PARP14 and PARP9/DTX3L regulate interferon-induced ADP-ribosylation.

PARP-catalysed ADP-ribosylation (ADPr) is important in regulating various cellular pathways. Until recently, PARP-dependent mono-ADP-ribosylation has been poorly understood due to the lack of sensitive detection methods. Here, we utilised an improved antibody to detect mono-ADP-ribosylation. We visualised endogenous interferon (IFN)-induced ADP-ribosylation and show that PARP14 is a major enzyme responsible for this modification. Fittingly, this signalling is reversed by the macrodomain from SARS-CoV-2 (Mac1), providing a possible mechanism by which Mac1 counteracts the activity of antiviral PARPs. Our data also elucidate a major role of PARP9 and its binding partner, the E3 ubiquitin ligase DTX3L, in regulating PARP14 activity through protein-protein interactions and by the hydrolytic activity of PARP9 macrodomain 1. Finally, we also present the first visualisation of ADPr-dependent ubiquitylation in the IFN response. These approaches should further advance our understanding of IFN-induced ADPr and ubiquitin signalling processes and could shed light on how different pathogens avoid such defence pathways.

PARP14 inhibition restores PD-1 immune checkpoint inhibitor response following IFNγ-driven acquired resistance in preclinical cancer models.

Resistance mechanisms to immune checkpoint blockade therapy (ICBT) limit its response duration and magnitude. Paradoxically, Interferon γ (IFNγ), a key cytokine for cellular immunity, can promote ICBT resistance. Using syngeneic mouse tumour models, we confirm that chronic IFNγ exposure confers resistance to immunotherapy targeting PD-1 (α-PD-1) in immunocompetent female mice. We observe upregulation of poly-ADP ribosyl polymerase 14 (PARP14) in chronic IFNγ-treated cancer cell models, in patient melanoma with elevated IFNG expression, and in melanoma cell cultures from ICBT-progressing lesions characterised by elevated IFNγ signalling. Effector T cell infiltration is enhanced in tumours derived from cells pre-treated with IFNγ in immunocompetent female mice when PARP14 is pharmacologically inhibited or knocked down, while the presence of regulatory T cells is decreased, leading to restoration of α-PD-1 sensitivity. Finally, we determine that tumours which spontaneously relapse in immunocompetent female mice following α-PD-1 therapy upregulate IFNγ signalling and can also be re-sensitised upon receiving PARP14 inhibitor treatment, establishing PARP14 as an actionable target to reverse IFNγ-driven ICBT resistance.

Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop.

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo®). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

ADP-ribosyltransferases, an update on function and nomenclature.

ADP-ribosylation, a modification of proteins, nucleic acids, and metabolites, confers broad functions, including roles in stress responses elicited, for example, by DNA damage and viral infection and is involved in intra- and extracellular signaling, chromatin and transcriptional regulation, protein biosynthesis, and cell death. ADP-ribosylation is catalyzed by ADP-ribosyltransferases (ARTs), which transfer ADP-ribose from NAD+ onto substrates. The modification, which occurs as mono- or poly-ADP-ribosylation, is reversible due to the action of different ADP-ribosylhydrolases. Importantly, inhibitors of ARTs are approved or are being developed for clinical use. Moreover, ADP-ribosylhydrolases are being assessed as therapeutic targets, foremost as antiviral drugs and for oncological indications. Due to the development of novel reagents and major technological advances that allow the study of ADP-ribosylation in unprecedented detail, an increasing number of cellular processes and pathways are being identified that are regulated by ADP-ribosylation. In addition, characterization of biochemical and structural aspects of the ARTs and their catalytic activities have expanded our understanding of this protein family. This increased knowledge requires that a common nomenclature be used to describe the relevant enzymes. Therefore, in this viewpoint, we propose an updated and broadly supported nomenclature for mammalian ARTs that will facilitate future discussions when addressing the biochemistry and biology of ADP-ribosylation. This is combined with a brief description of the main functions of mammalian ARTs to illustrate the increasing diversity of mono- and poly-ADP-ribose mediated cellular processes.

PARP7 negatively regulates the type I interferon response in cancer cells and its inhibition triggers antitumor immunity.

PARP7 is a monoPARP that catalyzes the transfer of single units of ADP-ribose onto substrates to change their function. Here, we identify PARP7 as a negative regulator of nucleic acid sensing in tumor cells. Inhibition of PARP7 restores type I interferon (IFN) signaling responses to nucleic acids in tumor models. Restored signaling can directly inhibit cell proliferation and activate the immune system, both of which contribute to tumor regression. Oral dosing of the PARP7 small-molecule inhibitor, RBN-2397, results in complete tumor regression in a lung cancer xenograft and induces tumor-specific adaptive immune memory in an immunocompetent mouse cancer model, dependent on inducing type I IFN signaling in tumor cells. PARP7 is a therapeutic target whose inhibition induces both cancer cell-autonomous and immune stimulatory effects via enhanced IFN signaling. These data support the targeting of a monoPARP in cancer and introduce a potent and selective PARP7 inhibitor to enter clinical development.

Targeted Degradation of PARP14 Using a Heterobifunctional Small Molecule.

PARP14 is an interferon-stimulated gene that is overexpressed in multiple tumor types, influencing pro-tumor macrophage polarization as well as suppressing the antitumor inflammation response by modulating IFN-γ and IL-4 signaling. PARP14 is a 203 kDa protein that possesses a catalytic domain responsible for the transfer of mono-ADP-ribose to its substrates. PARP14 also contains three macrodomains and a WWE domain which are binding modules for mono-ADP-ribose and poly-ADP-ribose, respectively, in addition to two RNA recognition motifs. Catalytic inhibitors of PARP14 have been shown to reverse IL-4 driven pro-tumor gene expression in macrophages, however it is not clear what roles the non-enzymatic biomolecular recognition motifs play in PARP14-driven immunology and inflammation. To further understand this, we have discovered a heterobifunctional small molecule designed based on a catalytic inhibitor of PARP14 that binds in the enzyme's NAD+ -binding site and recruits cereblon to ubiquitinate it and selectively target it for degradation.

A potent and selective PARP14 inhibitor decreases protumor macrophage gene expression and elicits inflammatory responses in tumor explants.

PARP14 has been implicated by genetic knockout studies to promote protumor macrophage polarization and suppress the antitumor inflammatory response due to its role in modulating interleukin-4 (IL-4) and interferon-γ signaling pathways. Here, we describe structure-based design efforts leading to the discovery of a potent and highly selective PARP14 chemical probe. RBN012759 inhibits PARP14 with a biochemical half-maximal inhibitory concentration of 0.003 µM, exhibits >300-fold selectivity over all PARP family members, and its profile enables further study of PARP14 biology and disease association both in vitro and in vivo. Inhibition of PARP14 with RBN012759 reverses IL-4-driven protumor gene expression in macrophages and induces an inflammatory mRNA signature similar to that induced by immune checkpoint inhibitor therapy in primary human tumor explants. These data support an immune suppressive role of PARP14 in tumors and suggest potential utility of PARP14 inhibitors in the treatment of cancer.

Receptor-based mechanism of relative sensing and cell memory in mammalian signaling networks.

Detecting relative rather than absolute changes in extracellular signals enables cells to make decisions in constantly fluctuating environments. It is currently not well understood how mammalian signaling networks store the memories of past stimuli and subsequently use them to compute relative signals, that is perform fold change detection. Using the growth factor-activated PI3K-Akt signaling pathway, we develop here computational and analytical models, and experimentally validate a novel non-transcriptional mechanism of relative sensing in mammalian cells. This mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. The surface receptor abundance is regulated by background signal-dependent receptor endocytosis and down-regulation. We show the robustness and specificity of relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. Our results suggest that similar mechanisms of cell memory and fold change detection may be important in diverse signaling cascades and multiple biological contexts.

Torin2 Exploits Replication and Checkpoint Vulnerabilities to Cause Death of PI3K-Activated Triple-Negative Breast Cancer Cells.

Frequent mutation of PI3K/AKT/mTOR signaling pathway genes in human cancers has stimulated large investments in targeted drugs but clinical successes are rare. As a result, many cancers with high PI3K pathway activity, such as triple-negative breast cancer (TNBC), are treated primarily with chemotherapy. By systematically analyzing responses of TNBC cells to a diverse collection of PI3K pathway inhibitors, we find that one drug, Torin2, is unusually effective because it inhibits both mTOR and other PI3K-like kinases (PIKKs). In contrast to mTOR-selective inhibitors, Torin2 exploits dependencies on several kinases for S-phase progression and cell-cycle checkpoints, thereby causing accumulation of single-stranded DNA and death by replication catastrophe or mitotic failure. Thus, Torin2 and its chemical analogs represent a mechanistically distinct class of PI3K pathway inhibitors that are uniquely cytotoxic to TNBC cells. This insight could be translated therapeutically by further developing Torin2 analogs or combinations of existing mTOR and PIKK inhibitors.

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines.

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.

Enabling drug discovery for the PARP protein family through the detection of mono-ADP-ribosylation.

Poly-ADP-ribose polymerases (PARPs) are a family of enzymes responsible for transferring individual or chains of ADP-ribose subunits to substrate targets as a type of post-translational modification. PARPs regulate a wide variety of important cellular processes, ranging from DNA damage repair to antiviral response. However, most research to date has focused primarily on the polyPARPs, which catalyze the formation of ADP-ribose polymer chains, while the monoPARPs, which transfer individual ADP-ribose monomers, have not been studied as thoroughly. This is partially due to the lack of robust assays to measure mono-ADP-ribosylation in the cell. In this study, the recently developed MAR/PAR antibody has been shown to detect mono-ADP-ribosylation in cells, enabling the field to investigate the function and therapeutic potential of monoPARPs. In this study, the antibody was used in conjunction with engineered cell lines that overexpress various PARPs to establish a panel of assays to evaluate the potencies of literature-reported PARP inhibitors. These assays should be generally applicable to other PARP family members for future compound screening efforts. A convenient and generalizable workflow to identify and validate PARP substrates has been established. As an initial demonstration, aryl hydrocarbon receptor was verified as a direct PARP7 substrate and other novel substrates for this enzyme were also identified and validated. This workflow takes advantage of commercially available detection reagents and conventional mass spectrometry instrumentation and methods. Ultimately, these assays and methods will help drive research in the PARP field and benefit future therapeutics development.

Tensor clustering with algebraic constraints gives interpretable groups of crosstalk mechanisms in breast cancer.

We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our clustering method is general and can be tailored to a variety of applications in science and industry. We illustrate our method on a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. Each experiment consists of a cell line-ligand combination, and contains time-course measurements of the early signalling kinases MAPK and AKT at two different ligand dose levels. By imposing appropriate structural constraints and respecting the multi-indexed structure of the data, the analysis of clusters can be optimized for biological interpretation and therapeutic understanding. We then perform a systematic, large-scale exploration of mechanistic models of MAPK-AKT crosstalk for each cluster. This analysis allows us to quantify the heterogeneity of breast cancer cell subtypes, and leads to hypotheses about the signalling mechanisms that mediate the response of the cell lines to ligands.

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling.

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics.

Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency (GR50) and efficacy (GRmax) that are analogous to the more familiar IC50 and Emax values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ~4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/.

GRcalculator: an online tool for calculating and mining dose-response data.

BACKGROUND: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose-response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521-627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose-response assays and the reliability of pharmacogenomic associations (Hafner et al. 500-502, 2017). RESULTS: We describe here an interactive website ( www.grcalculator.org ) for calculation, analysis, and visualization of dose-response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics . CONCLUSIONS: GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose-response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose-response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program ( http://www.lincsproject.org /) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose-response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells.

Measuring the potencies of small-molecule drugs in cell lines is a critical aspect of preclinical pharmacology. Such experiments are also prototypical of high-throughput experiments in multi-well plates. The procedure is simple in principle, but many unrecognized factors can affect the results, potentially making data unreliable. The procedures for measuring drug response described here were developed by the NIH LINCS program to improve reproducibility. Key features include maximizing uniform cell growth during the assay period, accounting for the effects of cell density on response, and correcting sensitivity measures for differences in proliferation rates. Two related protocols are described: one involves an endpoint measure well-suited to large-scale studies and the second is a time-dependent measurement that reveals changes in response over time. The methods can be adapted to other types of plate-based experiments. © 2017 by John Wiley & Sons, Inc.

Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway.

Various genetic mutations associated with cancer are known to alter cell signaling, but it is not clear whether they dysregulate signaling pathways by altering the abundance of pathway proteins. Using a combination of RNA sequencing and ultrasensitive targeted proteomics, we defined the primary components-16 core proteins and 10 feedback regulators-of the epidermal growth factor receptor (EGFR)-mitogen-activated protein kinase (MAPK) pathway in normal human mammary epithelial cells and then quantified their absolute abundance across a panel of normal and breast cancer cell lines as well as fibroblasts. We found that core pathway proteins were present at very similar concentrations across all cell types, with a variance similar to that of proteins previously shown to display conserved abundances across species. In contrast, EGFR and transcriptionally controlled feedback regulators were present at highly variable concentrations. The absolute abundance of most core proteins was between 50,000 and 70,000 copies per cell, but the adaptors SOS1, SOS2, and GAB1 were found at far lower amounts (2000 to 5000 copies per cell). MAPK signaling showed saturation in all cells between 3000 and 10,000 occupied EGFRs, consistent with the idea that adaptors limit signaling. Our results suggest that the relative stoichiometry of core MAPK pathway proteins is very similar across different cell types, with cell-specific differences mostly restricted to variable amounts of feedback regulators and receptors. The low abundance of adaptors relative to EGFR could be responsible for previous observations that only a fraction of total cell surface EGFR is capable of rapid endocytosis, high-affinity binding, and mitogenic signaling.

Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs.

Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.

Systematic analysis of BRAF(V600E) melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis.

Drugs that inhibit RAF/MEK signaling, such as vemurafenib, elicit profound but often temporary anti-tumor responses in patients with BRAF(V) (600E) melanoma. Adaptive responses to RAF/MEK inhibition occur on a timescale of hours to days, involve homeostatic responses that reactivate MAP kinase signaling and compensatory mitogenic pathways, and attenuate the anti-tumor effects of RAF/MEK inhibitors. We profile adaptive responses across a panel of melanoma cell lines using multiplex biochemical measurement, single-cell assays, and statistical modeling and show that adaptation involves at least six signaling cascades that act to reduce drug potency (IC50) and maximal effect (i.e., Emax â‰ª 1). Among these cascades, we identify a role for JNK/c-Jun signaling in vemurafenib adaptation and show that RAF and JNK inhibitors synergize in cell killing. This arises because JNK inhibition prevents a subset of cells in a cycling population from becoming quiescent upon vemurafenib treatment, thereby reducing drug Emax. Our findings demonstrate the breadth and diversity of adaptive responses to RAF/MEK inhibition and a means to identify which steps in a signaling cascade are most predictive of phenotypic response.