Clinical Variant Analysis for Cancer

Golden Helix argues that in molecular cancer diagnostics, it is time to embrace software-aided decision-making

Andreas Scherer, PhD
Andreas Scherer, PhD
President and CEO
Golden Helix

Precision medicine uses genetic information from individual patients. This may include diagnosis, treatment, or prevention. Specifically, in the cancer space, data derived from next-generation sequencing (NGS) is used to diagnose and prognose diseases, select a targeted therapy, and potentially evaluate the suitability of a patient to be part of a clinical trial. The entire NGS-based cancer diagnostic space received a push in 2018 when Medicaid issued standard reimbursement codes for NGS-based tests. NGS allows us to look at any number of genes that are potentially involved in the oncogenesis of a patient’s tumor. The usage of an NGS platform is more efficient at a lower cost point than other methods such as Sanger sequencing and provides a much better resolution compared to microarrays. Hence, its adoption is spreading fast on a global basis.

To put this in perspective, I will elaborate on how this field is expanding at the macro level.

The global NGS market is valued at $5.70 billion and is expected to reach $16.35 billion by the year 2024. This represents a compound annual growth rate of 19.2%. Contributing to this growth are the following critical factors:

  • An increase in the number of disease treatment options, accompanied by the adoption of precision medicine and molecular diagnostics.
  • Advancements in NGS platforms, with sequencers providing increased throughput and improved data quality.
  • A decline in NGS capital requirements—while the sequencing technology itself improves, there are reduced capital requirements across multiple sequencing platform providers.
  • Changes in the regulatory environment, with an increase in acceptance of utilization of NGS-based tests in the clinic.
  • Changes in reimbursement, with payors increasingly willing to pay for these tests.
  • Funding for large-scale sequencing projects from both government and private sources.

These developments constitute a significant dilemma for the industry. On the one hand, we have a labor-intensive diagnostic process that requires expertise and attention to detail; on the other hand, a rapidly growing demand for NGS-based tests. Clinical laboratories in this space can expect to multiply their workload within the next four years, with similar if not greater growth anticipated beyond that. At the same time, there is already a shortage of clinical experts in the field of genetics with specific expertise in the NGS area. Automation is the only way to solve this dilemma.

The process of final variant classification and reporting often requires adjudicating multiple lines of evidence, including those revealed by decision trees and evidence-weighing systems. Automation of this process will help to eliminate the problems associated with human error and individual subjectivity. In addition, the automation of the informatics and creation of guided workflows will reduce the time and effort required for molecular pathologists and medical geneticists to sign off on clinical reports.

Over the last few years, I have had extensive dialogues with clinicians in this area. These discussions suggest that the key tasks for expert-level software include:

  • Delivery of consistent, high-quality interpretations: Clinical variant interpretation is a complex task. The person conducting the analysis must be well trained and have deep domain knowledge. In addition, over the course of a busy workday, maintenance with a high level of attention to detail is required. The quality of the analysis must be equally excellent regardless of when the work is being conducted—be it at 8:00 AM, 1:30 PM, right after lunch, or well into the evening hours.
  • Providing a framework for newer, less experienced clinicians: As the volume in a clinical laboratory ramps up, inevitably there will be a need to bring additional staff up to speed in conducting the analytics. In this context, an early version of VSClinical, Golden Helix’ clinical analytics platform, has proved helpful. VSClinical provides a framework that supports less experienced analysts so that they can confidently conduct high-quality work. In many instances, the product provides clear, specific guidance on how to answer certain questions. (If in doubt, the trainee can still engage with more senior staff members in the lab when necessary.) Overall, VSClinical can significantly reduce the ramp-up time it takes to bring new staff up to speed in the clinical interpretation of variants.
  • Staying abreast of new developments: A technology provider should invest a lot of time directly engaging with the community of end users, attending conferences, engaging with clients, and consulting with outside experts as part of the product development process. Based on ongoing feedback from these stakeholders, the provider should provide regular updates. In the case of variant analysis and interpretation software, we find that regular updates are reassuring to clients, who would otherwise face the burdensome process of staying abreast of new developments, but who might lack the time and resources to perform the necessary legwork.

The widespread implementation of NGS technologies has produced a vast number of variants to analyze and categorize. There are numerous data sources containing a wealth of knowledge about the clinical relevance of variants. In addition, there is a wide array of algorithms that help to assess how the presence of a variant functionally impacts a particular gene and the associated protein.

We are long past the point where these types of analytics can be conducted manually. Our first implementation of our clinical variant analysis solution was focused on the guidelines issued by the American College of Medical Genetics and Genomics. Our latest release allows the clinical variant interpretation of somatic variants according to the guidelines issued by the Association of Molecular Pathologists.

It is crucial to have the combined capability of both workflows available, as both germline and somatic variations can trigger cancer. The former occurs within a germ cell (egg or sperm) and is hereditary in nature. BRCA1 and BRCA2 are the most well-known examples. The latter occurs in any other type of cell except the germ cells. In either case, these mutations can either be activating or inactivating in nature.

Activating means that the mutation confers new or increased cell activity that is helpful to the development or spreading of the tumor. Inactivating means the loss or dampening of a cell function that inhibits the development or growth of tumors. For example, switching off a tumor suppressor gene. In fact, this is exactly what happens when BRCA1 is mutated. Another example would be the tumor-suppressor protein p53 encoded by TP53. The homozygous loss of p53 is often found in colon cancer, but also in breast and lung cancers.

Mutations can come in different forms. Here is an overview of those that are clinically most relevant:

  • Single-nucleotide variants triggering, for example, a missense or nonsense amino acid substitution.
  • Splice site alterations that impact the mRNA transcript. These mutations can potentially render an entire gene useless.
  • Copy number variants that either duplicate or delete entire chunks of DNA causing devasting damage on the
    molecular level. The tumor-suppressor Rb1 is often seen in retinoblastomas, for example.
  • Other structural rearrangements such as gene fusions, translocations, and inversions.

Based on the molecular profile of a sample, clinicians can determine suitable treatment options for cancer patients. These include the following:

  • Cancer drugs approved by the U.S. Food and Drug Admistration.
  • Off-label treatments for specific tumors, sometimes in conjunction with other treatments such as chemotherapy.
  • Recommendation to enroll a patient into a clinical trial based on the molecular profile of the tumor.

Molecular cancer diagnostics is a quickly evolving discipline. New papers describing treatment options and new associations between genes and cancers are published daily. Additionally, new clinical trials are opened regularly. Clinicians are under the obligation to apply state-of-the-art knowledge in their diagnostic and therapeutic decision making.

We have reached a level of complexity in the available data, information, and knowledge where the manual development of a defendable clinical report is extremely difficult. If not already, then very soon, software-aided decision making will be the only viable option to deal with this complex matter.


Andreas Scherer, PhD, is president and CEO of Golden Helix.

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