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The primary method used to test compounds for anticancer activity in vitro may produce inaccurate results, according to researchers.
Therefore, they have developed a new metric to evaluate a compound’s effect on cell proliferation—the drug-induced proliferation (DIP) rate.
They believe this metric, described in Nature Methods, overcomes the time-dependent bias of traditional proliferation assays.
“More than 90% of candidate cancer drugs fail in late-stage clinical trials, costing hundreds of millions of dollars,” said study author Vito Quaranta, MD, of Vanderbilt University School of Medicine in Nashville, Tennessee.
“The flawed in vitro drug discovery metric may not be the only responsible factor, but it may be worth pursuing an estimate of its impact.”
For more than 30 years, scientists have evaluated the ability of a compound to kill cells by adding the compound and counting how many cells are alive after 72 hours.
However, these proliferation assays, which measure cell number at a single time point, don’t take into account the bias introduced by exponential cell proliferation, even in the presence of the drug, said study author Darren Tyson, PhD, of Vanderbilt University School of Medicine.
“Cells are not uniform,” added Dr Quaranta. “They all proliferate exponentially but at different rates. At 72 hours, some cells will have doubled 3 times, and others will not have doubled at all.”
In addition, he noted, drugs don’t all behave the same way on every cell line. For example, a drug might have an immediate effect on one cell line and a delayed effect on another.
Therefore, he and his colleagues used a systems biology approach to demonstrate the time-dependent bias in static proliferation assays and to develop the time-independent DIP rate metric.
The researchers evaluated the responses of 4 different melanoma cell lines to the drug vemurafenib, currently used to treat melanoma, with the standard metric and with the DIP rate.
In one cell line, the team found a stark disagreement between the two metrics.
“The static metric says that the cell line is very sensitive to vemurafenib,” said Leonard Harris, PhD, of Vanderbilt University School of Medicine.
“However, our analysis shows this is not the case. A brief period of drug sensitivity, quickly followed by rebound, fools the static metric but not the DIP rate.”
The findings “suggest we should expect melanoma tumors treated with this drug to come back, and that’s what has happened, puzzling investigators,” Dr Quaranta said. “DIP rate analyses may help solve this conundrum, leading to better treatment strategies.”
These findings have 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,” Dr Quaranta said.
The Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases include drug response data along with genomic and proteomic data that detail each cell line’s molecular makeup.
“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 said. “If the metric by which you’ve evaluated the drug sensitivity of the cells is wrong, your statistical correlations are basically no good.” 
Image courtesy of PNAS
The primary method used to test compounds for anticancer activity in vitro may produce inaccurate results, according to researchers.
Therefore, they have developed a new metric to evaluate a compound’s effect on cell proliferation—the drug-induced proliferation (DIP) rate.
They believe this metric, described in Nature Methods, overcomes the time-dependent bias of traditional proliferation assays.
“More than 90% of candidate cancer drugs fail in late-stage clinical trials, costing hundreds of millions of dollars,” said study author Vito Quaranta, MD, of Vanderbilt University School of Medicine in Nashville, Tennessee.
“The flawed in vitro drug discovery metric may not be the only responsible factor, but it may be worth pursuing an estimate of its impact.”
For more than 30 years, scientists have evaluated the ability of a compound to kill cells by adding the compound and counting how many cells are alive after 72 hours.
However, these proliferation assays, which measure cell number at a single time point, don’t take into account the bias introduced by exponential cell proliferation, even in the presence of the drug, said study author Darren Tyson, PhD, of Vanderbilt University School of Medicine.
“Cells are not uniform,” added Dr Quaranta. “They all proliferate exponentially but at different rates. At 72 hours, some cells will have doubled 3 times, and others will not have doubled at all.”
In addition, he noted, drugs don’t all behave the same way on every cell line. For example, a drug might have an immediate effect on one cell line and a delayed effect on another.
Therefore, he and his colleagues used a systems biology approach to demonstrate the time-dependent bias in static proliferation assays and to develop the time-independent DIP rate metric.
The researchers evaluated the responses of 4 different melanoma cell lines to the drug vemurafenib, currently used to treat melanoma, with the standard metric and with the DIP rate.
In one cell line, the team found a stark disagreement between the two metrics.
“The static metric says that the cell line is very sensitive to vemurafenib,” said Leonard Harris, PhD, of Vanderbilt University School of Medicine.
“However, our analysis shows this is not the case. A brief period of drug sensitivity, quickly followed by rebound, fools the static metric but not the DIP rate.”
The findings “suggest we should expect melanoma tumors treated with this drug to come back, and that’s what has happened, puzzling investigators,” Dr Quaranta said. “DIP rate analyses may help solve this conundrum, leading to better treatment strategies.”
These findings have 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,” Dr Quaranta said.
The Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases include drug response data along with genomic and proteomic data that detail each cell line’s molecular makeup.
“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 said. “If the metric by which you’ve evaluated the drug sensitivity of the cells is wrong, your statistical correlations are basically no good.”
Image courtesy of PNAS
The primary method used to test compounds for anticancer activity in vitro may produce inaccurate results, according to researchers.
Therefore, they have developed a new metric to evaluate a compound’s effect on cell proliferation—the drug-induced proliferation (DIP) rate.
They believe this metric, described in Nature Methods, overcomes the time-dependent bias of traditional proliferation assays.
“More than 90% of candidate cancer drugs fail in late-stage clinical trials, costing hundreds of millions of dollars,” said study author Vito Quaranta, MD, of Vanderbilt University School of Medicine in Nashville, Tennessee.
“The flawed in vitro drug discovery metric may not be the only responsible factor, but it may be worth pursuing an estimate of its impact.”
For more than 30 years, scientists have evaluated the ability of a compound to kill cells by adding the compound and counting how many cells are alive after 72 hours.
However, these proliferation assays, which measure cell number at a single time point, don’t take into account the bias introduced by exponential cell proliferation, even in the presence of the drug, said study author Darren Tyson, PhD, of Vanderbilt University School of Medicine.
“Cells are not uniform,” added Dr Quaranta. “They all proliferate exponentially but at different rates. At 72 hours, some cells will have doubled 3 times, and others will not have doubled at all.”
In addition, he noted, drugs don’t all behave the same way on every cell line. For example, a drug might have an immediate effect on one cell line and a delayed effect on another.
Therefore, he and his colleagues used a systems biology approach to demonstrate the time-dependent bias in static proliferation assays and to develop the time-independent DIP rate metric.
The researchers evaluated the responses of 4 different melanoma cell lines to the drug vemurafenib, currently used to treat melanoma, with the standard metric and with the DIP rate.
In one cell line, the team found a stark disagreement between the two metrics.
“The static metric says that the cell line is very sensitive to vemurafenib,” said Leonard Harris, PhD, of Vanderbilt University School of Medicine.
“However, our analysis shows this is not the case. A brief period of drug sensitivity, quickly followed by rebound, fools the static metric but not the DIP rate.”
The findings “suggest we should expect melanoma tumors treated with this drug to come back, and that’s what has happened, puzzling investigators,” Dr Quaranta said. “DIP rate analyses may help solve this conundrum, leading to better treatment strategies.”
These findings have 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,” Dr Quaranta said.
The Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases include drug response data along with genomic and proteomic data that detail each cell line’s molecular makeup.
“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 said. “If the metric by which you’ve evaluated the drug sensitivity of the cells is wrong, your statistical correlations are basically no good.”