A decade ago, the term ‘personalized medicine’, as we understand it today, was introduced to the medical community. Despite the time that has passed since the term was introduced, there is still no universally accepted definition of personalized medicine; however, nearly all definitions have one expression in common, ‘the right treatment for the right patient’.
It is necessary to distinguish between personalized medicine and well-practised medicine. In the latter, the medical practitioner will not prescribe an incorrect medical treatment for his patient, whereas ‘personalized medicine’ refers to the tailoring of medical treatment based on individual characteristics and the genetic/genomic information of each patient.
Personalized medicine is the tailoring of treatment for defined subsets of patients based on their response to therapy and/or their risk of developing adverse effects. Over the past decade, significant improvement of genomic tools has greatly increased our understanding of the pathology of diseases at the molecular level; thus, several diseases, especially cancer, have been redefined at a molecular level. Today, the improved diagnostic criteria along with the rapid developments in molecular-targeted therapy offer several new strategies for targeted therapy.
The use of knowledge obtained from the genome and its derivatives (e.g. RNA, proteins and metabolites) guides treatment and/or medical decision-making. Standardization and integration of several vital elements into health care systems and – most of all – into clinical practice should be the starting point in reaching this aim. Several essential components are needed, including health risk assessment, family history, anamnesis and diagnosis, to reach a clinical decision that supports the complex risk and predictive information.
Health risk assessment is an element of personalized medicine and a standard method of evaluating an individual’s likelihood of developing chronic diseases (or disease events). Health risk assessment together with predictive models will certainly facilitate the assessment and prioritization of patients at risk of disease. Two attractive models have been introduced. First, the Framingham Risk Score, which is a gender-specific algorithm utilized to estimate the 10-year cardiovascular risk of an individual. This scoring used data obtained from the Framingham Heart Study that started in 1984. The model includes age, gender, total cholesterol, high-density lipoprotein cholesterol, smoking and systolic blood pressure.
Second, the Breast Cancer Risk Assessment Tool (Gail model) was designed at the US National Cancer Institute in collaboration with the National Surgical Adjuvant Breast and Bowel Project as a tool for health care providers. The formula calculates the risk of a woman developing breast cancer within the next 5 years, as well as within her expected lifetime (up to 90 years of age). It takes into account seven key risk factors for breast cancer, namely current age, age at first menstruation, age at giving birth to her first child, family history, number of breast biopsies, number of first biopsies showing hyperplasia and ethnicity.
In general, both models are of high clinical value, with special emphasis on personalized medicine. However, neither has been generally introduced as part of formal patient management plans because the evaluations lack the standards for both clinical data and the algorithms used.
Family health history is a simple and, so far, unrecognized tool that can be utilized to deliver personal health risk information. Family health history may connect the combination of shared genetic, environmental and lifestyle factors and integrate genetic/genomic risk information into a patient treatment strategy. Family health history assessment would constitute a step towards identifying persons at higher risk for disease, enabling pre-emptive and preventative strategies, including lifestyle changes, health screenings, testing and early prophylactic treatment if possible. The assessment and integration of family health history has not yet reached health care providers. To enable a robust family health history assessment, we should establish feasible standard collection protocols, easy access for health care providers and, most important of all, clinical guidance for interpretation and use. At present, data collections are mostly incomplete, difficult to interpret and variable in content among different health care providers. The integration of clinical decision tools with family health history systems will be an essential step in the advancement of personalized medicine.
Genomic information refers to the sequencing of the human genome, which has resulted in the discovery and classification of a variation of sequences among different populations and individuals, both healthy persons and those with various diseases. At present, 10–15 million common sequence variants of sufficient frequency (minor allele frequency of at least 5%) have been reported. Furthermore, the rare variants that are found in a few individuals will be accessible only by direct genome sequencing.
The integration of information obtained, from both individual genome sequences and information from relevant biomarkers in the expressed genome, with health system data for individuals will be crucial when including genomic information with personalized medicine.
Clinical decision making in combination with the new genomic era has opened up a wealth of possibilities for health care providers. However, it has remained underused until recently, similar to any new medical invention. One of the drawbacks is the long time required for clinical research results to be routinely implemented in clinical practice. Moreover, genomic interventions may be more difficult to implement in clinical practice than other traditional medical interventions owing to several factors, including limited familiarity of the clinician with genomics and the complexity of underlying data, that may need to be considered.
Application of personalized medicine
Susceptibility to disease, and risk of developing several diseases, can be quantified and anticipated well before the onset of disease. Even at birth stable genomics (e.g. DNA-based methods) can be utilized. DNA-based measurements usually do not change over a person’s lifetime. However, other methods, including metabolomics, proteomics and epigenetics, are dynamic and interact with and respond to environmental stimuli, lifestyle, diet and pathogens. Within the next few years, transcriptional profiles, protein expression patterns and levels of metabolites combined with dynamic multimodal imaging will provide precise models for screening individuals who are at high risk of developing a disease by observing early molecular manifestations, i.e. at subclinical status.
At the same time, the choice of treatment will be guided by the genetic background of the patient together with the molecular architecture of the disease.
Over the past few years, several studies have shown that women who carry mutations in either BRCA1 or BRCA2 (breast cancer, early-onset genes) have a high risk of developing breast and/or ovarian cancer. The current recommendation is that carriers of these mutations undergo genetic testing in order to provide a basis for decisions about surveillance or even surgical approaches to avoid cancer development.
Individuals in families with a history of colon cancer may be tested for genes such as MLH1 and MSH2 (DNA mismatch repair genes) that may identify those at high risk of developing colon cancer. Several other genome-based studies have identified genetic risk factors for common chronic diseases, including diabetes and heart disease, and other types of cancers.
One medication that has been subjected to targeted pharmacogenomics is warfarin. The oral anticoagulant is prescribed for long-term treatment and prevention of thromboembolic disorders. Warfarin is associated with a variety of complications, even after dose adjustment according to age, gender, weight and disease. Pharmacokinetic and pharmacodynamic studies of the drug showed that two genes are relevant in the determination of warfarin levels. One of these genes (CYP2C9) encodes cytochrome P450 2C9, which is responsible for 80% of the metabolic clearance of the more pharmacologically active S-enantiomer of warfarin. CYP2C9 is a polymorphic gene; up to 10-fold variation in clearance was reported. The second gene that can be a predictor of warfarin dosing is vitamin K epoxide reductase complex protein 1 (VKORC1). A consideration of the VKORC1 genotype or haplotype together with the CYP2C9 genotype and other factors, including age and weight, may account for 35–60% of the variability in warfarin dosing requirements. Several clinical studies are ongoing to establish whether the initial dose may be tailored to patients using CYP2C9 and VKORC1 genotyping.
The most successful examples of genome-based therapy involve targeted therapy in cancer. Two well-established genomic-based treatments of cancer pathology have moved cancer research into an era of personalized medicine or individualized therapy, namely trastuzumab (Herceptin®, Roche Products Ltd, Hertfordshire, UK) in breast cancer and imatinib (Glivec®, Novartis Pharmaceuticals, Surrey, UK) in chronic myeloid leukaemia (CML).
Tyrosine kinase, which binds to human epidermal growth factor (HER-2) receptor, is known to be amplified in approximately 20% of invasive breast cancers. The use of trastuzumab together with a diagnostic test for HER-2 overexpression has become an important therapy option in both the adjuvant and metastatic settings. Today, trastruzumab has become a hallmark for personalized medicine within the medical community.
The second successful example is imatinib in the treatment of CML. The Philadelphia chromosome is the product of a translocation between the long arms of chromosomes 9 and 22. It represents the first linkage of a molecular rearrangement with a specific disease. BCR–ABL is the fusion protein created as a result of this translocation, and it can induce a myeloproliferative disorder in CML. Imatinib is an inhibitor of the ABL tyrosine kinase and has become the first-choice treatment for CML. Given the specificity of the Philadelphia chromosome for CML, the translocation is used for diagnostic and therapeutic monitoring of response to imatinib.
Progress towards personalized medicine is ongoing. Today, there are a number of examples in which personalized medicine is influencing clinical decisions and shaping the modern health care system. At present, the progress in oncology is rapid, which is probably a result of the poor clinical outcome in cancer therapy. However, success outside of oncology is still limited. Several doubts have been expressed about whether or not personalized medicine will be able to deliver on its promise and/or whether or not the adoption of molecular diagnostics will ever be economically viable. Today, the personalized medicine concept has been formed. The next few years will show whether or not it is the right concept.