Assessing Genomic Variants for Targeted Therapy Options

Results from large panel biomarker testing may associate one or more genomic variants with targeted therapy options.  These targeted therapies may be FDA approved for the patient’s cancer, FDA approved for a different cancer (off-label) or primarily available through clinical trials. It can be challenging to know which, if any, of these therapies is most likely to benefit a patient since the evidence evolves over time. Genomic tumor boards serve an important role in weighing the available preliminary evidence and helping providers prioritize the options. The framework and tools below provide the general steps taken to assess the evidence supporting treatment options.

The following questions are considered for each genomic variant considered to be actionable.

What is the role of the gene?
Knowing role of the gene in the biological pathway targeted by the therapy can be helpful in assessing the likelihood of response. Some test reports list targeted therapies for variants that are a few steps removed from the biologic pathway that is targeted by the drug. Almost any pathogenic variant is going to activate some of the common downstream pathways, which is not sufficient to recommend off-label use, unless there is supporting clinical evidence.

What is the impact of the specific variant on gene function?
Genomic variants can have a range of effects on the function of the gene and impact to the protein product. They can be activating, leading to the overproduction of a protein or production of a protein irrespective of chemical signals from outside the cell (gain of function). Variants can also be inactivating, leading to less protein being produced or a protein that does not function as expected (loss of function). Both activating and inactivating variants can lead to a loss of regulation by inappropriately turning on or off pathways responsible for cell growth, proliferation, or death. However, some alterations may not affect the gene’s function (benign) or may be unknown in terms of their effect on the gene (variant of uncertain significance).

How does the therapy work?
Targeted treatments target genomic changes that drive cancer behaviors, which broadly include sustaining cell proliferation and growth, resisting cell death, and inducing angiogenesis. Existing treatments work on a variety of biological pathways and use different mechanisms. For example, tyrosine kinase inhibitors (e.g., erlotinib) block pro-growth signaling pathways while PARP inhibitors (e.g., niraparib) induce cell death. Understanding the intended mechanism of action of the treatment may be helpful in assessing the likelihood of its effectiveness in the presence of a specific variant.

Is the functional consequence of the variant compatible with the mechanism of action of the treatment?
Some reports identify treatment options that have been associated at the gene level but not necessarily at the variant level. Because variants can have different impacts on gene function, it is important to determine whether the therapy is compatible with the impact of the variant. For example, a therapy that blocks a pathway would be effective when there is a genomic variant that keeps the pathway activated, but it would be redundant with a genomic variant that inactivates the same pathway.

What is the strength of evidence supporting the therapy?
Data supporting the effectiveness of a therapy come from a variety of study types. Laboratories often categorize variants into different tiers (I, II, III, IV) based on the consensus recommendation from the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. These tiers are based on the strength of the evidence supporting effectiveness. Several groups have developed frameworks to help assess the strength of the evidence available. These frameworks typically rely on the stage of the research (e.g., preclinical, phase I clinical trial).

The strength of evidence may differ substantially based on cancer type and specific variant. While one goal of large panel genomic tumor testing is to identify treatment options that have been successful in other cancer types carrying the same variant(s), not all tumors react in the same way or to the same level. Therefore, it is important to assess the level of evidence available in a specific patient’s cancer type and with the specific variant identified.

There are different ways to assign levels of evidence (see examples below), but clinical data generally provides stronger evidence than pre-clinical data and data from individuals who share more characteristics with the patient being tested is stronger, but all levels of evidence can provide support or suggest a lack of effectiveness.

Next steps
Once you have assessed the evidence for each of the variants of interest on the report, prioritizing them requires additional considerations. Patient factors, including previous treatments, response to current treatment, health status, and interest and ability to participate in clinical trials are important to consider. If clinical trials are an option, it is important to assess those listed to determine exclusions as well as considering the approach (e.g., enrolling for specific variants vs. non-genetic enrollment criteria). Genomic tumor boards, if available, may be helpful at different steps during this process.

Framework for Assessing Targeted Treatment Options

Phases in the Decision-Making Process

Guiding Questions

Determine the role of the gene

  • What is the function of the gene?

Determine the impact of the specific variant on gene function

  • What is the impact of the variant on gene function (e.g., activating, inhibiting)?

Understand the mechanism of the targeted therapy

  • What pathway does the therapy target?
  • At what level does the therapy target (e.g., gene, variant)?
  • What is the mechanism of action of the therapy (e.g., inhibiting, activating)?

Assess whether the functional consequence of the variant is compatible with the mechanism of action of the treatment

  • Does the variant have the opposite impact of the treatment?
  • Does the variant convey resistance to the treatment?

Assess the strength of evidence supporting the effectiveness of the therapy

  • What type of studies provide data supporting effectiveness of therapy on cancers with this specific gene?
  • What type of studies provide data supporting effectiveness of therapy on cancers with this specific variant?
  • What is the strength of evidence to support use of the therapy in the patient’s specific cancer type?

Learn More     

Exploring Cancer Biomarker Testing (CME | CNE). Learn about benefits, limitations, and challenges of using cancer biomarker testing.

Interpreting Cancer Biomarker Testing – When is Additional Testing Needed? (CME|CNE). Learn when additional cancer biomarker testing is indicated for further evaluation of genome-informed therapy. 

Choosing the Best Genomic Tumor Test (CME | CNE). Learn about the benefits and limitations of different genomic tumor test options for patients with cancer and how to determine the best test for each patient.

How to Maximize the Genomic Tumor Board Experience. Suggests ways for you to make the most of the genomic tumor board experience.


Chakravarty D, Gao J, Phillips SM, et al. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017;2017:PO.17.00011.

Dumbrava EI, Meric-Bernstam F. Personalized cancer therapy-leveraging a knowledge base for clinical decision-making. Cold Spring Harb Mol Case Stud. 2018;4(2).

Li MM, Datto M, Duncavage EJ, et al. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017;19(1):4-23.

Meric-Bernstam F, Johnson A, Holla V, et al. A decision support framework for genomically informed investigational cancer therapy. J Natl Cancer Inst. 2015;107(7).


This resource was developed as part of the Maine Cancer Genomics Initiative (MCGI) and is supported by The Harold Alfond Foundation and The Jackson Laboratory.

Updated May 2023