Gene expression is used for risk stratification, classification of disease, and prediction of outcome of oncology patients.1,2 It was shown that gene expression can predict disease progression in breast cancer.3 Gene expression can be altered by various drugs, and volatile anesthetics (VA) can alter protein levels in rat brain.4 – 9 During surgery, many patients have general anesthesia with VA. We hypothesized that VA affect gene expression of tumor cells.
We exposed 2 tumor cell lines to VA and studied microarray gene expression profiles. The full genome gene expression levels were compared after incubating them with enflurane, isoflurane, desflurane, halothane, sevoflurane, or nitrous oxide (N2O). The neuronal cell line SH-SY5Y (neuroblastoma) was chosen because of the known effects of anesthetics on brain tissues. The MCF7 (adenocarcinoma) breast cell line was used because breast tumor–predictive gene expression signatures have been generated that are currently being tested for their applicability in clinical practice. As control, an experiment with air and 40% oxygen was used. The control incubations were performed in the same way as the experiments. To validate the findings, the sevoflurane, desflurane, and control experiments were performed in duplicate.
Detailed information on protocol, microarray analysis is available online as Supplemental Digital Content 1, http://links.lww.com/AA/A193.10,11
In the first approach, samples were clustered in different positions based on the algorithm's distance matrix used (Euclidian, Pearson, Covariance [data not shown]). In nearly all cases, the cell lines separated into distinct clusters indicating that for further analysis it was best to keep the cell lines separate. The older VA (enflurane, N2O, halothane) and the newer VA (isoflurane, sevoflurane, desflurane) often separated in clusters.
Second Approach: Cell Lines Tested Separately for Effects of Groups of VA
A significance analysis of microarrays (SAM) on the MCF7 cells only, using the total number of 25.649 reporters with a δ setting of 0.56 and the older VA and the newer VA as separate groups, resulted in a set of 186 reporters (false discovery rate [FDR] 18.1) that significantly separated these groups. These reporters were subsequently used to hierarchically cluster these reporters over all samples (Fig. 1). Figure 1 illustrates that in MCF7 cells (left), the newer VA (desflurane, sevoflurane, isoflurane) respond differently compared with the older VA (enflurane, N2O, halothane). The initially significantly increased expression levels (red) observed in the newer VA at the 10-minute time point are maintained in the later time points, whereas in the older VA they are reversed into significantly decreased expression levels (green). This indicates that the VA groups have profoundly different effects on the gene expression levels of this cell line. This set of 186 reporters does not separate the older from the newer VA groups in the SY5Y cells. Interestingly, using the same settings and VA grouping, an SAM performed on SY5Y cells' data did not yield any significant reporters. Only when the δ value was set to 0.24 were 4 reporters found (FDR 0.96). This indicates that in SY5Y cells, the older VA have similar effects on gene expression levels as the newer VA. The only effect that is observed is an overall increase in gene expression levels in later time points. All VA have this effect in a similar manner. To investigate whether the 186 reporters share functional aspects, an EASE analysis was performed using the 104 reporters with a LocusLink number. The top 10 gene ontology (GO) classes that were significantly enriched (after Bonferroni correction) include 69 reporters of which 26 were involved in DNA repair and 43 in the cell division cycle. The complete GO categories and Expression Analysis Systematic Explorer (EASE) table are presented online as Supplemental Digital Content 2 (Table 1, http://links.lww.com/AA/A194).
To examine whether VA can influence the gene expression levels of the 250 breast tumor fingerprint reporters, we performed an SAM with the older and newer VA groups. With a δ value of 0.58 and an FDR of 4.2, we found 32 reporters that significantly separated the VA groups (again, only for the MCF7 cells and not for the SY5Y cells). Figure 2 shows the cluster plots and centroid graphs for the significant reporters. It is clear that the older VA generate expression profiles in MCF7 cells that are different from the newer VA for the genes used in the published breast cancer profile. An EASE analysis using the 32 reporters shows 8 of them are, even after Bonferroni correction, significantly and predominantly involved in cell division (mitosis, M and S phase). A list of GO categories and EASE data are presented online as Supplemental Digital Content 3 (Table 2, http://links.lww.com/AA/A195).
The unique mechanisms of action of the VA on subclasses of genes that include, for example, serotonin receptors are displayed in Figure 3. Every VA up or down regulates in a time-related manner genes that express the serotonin receptor.
Our pilot study shows that VA have profound unique time-dependent effects on human tumor gene expression. Several genes have been identified that can predict breast cancer survival.2 These gene expression signatures or genetic fingerprints could be used to guide treatment in women with advanced breast cancer.1 Most patients undergo tumor excision during general anesthesia with VA. The type of anesthetic used during surgery is not usually considered when microarrays are analyzed. Older tumor samples, excised 15 to 20 years ago, are preferably selected for predictive microarray studies, because this permits a longer patient follow-up and hence better predictions for today's patients. The practice of anesthesia, however, has changed considerably over the last 20 years. Enflurane, halothane, and N2O were frequently used VA in the past, whereas isoflurane, sevoflurane, and desflurane have been introduced into our clinic in the last 20 years. Some hospitals have abandoned the use of N2O.
If VA can modulate genetic expression, this may have important effects for standardization of prognostic genetic fingerprints, and general anesthesia itself may have unknown effects on cell function that could have clinical consequences. Indeed, some of the genes described in the breast cancer fingerprint were affected by the anesthetic treatment. The differences in modulation of gene expression between the VA and cell lines are evident. Based on our findings, we hypothesize that the timing of tumor excision may influence the gene expression levels and this fact should be considered in microarray protocols.
Our findings are consistent with accumulating evidence that anesthetics may modulate gene expression in human and animal cells.4,12 – 15 Gene expression can also be influenced by tissue ischemia or surgical manipulation of the tumor.16 Activation of genes that activate the pathway that is involved in tumor apoptosis, mitosis, and DNA repair may be of clinical significance. Indeed, the use of VA may result in changes in gene expression and proteins that may contribute to clinical side effects of anesthesia or affect tumor characteristics.17 – 21
Our study has limitations. The experiment was terminated after 1 hour and the time intervals were randomly set at 10, 30, and 60 minutes. In this pilot, we used 1 minimum alveolar concentration of VA, but it is possible that different minimum alveolar concentration levels and different time intervals may influence the results. It is difficult to extrapolate these findings to in vivo studies. Repeat experiments would have improved the strength of the study. We started with the sevoflurane and desflurane experiments and because of the interesting and somewhat surprising findings we designed an extended experimental pilot study, which included all available VA. In this way, the sevoflurane, desflurane, and control experiments with air/oxygen were performed in duplicate. This gives an indication of the quality of the experiment. However, it is not known how many repeat experiments are needed for validation of the findings. With this amount of data and human genes, it will be impossible to find exactly the same results. In addition, when future clinical studies are performed on this subject, it will probably not be feasible to generate the same results, because gene expression is influenced in a time-dependent manner and genes are turned on and off by various mechanisms. The influence of VA is just one of these mechanisms. Patients will never receive exactly the same amount of VA during an operation with general anesthesia.
Of course, no clinical decisions can be made from our preliminary results. We can only hypothesize about the mechanisms and we do not understand why the VA act so differently on the cell level. This may be attributable to differences in their concentrations or chemical structure. Gene expression of various genes is affected as displayed in Figure 1. For example, RBBP8 (retinoblastoma binding protein 8) has an important function in DNA repair.22,23 CENPE (centromere protein E) is a modulator in genomic stability and suppression of tumor development.24,25 TFPI (tissue factor pathway inhibitor) is the natural inhibitor of tissue factor coagulant and signaling activities. It has been shown that TFPI exhibits antiangiogenic and antimetastatic effects in vitro and in vivo.26 In animal models of experimental metastasis, both circulating and tumor cell–associated TFPI are shown to significantly reduce tumor cell–induced coagulation activation and lung metastasis. Our approach does not allow any quantitative conclusions. However, we think that our observations are of importance to all doctors who treat patients with breast cancer and for researchers addressing gene expression for the prediction of treatment and prediction of recurrence of disease.
Our findings suggest that VA modulate gene expression in breast and brain tumor cell cultures in a unique and time-dependent manner. If modulation of gene expression does occur in vivo, these effects of VA may have implications for diagnosis, prognosis, and treatment of oncology patients.
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JMH, MH, and RMK helped design the study, conduct the study, analyze the data, and write the manuscript; MN helped conduct the study and analyze the data. SAL, WB, AV, and DS helped analyze the data and write the manuscript. All authors have seen the original study data, reviewed the analysis of the data, and approved the final manuscript. RMK is the author responsible for archiving the study files.
The results of this study were presented in part at the American Association for Cancer Research Meeting, April 14–18, 2007, Los Angeles, CA. The authors report no conflicts of interest.