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Biomarkers and Gene Tests - A Personal Insight by Dr Paul Debenham Executive Summary There is a bewildering array of biological markers that have been, and continue to be, identified and associated with disease prediction and prognosis. Genetic tests for gene mutations in well characterised biological processes are increasingly obvious candidates to add into the panoply of tests available for this purpose. The computation of well-being prediction based on biomarker data is becoming a mature science, at the same time the increasingly manageable access to gene analysis enables the partnering of all such information in the future. Thus genetic information, even from unknown genes amongst micro-array data, may be assimilated into calculations as just biomarker data points. Historically genetic data has been synonymous with monogenetic disorders, the low prevalence of which in the population at large has meant that such tests have not had a significant value to the insurance industry. However an increasing association with common disorders may change this stance. Further the potentially extensive use of gene markers tests in the drive to make better, safer drugs could bring many such tests to become commonplace and central to health care, and thus of interest to the insurance industry. As gene tests become just biomarker data points the insurance distinction between predictive and diagnostic classification may need to be re-examined. 1. Biomarkers Biomarkers have been defined in many different ways, for instance: (i) A characteristic that can be measured and evaluated as an indicator of normal biological processes, pathological processes or pharmacologic responses to therapeutic interventions (NIH Biomarkers Definitions Working Group, 1998) (ii) Any substance, structure or process that can be measured in the body or its products and can influence or predict the incidence of outcome of disease. (WHO International Programme on Chemical Safety) In the context of a drug development application the definition can have a slight change in emphasis: (iii) Any characteristic that can be objectively measured and evaluated as an indicator of biological processes (either normal or pathogenic), or to evaluate pharmacological response to therapeutic intervention. (Boguslavsky 2004) 1 Biomarkers are considered less relevant reference points compared to clinical end points that reflect how a patient feels, functions or survives. However a biomarker may become a suitable endpoint if it has gained the status of a surrogate marker. A surrogate marker is variously defined, but in essence is a marker that whilst not necessarily directly linked to the event yet is considered to reflect the outcome of importance, e.g. blood pressure is not directly linked to a heart attack event but it may be used as a surrogate marker for this because it is a risk factor for cardiovascular disease. A surrogate marker is expected to predict clinical benefit (or harm or lack of benefit and harm) based on epidemiological, therapeutic, pathophysiological or other scientific evidence. The NIH working group points out that probably only a few biomarkers are likely to achieve a consensus surrogate end point status. Perhaps the distinction between a biomarker and its elevated status as a surrogate end point is the extent to which the observer can place the emphasis on its role to indicate, or to predict, the biological end point under study. A subtlety hidden within the definitions must be the empowerment achieved by statistical analysis behind any specific association identified between the biomarker and the clinical end point. The reality of any study is that there are many more potential analytes than may be considered relevant for the biological end point than can be effectively validated and measured. Thus the balance between physiological endpoints employed such as heart rate, blood pressure, bone density etc., and laboratory analysis end points such as blood glucose, cholesterol, calcitonin etc. not only reflect the art of the possible but the practical variability and accuracy associated with those measurements. The potential multiplicity of biomarkers is bewildering as it includes enzymes, structural proteins, protein breakdown products, peptides, amino-acids, vitamins and derivatives, enzyme and co-factor conjugates, antibodies, lipids, lipoproteins, hormones, coagulation factors, sugars, endogenous compounds and their metabolites, nucleotides and nucleic acid intermediates, and micro-organisms etc. Of course genetics adds a new layer of complexity with 30,000 genes and their variations, transcription splice variants etc. Nonetheless genomics and transcriptomics are on the new agendas for biomarkers. Historically the choice of biomarkers to study to predict an aspect of health has been influenced by the current scientific knowledge in the field. This process is therefore biased and hypothesis-bound and potentially misleading. As an example there has been recent published debate about the utility of screening for Prostate Specific Antigen (PSA) as a surrogate for prostate cancer. This association has held sway since the late 1980s but is now questioned as whilst there is a correlation between PSA levels and the presence of cancer it appears more informative when monitored through serial screening than one-off analysis. PSA is a surrogate marker for benign prostatic hypertrophy and inevitably has 2 caused unnecessary biopsies and is now recognised as an imperfect cancer marker. It can be argued that a non-directed approach, capturing new datasets may be a valuable methodology for complex disease. However this rather hands over well-being prediction to the power of an algorithm that sits in some interested party's computer somewhere. 2. The more the merrier For any study of well-being, forecast of future health, or disease state there are probably minimum health biomarkers that we all provide to our doctors in general practice, let alone for specific study. This may include age, height, weight, gender, blood pressure, smoking, allergenic status, current medication and exercise. But of course family history, and thus genetics is captured indirectly as well. Within studies related to disease sensitivity or progression the drive is to increase the evidenced-based statistical models to better predict the onset and progression of chronic and preventable disease every bit as much as for cancers. Historically a single biomarker would be studied to generate a range of relative risk factors for disease and outcome. Increasingly a number of these risk estimates from different relevant biomarkers can be combined to generate a multi-variable prediction. Thus for coronary heart disease (CHD) the analysis of, for example, exercise, fibrinogen, albumin, lipoprotein A, and homocysteine from various different studies would be combined to give a variable concentration range for each biomarker and its associated risk. These ‘composite’ biomarkers are claimed to predict a disease state, or a drug's effect on disease, through the effects on a panel of disease-related markers. Thus the strategy proposed to evaluate the efficacy of, say, a statin in preventing CHD would be to argue that a composite biomarker analysis is more accurate than any single biomarker. Composite biomarkers are possible with sufficient input data and acceptable assumptions. Critical assumptions may include that all the biomarkers are essential to the causal pathways to disease, and that the relative weightings of the component biomarkers accurately reflect their biological importance. The use of composite biomarker panels coupled to statistical approaches are claimed to combine as a new tool that can improve the discrimination power, precision and efficiency of underwriting practice. By detecting subtle changes in the physiology that precede disease the approach is argued to predict the onset of chronic diseases by up to 10 years in advance. Models exist for CHD, stroke, type 2 diabetes, colon cancer, lung cancer, breast cancer, prostrate cancer, osteoporosis and chronic obstructive pulmonary disease. Underwriting remains a mixture of subjective analysis and numerical rating systems but one can imagine 3 that the new composite biomarker approach is attractive if it can offer more consistency and accuracy in risk classification. It is unclear whether there is any meaningful distinction in the nature or type of biomarker that might be used as a predictor of disease and those that may be used as markers for drug efficacy. Simplistically if, for example, the concentration of a particular biomarker in the blood is tightly correlated with risk of heart disease, then the lowering of that same biomarker’s concentration might be a suitable target point to assess the efficacy of a new drug for the treatment of that disease. The insurance industry could equally use that biomarker concentration as a predictor of patient risk, or as an indicator of a suitable medicinal regime. That being said, there are biomarkers that are of interest to the insurance industry that are not directly associated with conventional disease or its treatment. Thus the measurement of nicotine or cotinin from oral saliva is used as a marker for smoking cessation, or lack thereof. Other tests may look at IgG antibodies to HIV in saliva, or for traces of drugs of abuse. The long history of biomarkers for disease prediction has evolved to a large extent completely independently of that of genetic analysis. However the blueprint behind the family history must become increasingly relevant as we become competent at teasing out the detail. With the completion of the human genome sequencing in recent times, and the co-evolved technology that can now capture this diversity in tangible approaches, it is to be expected that genetics will start to contribute more significantly to the understanding of disease prediction. 3. Current gene markers and insurance Amongst those who die before their 65th year inherited disorders are the fifth most frequent cause of death. Most of such deaths result from inherited heart disorders followed by anomalies of the central nervous system as well as urogenital and gastrointestinal anomalies. Terminology can be confusing with respect to changes in DNA sequence. Any change in the DNA sequence from an accepted reference norm is termed a polymorphism, although a subset called single nucleotide polymorphisms (or SNPs) require any such change to be commonly observed (greater than 1%). Only polymorphisms causing a disruption to a gene’s function, or to the protein it codes for, are considered as mutations. The consideration of any newly discovered DNA sequence polymorphism in a gene with respect to whether it is a mutation or not requires either, or a combination of, molecular modelling, clinical data or disease association in family studies. Thus as biomarkers for disease the emphasis has been traditionally placed on mutations with an established provenance. 4 At least 6500 genes are known in which mutations lead to monogenetic disorders. These range from the disorders affecting lifestyle such as colour blindness, or short-sightedness, scaly skin, albinism and deafness through to the life threatening syndromes predisposing or causing heart disease, muscle atrophy and cancers. Currently of the order of 1000 genes have molecular genetic analyses possible on these diseases. However only a small number of genes have common mutations associated with predictable disease progression. Cystic Fibrosis (CF) is often cited as an example with one mutation in the CFTR gene, the delta 508 mutation, being the basis of CF in ~75% or more of cases in white Caucasians. Commercial tests available for CF screening have been designed to detect this mutation and around 30 to 70 less common mutations. However approximately 1390 different mutations have been recorded in the CFTR gene to date. Thus in theory the application of one of the commercial test regimes will miss illness causing mutations. Full gene sequence analysis is currently still considered time consuming and expensive. Very few genes are sufficiently well characterised to the extent of cataloguing all observed mutations with clinical prognosis or drug-responsiveness. This lack of current knowledge about mutation and disease prognosis means that that it remains difficult to predict the value of a genetic screen or test in most cases. Within the current knowledge base one sees Huntington’s Disease associated with the specific expansion of a triplet DNA base sequence in the gene. Conversely, whilst BRCA 1 and BRCA 2 gene mutations are associated with predisposition to breast and ovarian cancers, without family association to the disease one would need to resort to molecular modelling to predict the consequence of any new mutation identified. Within a family there may be sufficient family history to provide a one-off association between a specific genetic change and the disorder but these familial cases represent only a sub group of the total potentially affected population. Statistical analysis from population data however may provide sufficient insight that the technical validity of any test may not need to be 100% for it to be worth considering from an insurance perspective. An often-cited example is the mutation in the HLA DP B1 gene associated with occupational immune-mediated chronic beryllium disease (CBD). The specific position 69 mutation has high sensitivity (~94%) but a lower specificity (~70%) with respect to CBD amongst individuals occupationally exposed to respirable beryllium. Such a test could be used as a required job placement screen, or to reduce the frequency of lymphocyte proliferation tests, or as a voluntary job placement screen. Interestingly despite the poor specificity of the test one probabilistic analysis considered that such a test has significant utility in avoiding cases of CBD on a value-for-information base. This analysis was in terms of risk management and cost reduction programmes and not in the context of the ethical considerations and instigation of appropriate health and safety precautions. 5 Many commentators on the current use of genetics and health/employment insurance quote the Burlington Northern- Santa Fe Railroad company that tested employees for a deletion in the peripheral myelin protein 22 gene associated with carpal tunnel syndrome. The apparent intention was to try and identify a predisposition and avoid compensation for workers who develop this syndrome. This dispute was brought before the USA’s Equal Employment Opportunity Commission who found in the workers favour. In Germany the Kaufmannische Krankenkasse insurance company offered all its ~2 m clients haemochromatosis mutations test so that they could receive early treatment for the disease. 67 customers of the 4000 that took up the offer were told they were at high risk of the disease. In this case the results were only given to the clients and the results were not provided to the company. More generally, there is limited information about genetic tests offered by insurance companies to their clients, or companies to their employees. An Australia Institute of Actuaries report surveying two years (2000 -2002) of genetic testing in the country reported a total of 235 applications with the majority being for haemochromatosis (170), then Huntington’s (22), breast cancer (10), CF(8), Factor V Leiden (5) and then a few tests for each of myotonic dystrophy, Familial AdenomatousPolyposis, colorectal cancer, polycystic kidney disease, and single tests for Marfan Syndrome, Non Polyposis Colorectal Cancer, Muliple Endocrine Neoplasia, Charcot Marie Tooth Disease, Prothrombin gene mutation, Epidermolysis Bullosa, Tay Sachs Disease, Spinocerebellar Ataxia and Tuberous Sclerosis Complex. Given the near-religious zeal of anti-genetic testing campaigners to scrutinise the life insurance sector it is unlikely that extensive genetic biomarker testing is being undertaken and hidden from the public eye. Thus, despite the significant number of potential genetic tests that could be employed at this time, it would seem a deliberate strategy to not utilise, or cautiously use genetic biomarkers so far. A sense of realism prevails that there are only a small number of highly predictive genetic tests that might be of utility. Further it implies that either the power of probabilistic calculations cannot sufficiently rectify any shortfall in predictive accuracy for many of the tests, or that the financial or ethical framework for life insurance, or employment insurance has not justified such an approach to date. Importantly most opportunities for genetic testing in the population - mostly associated with family planning, or limited by acceptance criteria for cancers – are still only rarely taken. Thus the anti-selection potential for the insurance sector, in which the individual takes insurance after an undisclosed test, is currently low. However one can expect things to change. A growing discussion with respect to the value of newborn screening programmes heightens the potential for these tests to figure in future life history disclosures. In addition, and perhaps of more immediate impact is the growing move to involve genetic analysis in drug 6 development programmes. The expected consequence is that we are approaching a future in which the drug prescription will be associated with a diagnostic test to establish efficacy and/or avoidance of severe adverse reaction. The weight of exploratory genetics being undertaken by the pharmaceutical and academic communities associated with drug discovery should create drug-gene mutation linkages that will transform the public application of tests. The occurrence of such tests, their prevalence and their impact on well-being could radically change the insurance perspective on genetic tests. 4. Drug discovery gene tests In March 2005 the US Food and Drug Administration published its long awaited Guidance for Industry on Pharmacogenetic Data Submissions. This Guidance clearly signals the FDA's interest to take account of pharmacogenomic data but starts from the premise that this science is relatively new and that aspects of its methodology may mean that for certain cases the information is not yet suitable for regulatory decision making. However, it declared that some pharmacogenetic tests, primarily those associated with drug metabolism, have well accepted mechanistic and clinical significance suitable for integration into drug development decision making. These tests are defined in the Guidance as known valid biomarkers and additionally it introduces a less well-defined category of probable valid biomarkers, and then reflects on the broader field of exploratory pharmacogenomics. The probable valid biomarkers are referred to as where there is an available body of evidence to support clinical relevance but this has not as yet been widely accepted or validated in the scientific community. Often presentations in this field will show lists of twenty or so CYP P450 and 'phase II' liver metabolism genes, and these may well constitute the 'probable' range along with other well cited receptor or drug uptake gene systems. When it comes to the more exploratory pharmacogenomics analyses this may often relate to data generated from micro-arrays scanning large number of gene markers for change in expression co-ordinating with disease progression, drug response etc. There remains some debate about the accuracy, reproducibility and comparability between different array systems. Thus the validity of such exploratory data may well need to have defined methodological and control specification, let alone identification of the pertinent gene loci that provide the diagnostic information. Thus, whereas the life insurance approach to genetic tests has been defined by utility to predict a clinical end point, the drug discovery utility of a gene test (or an expression signature of a multiplex array of gene loci) is wider including the prediction of a synthetic compound’s ability to influence a transition in a clinical process, or the avoidance of adverse reactions to a new chemical entity. These are not conventional biomarker endpoints and as such open up the potential list of relevant genes for scrutiny in an unlimited and haphazard way. The ability of a 7 genetic test to predict the efficacy of a drug will lead to a requirement for the typing to be undertaken to ensure the best patient prognosis. Equally the ability of a genetic test to predict the avoidance of an adverse reaction for a drug may soon be required so that doctors are seen to not be liable for inappropriate care in their prescription management. It is claimed that approximately 10,000 deaths a year in the UK are related to adverse drug reactions and even that it is the fourth biggest killer from a meta-study of deaths in US hospitals. One can imagine therefore that these tests could actually constitute tests that the life insurance industry would encourage their clients to consider. Inevitably some of these pharmacogenetic and pharmacogenomic tests will involve gene mutations that have links to apparently unrelated disease or biological trait. It may be that certain mutations might simultaneously predispose the patient to cancer from smoking, or behavioral alterations or some late onset ill-heath, etc. The future prevalence of these tests and their possible link to disease might upset the anti-selection balance that has kept the insurance industry relatively quiescent to date. 5. Gene information with biomarkers The drive to ever-more accurately define clinical end points must inevitably take advantage of the combination of gene tests and biomarkers. Thus, whereas P53 protein has been a marker for cancer for over ten years and variants are associated with about 50% of cancers, or the KIT tyrosine kinase is associated with a number of cell malignancies etc, the specific screening of genes associated with the cancer in question should identify specific genetic changes that can be used to predict the specific nature of the disease. Used in combination with databases, developed over time to link genetic changes to clinical prognosis or cancer markers etc, the genetic biomarkers will become invaluable for prediction of disease and thus essential to be undertaken as part of the study. Another example may develop from research into the performance of statins to lower cholesterol and other lipid chemistries which is identifying particular gene variants that affect statin efficacy. Thus the measurement of the conventional biomarker, such as cholesterol, with respect to cardiovascular disease could in the future be coupled with gene information in order to best tailor the dosage and type of treatment to be utilised. In essence one can expect that the systematic combination of biomarkers and gene tests, accompanied by good databasing of illness progression and clinical outcome, will dissect the broad clinical definition of any disease into many sub- sets, each potentially with a specific strategy of treatment. Through the progression and acceptance of genomic micro-array analyses one can foresee that a key marker for a disease state or prediction may be a gene 8 expression change in an essentially unknown gene, observed perhaps as a change in intensity of a fluorescent spot in a gene micro-array. Whilst the understanding of what this gene exactly does may take years, its use as a pivotal maker may advance far more rapidly than the understanding of its relevance, let alone its association with any other human trait. Thus the past emphasis on understanding the gene and its function before placing weight on its use may be superseded by the unknown gene’s use as a reliable marker. Importantly the application of genetic studies to complement those traditionally predicting disease through biomarkers alone will provide individual patient perspectives rather than just generalised disease stratification. However such analyses may also track genes and the disease into the family group, whereas previously the use of the biomarker alone and the family history record may be insufficient to provide such information. Thus whilst a blood pressure test or trend does not raise ethical issues for the patient’s relatives, the investigation of his/her Factor V gene for mutations associated with thrombosis certainly does. These are ethical issues that have dogged the use of genetics, but would now need to be considered in many studies of well-being if the use of genetic tests became commonplace alongside conventional biomarkers. 6. The future There are two parallel tracks of analytical development in play at this time which may influence the insurance agenda in the mid term future, say within the next five years. Firstly, whether driven by the FDA expectation, or public pressure, there will be an increasing use of specific pharmacogenetic tests for the public at large linked to disease diagnosis and efficacy of treatment. These will be increasingly associated with common disorders such as heart disease. These tests will not just be about well characterised mutations in drug metabolising gene activity, but will also involve gene mutations regularly associated with disease progression. In the context of pharmacogenetics it is unclear whether such tests will be considered predictive or diagnostic and that may be a definition that is pivotal with respect to insurance access to such data. In the UK a moratorium exists on predictive genetic tests until November 2011. However other genetic tests used to confirm diagnosis are considered equivalent to other clinical technologies. In this context Insurers are permitted to seek, with customers consent, access to certain diagnostic genetic test results along with other clinical and family data. Secondly the capabilities of the biomarker algorithms will continue to improve at predicting disease outcome from an increasing number of biomarkers in combination. In this context unidentified gene markers in arrays associated with illness or disease progression may become key biomarkers. These exploratory gene markers, or gene expression markers, may even become linked to a particular medication monitoring requirement. If the status of these tests is 9 categorised as a biomarker is it therefore considered as just clinical data for insurance purposes? On a longer time scale there will develop a detailed association between the multitude of specific mutations in any particular gene, their clinical consequences and their responsiveness to particular drug therapies. Within any gene it is probable that certain mutations will have high predictive value irrespective of other genetic or environmental factors, whilst other mutations are only weakly predictive of disease or prognosis. Thus the eventual complexity of biomarker and genetic information could leave the assessment of risk in the hands of the truly empowered and motivated, that is the insurance industry. The insurance industry to date has not acted generally in a rash manner and presently indicates it is too early to determine the implications of future genetic capabilities. The perhaps inevitable nature of the developments described in this paper would imply that no-one can remain complacent. Paul Debenham August 05 Post script This paper is written as a personal, rather than a professional, insight into the complex interplay of biomarkers, illness and insurance. 10
"Biomarkers and gene tests distinct disciplines now merging"