Frequently Discussed Topics

With the declining costs of comprehensive genomic profiling and NGS assays, access to large amounts of genomic information will continue to increase, and its application into clinical practice and guidance of personalized medicine will continue to expand. The following are commonly encountered issues or frequently discussed topics related to NGS testing and data.

Companion diagnostics and standardization of assays

• Many targeted therapies are being co-developed with companion diagnostics; however, many multigene NGS assays or other laboratory-developed tests provide the same genomic or protein results as a companion diagnostic.
• The use of genomic results from testing other than the FDA-approved companion diagnostic may be considered off-label by some health insurers, despite providing comparable results.
• Under certain circumstances, appealing an insurer’s decision may be possible given evidence to support the equal nature of the laboratory-developed test results.
• The appeal of a companion diagnostic is that the assay dichotomizes patient populations into two groups, responders and nonresponders, whereas laboratory-developed tests may provide layers of genomic information, some of which may not be interpretable in relation to the therapy in question.
• In addition to the potential issues regarding companion diagnostics, the following points may be factored into the clinician’s decision process in selecting a genomic testing approach:
• There are countless options in clinical NGS testing platforms for clinicians to choose from, each designed and validated with unique bioinformatics frameworks, coverage bias, variant-calling algorithms and final clinical data reporting.
• The pharmaceutical industry is pairing with diagnostic testing companies more frequently to co-develop unique companion diagnostics for targeted therapies, which presents a logistical challenge, considering what clinicians may face if individual companion diagnostics are paired to each new drug approval.
• This is illustrated by the explosion of assays that have been developed to detect levels of PD-L1 for predicting response to immuno-oncology agents (eg, pembrolizumab [Keytruda, Merck], nivolumab [Opdivo, Bristol-Myers Squibb]). Currently, there are multiple antibody clones, thresholds and scoring conventions that differ by drug and tumor type.
• A recent comparison of four PD-L1 assays (The Blueprint PD-L1 IHC Assay Comparison Project) demonstrated differences in staining performance, and the researchers concluded interchanging assays may lead to “misclassification” of PD-L1 status.
• Conclusion - There is a dire need for industry-wide standardization of clinical NGS and companion diagnostics testing platforms.
• Moving Forward – Industry, government and health care entities will likely have to merge efforts to standardize precision medicine standards.

Passenger and driver mutations, tumor mutation burden and variant annotation

• Cancer arises from an accumulation of genetic alterations that cooperatively transform normal cells. On average, sequencing with whole-exome or whole-genome platforms yields between 30 to 65 variants, for which distinguishing between the disease-causing and random mutations, or drivers and passengers, is critical for clinical interpretation.
• The first layer of assessing raw NGS data lies in the bioinformatics data filtering, variant calling algorithms and decision support processes developed for the NGS platform.
• In a recent large-scale genome sequencing study, almost 95% of somatic variants were novel and not reported in public mutation databases (eg, Catalog of Somatic Mutations in Cancer), although a portion of variants did occur in therapeutically relevant genes.
• The second layer of assessing NGS data includes assigning variant annotation according to consensus guidelines endorsed by the American College of Medical Genetics and Genomics, Association for Molecular Pathology, ASCO and College of American Pathologists, where trained clinical molecular geneticists categorize somatic variants using a tiered system (eg, pathogenic, presumed pathogenic, variants of unknown clinical significance and benign variants) based on the strength of evidence and potential clinical impact.
• Some cancers exhibit very high mutational frequencies, also known as tumor mutation burden, in the range of >100/Mb (more than 100 mutations per megabase of DNA sequenced), which is often related to the underlying environmental exposures (eg, smoking).
• In tumors with high tumor mutation burden, classification of drivers or passengers is not as clinically relevant, as it is suggested that the high number of mutations act as neo-antigens that facilitate tumor clearance through activation of the immune system.
• Conclusion - Robust variant filtering, decision support platforms and a team of clinical molecular geneticists to annotate mutations are basic approaches to filtering large amounts of genomic data. In cancers with high mutational frequencies, distinguishing drivers and passengers may not be necessary, as a high tumor mutation burden represents an alternate therapeutically relevant genomic finding.
• Moving Forward – Development of shared variant annotation databases may help standardize reporting of sequence variants, and/or providing free access to such resources would ease the interpretation of NGS reports for researchers and clinicians.

Genetic heterogeneity and allele fraction

• Genetic heterogeneity describes a tumor comprised of a collection of mutations; some are important to survival (driver mutations), and some are bystanders (passengers).
• Diversity of genomic alterations exist between patients, between tumors within a patient and even within an individual tumor (intratumor heterogeneity).
• Subclonal evolution occurs during disease progression, in which genetic alterations that provide a survival advantage to the cells are selected, and variants that do not provide any survival advantage are lost. Treatment with targeted therapies applies a selective pressure on the tumor cells, where clonal evolution results in these changes (ie, resistance mechanism).
• Genetic heterogeneity presents the greatest clinical challenge in terms of treatment because, in theory, a tumor comprised of genetic diversity relies on multiple signaling nodes that can adapt to new environments, even those threatened with cancer therapeutics.
• Mutant allele fraction
• NGS assesses millions of pieces of DNA, provides a count of the pieces of DNA covering a genomic region and calculates the proportion of those pieces that exhibit a mutated sequence.
• The fraction of pieces exhibiting a mutation is called the mutant allele fraction (Figure 5).
• Mutations that contribute to the pathogenesis of a tumor, including driver mutations, usually have a higher mutant allele fraction. Because of their role in providing survival advantage and being present at the start of disease (initiation), they are likely present in more cells within a tumor.
• Conclusion – Genomic data from NGS provides a snapshot of the tumor’s genetic landscape. The tumor landscape is diverse and adaptable, which makes treatment of cancer with targeted therapies a clinical challenge. Mutant allele fraction is a measure of genetic heterogeneity but is dependent on the cells assessed.
• Moving Forward – Future strategies in the complex setting of genetic heterogeneity and clonal evolution may include development of therapies that target resistance mechanisms used in concert with serial screening (liquid biopsies). Cancer, therefore, would be approached as a chronic disease with the following series:
1. Targeted therapy
2. Detection of a resistance mechanism
3. Switch targeted therapy
4. Repeat

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