Today’s tools are transforming the impact of molecular data on patient and population health.
Molecular and genetic diagnostics have come a long way in a very short time. We can now sequence an individual’s genome in a fraction of the time and cost required only a decade ago. This opens the market reach significantly by providing access to the full view of a patient’s medical history and specific genomic information about the disease to be treated. Physicians can now provide the highest level of personalized care with the highest potential for successful outcomes.
The sad reality is that many of the illnesses to be treated have developed resistance to the go-to treatment options or many sick patients risk drug-drug interactions. The drug-resistant strains of bacteria create treatment challenges for healthcare practitioners all over the world. A possible solution to this problem is the use of advanced genetic methods to determine if a specific organism is resistant to the proposed treatment, allowing physicians to prescribe the treatment that makes the most sense for that particular strain.
In order to provide this kind of tailored treatment, modern genetic techniques including next-generation sequencing (NGS), whole genome sequencing (WGS) and a robust interconnected data repository are employed to assist the physician with diagnosis and treatment options.
General Drug Resistance
Since before Alexander Fleming discovered the benefits of the antibacterial compound penicillin, medical science has been working tirelessly to create and develop drugs to aid in the treatment of human ailments. For many years, these life-saving compounds proved effective allies in treating patients. Recently, researchers have discovered that some antimicrobial, anthelmintic and antineoplastic drugs have reduced effectiveness in curing certain diseases and treating certain conditions. This has led to the discovery of drug-resistant strains of what were historically treatable illnesses and diseases.
Some bacteria are not only capable of altering the enzymes targeted by antibiotics, but use enzymes to modify the antibiotic itself removing its capacity to harm the bacteria. Examples of target-altering pathogens include Staphylococcus aureus, vancomycin-resistant enterococci and macrolide-resistant Streptococcus. Examples of antibiotic-modifying microbes include Pseudomonas aeruginosa and aminoglycoside-resistant Acinetobacter baumannii.
At this point in our drug development cycles, the microbes are ahead of us for being able to advance their mutations making them resistant to drugs made to cure patients. Without sufficient funding to develop alternatives, the acquisition of drug resistance by pathogens will continue to be one of the more significant public health issues of our time.
NGS to Detect Drug Resistance
It’s not all doom and gloom. Next-generation sequencing marks a huge technological advancement in biological and medical science. NGS, also called “massively parallel sequencing” or “deep sequencing,” refers to a DNA sequencing technology that allows for the sequencing of millions of small DNA fragments at the same time. The resulting data pool is massive and allows for much quicker access to targeted information. The data pool can reach gigabytes in size, roughly a billion base pairs of DNA. What took 10 years of Sanger sequencing can be accomplished in two weeks by a single instrument or in a single day using multiple instruments dedicated to the task. NGS technologies have gotten to the point where one can completely sequence an entire human genome in a matter of days. Even better, the costs for individual whole genome sequences are on par with single DNA laboratory testing when NGS is used with high performance computing and other bioinformatics tools for big data analysis.
There are currently several NGS platforms using various sequencing technologies. All NGS techniques sequence millions of fragments of DNA in parallel. Bioinformatics analyses are used to piece together the fragments by mapping the individual reads to the human reference genome. The bases in the human genome are sequenced multiple times to ensure data accuracy. These processes can be used to sequence entire genomes or specific areas of interest down to individual genes.
In clinical settings, cancer research has embraced NGS in an effort to catalog mutations in many types of cancer. This will allow for new diagnostic methods and could lead to more successful treatment options. The accuracy, speed and ever-decreasing cost per sample make NGS the ideal platform for routine diagnostic screenings. NGS as an infectious disease diagnostic tool can reinforce accurate and rapid identification of infectious diseases in individual patients. Additionally, in times of public health crisis, NGS of the population at large can assist in disease surveillance and tracking.
In the same vein, the technology can be applied to use a targeted drug against the threat instead of broad spectrum antibiotics that can lead to drug resistance. As NGS is able to identify pathogens that may be missed in current standard culture assays and NGS has the sensitivity to distinguish between different pathogen strains, detect co-infections and uncover new pathogens so that treatments can be modified as needed to combat a specific and/or widespread threat.
Whole genome sequencing data generated by NGS techniques has been used successfully to detect drug resistance factors. This can be seen in Methicillin-resistant Staphylococcus aureus (MRSA), encoding a divergent mecA gene and developing the mecC MRSA, which has been shown to be resistant to virtually all β-lactam antibiotics. Another example of drug resistance is AmpC Beta-Lactamase in Escherichia coli, which has reduced susceptibility to amoxicillin-clavulanic acid, piperacillin-tazobactam, meaning treatment of infection, needs to rely on other antimicrobial drugs such as those based on Carbapenems to fight E. coli with this mutation. The rapid detection of individual strains of disease allows for the best treatments to be applied, subsequently providing a faster cure. There are other uses for drug resistance screening, such as for HIV, that are resistant to antiretroviral therapy, gene mutations in Leishmania and drug resistance in malaria.
Data suggests over two million people in the United States alone contract antibiotic resistant infections each year, which adds a significant fiscal burden to an already stressed healthcare system due to the longer treatment times, extended hospital stays and increased mortality rates. Drug-resistant organisms including vancomycin-resistant enterococcus, carbapenem-resistant enterobacteriaceae, and drug-resistant Neisseria gonorrhoeae pinpoint the need for efficient detection technologies that allow for effective treatment methods. Rapid detection and laser accurate treatment are the best tools in eradicating drug-resistant strains of diseases.
Personal Patient Perspective
The tools are available to provide an in-depth look at an individual’s health. Over time, changes and mutations can be logged and studied, providing the most precise treatment for a person. Clinical genomic data is no different than other complex medical data and should be handled in the same manner. The results of NGS laboratory tests should be read and interpreted by professionals (e.g., molecular geneticists, clinical geneticists, gene counselors, etc.) who have the understanding to interpret the results and deliver them to the patient and other care-providers so patients can make informed decisions.
NGS methods are not simple, the resultant data generated can be huge; the computer programs and data banks that analyze the data are complex; the physician reading the results needs to have a deep understanding in order to simplify it to clinical actions—or you need an LIS that can integrate with a big data vendor with the expertise and knowledge to take automatically delivered test results, run the intelligent algorithms applicable to that result and return the specific treatment profile recommended for that patient to the LIS for real-time use. Because NGS genetic tests have the ability to generate findings outside the realm of the target area, the question arises as to what data should be shared with the patient. All data is important and should be shared with the patient in a way that the most important results are prioritized and communicated by their healthcare professionals—ideally, a team approach.
By its very nature genetic findings may impact people other than the patient. At the recent AGBT conference, it was discussed that, during the NGS of free cell DNA (cfDNA) for cancer, the patient carried the markers for Huntington’s disease, which lead to two other members of the same family, different generations, also being diagnosed with this degenerative disease. NGS can be a powerful tool, but what information should be provided to the patent, their relatives and their physicians? The use of the liquid biopsy (circulating cell-free DNA)—a technique that is currently being used to non-invasively detect, monitor and treat some cancers—can be employed in the identification of mutations associated with acquired drug resistance. The use of cfDNA has the potential to speed up clinical decision making, monitor infection changes and assess treatment results. All in all, running NGS on cell-free DNA provides a snapshot of a person’s health that would not be possible without the volume of data provided by sequencing to compare and analyze the vast known genetic data that gene banks have made available.
Intelligent Data Exchange
In the current clinical setting, medical tests are ordered, samples are collected, clinical tests are performed and results are delivered. The use of computers to facilitate this process has helped reduce errors, decrease result reporting time and create a data repository. The backbone of this system is the laboratory information system (LIS), which receives the test orders in one of many ways, including manually via paper requisition, digitally by physician office interface, digitally via web based order entry, electronically through EMR or from a host of other interfaces. The LIS then handles the test workflow management, data entry (patient and test data), results reporting, CPT and ICD code capturing and interfacing and integration with other systems as well as supporting regulatory compliance by monitoring aspects of the laboratory and providing users with feedback.
In addition to the basic features of modern laboratory information systems—intuitive user interface, efficient sample acquisition, robust reporting, integrations with laboratory instruments, highly configurable workflows and workflow tracking, automation, integrated regulatory compliance, a portal for status and results and the ability to extend functionality through an application programming interface—a molecular LIS doing NGS (and other complex molecular testing) needs to be able to handle the complexities of molecular testing, the data generated by sequencing instruments, integrate with genomic data repositories in a bi-directional manner, integrate with other systems to provide a complete picture of a patient’s past and current health.
NGS has allowed for fast, efficient and accurate clinical genetic diagnosis to improve patient outcomes. Whether it’s sequencing an individual’s entire genome to detect and treat disease or sequencing a specific bacteria to determine its drug resistance, NGS is a powerful tool for medical professionals to treat patients. The availability of a complete patient’s medical history coupled with genetic test results enables healthcare professionals to provide the highest level of personalized care with the highest potential for successful treatment.