Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells

Mario Niepel1, Marc Hafner1, Mirra Chung1, Peter K. Sorger1

1 HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
Publication Name:  Current Protocols in Chemical Biology
Unit Number:   
DOI:  10.1002/cpch.21
Online Posting Date:  June, 2017
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Measuring the potencies of small‐molecule drugs in cell lines is a critical aspect of preclinical pharmacology. Such experiments are also prototypical of high‐throughput experiments in multi‐well plates. The procedure is simple in principle, but many unrecognized factors can affect the results, potentially making data unreliable. The procedures for measuring drug response described here were developed by the NIH LINCS program to improve reproducibility. Key features include maximizing uniform cell growth during the assay period, accounting for the effects of cell density on response, and correcting sensitivity measures for differences in proliferation rates. Two related protocols are described: one involves an endpoint measure well‐suited to large‐scale studies and the second is a time‐dependent measurement that reveals changes in response over time. The methods can be adapted to other types of plate‐based experiments. © 2017 by John Wiley & Sons, Inc.

Keywords: dose‐response; GR50; GRmax; growth rate inhibition; cancer; drug sensitivity; drug resistance

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Table of Contents

  • Introduction
  • Strategic Planning
  • Basic Protocol 1: Endpoint Measurement of Drug Sensitivity
  • Basic Protocol 2: Time‐Dependent Measurement of Drug Sensitivity
  • Reagents and Solutions
  • Commentary
  • Literature Cited
  • Figures
  • Tables
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Basic Protocol 1: Endpoint Measurement of Drug Sensitivity

  • MCF 10A cells (ATCC #CRL‐10317) labeled with H2B‐mCherry (Hafner et al., )
  • Complete growth medium for MCF 10A cells (see recipe)
  • Phosphate‐buffered saline (PBS; Thermo Fisher Scientific, cat. no. 21‐040‐CV)
  • 0.25% Trypsin/2.21 mM EDTA in HBSS (Thermo Fisher Scientific, cat. no. 25‐053‐CL)
  • 0.4% trypan blue (Thermo Fisher Scientific, cat. no. 15250‐061)
  • 10 mM stock solutions in DMSO of drugs for treatment (also see recipe):
    • Etoposide (Selleck Chemicals, cat. no. S1225)
    • Taxol (Selleck Chemicals, cat. no. S1150)
    • Trametinib (Selleck Chemicals, cat. no. S2673)
    • Neratinib (Selleck Chemicals, cat. no. S2150)
  • Staining solution (see recipe)
  • Fixation solution (see recipe)
  • 15‐cm tissue culture–treated Petri dishes
  • 50‐ml conical polypropylene tubes (e.g., Corning Falcon)
  • TC20 Automated Cell Counter (BioRad; preferred) or hemacytometer and suitable microscope (also see Phelan & May, )
  • CellCarrier tissue culture–treated optical clear‐bottom 384‐well plates (PerkinElmer, cat. no. 6007558)
  • Multidrop Combi Reagent Dispenser (Thermo Fisher Scientific, cat. no. 5840300; preferred) or multi‐well pipettors
  • D300 Digital Dispenser (Hewlett‐Packard; preferred) or multiwell pipettors
  • Operetta High‐Content Imaging System (Perkin Elmer) or other microscope system able to read multi‐well plates, such as the IN Cell Analyzer (General Electric) or the ImageXpress System (Molecular Devices)
  • EL406 Washer Dispenser (BioTek; preferred) or other automated plate washer/dispenser
  • Microseal ‘F’ Sealing Foil plate seals (Bio‐Rad, cat. no. MSF1001)
  • Columbus Image Data Storage and Analysis System (PerkinElmer) or alternative software to automate counting of viable cells, such as Cell Profiler (Carpenter et al., )
  • Additional reagents and equipment for cell culture including trypsinization and cell counting (Phelan & May, )
NOTE: The materials listed here were used to create the MCF 10A data presented in this protocol, but different cell lines require the use of different reagents. We encourage users, if possible, to run control experiments with MCF 10A cells as a means of calibrating their results against ours.

Basic Protocol 2: Time‐Dependent Measurement of Drug Sensitivity

  Additional Materials (also see protocol 1)
  • Complete microscopy growth medium (see recipe)
  • YOYO‐1 Iodide (ThermoFisher Scientific, Y3601)
  • Operetta High‐Content Imaging System (Perkin Elmer) with an environmental chamber, attached robotic arm, and tissue culture incubator
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Key References
  Hafner et al. (2016). See above.
  This paper describes the theory behind GR metrics as a means to parameterize drug sensitivity. Explains why they are superior to classical metrics based on relative cell counts, and shows how GR data can be applied to gain insights into biological mechanisms of drug response.
  Hafner et al. (2017). See above.
  This article is the companion to the present article and focuses on the computational pipeline needed to run drug‐response experiments effectively. It can be used independently of the protocols described here, but for optimal results, both methods should be implemented together.
  Haibe‐Kains et al. (2013). See above.
  This work describes inconsistencies among recently published, large‐scale, pharmacogenomic studies. The authors identify significant discrepancies between the two studies.
  Haverty et al. (2016). See above.
  This manuscript explores the reproducibility of large‐scale pharmacogenomic profiling efforts, paying particular attention how variations in the experimental procedure can affect the outcome of drug‐response measurements.
Internet Resources‐natmethods‐2016/.
  This is the landing page on the Harvard Medical School LINCS Web site for the paper describing the GR metrics (Hafner et al., ). It summarizes the method, provides all data from the paper, and links out to additional tools related to calculating GR metrics.
  The GR Calculator Web site provides explanations of GR metrics, examples of dose‐response datasets collected by the LINCS consortium, and the option of uploading a user's own data to automatically extract the GR metric parameters using the online GR calculator tool.
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