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Gene Expression Profile May Help Predict Chemotherapy Response

NEW YORK (GenomeWeb News) – In work that could have implications for understanding why some patients respond better to chemotherapy than others, American researchers have identified a gene expression signature that can predict whether an individual’s cells will die after being exposed to a specific DNA-damaging agent.
 
The team, from the Massachusetts Institute of Technology, screened dozens of white blood cell lines with an alkylating agent called MNNG and looked at gene expression patterns associated with sensitivity and resistance. The work, appearing online today in Genes and Development, pinpointed a set of 48 genes whose pre-treatment expression could predict MNNG-induced cell death with 94 percent accuracy.
 
“A cell line from one person would be killed dramatically, while that from another person was resistant to exposure,” co-lead author Rebecca Fry, a University of North Carolina researcher who was formerly at MIT, said in a statement. “It wasn’t known that cell lines from different people could have such dramatic differences in responses.”
 
In the past, several studies have demonstrated that human lymphoblastoid cells, immortalized white blood cells, can have very different gene expression profiles. But it wasn’t known whether this would influence the cells’ response to external challenges.
 
“[W]hile it is known that human lymphoblastoid cells derived from different healthy individuals display considerable variation in their transcription profiles, the influence this variation has on the response to environmental and chemotherapeutic agents is unknown,” the authors noted.
 
For this study, the researchers used the DNA-damaging agent MNNG to screen cells. The compound alkylates certain DNA bases, leading to mutagenesis. Some of this damage can be repaired by the DNA methyltransferase MGMT. But if it isn’t, the DNA mismatch repair or MMR pathway targets damaged DNA bases and sets off the process of cell death via apoptosis.
 
Consequently, cells with reduced MGMT activity but a functional MMR pathway are expected to be more sensitive to MNNG, whereas cells deficient in both pathways are more MNNG resistant but accumulate mutations when exposed to the compound.
 
The researchers used the Affymetrix GeneChip Human Genome U133 Plus 2.0 array to measure gene expression in 24 human lymphoblastoid cell lines before and after MNNG treatment. The lines were previously generated from unrelated, healthy individuals from a variety of ancestries.
 
They then focused in on the most- and least-sensitive cell lines, comparing gene expression between the two groups. Overall, they identified 240 genes that were differentially expressed between the most- and least-sensitive cell lines.
 
Using computational models to pinpoint differentially expressed genes with positive or negative correlations, they identified 48 genes whose basal (pre-treatment) expression could predict MNNG-sensitivity with 94 percent accuracy.
 
“That basal gene expression is the most accurate predictor of alkylation sensitivity bodes well for translating these findings to a clinical setting, for example to predict whether a tumor will respond to alkylation chemotherapy,” Fry and her colleagues wrote.
 
Next, the researchers confirmed these results by gauging the transcript levels for two genes — MGMT and C21ORF56 — that are more highly expressed in MNNG resistant cells. Interestingly, they detected only a weak positive association between MGMT expression and MNNG resistance.
 
That means that the new gene expression test may prove more valuable than the current approach for predicting chemotherapy response, the authors noted. “MGMT silencing is currently being used as a prognostic indicator of successful alkylation chemotherapy for glioblastoma; our results suggest that expression levels for the 48 genes described here may prove a more accurate indicator,” they wrote.
 
By doing additional cell-based experiments and network analyses, the researchers put together additional genes and pathways associated with alkylation, including a network of more than 100 inter-related genes.
 
Even so, the 48-gene signature in pre-treatment cells appeared to be the most powerful predictor of MNNG sensitivity — a finding that the team hopes may eventually inform decisions made in a clinical setting.
 
“These findings may have profound implications in the clinical setting where the collective set of 48 genes may be used as predictors and modulators of cellular sensitivity to cancer chemotherapeutics,” the authors wrote.
 

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