed during the hydrolysis of various phosphoesters. Kinetic analyses support the existence of such compounds, although they are not isolated. In an attempt to create a stable example, 2 equiv of a ligand possessing a very high effective molarity were attached to a central phosphorus. Instead of obtaining a hydroxyphosphorane, analysis by (31)P and (19)F NMR spectroscopy and X-ray crystallography showed the product to be a phosphotriester. The reason for this is discussed.