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Commentary
Predictive medicine and biomarkers: the case of rare diseases “With the aid of translational bioinformatics, the construction of molecular networks and pathways relevant to specific rare disorders is increasingly possible. Bioinformatic analyses of data from gene-expression arrays, proteomics studies and clinical observations on patients with RDs can define signatures of fundamental disease mechanisms.” KEYWORDS: ’omics n personalized medicine n rare diseases n therapeutic approach
Rare diseases & personalised medicine: the state of art For decades, geneticists have had only one way to find the genes underlying Mendelian disorders, through the study of the members of a family, where diseased individuals were primarily identified by symptoms and phenotypes. However, this approach does not apply to mutations that occur spontaneously and few affected individuals can be found, or the disease is rare. In addition, of course this approach does not take into consideration the complex interactions between the genome and the endogenous/environmental agents. The use of bioinformatic approaches would facilitate the development of new therapeutic algorithms aimed at harmonizing such complex information into a useful tool for health operators. In the postgenome era the ’omics technologies (e.g., genomics, pharmacogenomics, proteomics, epigenomics, interactomics, metabolomics and so on) enable innovative approaches in diagnosis, drug development and individualized therapy. According to the definition adopted by the European Community (Regulation EC 141/2000), rare diseases (RDs) are characterized by a prevalence lower than five out 10,000 inhabitants. As reported by Palau in this issue RDs are estimated to be approximately 7000–8000 different conditions, the majority of which are of genetic origin [1] . The others may be owing to multifactorial determinants. They are characterized for being severe, chronic, progressive and therefore they constitute a threat to survival. RD patients face a lack of access to correct diagnosis, information and public awareness, scientific knowledge and expertise, research, therapeutic development, appropriate healthcare and high
costs for most of the few existing drugs, inequalities in access to treatment and care, and lack of specialized social services. In the wide spectrum of RDs, a good level of knowledge is limited to only approximately 1200 of these and in general diagnosis is very slow. New approaches are now focusing on the concept of personalized medicine, whereby each patient would be able to receive the appropriate individual treatment based on his/her personal genetic and metabolic background. RDs are currently becoming targets for such new approaches, owing to the view that individual susceptibility might now be explained by the subject’s genetic background, whether mono- or poly-genic, and by epigenetic changes. Based on this paradigm, a massive increase in new scientific data, generated mainly by different types of novel high-throughput technologies, is resulting in a great amount of traditional phenotypes being split into different diseases, on the understanding that these would have different risk factors, different risk prognoses or, at the very least, different inherited mechanisms. Epidemiology could take advantage from such new approaches, thus playing its role in RD research by finding solutions to the lack of descriptive knowledge and proposing methods for analyzing risk and prognostic factors, drug efficacy and efficiency, and social modifiers of disease. Without this type of knowledge, prevention would not – apart from some sporadic exceptions – be achievable. The integration of data-dense information from the different ’omics platforms at the individual and population levels is an essential step to reap the benefits of ’omics technologies for
10.2217/PME.12.17 © 2012 Future Medicine Ltd
Personalized Medicine (2012) 9(2), 143–146
Domenica Taruscio Author for correspondence: National Centre for Rare Diseases, Istituto Superiore di Sanità, Viale Regina Elena, 299-00161, Rome, Italy Tel.: +39 064 990 4016 Fax: +39 064 990 4370
[email protected]
Marco Salvatore‡ National Centre for Rare Diseases, Istituto Superiore di Sanità, Viale Regina Elena, 299-00161, Rome, Italy ‡ Author contributed equally
Armando Magrelli‡ National Centre for Rare Diseases, Istituto Superiore di Sanità, Viale Regina Elena, 299-00161, Rome, Italy ‡ Author contributed equally
Rosella Tomanin Laboratory of Diagnosis & Therapy of Lysosomal Storage Disorders, Women’s & Children’s Health Department University of Padova, Padova, Italy
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healthcare. A key success factor will be the establishment and the harmonization of databases and bioresources, including standardization and quality control aspects of the data and samples collected. Applying ’omics approaches in a chosen group of RDs should help in understanding the clinical heterogeneity of certain individual RDs, as well as help to reveal pathophysiological commonalities between clinically disparate RDs. Collaboration between clinicians and ’omics scientists will hence be vital for improving the interpretation of clinical data and, in particular, the definition of harmonized ontologies. In addition, with the aim to support future clinical trials based on the most recent technologies, appropriate in silico, in vitro and/or in vivo models should be used and developed, focused on obtaining: Deep phenotyping of patients, including use of ’omics technologies for better understanding of disease allowing the development of novel diagnostic tools and treatments; Development of the relevant technologies and software for adequate translation of the information obtained to a clinical setting for diagnostic or screening purposes; Appropriate quality control, standardization and statistical treatment of data must be addressed; Reference ’omics profiles of diseases should be established, to set or confirm a diagnosis; Development of appropriate preventive or therapeutic personalized interventions. With the aid of translational bioinformatics, the construction of molecular networks and pathways relevant to specific rare disorders is increasingly possible [2,3] . Bioinformatic analyses of data from gene-expression arrays, proteomics studies and clinical observations on patients with RDs can define signatures of fundamental disease mechanisms [4,5] . Integration of this information with signatures of drug activities or therapeutic responses could intuitively promote discoveries related to the etiology, pathogenesis and treatment of unclassified or poorly identifiable disorders. As it has recently been demonstrated, individualized therapeutic approaches have become a realistic perspective for therapies that are more effective and less prone to side effects and economically reasonable, as in the case of chronic liver disease. With the aim not only to expand the current knowledge base through basic research on the underlying disease processes and treatment options, but also to identify and characterize 144
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biomarkers, the creation of genetic fingerprints for individualized diagnosis, prognosis and treatment of patients is becoming an important tool for translational medicine. For certain liver diseases personalized therapy approaches already exist. Examples are the determination of viral genotypes, viral kinetics and genotyping of the IL28B polymorphism to optimize the treatment of chronic hepatitis C [6] .
Use of biomarkers in personalized medicines for RDs An important step to speed up personalized medicine for RDs involves the identification of biomarkers to monitor diagnosis and responses to therapy. Biomarkers have multiple uses, in fact they also represent an innovative approach to identify new therapeutic targets and to develop drugs for RDs. Developing and validating biomarkers is not a trivial undertaking even for common conditions, but it is highly relevant for RDs and warrants concerted attention, as summarized in the recommendations on biomarker evaluation in the Institute of Medicine report [101] .
“RDs are currently becoming targets for such new approaches, owing to the view that individual susceptibility might now be explained by the subject’s genetic background...”
Biomarkers play a critical role in disease diagnosis and treatment, especially for the early detection of cancer, to enable screening of asymptomatic populations. Recent ’omics technologies, such as transcriptomics, proteomics, metabonomics and others, are importantly accelerating the rate of biomarkers discovery [7] . Several research initiatives are investigating biomarkers for RDs, for example, Huntington’s disease [8] , pulmonary arterial hypertension [9] , Hailey–Hailey disease [10,11] , hepatoblastoma [12] and multiple osteocondromas [13] . However, the approaches for validating biomarkers have yet to be addressed clearly and the gap between discovery and clinically validated biomarkers has significantly increased. Biomarker validation for cancer prediction refers to the confirmation of accuracy, reproducibility and effectiveness of biomarkers in detecting cancer [14] . The validation of clinically useful biomarkers from high-dimensional ’omic experiments poses great challenges to the scientific community. The major challenge for biomarker validation is the high degree of variability of biomarker levels across the human population and the future science group
Predictive medicine & biomarkers: the case of rare diseases
considerable molecular heterogeneity of individual RDs [15] . In order to validate panels of candidate biomarkers in population-based sample collections, novel detection technologies with high throughput, high precision and high sensitivity, but low sample consumptions are required, especially when a personalized approach is applied to RDs. The rapid emergence of new biomarkers, the enormous costs for validating them, the pace of validation and the choice of which biomarkers to validate are currently dominated by commercial considerations. Because putative biomarkers that are discovered are not useful without clinical validation, a major bottleneck occurs. So far, commercial considerations regarding biomarkers also distort the science. Vendors of biomarkers invest in creating knowledge bases that annotate the results of their assays. These knowledge bases informed and improved with data derived from the clinical use of the tests, define the utility of the test in the particular clinical setting and become the intellectual property of the biomarker vendor. These databases are not available to the scientific community and therefore cannot be replicated or used for accelerated discovery and validation [16,17] .
“Applying ’omics approaches in a chosen group of RDs should help in understanding the clinical heterogeneity of certain individual RDs, as well as help to reveal pathophysiological commonalities between clinically disparate RDs.” On the other hand, the scientific community that would have the potential to set up functional and integrated public biomarker databases cannot compete with companies from the economical point of view. Thus, big institutions, such as governments and public agencies, should invest economical energies for the identification of molecular biomarkers, receiving a return in terms of money savings for public health, considering that at the moment approximately 90% of drugs currently on the market work in only 40% of individuals [16] , therefore turning out to be useless to the other 60% of the treated population. At the moment the progression towards the primary step of the strategy, which is to generate validated data available to the medical–scientific community and, in particular, to the clinicians, is difficult to realize without the availability of public databases available, under controlled access, to health operators and patients’ associations. More importantly, beyond availability of future science group
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databases, energies must be directed towards the cross-validation of data and to the generation of new software cross-connecting all data, thus providing new tools for faster and more reliable laboratory and clinical diagnosis as well as personalized therapeutic interventions. As stated during the recent ‘Personalized medicine for the European citizen – towards more precise medicine for the diagnosis, treatment and prevention of disease (iPM)’ summit (18–20 October 2011, The Hague, The Netherlands), RDs represent, in a way, the tip of the iceberg, symbolizing common diseases and RDs as a continuum, and they are so numerous that estimation from the EU are even higher than between 27 and 36 million people affected, due to a larger population base. Therefore, as a paradox, RDs are so individually rare, but so close to each of us. The main challenges in biomarkers identification for personalized medicine in RDs, which might be partly common to other disease categories and partly specific are: to develop integrated networks of information for specific phenotypes (holistic approach) and validate them for individualized therapies; to identify more biomarkers required to lead intervention in the presymptomatic phases [16] ; to raise and increase our awareness of RDs, improving clinical and molecular diagnosis, which would allow an early application of the available therapeutic interventions; the need to identify new therapeutic targets and noninvasive therapeutic technologies for a group, as RDs, which has been often disregarded by pharmaceutical companies; the need to specifically guarantee RD data protection, considering that for very rare diseases it might be quite easy to recognize the patients. RDs can be considered as a benchmark for testing models and can help us understanding other diseases, thus leading the way to personalized medicine.
Conclusion & future perspective The ’omics studies coupled with in silico approaches have great potential in identifying biomarkers of pathogenesis, as well as indicators of therapeutic efficacy. In the cancer field, in which this technological approach has been so far most widely applied, genes regulating drug response in vitro have been identified and they may provide the first step in the discovery of predictive biomarkers to stratify patients for distinct personalized therapeutic intervention. However, although these findings may allow for the development of predictive biomarkers of treatment efficacy, heterogeneity of RDs will www.futuremedicine.com
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limit the effective treatment of this disease, as has been clearly stated in the case of rare tumors [18] . The fulfillment of a personalized approach to diagnosis and treatment of pathologies goes mostly through the application of the new ’omics technologies to the analysis of ‘old samples’, in other words, only the availability of these new technologies might make possible the personalized approach to medicine, providing in depth registrations of extremely complex molecular scenarios. In the postgenomic era, the progressive reduction of timing and costs of analysis has importantly encouraged the application of these technologies. This is progressively leading to the collection of huge amounts of data, which are now facing a main problem: importantly, we still lack methods for data interpretation. Thus, the gap between available data and ability of interpretation, identifying useful and reliable biomarkers, is exponentially increasing. At the moment this remains the most relevant limitation and much effort and determination will have to be invested to quickly narrow this gap.
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Finally, healthcare would shift from ‘treatments of last resort’ to a system that emphasizes disease-risk prediction, prevention and early therapeutic intervention. RDs would be profiled and end up as many molecularly defined subtypes, each with its own tailored, highly efficacious therapy. Genotyping and molecular profiling would be used to restore drugs that had been dropped because of a poor therapeutic index. Genetic polymorphisms linked to variability in patient responses would allow drug dosage to be tailored according to understood disorders. Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
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