Almost two decades ago, mutations in the EGFR kinase domain were identified as the first targetable alterations in lung cancer. These two “classical” mutations, a point mutation in exon 21 (L858R) and deletions in exon 19, were found to underlie the marked sensitivity that some patients had to the two first-generation EGFR tyrosine kinase inhibitors (TKIs) (erlotinib and gefitinib) tested at the time.1,2,3 While these classical EGFR mutations are the most common EGFR mutations, accounting for roughly 70% of EGFR-positive non-small cell lung cancer (NSCLC) cases, the remaining 30%4 with non-classical or atypical mutations have received much less attention.
The major challenge in studying atypical EGFR mutations has been their heterogeneity. There are more than 100 individual or compound EGFR mutations, mainly located in the kinase domains encoded by exons 18 to 21 of the EGFR gene.4 Some of these mutations are known to respond well to first- or second-generation TKIs, such as afatinib, while others clearly do not.5
But most atypical mutations do not have a specific EGFR TKI for which they are approved. Since it is not practical to conduct separate clinical trials for each individual mutation, a critical issue in the field is how to best group atypical EGFR mutations together to facilitate clinical testing and match patients with the most appropriate drug. One approach to grouping these mutations is based on exons. While this has proved useful for exon 20 insertions,6 for other atypical mutations there appears to be no association between exon and drug response.
To address this need, we recently tested a structure-based classification of EGFR mutations that can be used to group mutations based on their likelihood of response to specific types of drugs. After analyzing more than 70 atypical EGFR mutations, we identified four structure classes of EGFR mutations:4
- Exon 20 loop insertion,
- P-loop aC helix compressing (PACC), and
- T790M-like groups.
Each class of mutations has a distinct structure in the adenosine triphosphate (ATP) binding pocket. However, within each class, different mutations share similarities, and therefore, mutations within each class demonstrate a similar response to different EGFR TKIs.
For example, the classical-like group, which includes the exon 19 deletion and the L858R mutation, and the T790M-like group have great sensitivity to third-generation EGFR TKIs.4 However, PACC mutations are more sensitive to second-generation TKIs.4 This four-class system much better predicts response to EGFR TKIs than the exon-based system.
In addition, this type of classification may facilitate clinical trials for atypical mutations. While an individual atypical mutation may be rare, when atypical mutations are grouped, the total prevalence may be more substantial.
For example, PACC mutation occurs in 13.7% of all EGFR-positive cases, and less than half have an FDA-approved medication. In comparison, exon 20 loop insertions make up about 5.8% of all EGFR-positive cases.4 The lack of approved TKI for PACC mutations is a large unmet need, as many of the patients with EGFR PACC mutant lung cancer don’t have a tailored treatment plan.
While third-generation EGFR TKIs are generally less toxic and more efficacious in treating lung cancers with classical EGFR mutations, we found second-generation TKIs can work better for PACC mutations. In our clinical case analysis, we confirmed that finding.
In lung cancer patients with PACC mutations, first- or third-generation TKIs render limited benefit with time-to-treatment failure (TTF) at 10 and 4.1 months respectively. In comparison, those lung cancers showed enhanced sensitivity to second-generation TKIs afatinib, poziotinib, and dacomitinib with TTF at 17.7 to 21.7 months.4
Therefore, it is critically important to recognize PACC mutations and offer patients the right type of EGFR TKI to achieve maximum clinical benefit.
Now that we’ve shown PACC mutation cases represent a distinct patient group, we should be able to design TKIs and clinical trials for patients with this class of EGFR mutation. Using this classification system, we can conduct clinical trials more effectively and quickly obtain sufficient data to support approvals for this group of patients with unmet needs.
In summary, the oncogenic mutations in EGFR have traditionally been defined based on the exon location. Heterogenous responses to EGFR TKIs have been found in different mutations within the same exon, which present a great challenge for clinical practice and trial design.
We established a new structure-based approach, which classifies EGFR mutations into four major groups based on sensitivity to different generations of EGFR TKIs. This classification provides a system for selecting the most appropriate TKI for all EGFR mutations and can be used as the basis for future EGFR TKI trial designs, which we hope will eventually help physicians tailor the choice of EGFR TKI to a patient’s specific mutation.
- 1. Paez, JG et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497-1500, doi:10.1126/science.1099314 (2004).
- 2. Lynch, TJ et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350, 2129-2139, doi:10.1056/NEJMoa040938 (2004).
- 3. Pao, W et al. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A 101, 13306-13311, doi:10.1073/pnas.0405220101 (2004).
- 4. Robichaux, JP et al. Structure-based classification predicts drug response in EGFR-mutant NSCLC. Nature 597, 732-737, doi:10.1038/s41586-021-03898-1 (2021).
- 5. FDA broadens afatinib indication to previously untreated, metastatic NSCLC with other non-resistant EGFR mutations
- 6. Robichaux JP, Elamin YY, Tan Z, et al. Mechanisms and clinical activity of an EGFR and HER2 exon 20-selective kinase inhibitor in non-small cell lung cancer. Nat Med. 2018;24(5):638-646. doi:10.1038/s41591-018-0007-9