image: Survival curves reveal the clinical impact: patients with the CLASS 3G mutation (red line) progress quickly on single-agent targeted therapy, but their outcomes improve dramatically when starting with a combination of targeted therapy and chemotherapy (green line).
Credit: ©Science China Press
For many lung cancer patients, a mutation in the EGFR gene makes their tumors vulnerable to targeted drugs. Yet, about a quarter of patients with the most common type of EGFR mutation inexplicably see limited benefit. A new study reveals the reason lies not in the presence of the mutation, but in its precise 3D architecture.
Researchers from the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, have developed a new way to classify EGFR exon 19 deletion mutations based on the structural changes they cause in the resulting protein. Publishing in Science China Life Sciences, the team reports identifying a distinct subgroup called “CLASS 3G”. This subtype twists the protein into a shape that third-generation targeted drugs like osimertinib grip less tightly.
“Think of the drug as a key and the mutated protein as a lock,” explained by Docter Wei Zhuang. “Most mutations are similar locks that the key fits well. But CLASS 3G alters the lock just enough that the key doesn’t turn as smoothly, weakening the treatment’s effect.”
This structural insight directly translates to patient outcomes. In their analysis of clinical cohorts, those with CLASS 3G mutations had their cancer progress in about half the time when treated with a third-generation drug alone, compared to patients with other subtypes.
Crucially, the study also lights a way forward. For patients identified as having the CLASS 3G subtype, the data suggest beginning treatment with a combination of the targeted drug and chemotherapy more than doubled the time before the cancer advanced. This finding provides doctors with a potential biomarker to guide more personalized and effective first-line treatment decisions.
“This moves us beyond just finding the mutation to understanding its functional consequence,” says Doctor Zhuang. “By reading the structural ‘fine print’ of the mutation, we can better match patients to the most effective strategy from day one.”