Technologies and methods
Sequencing-based genomic technologies, which mainly employ traditional Sanger Sequencing technology (capillary electrophoresis), are not new to the clinical laboratory and range from infectious disease to cancer diagnostics. Like other molecular diagnostics, Sanger Sequencing enables the analysis of specific gene sequences for identifying a pathogenic agent, predicting or monitoring disease, selecting treatment options, and determining a treatment’s efficacy. Examples include the sequencing of the human immunodeficiency virus (HIV) to monitor the susceptibility or resistance to HIV antiviral medications, the sequencing of the cystic fibrosis (CF) gene or sequencing of the BRCA1 and BRCA2 genes in breast cancer. NGS-based technologies have a great potential to identify, classify and analyze disease sub-types, in particular in the elucidation of the contribution of rare, highly penetrant mutations to the genetic architecture of complex disorders, for instance in the monogenic form of morbid obesity and Type 2 diabetes. Such knowledge is very valuable in the delivery of personalized medicine
Also, for rare and orphan disease in general, NGS- and biomarker-based analyses can help to identify relevant loci in disorders and thus might stimulate drug development in this area.
In oncology, NGS-based systems will allow for the rapid sequencing of tumor genomes to direct cancer treatments, pathogen genomes to determine drug resistance, or cell-free DNA to identify chromosomal abnormalities in pregnancy, and offer undisputed advantages over Sanger Sequencing. The time involved to generate and analyze genomic data in a medical context represents one of the major challenges en route to apply NGS technologies as a standard tool in clinical practice. However, as technologies develop and progress, sequencing at a cost of US$ 1,000 might be possible in the near future. If genomic data feed clinical decision-support systems to evaluate the best possible therapeutic intervention, then these results should ideally be available within a short timeframe, i.e. rather within a few days than weeks. To achieve this, the steps performed during the analyses, which include library preparation, sequencing runs and the integrated data interpretation, must be fast, reliable and fully automated. The integrated and advanced analysis of genomic data comes along with the collection of such data; automatic analysis software for detecting somatic mutations is being developed and is tested for its sensitivity, specificity and accuracy. The challenge lies in assigning the biological significance of each somatic mutation and integrating each data type, viz. genomic, epigenomic or transcriptomic, to predict the biological impact. The biological understanding of the human genome is far from complete and although somatic mutations and aberrations may be detected, the information would not be informative to the clinicians if the genes affected have not been studied so far or if the pathways they are involved in are unknown.
Another challenge is the starting material required to generate the sequencing libraries. Samples with a limited amount of starting material, e.g. from core biopsies, are beyond the scope of the most thorough genomic analyses. For transcriptomic libraries, typically a depletion of the ribosomal RNA (rRNA) is required to allow the signal from expressed genes to be detected and such depletion does not work well with degraded RNA, affecting mostly formalin-fixed samples. Normal DNA can be derived from blood samples; however, gene expression and epigenomic variation differ significantly in different tissues. Thus, adjacent healthy tissue is required to determine the default and “normal” status of the gene expression, epigenome and trancriptome. Depending on the size and location of a specific tumor, this could be very challenging.
Predictive, preventive, personalized and participatory medicine
As a result to all current efforts to translate the concepts of personalized healthcare into the clinic, personalized medicine will become participatory and this implies patient decisions about their own health. As a prerequisite, such a new paradigm requires powerful tools to securely handle significant amounts of personal information with the approach introduced as P4 medicine that is predictive, preventive, personalized and participatory. The main benefits from P4 medicine include enabling the design of drugs based on proteins and RNAs molecules (Rossbach, 2010) associated with genes and diseases, developing therapies that are highly targeted to specific diseases in order to maximize the effects of therapeutic interventions while minimizing side-effects or damaging healthy tissues. Avoiding trial and error phases through better and truly personalized drugs, using genetic profiles to identify the best possible drug and therapy for a given patient and reducing adverse effects are instances of personalized medicine goals, will benefit not only patients, but the public healthcare systems in general.
The P4 medical approach will also help to select the right drug for the right patient at the right time, avoiding that valuable drugs are prescribed to unsusceptible patients and preventing potentially harmful side-effects (Figure 4).
In this regard, novel genome-based diagnostic technologies offer a significant progress in medical practice in comparison with the current prevention methods; a combination of genetic knowledge and clinical studies is expected to impart a significant advance toward preventive medicine and subsequent prospective medicine.
Genomic literacy: education and counseling
Large-scale sequencing project in cancer medicine are crucial since cancer is a highly complex genetic disease and the hereditary causative mutation of a combination of mutations is only know for a very small number of familial cancers. As such, NGS approaches translated into clinical practice have the potential to unravel some of this genetic complexity. Such knowledge can provide genetic counselors with information that is required to feed clinical decision-support systems when selecting the best possible treatment plan and therapeutic intervention for an individual patient. Also, such data would provide the information necessary to inform family members of potential risks of a disease and to ensure that these family members have access to the most appropriate screening programs and preventions. The knowledge gained also leads to the development of more preventive rather than curative therapies.
Economic aspects
Along with the discussions of translating genomic knowledge into clinical practice come considerations of the economic value, namely careful assessments of benefits, risks and costs (Collins et al., 2003; Haga and Burke 2004). With regards to the cost-effectiveness of different genomic screening strategies, more evidence is needed since most analyses so far do not take the impact of testing on quality of life into account. Assessments should consider the complexity for screening for genomic aberrations, including the development of reliable algorithms for integrated data analysis. Incorporating subgroups or family members of affected patients, should also be considered. To assess the value of personalized medicine, it is important to define approaches that consider both diagnostic tests and the treatment with drugs. Genome-based diagnostic tests should elucidate whether the detected mutations are relevant to the given phenotype and if this phenotype can be linked to clinical predictions. From a market’s perspective, the diagnostic market represents a large and fast growing opportunity for NGS, much greater than the currently addressed life science research market, which is mostly driven by biopharmaceutical research and development (R&D) or government funding. However, the diagnostics market also entails much larger regulatory and reimbursement hurdles. Thus, to successfully translate NGS-based systems into clinical practice, it will be crucial to develop valuable, user-friendly, reliable, and cost-effective diagnostic tests. To facilitate routine testing in clinical practice and laboratories, the market is demanding cost effective and simple-to-perform tests that have cleared the many regulatory hurdles. Also, automation is playing a key role in the development of diagnostic tests that are easier and less expensive to operate. Given the re-emergence of infectious threats, viz. multidrug-resistant TB, new strains of HIV or H1N1, infectious disease testing represents a large portion of the current market for molecular diagnostics. Here, pharmacogenomics will likely be the most immediate new opportunity in the field and many pharmaceutical companies invest significantly in pharmacogenomics in anticipation of shaving years off the drug discovery and approval process, bringing potentially lucrative drugs to market much sooner (Constance, 2010).
In regards to personalized cancer diagnostics, the molecular oncology diagnostics sector is expected to grow at a compound annual growth rate (CAGR) of approx. 18% per annum; through 2015, the molecular diagnostics market will grow at double-digit pace, achieving an overall 14% CAGR to meet increasing demand for personalized medicine. Key areas of growth include not only oncology an infectious diseases, but also genetic testing and biobanking with a wide variety of drugs in late preclinical and early clinical trials being targeted to disease-specific gene and protein defects that will require co-approval of diagnostic and therapeutic products by the regulatory authorities.
Ethical aspects
Ethical issues come along with the advancements and progression of translational genomics and the debate is mostly about discrimination, confidentiality and data security or informed consent. Unlike most diagnostic tests, whole genome sequencing allows the identification of an individual and related family members. Thus, concerns regarding data security and privacy are raised and access to genomic patient data must be restricted to the use in clinical practice.
As discussed, there are both substantial challenges and benefits in translating genetic results about disease susceptibilities or genomic data into clinical practice. Studies on individual (cancer) genomes and genetic susceptibility produce large volumes of biomedical data on a wide range of phenotypes. The impact of genetic data can extend well beyond clinical utility as there may be emotional, rather than practical benefits and risks of genetic risk information if provided to a patient; the emotional meaning may be more relevant for very large amounts of genetic information and information on susceptibility and risk, rather than prediction/diagnosis and people may react very differently to this kind of data, based on personality/coping strategy and the context of the information. Thus, patients should be encouraged to consider the range of results, how they might cope, and the range of possible actions/reactions before performing NGS-based diagnostic tests. As genetic studies on susceptibility to disease proliferate, it s necessary to demand replication and understanding of population attributable risks of significant findings. As patients and consumers seek out genetic profiling and will ask for such profiles, physicians need to understand the clinical validity and limitations of the data for making health-related decisions and should be trained to interpret the complex results of personal genomic sequencing data.
We need well-designed studies about how patients/consumers are affected by genetic data, especially large amounts of data, in order to understand the risk and benefits.
Another aspect is the equality of access in relation to further delivery of personalized treatments since novel drugs in this particular field are very expensive and ubiquitous reimbursement schemes are not in place in most countries yet.