Revolutionizing Cancer Cell Line Screening
Rapid and cost-effective method to generate large-scale screening datasetsCancer cell lines are essential models for cancer research. These models of cancer are used to identify drug sensitivities to find new cancer therapies. To enable the systematic evaluation of cancer vulnerabilities, the Broad Institute, in collaboration with Novartis, has spent considerable eﬀort on collecting and generating hundreds of cell lines representing diverse lineages and genetic backgrounds into the Cancer Cell Line Encyclopedia (CCLE) (Barretina et. al., 2012). CCLE has collected cancer cell models to further progress cancer research. These models have been extensively characterized to identify new markers of cancer. In order to make this large set of models faster to screen, we have developed a multiplexed technology to screen cell lines in mixtures. This technology called PRISM (Profiling Relative Inhibition Simultaneously in Mixtures) enables a rapid and cost-effective screening method to screen hundreds of cell lines with thousands of drugs. PRISM technology has reduced the amount of time to screen a large panel of cell lines from years to months.
PRISM can be used to identify novel cancer therapies, repurpose existing therapies of any indication, or help identify a new patient population for existing cancer treatments. Besides the ability to screen hundreds of cell lines in less time, a distinct advantage of working with the Broad is our genomic profiling of the CCLE dataset. This data set, when compared to the compound sensitivities, could identify genomic features of sensitivity or resistance. Gene expression, proteomics, methylation, copy number, and Achilles dependencies are only some of the features that are analyzed when screening compounds with so many cell lines.
By leveraging these genomic characterizations, PRISM and the Dependency Map will be instrumental in changing the way new cancer drugs are discovered.
Phenotypic screening with novel compounds.
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Drug repurposing: new indications for existing drugs
Targeted screening identifies biomarkers and sensitive cell lines/lineages
PRISM TechnologyPRISM technology is based on the integration of a unique identifier, a small DNA barcode, into the genome of each cancer cell line. Once a unique barcode identifier has been introduced, multiple cell lines can be mixed and screened in a pooled format against a large panel of chemical compounds. Cancer cell lines are mixed together by doubling time with the same cell number per cell line. After exposure to the compounds, cell lines are lysed to isolate gDNA or mRNA, and barcodes are amplified and quantified. Changes of barcode counts for each cell model in comparison to control are a direct measure for its sensitivity to the tested drug. Sensitivity profiles for each compound are then compared against all molecular features of the cancer dependency map, including CCLE profiling and Achilles profiling. From this analysis, biomarkers are identified and prioritized by significance.
Generation of barcoded cancer models for PRISM screens
Over 800 genomically characterized CCLE cell lines have been adapted to RPMI-1640 media and barcoded with a DNA barcode. All cell lines are tested for mycoplasma, verified with SNP fingerprinting, and the barcode identity is confirmed. Cell lines are then mixed together in pools according to doubling time.
PRISM Viability Assay
The barcoded cell lines are mixed together in a pool and plated. Cells are treated with compounds, and after a five day incubation, the cells are lysed and mRNA or gDNA is isolated. The barcode sequences are then amplified by PCR and detected with Next Gen Sequencing or by a Luminex scanner. The quantity of each barcode remaining after treatment serves as a readout to generate cell line sensitivity signatures for each compound.
Channing Yu, Aristotle M. Mannan, Griselda Metta Yvone, Kenneth N. Ross, Yan-Ling Zhang, Melissa A. Marton, Bradley R. Taylor, Andrew Crenshaw, Joshua Z. Gould, Pablo Tamayo, Barbara A. Weir, Aviad Tsherniak, Bang Wong, Levi A. Garraway, Alykhan F. Shamji, Michelle A. Palmer, Michael A. Foley, Wendy Winckler, Stuart L. Schreiber, Andrew L. Kung, & Todd R Golub. 2016. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines Nature Biotechnology 34 (4): 419–23. https://doi.org/10.1038/nbt.3460.