Publications by authors named "Elizabeth A Coker"

12 Publications

  • Page 1 of 1

SLFN11 informs on standard of care and novel treatments in a wide range of cancer models.

Br J Cancer 2021 Mar 18;124(5):951-962. Epub 2020 Dec 18.

Bioscience, Oncology R&D, AstraZeneca, Cambridge, UK.

Background: Schlafen 11 (SLFN11) has been linked with response to DNA-damaging agents (DDA) and PARP inhibitors. An in-depth understanding of several aspects of its role as a biomarker in cancer is missing, as is a comprehensive analysis of the clinical significance of SLFN11 as a predictive biomarker to DDA and/or DNA damage-response inhibitor (DDRi) therapies.

Methods: We used a multidisciplinary effort combining specific immunohistochemistry, pharmacology tests, anticancer combination therapies and mechanistic studies to assess SLFN11 as a potential biomarker for stratification of patients treated with several DDA and/or DDRi in the preclinical and clinical setting.

Results: SLFN11 protein associated with both preclinical and patient treatment response to DDA, but not to non-DDA or DDRi therapies, such as WEE1 inhibitor or olaparib in breast cancer. SLFN11-low/absent cancers were identified across different tumour types tested. Combinations of DDA with DDRi targeting the replication-stress response (ATR, CHK1 and WEE1) could re-sensitise SLFN11-absent/low cancer models to the DDA treatment and were effective in upper gastrointestinal and genitourinary malignancies.

Conclusion: SLFN11 informs on the standard of care chemotherapy based on DDA and the effect of selected combinations with ATR, WEE1 or CHK1 inhibitor in a wide range of cancer types and models.
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http://dx.doi.org/10.1038/s41416-020-01199-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921667PMC
March 2021

AZD0364 Is a Potent and Selective ERK1/2 Inhibitor That Enhances Antitumor Activity in -Mutant Tumor Models when Combined with the MEK Inhibitor, Selumetinib.

Mol Cancer Ther 2021 02 3;20(2):238-249. Epub 2020 Dec 3.

Bioscience, Oncology R&D, AstraZeneca, Cambridge, England, United Kingdom.

The RAS-regulated RAF-MEK1/2-ERK1/2 (RAS/MAPK) signaling pathway is a major driver in oncogenesis and is frequently dysregulated in human cancers, primarily by mutations in or genes. The clinical benefit of inhibitors of this pathway as single agents has only been realized in -mutant melanoma, with limited effect of single-agent pathway inhibitors in -mutant tumors. Combined inhibition of multiple nodes within this pathway, such as MEK1/2 and ERK1/2, may be necessary to effectively suppress pathway signaling in -mutant tumors and achieve meaningful clinical benefit. Here, we report the discovery and characterization of AZD0364, a novel, reversible, ATP-competitive ERK1/2 inhibitor with high potency and kinase selectivity. , AZD0364 treatment resulted in inhibition of proximal and distal biomarkers and reduced proliferation in sensitive -mutant and -mutant cell lines. In multiple xenograft models, AZD0364 showed dose- and time-dependent modulation of ERK1/2-dependent signaling biomarkers resulting in tumor regression in sensitive - and -mutant xenografts. We demonstrate that AZD0364 in combination with the MEK1/2 inhibitor, selumetinib (AZD6244 and ARRY142886), enhances efficacy in -mutant preclinical models that are moderately sensitive or resistant to MEK1/2 inhibition. This combination results in deeper and more durable suppression of the RAS/MAPK signaling pathway that is not achievable with single-agent treatment. The AZD0364 and selumetinib combination also results in significant tumor regressions in multiple -mutant xenograft models. The combination of ERK1/2 and MEK1/2 inhibition thereby represents a viable clinical approach to target -mutant tumors.
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http://dx.doi.org/10.1158/1535-7163.MCT-20-0002DOI Listing
February 2021

canSAR: update to the cancer translational research and drug discovery knowledgebase.

Nucleic Acids Res 2021 01;49(D1):D1074-D1082

Department of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.
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http://dx.doi.org/10.1093/nar/gkaa1059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7778970PMC
January 2021

AZD4320, A Dual Inhibitor of Bcl-2 and Bcl-x, Induces Tumor Regression in Hematologic Cancer Models without Dose-limiting Thrombocytopenia.

Clin Cancer Res 2020 Dec 28;26(24):6535-6549. Epub 2020 Sep 28.

DMPK, Oncology R&D, AstraZeneca, Boston, Massachusetts.

Purpose: Targeting Bcl-2 family members upregulated in multiple cancers has emerged as an important area of cancer therapeutics. While venetoclax, a Bcl-2-selective inhibitor, has had success in the clinic, another family member, Bcl-x, has also emerged as an important target and as a mechanism of resistance. Therefore, we developed a dual Bcl-2/Bcl-x inhibitor that broadens the therapeutic activity while minimizing Bcl-x-mediated thrombocytopenia.

Experimental Design: We used structure-based chemistry to design a small-molecule inhibitor of Bcl-2 and Bcl-x and assessed the activity against cell lines, patient samples, and models. We applied pharmacokinetic/pharmacodynamic (PK/PD) modeling to integrate our understanding of on-target activity of the dual inhibitor in tumors and platelets across dose levels and over time.

Results: We discovered AZD4320, which has nanomolar affinity for Bcl-2 and Bcl-x, and mechanistically drives cell death through the mitochondrial apoptotic pathway. AZD4320 demonstrates activity in both Bcl-2- and Bcl-x-dependent hematologic cancer cell lines and enhanced activity in acute myeloid leukemia (AML) patient samples compared with the Bcl-2-selective agent venetoclax. A single intravenous bolus dose of AZD4320 induces tumor regression with transient thrombocytopenia, which recovers in less than a week, suggesting a clinical weekly schedule would enable targeting of Bcl-2/Bcl-x-dependent tumors without incurring dose-limiting thrombocytopenia. AZD4320 demonstrates monotherapy activity in patient-derived AML and venetoclax-resistant xenograft models.

Conclusions: AZD4320 is a potent molecule with manageable thrombocytopenia risk to explore the utility of a dual Bcl-2/Bcl-x inhibitor across a broad range of tumor types with dysregulation of Bcl-2 prosurvival proteins.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-0863DOI Listing
December 2020

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.

Mol Syst Biol 2020 07;16(7):e9405

Wellcome Sanger Institute, Hinxton, UK.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.
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http://dx.doi.org/10.15252/msb.20199405DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336273PMC
July 2020

Differences in Signaling Patterns on PI3K Inhibition Reveal Context Specificity in -Mutant Cancers.

Mol Cancer Ther 2019 08 1;18(8):1396-1404. Epub 2019 Jul 1.

Division of Cancer Therapeutics, The Institute of Cancer Research, London, United Kingdom.

It is increasingly appreciated that drug response to different cancers driven by the same oncogene is different and may relate to differences in rewiring of signal transduction. We aimed to study differences in dynamic signaling changes within mutant (), non-small cell lung cancer (NSCLC), colorectal cancer, and pancreatic ductal adenocarcinoma (PDAC) cells. We used an antibody-based phosphoproteomic platform to study changes in 50 phosphoproteins caused by seven targeted anticancer drugs in a panel of 30 cell lines and cancer cells isolated from 10 patients with cancers. We report for the first time significant differences in dynamic signaling between colorectal cancer and NSCLC cell lines exposed to clinically relevant equimolar concentrations of the pan-PI3K inhibitor pictilisib including a lack of reduction of p-AKTser473 in colorectal cancer cell lines ( = 0.037) and lack of compensatory increase in p-MEK in NSCLC cell lines ( = 0.036). Differences in rewiring of signal transduction between tumor types driven by cancers exist and influence response to combination therapy using targeted agents.
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http://dx.doi.org/10.1158/1535-7163.MCT-18-0727DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679718PMC
August 2019

Leveraging Human Genetics to Guide Cancer Drug Development.

JCO Clin Cancer Inform 2018 12;2:1-11

All authors: The Institute of Cancer Research, London, United Kingdom.

Purpose: The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development and repurposing in oncology.

Materials And Methods: Across 37 cancers, we identified 955 genetic risk variants from the National Human Genome Research Institute-European Bioinformatics Institute genome-wide association study catalog. We linked these variants to target genes using strategies that were based on linkage disequilibrium, DNA three-dimensional structure, and integration of predicted gene function and expression. With the use of the Informa Pharmaprojects database, we identified genes that are targets of unique drugs and assessed the level of enrichment that would be afforded by incorporation of genetic information in preclinical and phase II studies. For targets not under development, we implemented machine learning approaches to assess druggability.

Results: For all preclinical targets incorporating genetic information, a 2.00-fold enrichment of a drug being successfully approved could be achieved (95% CI, 1.14- to 3.48-fold; P = .02). For phase II targets, a 2.75-fold enrichment could be achieved (95% CI, 1.42- to 5.35-fold; P < .001). Application of genetic information suggests potential repurposing of 15 approved nononcology drugs.

Conclusion: The findings illustrate the value of using insights from the genetics of inherited cancer susceptibility discovery projects as part of a data-driven strategy to inform drug discovery. Support for cancer germline genetic information for prospective targets is available online from the Institute of Cancer Research.
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http://dx.doi.org/10.1200/CCI.18.00077DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874034PMC
December 2018

canSAR: update to the cancer translational research and drug discovery knowledgebase.

Nucleic Acids Res 2019 01;47(D1):D917-D922

Department of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.

canSAR (http://cansar.icr.ac.uk) is a public, freely available, integrative translational research and drug discovery knowlegebase. canSAR informs researchers to help solve key bottlenecks in cancer translation and drug discovery. It integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and unique, comprehensive and orthogonal 'druggability' assessments. canSAR is widely used internationally by academia and industry. Here we describe major enhancements to canSAR including new and expanded data. We also describe the first components of canSARblack-an advanced, responsive, multi-device compatible redesign of canSAR with a question-led interface.
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http://dx.doi.org/10.1093/nar/gky1129DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323893PMC
January 2019

Simulated ablation for detection of cells impacting paracrine signalling in histology analysis.

Math Med Biol 2019 03;36(1):93-112

Department of Cancer Imaging and Metabolism, Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

Intra-tumour phenotypic heterogeneity limits accuracy of clinical diagnostics and hampers the efficiency of anti-cancer therapies. Dealing with this cellular heterogeneity requires adequate understanding of its sources, which is extremely difficult, as phenotypes of tumour cells integrate hardwired (epi)mutational differences with the dynamic responses to microenvironmental cues. The later comes in form of both direct physical interactions, as well as inputs from gradients of secreted signalling molecules. Furthermore, tumour cells can not only receive microenvironmental cues, but also produce them. Despite high biological and clinical importance of understanding spatial aspects of paracrine signaling, adequate research tools are largely lacking. Here, a partial differential equation (PDE)-based mathematical model is developed that mimics the process of cell ablation. This model suggests how each cell might contribute to the microenvironment by either absorbing or secreting diffusible factors, and quantifies the extent to which observed intensities can be explained via diffusion-mediated signalling. The model allows for the separation of phenotypic responses to signalling gradients within tumour microenvironments from the combined influence of responses mediated by direct physical contact and hardwired (epi)genetic differences. The method is applied to a multi-channel immunofluorescence in situ hybridisation (iFISH)-stained breast cancer histological specimen, and correlations are investigated between: HER2 gene amplification, HER2 protein expression and cell interaction with the diffusible microenvironment. This approach allows partial deconvolution of the complex inputs that shape phenotypic heterogeneity of tumour cells and identifies cells that significantly impact gradients of signalling molecules.
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http://dx.doi.org/10.1093/imammb/dqx022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197102PMC
March 2019

SiGNet: A signaling network data simulator to enable signaling network inference.

PLoS One 2017 17;12(5):e0177701. Epub 2017 May 17.

Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, United Kingdom.

Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177701PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435248PMC
September 2017

canSAR: an updated cancer research and drug discovery knowledgebase.

Nucleic Acids Res 2016 Jan 15;44(D1):D938-43. Epub 2015 Dec 15.

Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, SM2 5NG, UK

canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and druggability data. canSAR is widely used to rapidly access information and help interpret experimental data in a translational and drug discovery context. Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities, new disease and cancer cell line summaries and new and enhanced batch analysis tools.
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http://dx.doi.org/10.1093/nar/gkv1030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702774PMC
January 2016

canSAR: updated cancer research and drug discovery knowledgebase.

Nucleic Acids Res 2014 Jan 3;42(Database issue):D1040-7. Epub 2013 Dec 3.

Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, SM2 5NG, UK.

canSAR (http://cansar.icr.ac.uk) is a public integrative cancer-focused knowledgebase for the support of cancer translational research and drug discovery. Through the integration of biological, pharmacological, chemical, structural biology and protein network data, it provides a single information portal to answer complex multidisciplinary questions including--among many others--what is known about a protein, in which cancers is it expressed or mutated, and what chemical tools and cell line models can be used to experimentally probe its activity? What is known about a drug, its cellular sensitivity profile and what proteins is it known to bind that may explain unusual bioactivity? Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities and new target, cancer cell line, protein family and 3D structure summaries and tools.
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http://dx.doi.org/10.1093/nar/gkt1182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3964944PMC
January 2014