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2. DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy

Author

Chao Fang, Dong Xu, Jing Su, Jonathan R Dry, and Bolan Linghu

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Immuno-oncology (IO) therapies have transformed the therapeutic landscape of non-small cell lung cancer (NSCLC). However, patient responses to IO are variable and influenced by a heterogeneous combination of health, immune, and tumor factors. There is a pressing need to discover the distinct NSCLC subgroups that influence response. We have developed a deep patient graph convolutional network, we call “DeePaN”, to discover NSCLC complexity across data modalities impacting IO benefit. DeePaN employs high-dimensional data derived from both real-world evidence (RWE)-based electronic health records (EHRs) and genomics across 1937 IO-treated NSCLC patients. DeePaN demonstrated effectiveness to stratify patients into subgroups with significantly different (P-value of 2.2 × 10−11) overall median survival of 20.35 months and 9.42 months post-IO therapy. Significant differences in IO outcome were not seen from multiple non-graph-based unsupervised methods. Furthermore, we demonstrate that patient stratification from DeePaN has the potential to augment the emerging IO biomarker of tumor mutation burden (TMB). Characterization of the subgroups discovered by DeePaN indicates potential to inform IO therapeutic insight, including the enrichment of mutated KRAS and high blood monocyte count in the IO beneficial and IO non-beneficial subgroups, respectively. Our work has proven the concept that graph-based AI is feasible and can effectively integrate high-dimensional genomic and EHR data to meaningfully stratify cancer patients on distinct clinical outcomes, with potential to inform precision oncology.

Published
2021
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3. A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

Author

Dennis Wang, James Hensman, Ginte Kutkaite, Tzen S Toh, Ana Galhoz, GDSC Screening Team, Jonathan R Dry, Julio Saez-Rodriguez, Mathew J Garnett, Michael P Menden, and Frank Dondelinger

Subjects
pharmacogenomics, biomarkers, machine learning, drug prediction, statistical inference, uncertainty estimation, Medicine, Science, Biology (General), QH301-705.5
Abstract

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells’ response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

Published
2020
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4. 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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5. 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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6. RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib.

Author

James R Bradford, Matthew Farren, Steve J Powell, Sarah Runswick, Susie L Weston, Helen Brown, Oona Delpuech, Mark Wappett, Neil R Smith, T Hedley Carr, Jonathan R Dry, Neil J Gibson, and Simon T Barry

Subjects
Medicine, Science
Abstract

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers.

Published
2013
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8. 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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10. 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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12. 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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14. 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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15. 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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17. 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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18. 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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20. 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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22. 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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23. 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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24. 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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25. 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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26. 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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27. 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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28. 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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29. 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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30. 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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31. 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

Published
2023
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32. 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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33. 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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34. 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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35. 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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36. 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)

Published
2023
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37. 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)

Published
2023
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38. 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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39. 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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40. 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)

Published
2023
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41. 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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42. 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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43. 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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44. 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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45. Supplementary Figure Legend 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 Figure Legend. Legends for Supplementary Figures S1-S6.

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

Published
2023
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47. Data 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

Resistance to targeted EGFR inhibitors is likely to develop in EGFR-mutant lung cancers. Early identification of innate or acquired resistance mechanisms to these agents is essential to direct development of future therapies. We describe the detection of heterogeneous mechanisms of resistance within populations of EGFR-mutant cells (PC9 and/or NCI-H1975) with acquired resistance to current and newly developed EGFR tyrosine kinase inhibitors, including AZD9291. We report the detection of NRAS mutations, including a novel E63K mutation, and a gain of copy number of WT NRAS or WT KRAS in cell populations resistant to gefitinib, afatinib, WZ4002, or AZD9291. Compared with parental cells, a number of resistant cell populations were more sensitive to inhibition by the MEK inhibitor selumetinib (AZD6244; ARRY-142886) when treated in combination with the originating EGFR inhibitor. In vitro, a combination of AZD9291 with selumetinib prevented emergence of resistance in PC9 cells and delayed resistance in NCI-H1975 cells. In vivo, concomitant dosing of AZD9291 with selumetinib caused regression of AZD9291-resistant tumors in an EGFRm/T790M transgenic model. Our data support the use of a combination of AZD9291 with a MEK inhibitor to delay or prevent resistance to AZD9291 in EGFRm and/or EGFRm/T790M tumors. Furthermore, these findings suggest that NRAS modifications in tumor samples from patients who have progressed on current or EGFR inhibitors in development may support subsequent treatment with a combination of EGFR and MEK inhibition. Cancer Res; 75(12); 2489–500. ©2015 AACR.

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

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

Published
2023
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50. Supplementary Tables S1-S4 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 Tables S1-S4. Generation of resistant cell populations (S1); Small molecule inhibitors (S2); IC50 (µM) values from cell growth inhibition assays comparing compound sensitivity between parental and resistant cell populations (S3); Genetic analysis of resistant cell populations (S4).

Published
2023
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