Surgical genomics is here

Surgical research review Surgical genomics is here Jeffrey L. Johnson, MD, and Alan H. Harken, MD, Denver, Colo

From the Department of Surgery, Denver Health Medical Center, Denver, Colo

ALL OF US HAVE PERFORMED the same, welldesigned surgical procedure on two apparently identical patients with frighteningly discordant results. What happened? Prior to surgery, we responsibly assessed the comorbid disease, medications, allergies, habits, and even lifestyles of these two patients. Our preoperative preparation, operative technique, and postoperative support were apparently superimposable. One patient, however, had a smooth postoperative course, and the other succumbed to multiple organ failure. It is now increasingly clear, to even the most recalcitrant among us,1 that patients exhibit unique patterns in response to medications, infection, and neurohormonal (surgical) stress. The purposes of this general review are: 1) to cover the concepts of molecular coding (genes) and common variants (gene polymorphisms) relevant to the practice of surgery; 2) to relate genetic alterations to the functional clinical response to surgical stress; 3) to briefly describe the tools available for modern genetic and bioinformatics analysis; and 4) to raise practical and ethical issues that surface in the study and analysis of genetic information. GENES AND GENE POLYMORPHISMS A gene is the sequence of nucleotides that both codes for a specific protein and regulates its production. Genes, then, contain the instructions for assembling the protein (contained in “exons” from Accepted November 18, 2002. Surgery 2003;133:127-32. Supported in part by an American College of Surgeons Faculty Research Fellowship and National Institutes of Health grants GM 49222 and GM 08315. Reprint requests: Jeffrey L. Johnson, MD, Department of Surgery, MC 0206, Denver Health Medical Center, 777 Bancock Street, Denver CO 80204. © 2003, Mosby, Inc. All rights reserved. 0039-6060/2003/$30.00+0 doi:10.1067/msy.2003.75

EXpressed regions) and other elements, which are not translated into protein (“introns” from INTervening regions). Introns may include sequences required for trafficking of messenger RNA through a cell, sequences that determine the timing and amount of gene expression (eg, promoters/repressors) and areas with no apparent function. Promoter regions are of particular interest to the physiology of surgical stress because they control the timing and magnitude of transcription of gene into message. Indeed, most proteins implicated in the “stress response” to surgery are produced in response to nuclear signals that facilitate the production of new messages for that protein. One prototypical stress response is expression of the Interleukin-6 (IL-6) gene. This gene is activated when transcription factors such as NF-kB and nuclear factor IL-6 (NF-IL6) are translocated to the nucleus, bind to the promoter region of the IL-6 gene, and facilitate the binding of RNA polymerases responsible for generating messenger RNA. Ribonucleic acid polymerase then generates messenger RNA (mRNA), which is then translated into the IL-6 protein (Figure 1). While the net effect of IL-6 remains abstruse, it coordinates a number of changes that are pivotal in the response to surgical stress. Among its many effects, it stimulates the maturation of B lymphocytes, stimulates a switch of hepatic protein synthesis to acute phase reactants, and increases expression of adhesion molecules on endothelial surfaces.2 Levels of IL-6 are a robust marker of ongoing inflammation, degree of injury, and risk for poor outcome.3 Although remarkably similar,1 our patients’ genes are not identical. A “mutation” (used as a noun) technically is any difference in a gene between one individual and the next. The most common use of the term “mutation” also implies an easily observable change (phenotype) in the person due to that molecular variation. The common use of the word “mutation” also implies that this single change produces an abnormal or disSURGERY 127

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eased state. In its simplest form, mutation indicates a single nucleotide alteration producing a recognizable change in phenotype. For example, mutations resulting in the phenotype of cystic fibrosis are variants of a chloride transporter gene. The term “polymorphism,” on the other hand, is used to describe genetic differences that are common (by convention, present in more than 1% of a population), but do not invariably produce an easily observable phenotypic change. Further, multiple polymorphisms may synergize to produce a vulnerable or even diseased state. The genes encoding blood type, for example, are polymorphic in the population. While these are also technically “mutations,” they do not carry the same connotation of dysfunction. Now that the human genome has been sequenced, a draft reference exists against which all individuals can be compared. Indeed, real time, web-based programs exist (www.ncbi.nlm.nih.gov/ BLAST) allowing users to compare any stretch of nucleotides to known sequences from the human genome. As more and more comparisons are performed, the number of variations found in the population is proving to be enormous. Single nucleotide polymorphisms (SNPs, pronounced “snips”) are the most frequent.4 A SNP is merely a change of one nucleotide at a single locus. Conservative estimates suggest there are approximately 1.5 million SNPs in the human genome. Referring again to IL-6, there are four loci, which commonly vary, in the first 650 base pairs of the promoter region directly upstream of the first exon. Figure 2 shows three SNPs in the IL-6 promoter and their location relative to the first exon of the gene, and binding sites for transcription factors. The question becomes—do SNPS count? Some SNPs occur in exons and so may directly alter the code for an amino acid in the translated protein product. These types of SNPs change the structure, and likely the function, of the protein produced. Conservatively, there are 55,000 of these “coding SNPs,” and these are surfacing on a daily basis. Indeed, multiple research groups are developing SNP libraries, the most prominent of which is in the public domain (snp.cshl.org). For instance, the proteins TNF alpha, Interleukin-1 beta, Platelet Activating Factor receptor, Toll-like Receptor 4, and beta adrenergic receptor all have coding SNPs; that is to say, more than 1% of the population exhibit a variation in these genes and therefore produce a slightly altered protein. It is reasonable to postulate that patients harboring these variant proteins will respond differently to surgical stress.

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SNPs also occur in introns. Whereas a large number of these may be genetic “noise,” if they occur in promoter regions of inducible genes, they may alter the ease with which a gene is activated during a stress response. For example, polymorphisms in the IL-6 promoter have been implicated in the stimulated production of IL-6.5 In a parallel fashion, patients undergoing cardiopulmonary bypass who are heterozygous for the G allele at position -174 have statistically higher levels of IL-6 postoperatively and, interestingly, this translates into longer lengths of hospital stay.6 FUNCTIONAL GENOMICS OF VARIABILITY The significance of variability in the genome on the stress response of the patient (the physiome) is difficult, but important to decipher. There are two interrogation strategies for this deciphering process: the candidate-gene approach, and the genome-wide approach. In the candidate-gene approach, a specific gene polymorphism in a gene with a disease-related function is studied. The frequency of this polymorphism in a diseased population then can be compared to the frequency of that SNP in a matched healthy population. For example, patients harboring a polymorphism in the promoter for plasminogen activator inhibitor-1 are more likely to succumb after severe injury.7 Similarly, patients with abdominal aortic aneurysms who have the C allele at -174 in the IL-6 promoter (see Figure 2) have a higher risk of cardiovascular mortality.8 A selected list of candidate genes with known variants is provided in the Table. An attractive alternative to the candidate-gene approach are the high throughput “genome-wide” associative studies.9 These techniques can evaluate thousands of SNPs simultaneously, which permits, for example, the examination of the effect of time (age) on the prevalence of thousands of SNPs in a population—a genome-wide screen. If the prevalence of a particular SNP in a pooled group of octogenarians is remarkably higher than that in a pooled group of patients in their forties, this may reflect a variation associated with a decreased risk of death. Similarly, one could screen patients with a specific disease for SNPs that occur in this population more commonly than a control population. Although genome-wide approaches are attractive, they do not distinguish coding (exons) from non-coding (introns) SNPs. Some of the variations implicated may be in regions of DNA that are not even genes; furthermore, many of the genes identified may be of unknown function. Thus, genome-wide SNP studies should be generally thought of as hypothesis-generating tools rather than hypothesis-testing tools.

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Figure 1. The transcriptional regulation of interleukin-6 production. A signal received from a cell membrane receptor results in the activation of transcription factors NF IL-6 and NF-kB. These factors translocate to the nucleus where they facilitate the binding of RNA polymerase to the IL-6 promoter region. The polymerase generates IL-6 messenger RNA, which is translated into IL-6 protein. IL-6 is subsequently exported from the cell.

Regardless of the approach (candidate-gene or genome-wide), purely associative studies ignore the fact that genes are not inherited independently, but rather travel as groups. An association between a SNP and a disease may merely reflect a linkage to the real culprit—genetic variation. Further, finding a comparison group for a diseased population can be problematic; the most common control is DNA from blood donors, which is an extremely select population. The sheer number of SNPs worsens the problem. The effect of a SNP at one site may be modified by a SNP at another site; it may even be the combination of a certain set of SNPs—or haplotype—that determines responsiveness. If there are 1.5 million SNPs, there are potentially 1.5 million factorial combinations of those SNPs. Given the daunting number of comparisons that can be made between SNPs and disease states, some of these comparisons will suggest a relationship that does not exist. This problem has encouraged the burgeoning field of bioinformatics to catalog combinations and create statistically valid correlations between them and physiologic outcomes. Guide-

lines for interpreting these blizzards of information are only now emerging.10 THE TRANSCRIPTOME OF SURGICAL STRESS AND TRAUMA Studying the effect of genetic variation on outcome is arguably premature when we do not really know which genes are “turned on” in response to surgical stress. In other words, it’s not very useful to identify phone numbers precisely if we are in the wrong area code. Pioneering work in this area has been completed for human endotoxemia, and has been started in human sepsis (CEPSIS consortium). The Trauma/Burn Glue Grant will soon begin to define the set of genes transcribed after surgical stress, burns, or trauma—the “transcriptome” of injury. A “glue” grant is an NIH funding mechanism (RFA-GM-01-004) specifically designed as a multiinstitutional effort in which the expertise of contributing institutions is combined to produce large scale translational research. The trauma/ burn glue grant will use animal modeling and human studies to catalog gene expression after sepsis

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Figure 2. Single nucleotide polymorphisms in the promoter region of the interleukin-6 gene. The horizontal line represent the region of DNA. The three boxes above the line identify areas where the DNA can vary (polymorphisms) and indicates the frequencies of each variation. The boxes below the line indicate regions where selected transcription factors bind. Numbers on either end of the stretch of DNA and at the site of polymorphisms indicate position (number of nucleotides) relative to the first exon of the gene.

and injury. Another important feature of this particular effort is that results will become available in the public domain. Gene profiles that are made anonymous (oligonucleotide arrays, see below) ultimately will be uploaded to web sites so that the entire scientific community can interpret the results, and presumably initiate investigations of candidate genes. At present, one technique favored to identify suspect genes is the high-density oligonucleotide array.11 Although this may sound complicated, the underlying concept is quite simple. Unique segments (oligonucleotides) of tens of thousands of genes (high density!) can be printed on different materials in a precise layout (arrays, or “gene chips”). This permits detection of messenger RNA for all of those genes with the following technique. A tissue sample is taken, the messenger RNA is extracted and reverse-transcribed using labeled nucleotides (copied from RNA to DNA, thereby creating a complementary or cDNA). This cDNA thus faithfully represents the type and amount of mRNA present in a tissue at that snapshot in time. The cDNA is then incubated with the pre-printed array. Sequences in a clinical sample that are complementary to those on the array will bind. Computerized imagers then can detect the amount of cDNA bound for each of the thousands of genes encoded on the chip, which is an expression profile. Expression profiling also can be accomplished on arrays using more complete cDNAs or RNAs. Further, production of gene products (the pro-

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teome) can be profiled using arrays printed with oligonucletodies that recognize specific proteins (aptamers). Expression profiling allows the investigator to identify a set of genes that are over- or under-expressed in different disease states, ages, cell types, or even at different times of day. For example, there may be a set of genes transcribed in response to postoperative infection, a profile, or genomic signature for nosocomial infection. Or, there may set of genes transcribed that predict progression to ARDS after shock, a molecular signature of impending lung injury. Oligonucleotide arrays permit a view of the transcriptome, albeit a snapshot, at a specific time. But we don’t always know the function of these genes, and we don’t know whether the message is actually transcribed into functional protein. Furthermore, the amount of data generated from even a single time point after a single insult is quite staggering; how do we pick a guilty gene out of hundreds that change expression after an insult? How do we make thousands of comparisons in a statistically valid fashion? Moreover, since each array analysis may represent just a snapshot in time, are we permitted to apply our conclusions to other individuals at other times? Expanding expression profiling to multiple time points in multiple patients compounds the problem. Experts in bioinformatics are developing techniques with which to estimate the importance of increases or decreases in message for specific genes. A rule of thumb is that 20% of the time is spent collecting the data and 80% of the time analyzing it. At present, the preferred analytic methods include principal component analysis, artificial neural networks, and hierarchical clustering.12-14 These statistical strategies allow for a set of candidate genes to be identified from a set of genes that are changing. Like associative studies, ground rules for interpreting expression arrays are only now emerging.15 It is imperative that clinicians remain involved in the process of expression analysis for the discovery of candidate genes. Samples must be prepared from homogeneous patients with a precisely defined disease in a regimented fashion and compared to appropriately matched controls. Due to the huge amount of data, it is easy to establish clinically irrelevant associations. For example, certain genes have diurnal variation to their expression. If messenger RNA is collected from injured patients (typically collected mostly at night) and compared with mRNA from volunteers (typically collected during the day), expression profiling may reveal a set of candidate genes that can predict whether the sun is up. Alternatively, if the study group consists of penetrating trauma patients (mostly men) and

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Table. Examples of gene polymorphisms implicated in the response to stress. The second column indicates whether the polymorphism is found in a regulatory element of the gene (promoter), in a portion of the genes that codes for the protein (exon), or in a portion of the gene that is not expressed as protein but may serve some unknown function (intron.). Gene

Location of Polymorphism

Interleukin 6

Promoter

TNF alpha Plasminogen Activator Inhibitor Vascular Endothelial Growth Factor Interleukin-1 RA Toll-Like Receptor 4 Platelet Activating Factor Receptor

Promoter Promoter Promoter Intron Exon Exon

Interleukin-10

Promoter

the control volunteers come from the local women’s college, one may find a great set of genes that relate exclusively to gender. Although these examples seem trivial, similar genetic traps have been set and have snared unsuspecting geneticists. Expression profiling can either yield exciting new hypotheses or a prodigious stream of incomprehensible garbage. ARE WE READY? Genomic analysis undoubtedly will yield exciting new diagnostic and therapeutic opportunities. First, gene polymorphisms placing patients at risk for poor perioperative outcomes may allow us to more precisely counsel patients regarding high morbidity interventions. Identification of genetic diatheses for perioperative complications may allow us to apply prophylactic measures to populations who will most benefit. For example, if a gene polymorphism predicts susceptibility to wound infection, expanded preemptive measures might be explored. Second, gene expression profiles unique to certain clinical situations (eg, types of cancer, infection, ischemia) may be described, thus permitting early detection and treatment. Third, identification of new candidate genes pivotal in the stress response also will lead to new therapy. For example, sepsis studies targeting specific inflammatory mediators like TNF have proven a disappointment. This may be in part due to redundant mechanisms for the immunoinflammatory response. We are likely to find that these other mediators’ effects have substantial genetically based variation. Some patients may be TNF overproducers, others IL-6 overproducers, and still others IL-10 overproducers. New sepsis trials

Proposed Effect IL-6 production and length of stay after cardiopulmonary bypass6; AAA-associated cardiovascular risk8 Susceptibility to infectious disease, inflammation Risk of death after trauma7 Risk of renal allograft rejection Risk of death after Sepsis; Inflammatory bowel disease Altered susceptibility to bacterial lipopolysaccharide Altered intracellular signaling. Magnitude of a septic response. High vs low IL-10 producers. Ability to control inflammation.

present a unique opportunity to investigate these questions. We now can identify genes that are pivotal in the stress response, and document variations between individuals that will affect their tolerance to surgical stress or portend unique responses to surgical diseases. How we choose to use these data is a matter of medical and ethical debate. On one hand, it is argued that cataloging these variations is no different than, say, a good medical history, which allows us to tailor an individual’s therapy. Indeed, a family history that includes a parent with early myocardial infarction may hold more predictive power than many of the newly discovered genetic associations. A recent review of pharmacogenomics proposes that a hospital should keep a patient’s genome on file to preempt any of the known idiosyncratic reactions to drugs due to gene polymorphisms.16 But it must be acknowledged that genetic variation has consequences far beyond medical care and the individual patient. Family members of our patients may carry a variation that puts them at risk. Should we test them all? Third party payors undoubtedly will take a keen interest in polymorphisms that have significant health care (ie, cost) implications for the patient. Patients may not want to share their DNA with their doctors, not to mention their HMOs. Laws addressing these issues are already on the books. At one extreme, one’s DNA is personal property; it would be a violation of one’s rights for another party (insurance company, employer or even surgeon) to access it.17 At the other extreme, one’s DNA is an important window to allow appropriate medical care. We must find ways for our patients to benefit from surgical

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genomics without invading their privacy or placing their socioeconomic status at risk.18 Surgical genomics is here. In the coming years, we will be inundated with SNPs, chips, correlative trips, and legal quips. Translating this evolving field into coherent messages for our patients and their care will be a formidable challenge. As surgeons, we have no reasonable alternative but to get ready. We can be part of the steamroller or part of the road. REFERENCES 1. Shames BD, Selzman CH, Meng X, et al. Genes don’t count. Arch Surg 1998; 133:667-9. 2. Kishimoto T, Akira S, Narazaki M, Taga T. Interleukin-6 family of cytokines and gp130. Blood 1995; 86:1243-54. 3. Biffl WL, Moore EE, Moore FA. Interleukin-6 In The Injured Patient. Marker Of Injury Or Mediator Of Inflammation? Ann Surg 1996; 224:647-64. 4. Syvanen AC. Accessing genetic variation: genotyping single nucleotide polymorphisms. Nat Rev Genet 2001; 2:930-42. 5. Terry CF, Loukaci V, Green FR. Cooperative influence of genetic polymorphisms on interleukin 6 transcriptional regulation. J Biol Chem 2000; 275:18138-44. 6. Brull DJ, Montgomery HE, Sanders J, et al. Interleukin-6 gene -174g>c and -572g>c promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler Thromb Vasc Biol 2001; 21:1458-63.

Surgery February 2003 7. Menges T, Hermans PW, Little SG, et al. Plasminogen-activator-inhibitor-1 4G/5G promoter polymorphism and prognosis of severely injured patients. Lancet 2001; 357:1096-7. 8. Jones KG, Brull DJ, Brown LC, et al. Interleukin-6 and the Prognosis of Abdominal Aortic Aneurysms. Circulation 2001; 103:2260-5. 9. Shi MM. Enabling large-scale pharmacogenetic studies by high-throughput mutation detection and genotyping technologies. Clin Chem 2001; 47:164-72. 10. Devlin B, Roeder K, Bacanu SA. Unbiased methods for population-based association studies. Genet Epidemiol 2001; 21:273-84. 11. Fernandez Zapico ME, Ahmad US, Urrutia R. DNA microarrays: Revolutionary insight into the living genome. Surgery 2001; 130:403-7. 12. Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics 2001; 17:763-74. 13. Yang YH, Speed T. Design issues for cDNA microarray experiments. Nat Rev Genetics 2002; 3:579-88. 14. Mills APJ. Gene expression profiling diagnosis through DNA molecular computation. Trends Biotechnol 2002; 20:137-40. 15. Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001; 29:365-71. 16. Phillips KA, Veenstra DL, Oren E, et al. Potential role of pharmacogenomics in reducing adverse drug reactions: a systematic review. Jama 2001; 286:2270-9. 17. Annas GJ. The limits of state laws to protect genetic information. N Engl J Med 2001; 345:385-8. 18. Greely HT. Human genomics research. New challenges for research ethics. Perspect Biol Med 2001; 44:221-9.

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