Over the past decade, advancements in genetic testing have revolutionized how we diagnose, classify, and treat cancer patients. Scientific journals are saturated with articles describing a gene or list of genes involved in carcinogenesis that may serve as novel candidates for precision therapies. Similarly, advertisements in mainstream media frequently describe the personalization of cancer care. The overall pace at which genetic information is being collected from tumors is daunting even to experts. As a result, there is not only enthusiasm, but also confusion, on what genomic testing offers cancer patients. This article will briefly describe how cancer testing is evolving, consider different viewpoints on genomic testing of solid tumors, and discuss future clinical testing needs.
The Current State of Genetic Testing for Cancer Specimens
Clinical laboratories have multiple methods available in their testing toolbox to assess genetic changes in tumor specimens. Tests that assess intact cells (e.g., fluorescence in situ hybridization [FISH]) are typically performed directly on unstained formalin-fixed paraffin-embedded (FFPE) slides.
One benefit of testing intact specimens with methods like FISH is that specific alterations (e.g., gene or chromosomal amplifications, deletions, rearrangements) can be identified in rare cells among hundreds or thousands of cells without the alteration. However, in situ methods can only assess a limited number of specific targets in a single sample, a significant drawback as the number of therapeutically targetable genes increases. Con-sequently, clinical laboratories are implementing larger-scale genomic tests that simultaneously interrogate multiple genes and chromosomes.
A similar transition is occurring for molecular genetic tests. The more commonly used molecular methods for cancers include Sanger sequencing, quantitative real time PCR (qPCR), reverse transcription PCR (RT-PCR), mass spectrometry, molecular inversion probe array, high resolution melt curve analysis, and allele-specific PCR. This testing generally begins by obtaining pathologist-selected serial sections (usually 5 to 10 slides at 5-10 µ thickness). Areas containing adequate tumor are macrodissected from the unstained slides with a scalpel or razor. These samples are then deparaffinized and nucleic acids (DNA and/or RNA) are extracted using one of many commercially available nucleic acid extraction kits. A clinical laboratory can perform any of the aforementioned chemistries with isolated nucleic acid samples.
Similar to the limitations with FISH testing, most of these molecular techniques target relatively few genes. The overwhelming need to test multiple markers on an individual tumor with limited tissue has caused a recent migration to next-generation sequencing (NGS) cancer testing.
NGS Testing of Solid Tumors
NGS does not represent a single technique but rather a collection of technologies that has revolutionized clinical testing for hereditary diseases and diverse cancers. NGS testing, similar to other molecular assays, begins by extraction of the nucleic acids followed by quantification with a fluorimetric dye-based platform or qPCR. A substantial challenge of solid tumor testing is obtaining adequate amounts of high-quality DNA from FFPE fixed by standard methods.
Although FFPE tissue is an excellent source of DNA and is the primary long-term preservation method in clinical laboratories, nucleic acids are compromised during FFPE processing. Formaldehyde, the effective component of formalin, generates cross-linking between nucleic acids and proteins and causes nucleic acid fragmentation. As a result, laboratories are required to design solid tumor assays that amplify short fragments of DNA (approximately 150 base pairs), in contrast to hereditary or hematologic cancer assays that assess longer fragments of DNA from blood specimens. This obstacle is not unique to NGS assays, but has become more problematic as we move to large, multiplex NGS assays that comprise thousands of primer sets within an individual tube.
Library preparation (i.e., target enrichment) begins after the DNA has been extracted and quantified. The two primary target enrichment methods used in clinical practice include amplicon enrichment through PCR and hybrid capture or probe-hybridization capture. Because most solid tumor testing involves FFPE specimens which yield lower quality/quantity nucleic acids, PCR amplicon testing is often preferred because it requires as little as 10 ng DNA, requires less time than hybrid capture, and involves relatively straightforward bioinformatics. Drawbacks of conventional PCR methodologies include potential allele bias and limited ability to detect copy number variants (CNV) or uncommon fusions/rearrangements.
Nucleic acids obtained from fresh frozen tissue, peripheral blood, or bone marrow samples yield larger amounts of high-quality DNA and are more amenable to probe-hybridization-based capture methods. These technologies utilize biotinylated probes that are hybridized to targeted regions that then bind to streptavidin beads. Non-bound DNA is washed away and bound DNA is subsequently amplified. Hybrid capture is more reliable for detecting CNV and fusions compared to PCR target enrichment. Disadvantages include a higher DNA requirement (≥50 ng) and more complex bioinformatics.
Following target enrichment, specimens can be sequenced using one of many different NGS “boxes,” including MiSeq, HiSeq, and NextSeq instruments from Illumina, Ion Torrent Personal Genome Machine or Ion Proton instruments from ThermoFisher, or the PACBIO RSII instrument from Pacific Biosciences. Each of these instruments, similar to the chemistries, have pros and cons. However, laboratories should be aware that much of the NGS work begins after physical sequencing of the tumor specimen, and sequencing instrumentation is only one of many financial considerations associated with NGS testing.
A primary difference between NGS and other molecular methods is bioinformatics. This task/pipeline includes generating sequences (i.e., reads) and assigning base quality scores from the sequencer, aligning these reads to a reference genome, calling variants (identifying differences between the nucleotides and the reference sequence at a defined threshold/cutoff), and annotating and displaying these variants in a program accessible to the end user.
Specific quality metrics are part of the bioinformatics analysis and include measures such as base scoring, measuring read depth and uniformity of coverage, and other sequencing metrics. Laboratories should select their bioinformatics program based on the particular NGS application, the type of samples sequenced, and the clinical context of the gene panel.
Clinical laboratories' main goal is to take the data provided by the bioinformatics pipeline and turn it into useful clinical information for the patient. The report provided to clinicians generally includes the identified mutations and appropriate targeted therapies or information on pertinent clinical trials associated with the detected alterations. However, the larger the gene panel, the higher the likelihood of reporting variants of uncertain significance (VUS). These alterations represent either rare variants that have not been functionally studied to know their pathogenicity or more common variants that have inconsistent pathogenicity findings. As more data is collected and made available to all laboratories performing tumor testing, the quality and amount of information given to clinicians and patients should improve.
The Pessimistic View of NGS Testing
Translating NGS technology into clinical laboratories has been met with criticism and reluctance. Multiple factors have deterred some laboratories from using NGS testing. One of the main reasons is cost. The initial cost of the sequencing box may be hundreds of thousands of dollars, chemicals cost hundreds of dollars per sample, and data storage is also expensive. Operating the sequencer is also pricey due to batch effect: if only one or two patients need to be tested over a short period of time, the cost per sample becomes very high. Additional expenses are a bioinformatics software package and preferably a bioinformatician to run and maintain the software.
Another obstacle to developing clinical NGS testing is determining both a gene list and its clinical utility. Clinicians who are hesitant to run NGS panels often recommend and order only Food and Drug Administration (FDA)-approved tests, which are predominantly single gene assays with clear clinical utility. Some argue that many genes within NGS panels do not represent targetable genes and laboratories are merely performing research studies at patients' cost. The same individuals will successfully point out that if 10 experts are asked to list their clinically pertinent genes for an individual tumor, there will likely be 10 different answers.
Moreover, as the number of genes tested increases, so does the number of variants that need to be reviewed. Laboratories using NGS have likely hired additional personnel to review and interpret the variants. Although common variants may require little review time, variant review for rarer alterations can easily exceed 30 minutes per variant, especially when significant literature review is required to determine pathogenicity. A similar drawback of NGS testing of multiple genes is VUS. Identifying and reporting multiple VUS in genes can cause confusion for determining treatment options, not to mention anxiety for patients.
Test reimbursement policies also complicate the use of NGS testing. As laboratories use more expensive NGS testing, insurers have little tolerance to pay for expensive assays that target genes with questionable clinical utility. Payers and NGS pessimists can generally find a gene or mutation within a NGS panel that they argue has not been shown to change the way a patient is treated. As a result, comprehensive cancer panels with hundreds of genes often have more difficulty getting reimbursed than smaller panels targeting a specific tumor with a clear clinical purpose.
The future of clinical NGS also will be heavily influenced by future regulatory guidelines, such as FDA's proposed regulation of laboratory-developed tests.
The Optimistic View of NGS Testing
The fact that many large academic and reference laboratories have adopted NGS suggests that its benefits outweigh the drawbacks. Ironically, cost not only is the first issue identified by those critical of NGS testing, but also one of the first variables discussed by those in support of NGS testing. The difference is how one calculates cost. Nearly all laboratories agree that an NGS test costs more than a single-gene oncology assay. However, if a clinician follows professional guidelines and orders KRAS, NRAS, and BRAF testing on a metastatic colorectal cancer specimen to determine whether the patient will respond to an EGFR inhibitor, then it is more cost effective to run a single NGS panel that includes these genes compared to three separate single-gene assays. It is also possible to exhaust the tissue within small biopsies or cytology specimens if a laboratory has to perform multiple test-specific DNA extractions in order to run multiple gene assays. Therefore, NGS testing allows laboratories to test more genes with limited tissue—in effect saving money and preventing the need for additional biopsies or surgical resections.
Similarly, using FDA-approved single-gene testing also limits the number of detectable mutations. This can result in many patients with undetected mutations that may under- or overestimate the number who would benefit from therapy. With the cost of targeted therapy drugs in the hundreds of thousands of dollars, a larger assay that assesses rarer mutations is ultimately more efficient and better for patients. This approach allows the right patients to get the right drugs.
Clinical utility is also a hot topic when discussing NGS, but it is often in the eye of the beholder. If you are a patient with a life-threatening cancer and have exhausted standard therapies, then a large NGS panel to assess for gene mutations amenable to a non-standard targeted therapy or clinical trial drug would be considered clinically useful. However, if you are a payer, the lack of clinical trial data supporting a rare variant/drug combination would suggest unproven clinical utility.
These conflicting perspectives lead to disagreement over “actionable” genes. Determining the optimal gene list is one of the most difficult parts of test development because many of the potential targetable alterations are rare and it is impossible to get enough clinical trial data to fulfill historic requirements for a “proven” drug/companion diagnostic. As a result, different approaches to clinical trials are now being implemented that look for rarer variants. For instance, the National Cancer Institute's Molecular Analysis for Therapy Choice trial will assess as many as 3,000 patient tumors to determine whether a larger NGS approach results in improved care for cancer patients.
NGS technologies possess distinct advantages over other platforms. NGS is currently the only platform that assesses small amounts of tissue for thousands of variants with high accuracy. When designed properly, NGS assesses for many types of genetic alterations including point mutations, insertions/deletions, and CNVs. The technology also provides good analytical sensitivity and specificity.
Analytical sensitivity is a key performance characteristic of an oncology test because it describes the ability of an assay to detect genetic changes at low frequencies within a background of both non-mutant tumor DNA and non-tumor DNA. For example, if DNA is extracted from a FFPE slide containing 20% tumor cells among 80% non-tumor epithelium, then only 10% of tested alleles may harbor an oncogenic mutation (since only 1 of the 2 alleles within the tumor is mutated). Therefore, the analytical sensitivity of an assay to detect this mutation would need to be ≤10%. The superior analytical sensitivity of NGS assays compared to Sanger sequencing methods (5–10% vs. 20%, respectively) renders NGS an attractive option for oncology testing.
The Future of Cancer Testing
The past decade has been very innovative for oncology, including a paradigm shift with increasing emphasis to customize therapies based on a tumor’s molecular signature. Although the future of oncology testing is difficult to predict, clear advancements should be expected.
First, oncology testing will continue to improve with newer NGS technologies and bioinformatics that will likely decrease the cost of testing, reduce turnaround time, and improve accuracy. New chemistries and more advanced gene panels will also allow clinical laboratories to more easily perform not only single-nucleotide variant analysis, but also assess for more complex alterations including fusions, chromosomal rearrangements, and CNVs.
This revolution in testing will likely result in modified laboratory structures. Fewer methodologies and tests will reduce the number of “wet lab” personnel required to conduct the chemistries but increase the need for bioinformaticians and non-wet lab personnel—genetic counselors, laboratory directors—to identify, review, and interpret the large number of genetic variants identified by NGS.
Increased testing will generate tremendous amounts of data that can be used to better identify appropriate treatment options. The explosion of new targeted drugs and increased use of immunotherapies will push laboratories to better define these targets, whether genetic, protein-based, or other. This testing will not be limited to FFPE tissue, but will quickly progress to blood-based testing of both cell free DNA and circulating tumor cells.
As more patients receive targeted therapies, a demand will rise for identifying and testing of post-therapy resistance markers. NGS testing will undoubtedly be part of these advancements, but newer technologies may also prevail. Regardless of the platform, the future of oncology testing will be molded by big data, new drugs, clinical trial results, the regulatory environment, and the reimbursement landscape.
Benjamin R. Kipp, PhD, is co-director of the clinical genome sequencing laboratory, co-director of the molecular genetics laboratory, and an associate professor of laboratory medicine and pathology at the Mayo Clinic College of Medicine in Rochester, Minnesota. +EMAIL: [email protected]
Acknowledgements
I would like to thank Emily Barr Fritcher and Jesse Voss for their assistance in reviewing and editing this publication.
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