Chapter 53: Genomics and Acute Care Surgery

Outcomes after traumatic injury, perhaps more than in any other surgical disease, have improved through standardization of care. Nevertheless and despite seemingly ideal care, unexpected outcomes occur. Why do patients with similar injuries or severity of acute illness, despite receiving comparable and appropriate treatment, often follow different paths? While one patient recovers uneventfully from massive transfusion for hemorrhagic shock after a motor vehicle collision, another follows a prolonged course complicated by nosocomial pneumonia and organ failure. Consider also, the seemingly more straightforward task of preventing or treating deep venous thromboembolic disease. Despite our understanding of the biology of coagulation and pharmacotherapeutic strategies, venous thromboses and pulmonary emboli still occur, often with fatal consequences.

Numerous clinical, environmental, and genetic factors contribute to variability in the inflammatory response, variable rates of drug metabolism and risk for venous thromboembolic disease. The completion of the Human Genome Project and technological advances in sequencing and small molecule identification have provided the foundation on which to build knowledge of genetic variation in both humans and animal models.¹ This, in turn, has been used to establish genotype–phenotype associations for common polygenic diseases, such as diabetes mellitus, cancer, and hypertension. Using this genetic variability to understand disease biology and to better direct therapy (such as drug selection and dosing) are the goals of the field of “genomic medicine.”² Understanding the information contained in an individual’s genetic fingerprint has the potential for further decreasing the disability and mortality secondary to traumatic injury.

At the molecular level, differences in DNA sequence can alter RNA transcription and protein translation, which thereby alter clinical phenotypes. For instance, drug absorption, metabolism, and excretion are all affected by genetic variation and are the focus of an emerging field of study called pharmacogenomics. The fields of proteomics and metabolomics are additional pieces of systems biology that are promising new discoveries and improvements in patient care. Taken together these form the foundation of personalized medicine. This chapter focuses on select aspects of these fields and their potential application to the care of critically ill and injured surgical patients.

Since the discovery and publication of the molecular structure of nucleic acids by Watson and Crick in 1954, the genetic basis for many conditions has been determined; however, misconceptions remain regarding the role of genetics and genomics in clinical medicine.³ Despite the commonly held notion that genetics had little influence on clinical medicine in the past, genetics has, in fact, played an important role in understanding disease, but only for a small number of conditions and patients. As a result of these, we have entered a period of tremendous growth in our knowledge of genomics that will eventually influence care for all patients.¹ In order for clinicians to understand and participate in these advances, we must become “literate” in the language of genomic medicine. Box 53-1 includes some important definitions of genetic concepts for clinicians, some of which will be more completely developed below.

Much is known about the human genome, including the fact that the minority of the 3 gigabases of DNA sequence codes for proteins. It is estimated that only 2% of our DNA sequence includes the codes for approximately 20,000 protein-coding genes. The function of the remaining 98% of DNA is perhaps the most fascinating aspect of genomics. Figure 53-1 illustrates the structure of a typical gene. The 5′ control region, often termed the “promoter,” includes DNA sequences that recognize and bind to proteins called transcription factors, whose function is to modify gene expression by controlling transcription. The “start codon” is a series of nucleotides that are recognized by the transcriptional machinery, which initiates the generation of messenger RNA (mRNA) from DNA. Not all of this mRNA sequence encodes for protein. Exons are the regions of DNA that are transcribed into RNA to make amino acids. In most genes, multiple exons are separated by introns, which are removed from mRNA prior to translation into protein. The end of transcription is signaled by a series of nucleotides at the end of the coding sequence, referred to as a “stop codon.” The DNA sequence after this stop codon, termed the 3′ control region, can influence the rate of gene transcription and may affect the stability of the mRNA sequence and its subsequent translation into protein, also.

Despite our limited knowledge of the function of much of the genome, our knowledge of the genetic basis for disease is extensive. Basic research and clinical observation have elucidated the inheritance of single gene, Mendelian disorders (transmitted according to Mendel’s laws of inheritance). Most of these are uncommon and, taken together, the most prevalent (eg, cystic fibrosis and hemochromatosis), affect no more than one in several hundred people; however, when the genetic variant is present, its effect on individual patients is substantial. Furthermore, understanding the mechanisms underlying many monogenic disorders has provided pathophysiological information about related, more common disorders. For instance, we have learned a great deal about the pathophysiology of cardiovascular disease from discoveries related to familial hypercholesterolemia, a rare genetic disorder leading to premature atherosclerosis.

The most common type of DNA sequence variation is single base substitutions termed single nucleotide polymorphisms (SNPs). As naturally occurring, SNPs have emerged as important genetic markers for studying multifactorial diseases.⁴ Polymorphisms occur in all individuals and, by strict definition, SNPs exist with a frequency of greater than 1%.⁵ Therefore, the term SNP does not include numerous other single base substitutions in which the least common allele is present at a frequency of less than equal to 1% (such as “personal mutations” that may be limited to one family), nor does the term encompass other variations such as insertion/deletion polymorphisms. Importantly, SNPs should be distinguished from disease “causing” mutations, which are generally much less common, but have higher penetrance (eg, sickle cell anemia). SNPs typically do not cause disease, but, in aggregate, may influence the risk for developing a disease (eg, diabetes mellitus, asthma) or the outcome from a disease or condition (eg, death from sepsis, rate of atherosclerosis in recipients of cardiac transplants).^6,7,8 SNPs that exist in mRNA-coding regions (exons) of DNA can lead to amino acid substitutions (termed a missense mutation) and, therefore, may change the structure and function of the resultant protein. This type of variant potentially has the most profound impact on protein function; however, this type of SNP is the least common.⁹ SNPs in the regulatory (promoter) region of a gene may influence (increase or, more often, decrease) transcription of that gene, and may influence the amount of protein available.¹⁰ Yet other SNPs may not directly alter protein abundance or function, but may be important markers for other (unidentified) functional variants—a concept known as linkage.⁹ Similarly, haplotypes can add complexity to this concept. A haplotype is a group of genes that are commonly inherited together, for instance, a group of SNPs on the same chromosome. Within a haplotype, a nonfunctional SNP may mark the presence of one or a group of other polymorphisms that directly alter a protein either in amount or function.

The analysis of SNPs has been facilitated by the two related following developments: (1) the establishment of large data repositories of SNPs and (2) the availability of relatively affordable “high-throughput” methods for genotyping. Recent techniques have identified at least 10 million SNPs interspersed throughout the human genome, at a frequency of one SNP per ~300 basepairs.¹¹ Their role in critical illness, both as risk factors and in determining treatment response, has been investigated for several candidate genes.¹² It is hoped that these studies will contribute to the understanding of the biology of critical illness and toward characterizing individual patient risk, treatment response and outcome.¹³

Microsatellite and insertion/deletion polymorphisms represent other genetic variations that may be used to characterize an individual’s risk for disease and response to therapy. Microsatellite or simple sequence repeat (SSR) polymorphisms are created by the presence of short sequences (generally 2–4 base pairs) repeated multiple times in tandem. Insertion/deletion polymorphisms are characterized by the presence or absence of a single base in some cases or a longer fragment in others. Microsatellite and insertion/deletion polymorphisms are generally considered to be markers for other functional variants and not themselves functional, but in some cases may directly alter gene activity.^14,15 Typically, they occur in nonfunctional regions or in gene regulatory regions and not in coding regions as they would likely lead to a nonfunctional protein.

Important advances in genomic analysis have been made using genome-wide association studies (GWAS) and, more recently, “next generation sequencing,” which generally focus on the exon regions of the genome (exome sequencing).^16,17 In a GWAS, hundreds of thousands of SNPs are genotyped and analyzed for association with a particular phenotype. This method has been used to identify numerous candidate SNPs for complex diseases including inflammatory bowel disease, breast cancer, and coronary artery disease.^18,19,20 The sheer amount of data gathered from a GWAS, however, can present significant statistical problems.²¹ Exome sequencing, as the term implies, involves sequencing the protein-coding genes of the genome.²² Exome sequencing will identify rare variants directly. These rare variants are typically missed by GWAS, which rely on relatively common haplotype-tagging SNPs to capture variability across the genome. Therefore, exome sequencing holds promise for digging more deeply into our genome.^23,24

Transcription of DNA into mRNA, commonly called gene expression, is the first step in the path to new protein production. The specificity of Watson–Crick base pairing allows for the measurement of mRNA abundance using technology similar to that enabling DNA sequencing. Only a short sequence (<100 nucleotides) is required to uniquely identify each gene and its associated mRNA. Small wafers arrayed with minute quantities of short, gene-specific nucleotides (called microarrays) are now used routinely to monitor gene expression for all human genes in any tissue of interest. The field of measuring the dynamics of mRNA abundance across the genome is called gene expression profiling. This is used to estimate changes in global gene transcription based on the good correlation between alterations in relative RNA abundance as measured by microarrays and changes in gene transcription. Of note, the correlation between changes in gene expression and gene translation (protein production) is not as strong.²⁵

The application of new computational approaches to gene expression data has revealed novel insight into the biology of the response to injury.^26,27 For example, network-based analysis of the gene expression of circulating white blood cells revealed that acute systemic inflammation causes a transient alteration in numerous pathways and networks, including those involved in immunity and modulation of leukocyte translational machinery.^28,29,30

In addition to improving our understanding of pathophysiology, gene expression profiling may help improve disease classification and prognostication.³¹ The potential application to diagnostics and therapeutics in critically ill and injured patients is considerable.³² If one assigns baseline or “reference” status to the averaged leukocyte gene expression profiles of healthy human volunteers, gene expression profiles from patients 12 hours after injury can be used to compute a difference-from-reference (DFR) score for each individual.³³ Scores early after injury were associated with subsequent adverse outcomes, including multiple organ dysfunction, length of mechanical ventilation and hospital stay, and infection rates. Moreover, differences in gene expression are biologically consistent with the complications that develop. For example, patients who develop gram-negative bacteremia days to weeks after injury have evidence of immune suppression within 4 days of their injury. This immune suppression is broad and includes impairment of innate and adaptive immunity.²⁹ This information may allow us to intervene earlier in order to prevent complications.

Genomic medicine holds the promise of personalized medicine. In this context, individualized treatment is designed based upon a patient’s gene-associated disease risks. Venous thromboembolic disease (VTE) is one example where understanding genetic risk factors may be important for optimizing care for critically ill patients. Clot formation is determined by a delicate balance of procoagulant and anticoagulant processes interacting via complex system of cofactors and inhibitors regulated by an elaborate feedback mechanism.³⁴ When this balance is disrupted, abnormal bleeding or clotting may ensue, a common occurrence in the intensive care unit (ICU).

Both hereditary and acquired risk factors contribute to the occurrence of VTE; however, the role of hereditary factors in post-traumatic VTE is unknown. There are five established genetic defects considered as risk factors for venous thromboembolism (see Table 53-1). Deficiencies in protein C, protein S, and antithrombin III lead to defects in the anticoagulant pathways of blood coagulation and together are found in approximately 15% of people with familial thrombophilia.³⁵ A factor V Leiden mutation and increased prothrombin associated with the prothrombin 20210A allele are much more prevalent and, together, may account for more than 60% of familial thrombophilia. Whereas protein C, protein S, and antithrombin deficiencies all involve defects in anticoagulant pathways, the factor V Leiden mutation and the prothrombin gene mutation involve procoagulant factors. Multiple genetic defects are responsible for protein C, protein S and antithrombin deficiencies, but both the factor V Leiden and prothrombin defects are caused by single mutations. These mutations are particularly relevant in patients with VTE occurring in the absence of a clear risk factor such as a traumatic injury. Up to 50% of such individuals have an underlying defect including these genetic causes, which may contribute to the development of a VTE even when the patients receive appropriate pharmacologic prophylaxis.³⁵ Although traumatic injury, particularly extremity fractures and spinal cord injury, are sufficient to lead to VTE in the absence of prophylaxis, it is possible that some failures of prophylaxis may be, in part, due to the aforementioned genetic variants.

Vessel wall damage, venous stasis, and increased activation of clotting factors remain the fundamental basis for our understanding of thrombosis; however, there are numerous additional pathways that may contribute to VTE risk after trauma. Variation in other genes have been associated with VTE.³⁶

Presently, there is not enough information to know the role of these genetic variants in post-traumatic VTE and, therefore, it is not recommended that patients with so-called provoked VTE undergo any type of genetic screening. The rationale is, in part, due to the notion that treatment will not be affected by the presence of these mutations.³⁷ If the risk of failure of conventional prevention strategies is related to mutations causing thrombophilia, however, it is possible that more aggressive prophylaxis for those at the greatest risk for failure would lower the rate of VTE in such individuals.

Clinical genetic testing has grown over the past decades, but generally has been successfully applied to Mendelian diseases. Given the complexities of drug dosing and the risks for serious complications in critically ill patients, the potential application of pharmacogenomics is considerable. Of interest, clinical genetic testing to identify genetic variants to help guide pharmacotherapy is presently quite limited.³⁸

Genetic variation influences the risk for and outcome from acute surgical illness and its complications, such as infection and acute lung injury (ALI).^12,39 The earliest evidence favoring a role for genetic differences in infection risk and outcome came from an epidemiological study of adoptees and their biologic and adoptive parents. A strong association between early death from infection in adoptees and their biologic, but not adoptive, parents suggested a genetic influence on the risk for and outcome from infection.⁴⁰ Subsequent studies determined that inflammatory responses in blood were heritable and were associated with outcomes from meningococcal sepsis.⁴¹ Following these initial observations, reports have identified associations between specific SNPs and sepsis.^42,43 Unfortunately, many of the initially striking associations between SNPs and sepsis risk and outcome have not been independently replicated.^44,45

Genetic differences may help to explain why “antimediator” therapies, often observed to be beneficial in preclinical models and small clinical trials, have not improved outcomes in Phase III clinical trials.^46,47,48 Much of our understanding of the biology of inflammation comes from highly controlled models systems. Examples include the discovery of the roles of tumor necrosis factor-α (TNF-α) and toll-like receptor-4 (TLR4) in the inflammatory response.^49,50 In genetically diverse populations (like humans), genetic variability creates sufficient background “noise,” above which it may be difficult to discern true treatment effects. This is where pharmacogenomics may be the most useful. In addition, new discoveries continue to refine our understanding of the pathways involved in the response to infection and inflammation, improving our understanding of human inflammation and sepsis.⁵¹ These pathways are likely influenced by genetic variation that will influence clinical outcomes. Better risk stratification by clinical and genetic factors will better inform therapeutic decisions, directing therapy to patients who are most likely to benefit.^26,28,52

The usefulness of individual SNPs to predict predisposition to infection or organ failure and response to therapies is presently limited. The first challenge is to reproduce associations that have been identified in the original investigations. This has been achieved for a few genes, including TNF-α and TLR1,^8,45,48 however, the increased risk associated with these and other genetic variants are relatively small and may not be sufficient to warrant specific treatment strategies.

Moreover, the largest, most robust GWAS studies published to date on polygenic diseases like diabetes mellitus, heart disease, and cancer indicate that the additional informational value for predicting a given phenotype is small.⁹ Once the predictive SNPs have been identified, it will be necessary to determine whether directing interventions to genetically “high-risk” patients improves outcomes.

In sepsis and shock, like most common diseases including heart disease, diabetes mellitus, hypertension, and cancer, multiple genetic and environmental factors influence an individual’s risk and clinical course. Within infectious and inflammatory disorders, patient factors, such as age and comorbidity, and disease factors, such as infectious strain, exposure, and concomitant injury, contribute to disease risk and severity making the study of genetic risk factors particularly challenging.

A growing body of information indicates that genomic data will improve our understanding of common diseases including sepsis and organ failure.^26,28 Similarly, genetic influences on many biological responses, such as drug metabolism, contribute to clinically important differences in treatment response. Genomics, systems biology and personalized medicine will potentially transform the care of critically ill and injured patients. Gene expression studies have provided new genomics tools and significant novel insight that may facilitate more accurate diagnosis and therapy. The successful integration of genomics into caring for critically ill and injured patients demands that clinicians become genomically literate.³⁵ This chapter provides a framework to develop that skill.