Standard-of-Care Approaches in EGFR-Mutant NSCLC

Transcript:

Charu Aggarwal, MD, MPH: We are going to move on and talk about [the] treatment of lung cancer with uncommon EGFR mutations next. Dr [Martin] Dietrich, I’ll come to you first and ask you to briefly describe the standard-of-care treatment for patients with EGFR mutations, if you could walk us through that.

Martin Dietrich, MD, PhD: That’s a space that [has] had a lot of movement recently, so I’m happy to do that. I would classify them into numerous subsets. I think EGFR exon 19 deletions—I specifically mean the 746-750 deletions and the LA58R substitutions in exon 21—are kind of the main domain, the most common mutations that we’re seeing in both exons, and obviously as a major contributor to EGFR mutations in the TKI [tyrosine kinase inhibitor] domain overall. I think the standard of care here is an osimertinib third-generation-based TKI treatment for the most part. And I’ll talk a little bit about the most recent updates there. We’ve seen great movement in EGFR exon 20. We’ve lost, unfortunately, exon 20 targeting by TKI with more osimertinib.

The confirmatory study was negative and didn’t show any superior outcomes. And the company has asked the FDA to voluntarily withdraw the label. So we’ve moved the TKI out, and we moved the antibody-based strategy into the first-line setting with chemotherapy combinations. The PAPILLON [NCT04538664] study that was just revealed at ESMO [the European Society for Medical Oncology’s annual meeting]. We see superior outcomes combining platinum-based chemotherapy plus amivantamab as a bispecific antibody for CMET and theEGFR dual targeting at the same time. I think there’s an interest in looking at what the subgroups are. Then we have the “uncommon mutations” in EGFR exons 18 and 21, and I think 7.19 and 8.61. The amino acid locations here are the most interesting ones. And we have some data there for afatinib, where it’s approved, but also support for osimertinib. But I think it simplifies the way we are looking at mutations.

By classifying them by exons, I think we have a sort of umbrella over different mutations. But I think we have to be very aware of how specific the different mutations are and how divergent the responses are according to the treatments that we are applying. So taking a close look at each mutation specifically here [is] very important. We’ve seen this, particularly with the EGFR exon 20 mutations. We classify them into Neolub, Farlub, and Helicual mutations on an overall basis. But there are so many mutations in this insertion spectrum that we really don’t know all the details yet. And I think there’s still a lot to be learned. I think the take-home message is not all EGFR mutations are the same, and at the very least, we should classify by exons. I think we will learn more and more about these mutations; unfortunately, often we default to a case report level of evidence for some of these mutations. Again, a very heterogeneous family of mutations that we’re seeing. Certainly even more of an interest in looking at strategies for targeting EGFR that is mutation independent. And I think with amivantamab we have an agent that would encapsulate all those mutations in 1 space. We have an extracellular approach that is mutation independent. I think those are the 2 studies that are asking for these mutations in these higher-risk, lower-efficacy situations. I have seen at ASCO [American Society of Clinical Oncology’s annual meeting] the FLAURA 2 study [NCT04035486] that showed a significant improvement in progression-free survival [PFS] building on osimertinib with a combination of chemotherapy with about a 9-month PFS benefit. We don’t have a clear signal for overall survival benefit there, but what’s very obvious is the high-risk group seems to be doing relatively better. I think that’s the take-home message—there’s still a lot to be learned.

Charu Aggarwal, MD, MPH: Absolutely, and you summarized some of the new and emerging data really well. You know, there are more than 100 different types of EGFR mutations that we see in the clinic. Most of them are without an approved TKI. Martin [Dietrich], what do you think are some of our challenges with the way we currently think about the classification?

Martin Dietrich, MD, PhD: I think we’re moving from a one-size-fits-all approach into an exon-specific approach. But we also understand that even exon specificity is not enough to classify them. So we’re looking layers deeper and covering mutations on a one-off basis. But again, this is a classification that is not necessarily yet ready for clinic. I think we learned from preclinical models that we see different utility of agents. I think what we’ve never understood—we’re withdrawing from the market—but we saw about 30% of patients with these beautiful long tails of responses. I think we never really defined who those patients are. And I think there is a suspicion that there would be a molecular definition. The same is true for EGFR targeting in exon 19, 18, and 21 with osimertinib vs afatinib. Again, those are classifications that have been proposed in different settings. However, the level of evidence clinically that correlates to these mutations and the understanding of these mutations are still very slim. So we’re trying to look at EGFR in a much more unified version than it truly is. I think this is one of the most complex families of mutations that we’re seeing. We certainly need tailored approaches to each mutation at the beginning. And it is certainly not one-size-fits-all anymore.

Charu Aggarwal, MD, MPH: Exactly, and I think, as you mentioned, exon-based approaches or classification may work to some extent. We can think about exon 18, 19, 20, and 21, but I think we’re also moving toward, as you mentioned, more of a structure-based approach or structure function–based groups where we can think about these mutations as classical. We can think of exon 20 insertion–like mutations. We can also look at PAK mutations. And finally, we can look at T790M-like mutations all within that framework of structure-based mutations.

And this is really research coming out from John Heymach [MD, PhD,] and team at [The University of Texas] MD Anderson Cancer Center, which may actually have implications for us in the future in terms of drug development because we can actually pick this group of structure-based classification and look at particular TKIs that may work best.

Transcript edited for clarity.