High-throughput validation of ceRNA regulatory networks
Chiu, H. S., M. R. Martinez, M. Bansal, A. Subramanian, T. R. Golub, X. Yang, P. Sumazin, and A. Califano. 2017. “High-Throughput Validation of CeRNA Regulatory Networks”. BMC Genomics 18: 418.
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A Maltose-Binding Protein Fusion Construct Yields a Robust Crystallography Platform for MCL1
Clifton, M. C., D. M. Dranow, A. Leed, B. Fulroth, J. W. Fairman, J. Abendroth, K. A. Atkins, et al. 2015. “A Maltose-Binding Protein Fusion Construct Yields a Robust Crystallography Platform for MCL1”. PLoS One 10: e0125010.
Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling
Corsello, S. M., R. T. Nagari, R. D. Spangler, J. Rossen, M. Kocak, J. G. Bryan, R. Humeidi, et al. 2020. “Discovering the Anti-Cancer Potential of Non-Oncology Drugs by Systematic Viability Profiling”. Nat Cancer 1: 235-48.
Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies
Cowley, G. S., B. A. Weir, F. Vazquez, P. Tamayo, J. A. Scott, S. Rusin, A. East-Seletsky, et al. 2014. “Parallel Genome-Scale Loss of Function Screens in 216 Cancer Cell Lines for the Identification of Context-Specific Genetic Dependencies”. Sci Data 1: 140035.
Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets
Dempster, J. M., C. Pacini, S. Pantel, F. M. Behan, T. Green, J. Krill-Burger, C. M. Beaver, et al. 2019. “Agreement Between Two Large Pan-Cancer CRISPR-Cas9 Gene Dependency Data Sets”. Nat Commun 10: 5817.
Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors
Adalsteinsson, V. A., G. Ha, S. S. Freeman, A. D. Choudhury, D. G. Stover, H. A. Parsons, G. Gydush, et al. 2017. “Scalable Whole-Exome Sequencing of Cell-Free DNA Reveals High Concordance With Metastatic Tumors”. Nat Commun 8: 1324.
A strategy for oligonucleotide microarray probe reduction
Antipova, A. A., P. Tamayo, and T. R. Golub. 2002. “A Strategy for Oligonucleotide Microarray Probe Reduction”. Genome Biol 3: RESEARCH0073.
Inhibition of FLT3 in MLL. Validation of a therapeutic target identified by gene expression based classification
Armstrong, S. A., A. L. Kung, M. E. Mabon, L. B. Silverman, R. W. Stam, M. L. Den Boer, R. Pieters, et al. 2003. “Inhibition of FLT3 in MLL. Validation of a Therapeutic Target Identified by Gene Expression Based Classification”. Cancer Cell 3: 173-83.
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., G. Caponigro, N. Stransky, K. Venkatesan, A. A. Margolin, S. Kim, C. J. Wilson, et al. 2012. “The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity”. Nature 483: 603-7.
Stem cell gene expression programs influence clinical outcome in human leukemia
Eppert, K., K. Takenaka, E. R. Lechman, L. Waldron, B. Nilsson, P. Galen, K. H. Metzeler, et al. 2011. “Stem Cell Gene Expression Programs Influence Clinical Outcome in Human Leukemia”. Nat Med 17: 1086-93.