Making NGS Work for Oncologists

Smruti Vidwans, PhD, Chief Science Officer at CollabRx

Q: Your group has recently described an Actionability Framework for designing treatment strategies for cancers that are characterized by mutations. What is the basis and rationale for such an approach?
A: As Next Generation Sequencing (NGS) is increasingly adopted into clinical practice, physicians are faced with the daunting task of identifying variants that are clinically actionable – those that can help them select potential treatment options. In oncology, NGS technologies are used to profile tumor or liquid biopsies and identify variants in cancer-related genes. Cancer gene panels range in size from a handful of genes to several hundred. Depending on the size of the panel, many variants may be observed in tumors.
Genes in cancer panels can broadly be classified into three groups. The first group includes genes (e.g. BRAF, EGFR) that are validated for use in clinical decision-making based on related drug approvals, inclusion in treatment guidelines and a large body of supporting clinical data. The second group includes genes with ‘emerging’ data supporting actionability. Lastly, the vast majority of genes in large panels have limited data supporting use in the clinic and are primarily used for research purposes.
Clinical actionability of NGS data is context-dependent. Relevant factors include diagnosis, nature of observed variants, targetability of variants by one or more drugs, strength of evidence linking variants to therapies, potential interactions between variants within a sample etc.. There is another set of considerations, having to do with the patient as an individual, that is key for clinical actionability. Even when the molecular profile of a patient’s tumor clearly identifies one or more treatment options (e.g. when observed variants are in clinically validated genes such as BRAF), acting upon these treatment options is dependent on patient physiology, disease burden, history, patient goals and wishes and many others factors. For example, inclusion of a BRAF inhibitor in the next course of treatment will be influenced by whether the patient has already been treated with one and whether they have derived or are deriving clinical benefit from it. In the case of a treatment-naive patient with a BRAF mutation, the choice between a BRAF inhibitor and an immunotherapy will likely be influenced by factors such as burden of disease. Patient context becomes even more important if observed variants are in genes in the second group, for which treatment guidelines are not established, or for which clinical data are emerging.
The goal of NGS testing is to aid oncologists choose treatments most likely to help their patients based on molecular characteristics of their tumors. However, when NGS test results are not deeply integrated with other patient information, there is a real danger that they will become just another set of data that oncologists are confronted with, and are unable to derive benefit from, in the limited time they are able to spend with each patient. This is a shame for the patient, an impediment to achieving the promise of precision oncology, and will ultimately limit adoption of this technology in the clinic.
We’ve made great strides in providing oncologists with information linking molecular information to a variety of treatment options. Now we need to equip them with tools beyond the genetic test report that will enable them to find the most appropriate treatment choices for their patient at any given time.
For reference, download the poster from the Molecular Med Tri-Con 2016:
Download PDF of Poster
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