More Precise NSCLC Treatment Selection Boosted by Using EGFR Mutation Structure and Function

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Lung cancer, close-up
{Source: Science Photo Library-NCI/Getty Images]

Grouping epidermal growth factor receptor (EGFR) mutations by structure and function can be used to better match non-small cell lung cancer (NSCLC) patients to the most promising treatments, according to researchers from The University of Texas MD Anderson Cancer Center.

The findings, published today in Nature, identify four subgroups of mutations and introduce a new strategy for testing tyrosine kinase inhibitors (TKIs) and to uncover new clinical opportunities for approved targeted therapies.

The four EGFR-mutant NSCLC subgroups identified are:

  • “Classical-like” mutations, with little to no impact on drug binding
  • T790M-like mutations, which contain at least one mutation in the hydrophobic cleft and often are acquired after resistance to a first-generation targeted therapy
  • Exon 20 loop insertion mutations, characterized by insertions of additional amino acids in the loop after the C-terminal end of the αC-helix in exon 20
  • P-loop αC-helix compression (PACC) mutations on the interior surface of the ATP binding pocket or C-terminal end of the αC-helix

“More than 70 different EGFR mutations have been identified in patients, but drugs have only been approved for a handful of them. One of the immediate implications of our research is the discovery that therapies we already have may work for many of these mutations. For some mutations, older drugs may actually work better, and for other mutations, newer drugs work better,” said John Heymach, MD, PhD, chair of Thoracic/Head & Neck Medical Oncology and senior author of the study.

The current approach to testing new drugs in EGFR-mutant NSCLC is based on exon number, which indicates where the mutation occurs within a linear portion of the DNA. Grouping mutations by exon has produced mostly heterogeneous results in clinical studies, suggesting a poor correlation between exon number and drug sensitivity or resistance.

For this study, the researchers analyzed data from 16,175 patients with EGFR-mutant NSCLC from five different patient databases, including the Genomic Marker-Guided Therapy Initiative (GEMINI). Primary and co-occurring mutations were recorded for 11,619 patients. Of those, 67.1% had classical EGFR mutations, 30.8% had atypical EGFR mutations and 2.2% had both.

For both classical and atypical EGFR mutations, the team analyzed the time to treatment failure (TTF), and found a shorter TTF and lower overall survival for patients with atypical mutations regardless of treatment type. Patients with classical mutations treated with first- and third-generation TKIs had a longer TTF.

The researchers then created a panel of 76 cell lines with EGFR mutations and screened those cell lines against 18 EGFR inhibitors, which revealed the four distinct subgroups. Comparing the correlation to drug sensitivity by subgroup, versus exons, showed that the structure-based subgroups were more predictive than exon-based groups. The subgroup approach was further validated by machine learning to analyze data by classification and regression trees.

Classical-like mutations were sensitive to all classes of TKIs, particularly third-generation TKIs. Exon 20 loop insertion mutations remained the most heterogeneous subgroup, with certain mutations responding best to second-generation TKIs. T790M-like mutations were sensitive to ALK and PKC inhibitors, with some mutations retaining sensitivity to third-generation TKIs. PACC mutations were most sensitive to second-generation TKIs.

Organizing mutations based on how they impact the EGFR structure and drug binding instead, the researchers point out, allows for testing a drug across a whole group of mutations that are structurally similar at the same time.

“Right now, in the absence of guidance, clinicians often use the newest treatment for all EGFR mutations. This model can help us pick better therapies for patients immediately and hopefully develop better drugs for specific subgroups of mutations,” Heymach said.

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