Structure-based classification of EGFR mutations informs inhibitor selection for lung cancer therapy

Paul Yenerall; Ralf Kittler; John Minna

doi:10.1016/j.ccell.2021.10.012

Structure-based classification of EGFR mutations informs inhibitor selection for lung cancer therapy

Paul Yenerall, Ralf Kittler, John Minna

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

EGFR oncogenic mutations predict sensitivity to EGFR inhibitors in NSCLC, but less is known about EGFR “variants of unknown significance.” Using preclinical models, 3D structure analyses, and patient response data, Robichaux et al. show in Nature that mutations in structural regions of EGFR predict responses to different EGFR inhibitors.

Original language	English (US)
Pages (from-to)	1455-1457
Number of pages	3
Journal	Cancer Cell
Volume	39
Issue number	11
DOIs	https://doi.org/10.1016/j.ccell.2021.10.012
State	Published - Nov 8 2021

ASJC Scopus subject areas

Oncology
Cancer Research

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10.1016/j.ccell.2021.10.012

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title = "Structure-based classification of EGFR mutations informs inhibitor selection for lung cancer therapy",

abstract = "EGFR oncogenic mutations predict sensitivity to EGFR inhibitors in NSCLC, but less is known about EGFR “variants of unknown significance.” Using preclinical models, 3D structure analyses, and patient response data, Robichaux et al. show in Nature that mutations in structural regions of EGFR predict responses to different EGFR inhibitors.",

author = "Paul Yenerall and Ralf Kittler and John Minna",

note = "Publisher Copyright: {\textcopyright} 2021 Elsevier Inc.",

year = "2021",

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AU - Yenerall, Paul

AU - Kittler, Ralf

AU - Minna, John

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N2 - EGFR oncogenic mutations predict sensitivity to EGFR inhibitors in NSCLC, but less is known about EGFR “variants of unknown significance.” Using preclinical models, 3D structure analyses, and patient response data, Robichaux et al. show in Nature that mutations in structural regions of EGFR predict responses to different EGFR inhibitors.

AB - EGFR oncogenic mutations predict sensitivity to EGFR inhibitors in NSCLC, but less is known about EGFR “variants of unknown significance.” Using preclinical models, 3D structure analyses, and patient response data, Robichaux et al. show in Nature that mutations in structural regions of EGFR predict responses to different EGFR inhibitors.

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JO - Cancer Cell

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Structure-based classification of EGFR mutations informs inhibitor selection for lung cancer therapy

Abstract

ASJC Scopus subject areas

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