Clemons, Paul A.

Regulation of Ferroptotic Cancer Cell Death by GPX4

Yang, W. S., SriRamaratnam, R., Welsch, M. E., Shimada, K., Skouta, R., Viswanathan, V. S., Cheah, J. H., Clemons, P. A., Shamji, A. F., Clish, C. B., et al. (2014) · Cell

Ferroptosis and cancer prognosisCell death mechanisms and regulation

Cited by 7,713

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 5,257

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell

Gene expression and cancer classificationBioinformatics and Genomic Networks

Cited by 3,515

Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway

Viswanathan, V. S., Ryan, M. J., Dhruv, H. D., Gill, S., Eichhoff, O. M., Seashore-Ludlow, B., Kaffenberger, S. D., Eaton, J. K., Shimada, K., Aguirre, A. J., et al. (2017) · Nature

Ferroptosis and cancer prognosisCancer, Lipids, and Metabolism

Cited by 2,011

A GPX4-dependent cancer cell state underlies the clear-cell morphology and confers sensitivity to ferroptosis

Zou, Y., Palte, M. J., Deik, A. A., Li, H., Eaton, J. K., Wang, W., Tseng, Y.-Y., Deasy, R., Kost-Alimova, M., Dančík, V., et al. (2019) · Nature Communications

Ferroptosis and cancer prognosisCancer, Hypoxia, and Metabolism

Cited by 887

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

Seashore-Ludlow, B., Rees, M. G., Cheah, J. H., Cokol, M., Price, E. V., Coletti, M. E., Jones, V., Bodycombe, N. E., Soule, C. K., Gould, J., et al. (2015) · Cancer Discovery

Computational Drug Discovery MethodsCancer Genomics and Diagnostics

Cited by 813

Data-analysis strategies for image-based cell profiling

Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods

Cell Image Analysis TechniquesSingle-cell and spatial transcriptomics

Cited by 739

Inhibition of dihydroorotate dehydrogenase overcomes differentiation blockade in acute myeloid leukemia

Sykes, D. B., Kfoury, Y. S., Mercier, F. E., Wawer, M. J., Law, J. M., Haynes, M. K., Lewis, T. A., Schajnovitz, A., Jain, E., Lee, D., et al. (2016) · Cell

Acute Myeloid Leukemia ResearchBiochemical and Molecular Research

Cited by 500

Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

Gustafsdottir, S. M., Ljosa, V., Sokolnicki, K. L., Anthony Wilson, J., Walpita, D., Kemp, M. M., Petri Seiler, K., Carrel, H. A., Golub, T. R., Schreiber, S. L., et al. (2013) · PLoS ONE

Cell Image Analysis TechniquesComputational Drug Discovery Methods

Cited by 291

Phenothiazines induce PP2A-mediated apoptosis in T cell acute lymphoblastic leukemia

Gutierrez, A., Pan, L., Groen, R. W. J., Baleydier, F., Kentsis, A., Marineau, J., Grebliunaite, R., Kozakewich, E., Reed, C., Pflumio, F., et al. (2014) · Journal of Clinical Investigation

Acute Lymphoblastic Leukemia researchProtein Tyrosine Phosphatases

Cited by 220

An expanded universe of cancer targets

Hahn, W. C., Bader, J. S., Braun, T. P., Califano, A., Clemons, P. A., Druker, B. J., Ewald, A. J., Fu, H., Jagu, S., Kemp, C. J., et al. (2021) · Cell

Cancer Genomics and DiagnosticsChromatin Remodeling and Cancer

Cited by 213

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors

Alvarez, M. J., Subramaniam, P. S., Tang, L. H., Grunn, A., Aburi, M., Rieckhof, G., Komissarova, E. V., Hagan, E. A., Bodei, L., Clemons, P. A., et al. (2018) · Nature Genetics

Neuroendocrine Tumor Research AdvancesNeuroblastoma Research and Treatments

Cited by 210

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling

Wawer, M. J., Li, K., Gustafsdottir, S. M., Ljosa, V., Bodycombe, N. E., Marton, M. A., Sokolnicki, K. L., Bray, M.-A., Kemp, M. M., Winchester, E., et al. (2014) · Proceedings of the National Academy of Sciences

Computational Drug Discovery MethodsCell Image Analysis Techniques

Cited by 196

Stabilization of the Max Homodimer with a Small Molecule Attenuates Myc-Driven Transcription

Struntz, N. B., Chen, A., Deutzmann, A., Wilson, R. M., Stefan, E., Evans, H. L., Ramirez, M. A., Liang, T., Caballero, F., Wildschut, M. H. E., et al. (2019) · Cell Chemical Biology

Chemical Synthesis and AnalysisCancer-related Molecular Pathways

Cited by 130

Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics

de Waal, L., Lewis, T. A., Rees, M. G., Tsherniak, A., Wu, X., Choi, P. S., Gechijian, L., Hartigan, C., Faloon, P. W., Hickey, M. J., et al. (2016) · Nature Chemical Biology

Phosphodiesterase function and regulationComputational Drug Discovery Methods

Cited by 96

DNA-encoded library-enabled discovery of proximity-inducing small molecules

Mason, J. W., Chow, Y. T., Hudson, L., Tutter, A., Michaud, G., Westphal, M. V., Shu, W., Ma, X., Tan, Z. Y., Coley, C. W., et al. (2024) · Nature Chemical Biology

Protein Degradation and InhibitorsChemical Synthesis and Analysis

Cited by 83

Chemical Genomic Profiling of Biological Networks Using Graph Theory and Combinations of Small Molecule Perturbations

Haggarty, S. J., Clemons, P. A., & Schreiber, S. L. (2003) · Journal of the American Chemical Society

Bioinformatics and Genomic NetworksGene Regulatory Network Analysis

Cited by 61