Next generation sequencing (NGS) has allowed scientists to study a wider range of genes more quickly and cheaply than ever before. This powerful technology has completely transformed biology and has equipped researchers with more knowledge about how cells work — and what happens when they don’t. With newfound capabilities, NGS has gone from cutting-edge biology to a mainstay of the research lab in the past decade alone.
But NGS is not solely a research tool. In medicine, NGS is now being used as a tool for disease screening, diagnosis and treatment guidance. The strategy has the potential to provide faster, more accurate and cheaper diagnostics, especially in fields like oncology. The development of NGS biomarker tests may revolutionize medicine in the same way it has transformed research — assuming test developers can overcome the hurdles associated with these diagnostic tests.
The impact can be seen by the significant increase in the number of oncology clinical trials that now use biomarkers — up from less than one-fifth 20 years ago to now more than half. Additionally, of those trials using biomarkers, half now use more than one.
To create an effective test, researchers need to select the right biomarkers. Identifying the proper biomarkers is, however, fraught with challenges and potential missteps. Some of these difficulties are related to the disease itself. Tumors, for example, are not homogeneous, and developers need to ensure that all of these cell phenotypes are analyzed to get the most accurate test result. Other challenges have more to do with the test itself, such as the identification of genetic variants that have yet to be fully characterized.
Fortunately, there are solutions to these challenges. By addressing these difficulties head-on, companies can tap into the full power of NGS-based biomarkers to guide diagnosis and therapeutic decision making.
Challenge #1: Selecting the right biomarker
Compared to Sanger sequencing and PCR-based approaches, NGS is much more sensitive, and developers can select many more biomarkers to place on a single test. NGS can also allow test makers to ask slightly different questions. Instead of asking a simple yes/no question as to whether a patient’s DNA contains a specific genetic marker, an NGS-based test can determine whether a patient carries any number of genetic variants. Many of the common cancer gene panels contain 400-500 different genes, giving them a different scale than older genetic tests that look at only a small region of a single gene.
For NGS test developers, the question is no longer “can we identify this one variant?” Instead, the question is “how can we provide a complete and accurate picture of relevant variants carried by this patient?” Test developers and the medical community need to hone in on those genetic markers that will provide the most information for patients in terms of getting the correct diagnosis and identifying the most effective therapies. To create biomarker-based screening tests, developers will need to ensure that predictions of disease risk do more benefit than harm. This is a process that requires large clinical trials to show that the biomarker provides useful information — and the process is often lengthy and expensive. Doing the front-end work to identify the right biomarker beforehand can help to alleviate this challenge.
Challenge #2: Proving a biomarker’s clinical utility
A biomarker test doesn’t just need to be accurate — that is, show analytical validity — it also needs to provide usable information for patients and physicians. Known as clinical validity, this requires that a test can accurately identify, for example, those patients with a condition, and those without. A cancer screening panel with clinical validity will help identify drugs likely to be effective against a tumor, and those to which the cancer is resistant. Because many of these biomarkers are identified by pharmaceutical companies in the development of their therapies, these tests tend to be very drug-centric. They ask the question of whether a tumor will respond to ‘drug X.’ Even therapeutics in the same class can rely on slightly different biomarkers.
Showing clinical utility for an NGS-based biomarker screening test requires even more work. Not only does the biomarker have to accurately predict disease risk, but scientists also need to show that the additional screening provides enough benefit to outweigh any harm. Testing someone’s prostate-specific antigen levels, for example, will help detect more cases of prostate cancer, but the tests also have false positives that put patients through unnecessary invasive procedures. This is why some public health and preventive medicine organizations have questioned the routine use of this test.
Challenge #3: Variants of uncertain significance
Humans carry an average of a few million genetic variants. In most cases, these variations are silent and/or benign and have little impact on one’s life. Although this high rate of variation provides the fuel for natural selection and evolution, it also means that geneticists are continually discovering new variations in the human genome. As increasing numbers of individuals seek out genetic testing, scientists have identified more common polymorphisms. However, more people being tested also leads to the identification of very rare variants, especially in populations that are not well-characterized.
Occasionally, the impact of a new variant is obvious: It leads to a truncated or non-functional protein that is part of a molecular pathway relevant to a person’s symptoms. Often, however, a variant’s impact isn’t clear. These are known as variants of uncertain significance (VUS). When it comes to identifying the cause of a patient’s symptoms, a VUS creates a challenge. A geneticist can’t rule the variant out as a cause, but a VUS doesn’t provide enough certainty to steer treatment or to provide useful information to counsel a family about inheritance and testing.
These giant question marks create stress for families as they try to navigate a challenging situation. Although it’s not possible to eliminate VUS, selecting well-characterized biomarker genes can help reduce their impact. Another way to help reduce VUS is to foster broader data sharing, which relates to an additional challenge for biomarkers.
Challenge #4: Siloed and unshared data
Annotating and interpreting genetic test results requires laboratory scientists to compare an individual’s DNA to reference samples and examine links between genotype and phenotype. The process requires vast quantities of data that are not always readily available since much of this sequencing data goes unshared by testing companies.
The problem is especially apparent for rare diseases where genes may be less well-characterized and have fewer variants cataloged with accompanying phenotypic information. Research studies on the topic may only contain data on a small handful of individuals, which means scientists must pool data from a multitude of sources to determine the significance of a variant and whether it contributes to disease.This requires test makers and developers to create better methods for data sharing to reduce the number of VUS and increase the power of genetic tests to get answers for patients. Various initiatives from the National Institutes of Health and other organizations are trying to tackle this issue and pool data from various sources while protecting patient privacy, although these projects are ongoing.
Challenge #5: Tumor heterogeneity
Not all tumor cells are the same. Even cells within the same tumor may have different genotypes and different susceptibilities and resistances to therapeutics. When oncologists obtain a physical biopsy of a tumor, they may not gather a fully representative sample of cancer cells. It’s a well-known problem in oncology, and researchers are increasingly turning to NGS-based biomarkers to circumvent this problem with a noninvasive test.
When a cell dies, it releases its DNA into the bloodstream. This cell-free DNA has a half-life of minutes to hours, but it can be detected before enzymes break the genomes down into their building blocks. Most of the DNA circulating in the blood will be from normal, healthy cells. However, if a person has cancer, some of it will also be from tumor cells, and certain tests can detect these tiny amounts of circulating tumor DNA (ctDNA). The advantages of testing for ctDNA are obvious: It is possible to detect unknown metastatic tumors and minimal residual disease without having to find and biopsy tumor tissue.4 It’s also more straightforward to identify DNA from the full range of cancer cells. The procedure is also less invasive, requiring a simple blood draw rather than a surgical procedure.
Detecting the infinitesimal amounts of ctDNA in a blood sample requires extraordinarily deep sequencing. This makes it cost-prohibitive to perform whole genome sequencing on these samples. Instead, researchers have to take a more targeted approach, searching selected genomic regions for variants and biomarkers. The challenge for test designers comes back to the question of clinical validity and utility. If 5% of tumor cells are resistant to a certain chemotherapy drug, that’s likely to be clinically relevant information. But determining the proportion of cells that need to be positive for a specific biomarker and understanding the clinical implications for this is a challenging task.
Researchers also need to be cautious in the assumptions underlying these tests. Not all cancer cells will shed DNA at the same rate, making it even harder to interpret percentages and proportions. Furthermore, if a test does not detect a particular tumor-related mutation in the circulating DNA, it can be hard to know if that is because it really isn’t present, or the test simply wasn’t sensitive enough. Some ctDNA tests contain information about the fraction of circulating DNA that is tumor-derived to help provide clues about how representative the tumor DNA is and the trustworthiness of the answers. Additionally, not all data from ctDNA is medically actionable and more work needs to be done to establish the risk/benefit analysis for these types of testing.
Challenge #6: Patient-centric vs. drug-centric thinking
The design of the regulatory landscape in the U.S. and elsewhere in the world means that many biomarker tests are developed to determine whether a certain drug is likely to be effective in a certain patient. This creates a laundry list of yes/no questions that need to be evaluated one at a time. Take immune checkpoint inhibitors. There are currently several options on the market, and each test relies on a different biomarker. To find the right one, a patient has to ask whether ‘drug A’ will work, or ‘drug B’ or ‘drug C,’ and so on.
What would be more useful (and cost-effective) for patients is a test where they can ask, “Tell me about my tumor.” In a single test, you could get answers about a range of drugs and biomarkers rather than asking a series of yes/no questions. This process might also provide information about trade-offs, such as efficacy, the chances of developing resistance, and potential side effects.
Researchers call this patient-centric instead of drug-centric. On the surface, there doesn’t appear to be a huge gulf between these two ways of thinking. Yet if you go deeper, it’s a fundamentally different question being asked, and a different way of finding the answer. Making the shift from drug-centric to patient-centric thinking will require a major overhaul of the regulatory landscape, how drugs are tested in clinical trials, and how NGS-based biomarker tests are developed.
Challenge #7: Changing the regulatory landscape
Bringing a new test or drug to market is expensive, painstaking work. That’s why most biomarker tests are linked to specific pharmaceuticals — to determine whether a specific drug is appropriate for an individual patient. As a result, this is how the Food and Drug Administration (FDA) and other regulatory agencies approve the tests. Making a patient-centered test that is a panel of NGS biomarkers would require additional approvals, not to mention collaboration between competing pharma companies that may or may not have an incentive to work together. The shift would be a huge undertaking, but it would likely have major payoffs for patients.
By making NGS-based biomarker tests more useful, they may be able to steer diagnosis and treatment much earlier in cancer care. Many times, these tests are not used until a later stage, or when the current standard of care fails. As a result, a person’s cancer could become resistant, or they could have endured treatments and testing that were not necessary. Genomic profiling much earlier in treatment may not change cancer care for every patient, but it provides opportunities for better intervention and more appropriate care.
Newer types of biomarkers and immune profiling assays are being developed and becoming available. As the NGS field continues to mature, scientists will be able to harness the volumes of information that these biomarkers can provide. Although it remains to be seen precisely how these tests will change medicine, it’s becoming increasingly clear that there is potential for significant benefits for patients.
