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1. Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors

Author

Orsolya Papp, Viktória Jordán, Szabolcs Hetey, Róbert Balázs, Valér Kaszás, Árpád Bartha, Nóra N. Ordasi, Sebestyén Kamp, Bálint Farkas, Jerome Mettetal, Jonathan R. Dry, Duncan Young, Ben Sidders, Krishna C. Bulusu, and Daniel V. Veres

Subjects
Biology (General), QH301-705.5
Abstract

Abstract Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell ‘avatars’ capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

Published
2024
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2. Mixed responses to targeted therapy driven by chromosomal instability through p53 dysfunction and genome doubling

Author

Sebastijan Hobor, Maise Al Bakir, Crispin T. Hiley, Marcin Skrzypski, Alexander M. Frankell, Bjorn Bakker, Thomas B. K. Watkins, Aleksandra Markovets, Jonathan R. Dry, Andrew P. Brown, Jasper van der Aart, Hilda van den Bos, Diana Spierings, Dahmane Oukrif, Marco Novelli, Turja Chakrabarti, Adam H. Rabinowitz, Laila Ait Hassou, Saskia Litière, D. Lucas Kerr, Lisa Tan, Gavin Kelly, David A. Moore, Matthew J. Renshaw, Subramanian Venkatesan, William Hill, Ariana Huebner, Carlos Martínez-Ruiz, James R. M. Black, Wei Wu, Mihaela Angelova, Nicholas McGranahan, Julian Downward, Juliann Chmielecki, Carl Barrett, Kevin Litchfield, Su Kit Chew, Collin M. Blakely, Elza C. de Bruin, Floris Foijer, Karen H. Vousden, Trever G. Bivona, TRACERx consortium, Robert E. Hynds, Nnennaya Kanu, Simone Zaccaria, Eva Grönroos, and Charles Swanton

Subjects
Science
Abstract

Abstract The phenomenon of mixed/heterogenous treatment responses to cancer therapies within an individual patient presents a challenging clinical scenario. Furthermore, the molecular basis of mixed intra-patient tumor responses remains unclear. Here, we show that patients with metastatic lung adenocarcinoma harbouring co-mutations of EGFR and TP53, are more likely to have mixed intra-patient tumor responses to EGFR tyrosine kinase inhibition (TKI), compared to those with an EGFR mutation alone. The combined presence of whole genome doubling (WGD) and TP53 co-mutations leads to increased genome instability and genomic copy number aberrations in genes implicated in EGFR TKI resistance. Using mouse models and an in vitro isogenic p53-mutant model system, we provide evidence that WGD provides diverse routes to drug resistance by increasing the probability of acquiring copy-number gains or losses relative to non-WGD cells. These data provide a molecular basis for mixed tumor responses to targeted therapy, within an individual patient, with implications for therapeutic strategies.

Published
2024
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4. The landscape of therapeutic vulnerabilities in EGFR inhibitor osimertinib drug tolerant persister cells

Author

Steven W. Criscione, Matthew J. Martin, Derek B. Oien, Aparna Gorthi, Ricardo J. Miragaia, Jingwen Zhang, Huawei Chen, Daniel L. Karl, Kerrin Mendler, Aleksandra Markovets, Sladjana Gagrica, Oona Delpuech, Jonathan R. Dry, Michael Grondine, Maureen M. Hattersley, Jelena Urosevic, Nicolas Floc’h, Lisa Drew, Yi Yao, and Paul D. Smith

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Third-generation EGFR tyrosine kinase inhibitors (EGFR-TKIs), including osimertinib, an irreversible EGFR-TKI, are important treatments for non-small cell lung cancer with EGFR-TKI sensitizing or EGFR T790M resistance mutations. While patients treated with osimertinib show clinical benefit, disease progression and drug resistance are common. Emergence of de novo acquired resistance from a drug tolerant persister (DTP) cell population is one mechanism proposed to explain progression on osimertinib and other targeted cancer therapies. Here we profiled osimertinib DTPs using RNA-seq and ATAC-seq to characterize the features of these cells and performed drug screens to identify therapeutic vulnerabilities. We identified several vulnerabilities in osimertinib DTPs that were common across models, including sensitivity to MEK, AURKB, BRD4, and TEAD inhibition. We linked several of these vulnerabilities to gene regulatory changes, for example, TEAD vulnerability was consistent with evidence of Hippo pathway turning off in osimertinib DTPs. Last, we used genetic approaches using siRNA knockdown or CRISPR knockout to validate AURKB, BRD4, and TEAD as the direct targets responsible for the vulnerabilities observed in the drug screen.

Published
2022
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5. Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort

Author

Benjamin D. Leibowitz, Bonnie V. Dougherty, Joshua S. K. Bell, Joshuah Kapilivsky, Jackson Michuda, Andrew J. Sedgewick, Wesley A. Munson, Tushar A. Chandra, Jonathan R. Dry, Nike Beaubier, Catherine Igartua, and Timothy Taxter

Subjects
Gene expression profiling, Diagnostic biomarkers, Homologous recombination, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background With the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus HRD-DNA test. We further developed, validated, and explored the Tempus HRD-RNA model, which uses gene expression data from 16,750 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded tumor samples across numerous cancer types. Methods Genomic and transcriptomic profiling was performed using next-generation sequencing from Tempus xT, Tempus xO, Tempus xE, Tempus RS, and Tempus RS.v2 assays on 48,843 samples. Samples were labeled based on their BRCA1, BRCA2 and selected Homologous Recombination Repair pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression. Results In a sample of 2058 breast and 1216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively. Conclusions We predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.

Published
2022
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6. Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer

Author

Anna Gogleva, Dimitris Polychronopoulos, Matthias Pfeifer, Vladimir Poroshin, Michaël Ughetto, Matthew J. Martin, Hannah Thorpe, Aurelie Bornot, Paul D. Smith, Ben Sidders, Jonathan R. Dry, Miika Ahdesmäki, Ultan McDermott, Eliseo Papa, and Krishna C. Bulusu

Subjects
Science
Abstract

Resistance to EGFR inhibitors presents a major obstacle in treating non-small cell lung cancer. Here, the authors develop a recommender system ranking genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance.

Published
2022
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7. Landscape of homologous recombination deficiencies in solid tumours: analyses of two independent genomic datasets

Author

Zhongwu Lai, Matthew Brosnan, Ethan S. Sokol, Mingchao Xie, Jonathan R. Dry, Elizabeth A. Harrington, J. Carl Barrett, and Darren Hodgson

Subjects
Homologous recombination deficiency, Homologous recombination repair, Genomic loss of heterozygosity, Loss of function, cancer, Breast, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background DNA repair deficiencies are characteristic of cancer and homologous recombination deficiency (HRD) is the most common. HRD sensitizes tumour cells to PARP inhibitors so it is important to understand the landscape of HRD across different solid tumour types. Methods Germline and somatic BRCA mutations in breast and ovarian cancers were evaluated using sequencing data from The Cancer Genome Atlas (TCGA) database. Secondly, a larger independent genomic dataset was analysed to validate the TCGA results and determine the frequency of germline and somatic mutations across 15 different candidate homologous recombination repair (HRR) genes, and their relationship with the genetic events of bi-allelic loss, loss of heterozygosity (LOH) and tumour mutation burden (TMB). Results Approximately one-third of breast and ovarian cancer BRCA mutations were somatic. These showed a similar degree of bi-allelic loss and clinical outcomes to germline mutations, identifying potentially 50% more patients that may benefit from precision treatments. HRR mutations were present in sizable proportions in all tumour types analysed and were associated with high TMB and LOH scores. We also identified numerous BRCA reversion mutations across all tumour types. Conclusions Our results will facilitate future research into the efficacy of precision oncology treatments, including PARP and immune checkpoint inhibitors.

Published
2022
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8. A pan-cancer organoid platform for precision medicine

Author

Brian M. Larsen, Madhavi Kannan, Lee F. Langer, Benjamin D. Leibowitz, Aicha Bentaieb, Andrea Cancino, Igor Dolgalev, Bridgette E. Drummond, Jonathan R. Dry, Chi-Sing Ho, Gaurav Khullar, Benjamin A. Krantz, Brandon Mapes, Kelly E. McKinnon, Jessica Metti, Jason F. Perera, Tim A. Rand, Veronica Sanchez-Freire, Jenna M. Shaxted, Michelle M. Stein, Michael A. Streit, Yi-Hung Carol Tan, Yilin Zhang, Ende Zhao, Jagadish Venkataraman, Martin C. Stumpe, Jeffrey A. Borgia, Ashiq Masood, Daniel V.T. Catenacci, Jeremy V. Mathews, Demirkan B. Gursel, Jian-Jun Wei, Theodore H. Welling, Diane M. Simeone, Kevin P. White, Aly A. Khan, Catherine Igartua, and Ameen A. Salahudeen

Subjects
Biology (General), QH301-705.5
Abstract

Summary: Patient-derived tumor organoids (TOs) are emerging as high-fidelity models to study cancer biology and develop novel precision medicine therapeutics. However, utilizing TOs for systems-biology-based approaches has been limited by a lack of scalable and reproducible methods to develop and profile these models. We describe a robust pan-cancer TO platform with chemically defined media optimized on cultures acquired from over 1,000 patients. Crucially, we demonstrate tumor genetic and transcriptomic concordance utilizing this approach and further optimize defined minimal media for organoid initiation and propagation. Additionally, we demonstrate a neural-network-based high-throughput approach for label-free, light-microscopy-based drug assays capable of predicting patient-specific heterogeneity in drug responses with applicability across solid cancers. The pan-cancer platform, molecular data, and neural-network-based drug assay serve as resources to accelerate the broad implementation of organoid models in precision medicine research and personalized therapeutic profiling programs.

Published
2021
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9. Targeting melanoma’s MCL1 bias unleashes the apoptotic potential of BRAF and ERK1/2 pathway inhibitors

Author

Matthew J. Sale, Emma Minihane, Noel R. Monks, Rebecca Gilley, Frances M. Richards, Kevin P. Schifferli, Courtney L. Andersen, Emma J. Davies, Mario Aladren Vicente, Eiko Ozono, Aleksandra Markovets, Jonathan R. Dry, Lisa Drew, Vikki Flemington, Theresa Proia, Duncan I. Jodrell, Paul D. Smith, and Simon J. Cook

Subjects
Science
Abstract

BRAF or MEK1/2 inhibitors are cytostatic in melanoma and the surviving cells develop drug resistance. This study shows that the pro-survival pool is biased towards MCL1 in melanoma so that BRAF or MEK1/2 inhibitors are synthetic lethal with the MCL1 inhibitor AZD5991, improving tumour growth inhibition.

Published
2019
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10. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Michael P. Menden, Dennis Wang, Mike J. Mason, Bence Szalai, Krishna C. Bulusu, Yuanfang Guan, Thomas Yu, Jaewoo Kang, Minji Jeon, Russ Wolfinger, Tin Nguyen, Mikhail Zaslavskiy, AstraZeneca-Sanger Drug Combination DREAM Consortium, In Sock Jang, Zara Ghazoui, Mehmet Eren Ahsen, Robert Vogel, Elias Chaibub Neto, Thea Norman, Eric K. Y. Tang, Mathew J. Garnett, Giovanni Y. Di Veroli, Stephen Fawell, Gustavo Stolovitzky, Justin Guinney, Jonathan R. Dry, and Julio Saez-Rodriguez

Subjects
Science
Abstract

Resistance to first line treatment is a major hurdle in cancer treatment, that can be overcome with drug combinations. Here, the authors provide a large drug combination screen across cancer cell lines to benchmark crowdsourced methods and to computationally predict drug synergies.

Published
2019
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11. MEK1/2 inhibitor withdrawal reverses acquired resistance driven by BRAFV600E amplification whereas KRASG13D amplification promotes EMT-chemoresistance

Author

Matthew J. Sale, Kathryn Balmanno, Jayeta Saxena, Eiko Ozono, Katarzyna Wojdyla, Rebecca E. McIntyre, Rebecca Gilley, Anna Woroniuk, Karen D. Howarth, Gareth Hughes, Jonathan R. Dry, Mark J. Arends, Pilar Caro, David Oxley, Susan Ashton, David J. Adams, Julio Saez-Rodriguez, Paul D. Smith, and Simon J. Cook

Subjects
Science
Abstract

Colorectal cancer cells can acquire resistance to MEK inhibition due to BRAF or KRAS amplification. Here, the authors show that while MEK inhibitor withdrawal in BRAF mutant cells restores sensitivity to the inhibitor through the loss of BRAF amplification mediated by a p57-dependent mechanism, drug withdrawal from KRAS mutant cells does not restore sensitivity but results in EMT and chemoresistance.

Published
2019
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12. Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens

Author

Iñigo Ayestaran, Ana Galhoz, Elmar Spiegel, Ben Sidders, Jonathan R. Dry, Frank Dondelinger, Andreas Bender, Ultan McDermott, Francesco Iorio, and Michael P. Menden

Subjects
precision medicine, cancer, drug resistance, early drug discovery, biostatistics, biomarker discovery, Computer software, QA76.75-76.765
Abstract

Summary: High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFRT790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations. The Bigger Picture: Cancer drug resistance is the major challenge of modern oncology. Identifying resistance and its biomarkers will empower the next generation of precision medicines. High-throughput pharmacology screens in cancer cell lines have successfully identified drug-sensitivity biomarkers, but drug-resistance biomarkers are underexplored. Intrinsic drug-resistance events are often rare and experimentally indistinguishable from cytotoxicity or artifacts without prior knowledge. To address this, we investigate cell-line populations sensitized to a drug treatment (i.e., carrying established sensitivity biomarkers) and characterize those cell lines that do not respond as expected. We highlight unique genetic features harbored by these cell lines and confirm their linkage to drug resistance using CRISPR gene essentiality data. Our analysis and results pave the way for enhanced precision medicine, guide further CRISPR screens, and identify potential drug combinations to tackle resistance.

Published
2020
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13. Looking beyond the cancer cell for effective drug combinations

Author

Jonathan R. Dry, Mi Yang, and Julio Saez-Rodriguez

Subjects
Drug Combination, Antitumor Immune Response, Drug Synergy, Effective Drug Combination, Antigenic Burden, Medicine, Genetics, QH426-470
Abstract

Abstract Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.

Published
2016
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17. Contrived Materials and a Data Set for the Evaluation of Liquid Biopsy Tests

Author

Kyle M. Hernandez, Kelli S. Bramlett, Phaedra Agius, Jonathan Baden, Ru Cao, Omoshile Clement, Adam S. Corner, Jonathan Craft, Dennis A. Dean, Jonathan R. Dry, Kristina Grigaityte, Robert L. Grossman, James Hicks, Nikki Higa, Timothy R. Holzer, Jeffrey Jensen, Donald J. Johann, Sigrid Katz, Anand Kolatkar, Jennifer L. Keynton, Jerry S.H. Lee, Dianna Maar, Jean-Francois Martini, Christopher G. Meyer, Peter C. Roberts, Matt Ryder, Lea Salvatore, Jeoffrey J. Schageman, Stella Somiari, Daniel Stetson, Mark Stern, Liya Xu, and Lauren C. Leiman

Subjects
Molecular Medicine, Pathology and Forensic Medicine
Published
2023
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18. Supplementary Figures 1-5 from AZD5153: A Novel Bivalent BET Bromodomain Inhibitor Highly Active against Hematologic Malignancies

Author

Huawei Chen, Edwin Clark, Michael Zinda, Michael J. Waring, Stephen E. Fawell, Gordon B. Mills, Paul Lyne, Corinne Reimer, Jonathan R. Dry, Alfred A. Rabow, Tammie C. Yeh, Greg O'Connor, Graeme Walker, Miika J. Ahdesmaki, Larry Bao, Michael Collins, Deborah Lawson, Lillian Castriotta, Shenghua Wen, Jingwen Zhang, Tony Cheung, Scott Boiko, Ian L. Dale, Philip Petteruti, Wenxian Wang, Austin Dulak, Yi Yao, Maureen M. Hattersley, and Garrett W. Rhyasen

Abstract

Supplementary Figure 1. AZD5153 shows reduced BRD4 binding activity when BD2 function is abolished; Supplementary Figure 2. AZD5153 modulates MYC protein levels across hematologic cancer cell lines; Supplementary Figure 3. Gene Ontology (GO) analysis of the down-regulated transcripts by AZD5153 across all cell line types; Supplementary Figure 4. IC50 and percent maximal cell kill by AZD5153 and I-BET762 in five hematologic cell lines; Supplementary Figure 5. Tumor growth inhibition across six hematological xenograft Models

Published
2023
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20. Supplementary Table 3 from AZD5153: A Novel Bivalent BET Bromodomain Inhibitor Highly Active against Hematologic Malignancies

Author

Huawei Chen, Edwin Clark, Michael Zinda, Michael J. Waring, Stephen E. Fawell, Gordon B. Mills, Paul Lyne, Corinne Reimer, Jonathan R. Dry, Alfred A. Rabow, Tammie C. Yeh, Greg O'Connor, Graeme Walker, Miika J. Ahdesmaki, Larry Bao, Michael Collins, Deborah Lawson, Lillian Castriotta, Shenghua Wen, Jingwen Zhang, Tony Cheung, Scott Boiko, Ian L. Dale, Philip Petteruti, Wenxian Wang, Austin Dulak, Yi Yao, Maureen M. Hattersley, and Garrett W. Rhyasen

Abstract

Protein modulation by AZD5153 in hematological cell lines

Published
2023
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21. Supplementary figures from Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547

Author

Paul D. Smith, Elaine Kilgour, Jonathan R. Dry, Barry R. Davies, Joanne Wilson, Margaret Veldman-Jones, Sarah Cross, Pei Zhang, Dennis Wang, Michael Dymond, Robert Shaw, Dawn Baker, Lorraine Mooney, Claire Rooney, and Oona Delpuech

Abstract

Supplementary figures S1: volcano plot of insensitive cell lines treated by AZD4547 S2:Immunohistochemistry of p-S6 and p-ERK tissue sections from xenograft treated with AZD4547 S3:FGFR pathway modulation in xenograft models treated with AZD4547 S4: ANC3A xenograft tumour growth inhibition after treated with AZD4547 and/or AZD5363 S5: AZD4547 dynamic biomarkers in ANC3A model after 14 days treatment with AKT and FGFR inhibitors

Published
2023
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23. Supplementary Table 1 from AZD5153: A Novel Bivalent BET Bromodomain Inhibitor Highly Active against Hematologic Malignancies

Author

Huawei Chen, Edwin Clark, Michael Zinda, Michael J. Waring, Stephen E. Fawell, Gordon B. Mills, Paul Lyne, Corinne Reimer, Jonathan R. Dry, Alfred A. Rabow, Tammie C. Yeh, Greg O'Connor, Graeme Walker, Miika J. Ahdesmaki, Larry Bao, Michael Collins, Deborah Lawson, Lillian Castriotta, Shenghua Wen, Jingwen Zhang, Tony Cheung, Scott Boiko, Ian L. Dale, Philip Petteruti, Wenxian Wang, Austin Dulak, Yi Yao, Maureen M. Hattersley, and Garrett W. Rhyasen

Abstract

Cell line authentication information

Published
2023
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24. Data from Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547

Author

Paul D. Smith, Elaine Kilgour, Jonathan R. Dry, Barry R. Davies, Joanne Wilson, Margaret Veldman-Jones, Sarah Cross, Pei Zhang, Dennis Wang, Michael Dymond, Robert Shaw, Dawn Baker, Lorraine Mooney, Claire Rooney, and Oona Delpuech

Abstract

The challenge of developing effective pharmacodynamic biomarkers for preclinical and clinical testing of FGFR signaling inhibition is significant. Assays that rely on the measurement of phospho-protein epitopes can be limited by the availability of effective antibody detection reagents. Transcript profiling enables accurate quantification of many biomarkers and provides a broader representation of pathway modulation. To identify dynamic transcript biomarkers of FGFR signaling inhibition by AZD4547, a potent inhibitor of FGF receptors 1, 2, and 3, a gene expression profiling study was performed in FGFR2-amplified, drug-sensitive tumor cell lines. Consistent with known signaling pathways activated by FGFR, we identified transcript biomarkers downstream of the RAS-MAPK and PI3K/AKT pathways. Using different tumor cell lines in vitro and xenografts in vivo, we confirmed that some of these transcript biomarkers (DUSP6, ETV5, YPEL2) were modulated downstream of oncogenic FGFR1, 2, 3, whereas others showed selective modulation only by FGFR2 signaling (EGR1). These transcripts showed consistent time-dependent modulation, corresponding to the plasma exposure of AZD4547 and inhibition of phosphorylation of the downstream signaling molecules FRS2 or ERK. Combination of FGFR and AKT inhibition in an FGFR2-mutated endometrial cancer xenograft model enhanced modulation of transcript biomarkers from the PI3K/AKT pathway and tumor growth inhibition. These biomarkers were detected on the clinically validated nanoString platform. Taken together, these data identified novel dynamic transcript biomarkers of FGFR inhibition that were validated in a number of in vivo models, and which are more robustly modulated by FGFR inhibition than some conventional downstream signaling protein biomarkers. Mol Cancer Ther; 15(11); 2802–13. ©2016 AACR.

Published
2023
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26. supplementary Table 4 from Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547

Author

Paul D. Smith, Elaine Kilgour, Jonathan R. Dry, Barry R. Davies, Joanne Wilson, Margaret Veldman-Jones, Sarah Cross, Pei Zhang, Dennis Wang, Michael Dymond, Robert Shaw, Dawn Baker, Lorraine Mooney, Claire Rooney, and Oona Delpuech

Abstract

Differential gene expression and statistical analysis of cell lines treated with AZD4547 or DMSO over time

Published
2023
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28. Data from AZD5153: A Novel Bivalent BET Bromodomain Inhibitor Highly Active against Hematologic Malignancies

Author

Huawei Chen, Edwin Clark, Michael Zinda, Michael J. Waring, Stephen E. Fawell, Gordon B. Mills, Paul Lyne, Corinne Reimer, Jonathan R. Dry, Alfred A. Rabow, Tammie C. Yeh, Greg O'Connor, Graeme Walker, Miika J. Ahdesmaki, Larry Bao, Michael Collins, Deborah Lawson, Lillian Castriotta, Shenghua Wen, Jingwen Zhang, Tony Cheung, Scott Boiko, Ian L. Dale, Philip Petteruti, Wenxian Wang, Austin Dulak, Yi Yao, Maureen M. Hattersley, and Garrett W. Rhyasen

Abstract

The bromodomain and extraterminal (BET) protein BRD4 regulates gene expression via recruitment of transcriptional regulatory complexes to acetylated chromatin. Pharmacological targeting of BRD4 bromodomains by small molecule inhibitors has proven to be an effective means to disrupt aberrant transcriptional programs critical for tumor growth and/or survival. Herein, we report AZD5153, a potent, selective, and orally available BET/BRD4 bromodomain inhibitor possessing a bivalent binding mode. Unlike previously described monovalent inhibitors, AZD5153 ligates two bromodomains in BRD4 simultaneously. The enhanced avidity afforded through bivalent binding translates into increased cellular and antitumor activity in preclinical hematologic tumor models. In vivo administration of AZD5153 led to tumor stasis or regression in multiple xenograft models of acute myeloid leukemia, multiple myeloma, and diffuse large B-cell lymphoma. The relationship between AZD5153 exposure and efficacy suggests that prolonged BRD4 target coverage is a primary efficacy driver. AZD5153 treatment markedly affects transcriptional programs of MYC, E2F, and mTOR. Of note, mTOR pathway modulation is associated with cell line sensitivity to AZD5153. Transcriptional modulation of MYC and HEXIM1 was confirmed in AZD5153-treated human whole blood, thus supporting their use as clinical pharmacodynamic biomarkers. This study establishes AZD5153 as a highly potent, orally available BET/BRD4 inhibitor and provides a rationale for clinical development in hematologic malignancies. Mol Cancer Ther; 15(11); 2563–74. ©2016 AACR.

Published
2023
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29. supplementary Table 6 from Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547

Author

Paul D. Smith, Elaine Kilgour, Jonathan R. Dry, Barry R. Davies, Joanne Wilson, Margaret Veldman-Jones, Sarah Cross, Pei Zhang, Dennis Wang, Michael Dymond, Robert Shaw, Dawn Baker, Lorraine Mooney, Claire Rooney, and Oona Delpuech

Abstract

Differential gene expression and two statistical analysis of ANC3A xenograft after treatment by AZD4547 and/or AZD5363 compared to vehicle group, and Combination group compared to singled agent.

Published
2023
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30. supplementary Table 5 from Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547

Author

Paul D. Smith, Elaine Kilgour, Jonathan R. Dry, Barry R. Davies, Joanne Wilson, Margaret Veldman-Jones, Sarah Cross, Pei Zhang, Dennis Wang, Michael Dymond, Robert Shaw, Dawn Baker, Lorraine Mooney, Claire Rooney, and Oona Delpuech

Abstract

Statistical analysis of gene modulation between FGFR1, 2 and 3 dysregulated cell lines.

Published
2023
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31. Supplementary Table 2 from AZD5153: A Novel Bivalent BET Bromodomain Inhibitor Highly Active against Hematologic Malignancies

Author

Huawei Chen, Edwin Clark, Michael Zinda, Michael J. Waring, Stephen E. Fawell, Gordon B. Mills, Paul Lyne, Corinne Reimer, Jonathan R. Dry, Alfred A. Rabow, Tammie C. Yeh, Greg O'Connor, Graeme Walker, Miika J. Ahdesmaki, Larry Bao, Michael Collins, Deborah Lawson, Lillian Castriotta, Shenghua Wen, Jingwen Zhang, Tony Cheung, Scott Boiko, Ian L. Dale, Philip Petteruti, Wenxian Wang, Austin Dulak, Yi Yao, Maureen M. Hattersley, and Garrett W. Rhyasen

Abstract

Differentially expressed genes with AZD5153 treatment

Published
2023
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32. Supplementary Figures from Pharmacological Inhibition of PARP6 Triggers Multipolar Spindle Formation and Elicits Therapeutic Effects in Breast Cancer

Author

Huawei Chen, Corinne Reimer, Stephen E. Fawell, Keith Mikule, Michael Zinda, Edward W. Tate, Paul D. Lyne, Jonathan R. Dry, Deborah Lawson, Michelle L. Lamb, Jeffrey W. Johannes, David A. Scott, Dan Widzowski, Michele Mayo, Ryan T. Howard, Nancy Su, Jiaquan Wu, Farzin Gharahdaghi, Wenxian Wang, Xin Wang, Jingwen Zhang, Philip Petteruti, Tony Cheung, Shaun E. Grosskurth, and Zebin Wang

Abstract

Supplemental Fig S1- S6

Published
2023
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33. Data from PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models

Author

Carol J. Bult, Atul J. Butte, Helen Parkinson, Marcel Kool, Stefan M. Pfister, Frédéric Amant, S. John Weroha, Alana Welm, David M. Weinstock, Robert J. Wechsler-Reya, Emilie Vinolo, Livio Trusolino, Je Kyung Seong, Oscar M. Rueda, Daniel S. Peeper, James M. Olson, Steven B. Neuhauser, Enzo Medico, Jeremy Mason, K.C. Kent Lloyd, Michael T. Lewis, Tin O. Khor, Kristel Kemper, Jos Jonkers, Peter J. Houghton, Els Hermans, Melissa A. Haendel, Danielle Greenawalt, Neal C. Goodwin, Kristopher K. Frese, Stephane Ferretti, Yvonne A. Evrard, Olivier Duchamp, James H. Doroshow, Jonathan R. Dry, Heidi Dowst, Dominic A. Clark, Amanda L. Christie, Carlos Caldas, Annette T. Byrne, Matthew H. Brush, Alejandra Bruna, Andrea Bertotti, Debra M. Krupke, Dale A. Begley, Patrick Dunn, Jeffrey A. Wiser, Zhiping Gu, Sebastian Brabetz, Mark A. Murakami, Giorgio Inghirami, Theodore Goldstein, Nathalie Conte, and Terrence F. Meehan

Abstract

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.

Published
2023
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34. Data from Clinically Viable Gene Expression Assays with Potential for Predicting Benefit from MEK Inhibitors

Author

Darren R. Hodgson, Paul Smith, Christopher Womack, Nicky Whalley, J. Carl Barrett, Elizabeth A. Harrington, Jonathan R. Dry, Tom Liptrot, Alan Sharpe, and Roz Brant

Abstract

Purpose: To develop a clinically viable gene expression assay to measure RAS/RAF/MEK/ERK (RAS–ERK) pathway output suitable for hypothesis testing in non–small cell lung cancer (NSCLC) clinical studies.Experimental Design: A published MEK functional activation signature (MEK signature) that measures RAS–ERK functional output was optimized for NSCLC in silico. NanoString assays were developed for the NSCLC optimized MEK signature and the 147-gene RAS signature. First, platform transfer from Affymetrix to NanoString, and signature modulation following treatment with KRAS siRNA and MEK inhibitor, were investigated in cell lines. Second, the association of the signatures with KRAS mutation status, dynamic range, technical reproducibility, and spatial and temporal variation was investigated in NSCLC formalin-fixed paraffin-embedded tissue (FFPET) samples.Results: We observed a strong cross-platform correlation and modulation of signatures in vitro. Technical and biological replicates showed consistent signature scores that were robust to variation in input total RNA; conservation of scores between primary and metastatic tumor was statistically significant. There were statistically significant associations between high MEK (P = 0.028) and RAS (P = 0.003) signature scores and KRAS mutation in 50 NSCLC samples. The signatures identify overlapping but distinct candidate patient populations from each other and from KRAS mutation testing.Conclusions: We developed a technically and biologically robust NanoString gene expression assay of MEK pathway output, compatible with the quantities of FFPET routinely available. The gene signatures identified a different patient population for MEK inhibitor treatment compared with KRAS mutation testing. The predictive power of the MEK signature should be studied further in clinical trials. Clin Cancer Res; 23(6); 1471–80. ©2016 AACR.See related commentary by Xue and Lito, p. 1365

Published
2023
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35. Data from Pharmacological Inhibition of PARP6 Triggers Multipolar Spindle Formation and Elicits Therapeutic Effects in Breast Cancer

Author

Huawei Chen, Corinne Reimer, Stephen E. Fawell, Keith Mikule, Michael Zinda, Edward W. Tate, Paul D. Lyne, Jonathan R. Dry, Deborah Lawson, Michelle L. Lamb, Jeffrey W. Johannes, David A. Scott, Dan Widzowski, Michele Mayo, Ryan T. Howard, Nancy Su, Jiaquan Wu, Farzin Gharahdaghi, Wenxian Wang, Xin Wang, Jingwen Zhang, Philip Petteruti, Tony Cheung, Shaun E. Grosskurth, and Zebin Wang

Abstract

PARP proteins represent a class of post-translational modification enzymes with diverse cellular functions. Targeting PARPs has proven to be efficacious clinically, but exploration of the therapeutic potential of PARP inhibition has been limited to targeting poly(ADP-ribose) generating PARP, including PARP1/2/3 and tankyrases. The cancer-related functions of mono(ADP-ribose) generating PARP, including PARP6, remain largely uncharacterized. Here, we report a novel therapeutic strategy targeting PARP6 using the first reported PARP6 inhibitors. By screening a collection of PARP compounds for their ability to induce mitotic defects, we uncovered a robust correlation between PARP6 inhibition and induction of multipolar spindle (MPS) formation, which was phenocopied by PARP6 knockdown. Treatment with AZ0108, a PARP6 inhibitor with a favorable pharmacokinetic profile, potently induced the MPS phenotype, leading to apoptosis in a subset of breast cancer cells in vitro and antitumor effects in vivo. In addition, Chk1 was identified as a specific substrate of PARP6 and was further confirmed by enzymatic assays and by mass spectrometry. Furthermore, when modification of Chk1 was inhibited with AZ0108 in breast cancer cells, we observed marked upregulation of p-S345 Chk1 accompanied by defects in mitotic signaling. Together, these results establish proof-of-concept antitumor efficacy through PARP6 inhibition and highlight a novel function of PARP6 in maintaining centrosome integrity via direct ADP-ribosylation of Chk1 and modulation of its activity.Significance:These findings describe a new inhibitor of PARP6 and identify a novel function of PARP6 in regulating activation of Chk1 in breast cancer cells.

Published
2023
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36. S1 from PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models

Author

Carol J. Bult, Atul J. Butte, Helen Parkinson, Marcel Kool, Stefan M. Pfister, Frédéric Amant, S. John Weroha, Alana Welm, David M. Weinstock, Robert J. Wechsler-Reya, Emilie Vinolo, Livio Trusolino, Je Kyung Seong, Oscar M. Rueda, Daniel S. Peeper, James M. Olson, Steven B. Neuhauser, Enzo Medico, Jeremy Mason, K.C. Kent Lloyd, Michael T. Lewis, Tin O. Khor, Kristel Kemper, Jos Jonkers, Peter J. Houghton, Els Hermans, Melissa A. Haendel, Danielle Greenawalt, Neal C. Goodwin, Kristopher K. Frese, Stephane Ferretti, Yvonne A. Evrard, Olivier Duchamp, James H. Doroshow, Jonathan R. Dry, Heidi Dowst, Dominic A. Clark, Amanda L. Christie, Carlos Caldas, Annette T. Byrne, Matthew H. Brush, Alejandra Bruna, Andrea Bertotti, Debra M. Krupke, Dale A. Begley, Patrick Dunn, Jeffrey A. Wiser, Zhiping Gu, Sebastian Brabetz, Mark A. Murakami, Giorgio Inghirami, Theodore Goldstein, Nathalie Conte, and Terrence F. Meehan

Abstract

A figure showing the process of generating and using PDX models.

Published
2023
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37. Supplemental Tables S1 to S4 and Figures S1 to S5 including legends and footnotes from Clinically Viable Gene Expression Assays with Potential for Predicting Benefit from MEK Inhibitors

Author

Darren R. Hodgson, Paul Smith, Christopher Womack, Nicky Whalley, J. Carl Barrett, Elizabeth A. Harrington, Jonathan R. Dry, Tom Liptrot, Alan Sharpe, and Roz Brant

Abstract

Supplemental Table 1: Design of Affymetrix and NanoString probes Supplemental Table 2: Genes included in the NanoString Codeset, including genes up- and down-regulated by MEK, Cres and KRAS signatures, plus housekeeper genes Supplemental Table 3: Correlation between Affymetrix and NanoString data for Batch Comparison or New probes Supplemental Table 4: Pearson's correlation coefficients for MEK and Cres gene probes in NSCLC FFPET Supplemental Fig 1: The MEK signature is dynamic in lung cancer cell lines as demonstrated by MEK inhibition with selumetinib and siRNA mediated KRAS knockdown Supplemental Fig 2: Pearson's correlation coefficients for the most correlated MEK and Cres probes in NSCLC FFPET Supplemental Fig 3: NanoString gene expression range of MEK and CRes genes Supplemental Fig 4: Scatterplots showing correlations between MEK, Cres and RAS signatures Supplemental Fig 5: Pearson's correlations demonstrated a high agreement of MEK signature expression between gene probe batches

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2023
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38. Supplemental Materials from Pharmacological Inhibition of PARP6 Triggers Multipolar Spindle Formation and Elicits Therapeutic Effects in Breast Cancer

Author

Huawei Chen, Corinne Reimer, Stephen E. Fawell, Keith Mikule, Michael Zinda, Edward W. Tate, Paul D. Lyne, Jonathan R. Dry, Deborah Lawson, Michelle L. Lamb, Jeffrey W. Johannes, David A. Scott, Dan Widzowski, Michele Mayo, Ryan T. Howard, Nancy Su, Jiaquan Wu, Farzin Gharahdaghi, Wenxian Wang, Xin Wang, Jingwen Zhang, Philip Petteruti, Tony Cheung, Shaun E. Grosskurth, and Zebin Wang

Abstract

Supplemental materials

Published
2023
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39. Supplementary Table 7 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Table 7 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

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2023
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40. Supplementary Table 1 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Table 1 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

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2023
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41. Supplementary Table 3 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Table 3 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

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2023
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42. Supplementary Table 5 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Table 5 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

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2023
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43. Supplementary Table 6 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Table 6 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

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2023
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44. Supplementary Figures S1-S6 from Acquired Resistance to the Mutant-Selective EGFR Inhibitor AZD9291 Is Associated with Increased Dependence on RAS Signaling in Preclinical Models

Author

Darren A.E. Cross, William Pao, Brian Dougherty, Kenneth S. Thress, Garry Beran, Jonathan R. Dry, Paul D. Smith, Henry Brown, Claire H. Barnes, Elizabeth L. Christey O'Brien, Laura E. Ratcliffe, Ambar Ahmed, Miika J. Ahdesmaki, Sarah J. Ross, Eiki Ichihara, Paula Spitzler, Catherine B. Meador, Paul R. Fisher, Zhongwu Lai, Katherine J. Al-Kadhimi, Aleksandra A. Markovets, Daniel Stetson, and Catherine A. Eberlein

Abstract

Supplementary Figures S1-S6. Comparison of genetic alterations across multiple populations resistant to AZD9291 and other EGFR TKIs (S1); Treatment of resistant populations with AZD9291 (S2); Detection and Validation of a novel NRAS E63K mutation (S3); Lysates were prepared from parental PC9 and resistant populations analysed for levels of total and phosphorylated ERK, NRAS and KRAS by western blot (S4); The novel NRAS E63K mutation is an activating mutation that when expressed enhances resistance to cell growth inhibition by gefitinib or AZD9291 in EGFRm cell lines (S5); In vitro combination of AZD9291 with selumetinib induces more profound phenotype inhibition (S6).

Published
2023
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45. Supplementary Methods and References from Acquired Resistance to the Mutant-Selective EGFR Inhibitor AZD9291 Is Associated with Increased Dependence on RAS Signaling in Preclinical Models

Author

Darren A.E. Cross, William Pao, Brian Dougherty, Kenneth S. Thress, Garry Beran, Jonathan R. Dry, Paul D. Smith, Henry Brown, Claire H. Barnes, Elizabeth L. Christey O'Brien, Laura E. Ratcliffe, Ambar Ahmed, Miika J. Ahdesmaki, Sarah J. Ross, Eiki Ichihara, Paula Spitzler, Catherine B. Meador, Paul R. Fisher, Zhongwu Lai, Katherine J. Al-Kadhimi, Aleksandra A. Markovets, Daniel Stetson, and Catherine A. Eberlein

Abstract

Supplementary Methods and References. Description of additional methods and procedures used in the study. Also includes Supplementary References.

Published
2023
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46. Supplementary Figures 8-12 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Figures 8-12 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

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2023
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47. Supplementary Figure 5 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Figure 5 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Published
2023
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48. Supplementary Table 4 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Table 4 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Published
2023
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49. Supplementary Figure 1 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Figure 1 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Published
2023
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50. Supplementary Table 2 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Author

Paul D. Smith, Tim French, Nicholas K. Hayward, Neal Rosen, Richard S. Finn, David B. Solit, Feng Xing, Leisl Packer, Sugandha Ravishankar, Vanessa Bonazzi, Mitchell Stark, Barry S. Taylor, Judy Dering, Gillian Ellison, Helen Brown, Carolyn Haggerty, Andrew Cassidy, Garry Beran, Alexander Graham, Mark Cockerill, Natalie Byrne, Rose McCormack, Christine Chresta, Darren Hodgson, Sarah Runswick, Chris Harbron, Christine A. Pratilas, Sandra Pavey, and Jonathan R. Dry

Abstract

Supplementary Table 2 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Published
2023
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