Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 31

Related Article from
The research team decided to take a systems biology approach, a mixture of experimentation and mathematical modeling, to demonstrate the time-dependent bias in
static proliferation assays and to develop the time-independent DIP rate metric.

rather than merely cytostatic (cell growth inhibiting). Although cytostatic drugs may
initially have promising therapeutic effects, they may leave tumor cells alive that then
have the potential to cause the cancer to recur.

"Systems biology is what really makes the difference here," Dr. Quaranta remarked.
"It's about understanding cells-and life-as dynamic systems." This new study is
of particular importance in light of recent international efforts to generate data sets
that include the responses of thousands of cell lines to hundreds of compounds.
Using the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases will allow drug discovery scientists to include drug
response data along with genomic and proteomic data that detail each cell line's
molecular makeup.

The Vanderbilt team is currently in the process of identifying commercial entities that
can further refine the software and make it widely available to the research community to inform drug discovery.	
n

"The idea is to look for statistical correlations-these particular cell lines with this
particular makeup are sensitive to these types of compounds-to use these large databases as discovery tools for new therapeutic targets in cancer," Dr. Quaranta stated.
"If the metric by which you've evaluated the drug sensitivity of the cells is wrong,
your statistical correlations are basically no good."
The Vanderbilt team evaluated the responses from four different melanoma cell lines
to the drug vemurafenib, currently used to treat melanoma, with the standard metric-used for the CCLE and GDSC databases-and with the DIP rate. In one cell line,
they found a glaring disagreement between the two metrics.
"The static metric says that the cell line is very sensitive to vemurafenib. Howaever,
our analysis shows this is not the case," said co-lead study author Leonard Harris,
Ph.D., a systems biology postdoctoral fellow at Vanderbilt. "A brief period of drug
sensitivity, quickly followed by rebound, fools the static metric, but not the DIP rate."
Dr. Quaranta added that the findings "suggest we should expect melanoma tumors
treated with this drug to come back, and that's what has happened, puzzling investigators. DIP rate analyses may help solve this conundrum, leading to better treatment
strategies."
The researchers noted that using the DIP rate is possible because of advances in automation, robotics, microscopy, and image processing. Moreover, the DIP rate metric
offers another advantage-it can reveal which drugs are truly cytotoxic (cell killing),
31

| January, 2019


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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis

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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 1
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - Contents
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 3
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 4
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 5
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 6
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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 9
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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 11
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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 13
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 14
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 15
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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 18
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 19
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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 23
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 24
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 25
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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 31
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