Promising anti-proliferative indolic benzenesulfonamides alter mechanisms with sulfonamide nitrogen substituents.

Agents that cause apoptotic cell death by interfering with tubulin dynamics, such as vinblastine and paclitaxel, are an important class of chemotherapeutics. Unfortunately, these compounds are substrates for multidrug resistance (MDR) pumps, allowing cancer cells to gain resistance to these chemotherapeutics. The indolesulfonamide family of tubulin inhibitors are not excluded by MDR pumps and have a promising activity profile, although their high lipophilicity is a pharmacokinetic limitation for their clinical use. Here we present a new family of N-indolyl-3,4,5-trimethoxybenzenesulfonamide derivatives with modifications on the indole system at positions 1 and 3 and on the sulfonamide nitrogen. We synthesized and screened against HeLa cells 34 novel indolic benzenesulfonamides. The most potent derivatives (1.7-109 nM) were tested against a broad panel of cancer cell lines, which revealed that substituted benzenesulfonamides analogs had highest potency. Importantly, these compounds were only moderately toxic to non-tumorigenic cells, suggesting the presence of a therapeutic index. Consistent with known clinical anti-tubulin agents, these compounds arrested the cell cycle at G2/M phase. Mechanistically, they induced apoptosis via caspase 3/7 activation, which occurred during M arrest. The substituents on the sulfonamide nitrogen appeared to determine different mechanistic results and cell fates. These results suggest that the compounds act differently depending on the bridge substituents, thus making them very interesting as mechanistic probes as well as potential drugs for further development.

CD37 is a safe chimeric antigen receptor target to treat acute myeloid leukemia.

Acute myeloid leukemia (AML) is characterized by the accumulation of immature myeloid cells in the bone marrow and the peripheral blood. Nearly half of the AML patients relapse after standard induction therapy, and new forms of therapy are urgently needed. Chimeric antigen receptor (CAR) T therapy has so far not been successful in AML due to lack of efficacy and safety. Indeed, the most attractive antigen targets are stem cell markers such as CD33 or CD123. We demonstrate that CD37, a mature B cell marker, is expressed in AML samples, and its presence correlates with the European LeukemiaNet (ELN) 2017 risk stratification. We repurpose the anti-lymphoma CD37CAR for the treatment of AML and show that CD37CAR T cells specifically kill AML cells, secrete proinflammatory cytokines, and control cancer progression in vivo. Importantly, CD37CAR T cells display no toxicity toward hematopoietic stem cells. Thus, CD37 is a promising and safe CAR T cell AML target.

Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

Characterization of a selective, iron-chelating antifungal compound that disrupts fungal metabolism and synergizes with fluconazole.

Fungal infections are a growing global health concern due to the limited number of available antifungal therapies as well as the emergence of fungi that are resistant to first-line antimicrobials, particularly azoles and echinocandins. Development of novel, selective antifungal therapies is challenging due to similarities between fungal and mammalian cells. An attractive source of potential antifungal treatments is provided by ecological niches co-inhabited by bacteria, fungi, and multicellular organisms, where complex relationships between multiple organisms have resulted in evolution of a wide variety of selective antimicrobials. Here, we characterized several analogs of one such natural compound, collismycin A. We show that NR-6226C has antifungal activity against several pathogenic Candida species, including C. albicans and C. glabrata, whereas it only has little toxicity against mammalian cells. Mechanistically, NR-6226C selectively chelates iron, which is a limiting factor for pathogenic fungi during infection. As a result, NR-6226C treatment causes severe mitochondrial dysfunction, leading to formation of reactive oxygen species, metabolic reprogramming, and a severe reduction in ATP levels. Using an in vivo model for fungal infections, we show that NR-6226C significantly increases survival of Candida-infected Galleria mellonella larvae. Finally, our data indicate that NR-6226C synergizes strongly with fluconazole in inhibition of C. albicans. Taken together, NR-6226C is a promising antifungal compound that acts by chelating iron and disrupting mitochondrial functions.IMPORTANCEDrug-resistant fungal infections are an emerging global threat, and pan-resistance to current antifungal therapies is an increasing problem. Clearly, there is a need for new antifungal drugs. In this study, we characterized a novel antifungal agent, the collismycin analog NR-6226C. NR-6226C has a favorable toxicity profile for human cells, which is essential for further clinical development. We unraveled the mechanism of action of NR-6226C and found that it disrupts iron homeostasis and thereby depletes fungal cells of energy. Importantly, NR-6226C strongly potentiates the antifungal activity of fluconazole, thereby providing inroads for combination therapy that may reduce or prevent azole resistance. Thus, NR-6226C is a promising compound for further development into antifungal treatment.

Robust scoring of selective drug responses for patient-tailored therapy selection.

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling.

Current treatment selection for acute myeloid leukemia (AML) patients depends on risk stratification based on cytogenetic and genomic markers. However, the forecasting accuracy of treatment response remains modest, with most patients receiving intensive chemotherapy. Recently, ex vivo drug screening has gained traction in personalized treatment selection and as a tool for mapping patient groups based on relevant cancer dependencies. Here, we systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can assess risk for individual AML patients and may guide clinical decision-making.

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.

CELLULAR, A Cell Autophagy Imaging Dataset.

Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target. Image analysis to follow autophagy in cells, especially on high-content screens, has proven to be a bottleneck. Machine learning (ML) algorithms have recently emerged as crucial in analyzing images to efficiently extract information, thus contributing to a better understanding of the questions at hand. This paper presents CELLULAR, an open dataset consisting of images of cells expressing the autophagy reporter mRFP-EGFP-Atg8a with cell-specific segmentation masks. Each cell is annotated into either basal autophagy, activated autophagy, or unknown. Furthermore, we introduce some preliminary experiments using the dataset that can be used as a baseline for future research.

The human leukemic oncogene MLL-AF4 promotes hyperplastic growth of hematopoietic tissues in Drosophila larvae.

MLL-rearranged (MLL-r) leukemias are among the leukemic subtypes with poorest survival, and treatment options have barely improved over the last decades. Despite increasing molecular understanding of the mechanisms behind these hematopoietic malignancies, this knowledge has had poor translation into the clinic. Here, we report a Drosophila melanogaster model system to explore the pathways affected in MLL-r leukemia. We show that expression of the human leukemic oncogene MLL-AF4 in the Drosophila hematopoietic system resulted in increased levels of circulating hemocytes and an enlargement of the larval hematopoietic organ, the lymph gland. Strikingly, depletion of Drosophila orthologs of known interactors of MLL-AF4, such as DOT1L, rescued the leukemic phenotype. In agreement, treatment with small-molecule inhibitors of DOT1L also prevented the MLL-AF4-induced leukemia-like phenotype. Taken together, this model provides an in vivo system to unravel the genetic interactors involved in leukemogenesis and offers a system for improved biological understanding of MLL-r leukemia.

Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data.

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

Early response evaluation by single cell signaling profiling in acute myeloid leukemia.

Aberrant pro-survival signaling is a hallmark of cancer cells, but the response to chemotherapy is poorly understood. In this study, we investigate the initial signaling response to standard induction chemotherapy in a cohort of 32 acute myeloid leukemia (AML) patients, using 36-dimensional mass cytometry. Through supervised and unsupervised machine learning approaches, we find that reduction of extracellular-signal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in the myeloid cell compartment 24 h post-chemotherapy is a significant predictor of patient 5-year overall survival in this cohort. Validation by RNA sequencing shows induction of MAPK target gene expression in patients with high phospho-ERK1/2 24 h post-chemotherapy, while proteomics confirm an increase of the p38 prime target MAPK activated protein kinase 2 (MAPKAPK2). In this study, we demonstrate that mass cytometry can be a valuable tool for early response evaluation in AML and elucidate the potential of functional signaling analyses in precision oncology diagnostics.

Mitotic chromosome condensation resets chromatin to safeguard transcriptional homeostasis during interphase.

Mitotic entry correlates with the condensation of the chromosomes, changes in histone modifications, exclusion of transcription factors from DNA, and the broad downregulation of transcription. However, whether mitotic condensation influences transcription in the subsequent interphase is unknown. Here, we show that preventing one chromosome to condense during mitosis causes it to fail resetting of transcription. Rather, in the following interphase, the affected chromosome contains unusually high levels of the transcription machinery, resulting in abnormally high expression levels of genes in cis, including various transcription factors. This subsequently causes the activation of inducible transcriptional programs in trans, such as the GAL genes, even in the absence of the relevant stimuli. Thus, mitotic chromosome condensation exerts stringent control on interphase gene expression to ensure the maintenance of basic cellular functions and cell identity across cell divisions. Together, our study identifies the maintenance of transcriptional homeostasis during interphase as an unexpected function of mitosis and mitotic chromosome condensation.

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.

Analysis of the pheromone signaling pathway by RT-qPCR in the budding yeast Saccharomyces cerevisiae.

FUS3 and STE2 expression levels can be used as reporters for signaling through the pheromone pathway in the budding yeast Saccharomyces cerevisiae. Here, we describe an optimized protocol to measure the expression levels of FUS3 and STE2 using quantitative reverse transcription PCR (RT-qPCR). We describe the steps for comparing untreated and pheromone-treated yeast cells and how to quantify the changes in various deletion strains. The protocol can be applied to determine potential regulators of the pheromone pathway. For complete details on the use and execution of this protocol, please refer to Garcia et al. (2021).

Cell Cycle-Dependent Transcription: The Cyclin Dependent Kinase Cdk1 Is a Direct Regulator of Basal Transcription Machineries.

The cyclin-dependent kinase Cdk1 is best known for its function as master regulator of the cell cycle. It phosphorylates several key proteins to control progression through the different phases of the cell cycle. However, studies conducted several decades ago with mammalian cells revealed that Cdk1 also directly regulates the basal transcription machinery, most notably RNA polymerase II. More recent studies in the budding yeast Saccharomyces cerevisiae have revisited this function of Cdk1 and also revealed that Cdk1 directly controls RNA polymerase III activity. These studies have also provided novel insight into the physiological relevance of this process. For instance, cell cycle-stage-dependent activity of these complexes may be important for meeting the increased demand for various proteins involved in housekeeping, metabolism, and protein synthesis. Recent work also indicates that direct regulation of the RNA polymerase II machinery promotes cell cycle entry. Here, we provide an overview of the regulation of basal transcription by Cdk1, and we hypothesize that the original function of the primordial cell-cycle CDK was to regulate RNAPII and that it later evolved into specialized kinases that govern various aspects of the transcription machinery and the cell cycle.

Waves of sumoylation support transcription dynamics during adipocyte differentiation.

Tight control of gene expression networks required for adipose tissue formation and plasticity is essential for adaptation to energy needs and environmental cues. However, the mechanisms that orchestrate the global and dramatic transcriptional changes leading to adipocyte differentiation remain to be fully unraveled. We investigated the regulation of nascent transcription by the sumoylation pathway during adipocyte differentiation using SLAMseq and ChIPseq. We discovered that the sumoylation pathway has a dual function in differentiation; it supports the initial downregulation of pre-adipocyte-specific genes, while it promotes the establishment of the mature adipocyte transcriptional program. By characterizing endogenous sumoylome dynamics in differentiating adipocytes by mass spectrometry, we found that sumoylation of specific transcription factors like PPARγ/RXR and their co-factors are associated with the transcription of adipogenic genes. Finally, using RXR as a model, we found that sumoylation may regulate adipogenic transcription by supporting the chromatin occurrence of transcription factors. Our data demonstrate that the sumoylation pathway supports the rewiring of transcriptional networks required for formation of functional adipocytes. This study also provides the scientists in the field of cellular differentiation and development with an in-depth resource of the dynamics of the SUMO-chromatin landscape, SUMO-regulated transcription and endogenous sumoylation sites during adipocyte differentiation.

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.

Kel1 is a phosphorylation-regulated noise suppressor of the pheromone signaling pathway.

Mechanisms have evolved that allow cells to detect signals and generate an appropriate response. The accuracy of these responses relies on the ability of cells to discriminate between signal and noise. How cells filter noise in signaling pathways is not well understood. Here, we analyze noise suppression in the yeast pheromone signaling pathway and show that the poorly characterized protein Kel1 serves as a major noise suppressor and prevents cell death. At the molecular level, Kel1 prevents spontaneous activation of the pheromone response by inhibiting membrane recruitment of Ste5 and Far1. Only a hypophosphorylated form of Kel1 suppresses signaling, reduces noise, and prevents pheromone-associated cell death, and our data indicate that the MAPK Fus3 contributes to Kel1 phosphorylation. Taken together, Kel1 serves as a phospho-regulated suppressor of the pheromone pathway to reduce noise, inhibit spontaneous activation of the pathway, regulate mating efficiency, and prevent pheromone-associated cell death.