Cited by 283Lung Cancer Treatments and MutationsCell death mechanisms and regulationHER2/EGFR in Cancer Research

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BIM expression in treatment-naive cancers predicts responsiveness to kinase inhibitors

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Faber, Anthony C., Corcoran, Ryan B., Ebi, Hiromichi, Sequist, Lecia V., Waltman, Belinda A., Chung, Euiheon, Incio, Joao, Digumarthy, Subba R., Pollack, Sarah F., Song, Youngchul, Muzikansky, Alona, Lifshits, Eugene, Roberge, Sylvie, Coffman, Erik J., Benes, Cyril H., Gómez, Henry L., Baselga, José, Arteaga, Carlos L., Rivera, Miguel N., Dias-Santagata, Dora, Jain, Rakesh K., Engelman, Jeffrey A.·2011·Cancer Discovery

DOI 10.1158/2159-8290.cd-11-0106PMC PMC3229203

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Abstract

Cancers with specific genetic mutations are susceptible to selective kinase inhibitors. However, there is a wide spectrum of benefit among cancers harboring the same sensitizing genetic mutations. Herein, we measured apoptotic rates among cell lines sharing the same driver oncogene following treatment with the corresponding kinase inhibitor. There was a wide range of kinase inhibitor-induced apoptosis despite comparable inhibition of the target and associated downstream signaling pathways. Surprisingly, pretreatment RNA levels of the BH3-only pro-apoptotic BIM strongly predicted the capacity of EGFR, HER2, and PI3K inhibitors to induce apoptosis in EGFR-mutant, HER2-amplified, and PIK3CA-mutant cancers, respectively, but BIM levels did not predict responsiveness to standard chemotherapies. Furthermore, BIM RNA levels in EGFR-mutant lung cancer specimens predicted response and duration of clinical benefit from EGFR inhibitors. These findings suggest assessment of BIM levels in treatment-naïve tumor biopsies may indicate the degree of benefit from single-agent kinase inhibitors in multiple oncogene-addiction paradigms. Significance: In several oncogene-addiction paradigms, assessment of BIM RNA levels identifies those cancers that fail to have substantial apoptotic responses to kinase inhibitors. BIM RNA levels may be assessed in diagnostic cancer specimens to predict which patients will receive less benefit from single-agent kinase inhibitors. Cancer Discovery: 1(4); 352–65. ©2011 AACR. Read the Commentary on this article by Yoshida and Haura, p. 289 This article is highlighted in the In This Issue feature, p. 275

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Authors: Faber, A. C., Corcoran, R. B., Ebi, H., Sequist, L. V., Waltman, B. A., Chung, E., Incio, J., Digumarthy, S. R., Pollack, S. F., Song, Y., Muzikansky, A., Lifshits, E., Roberge, S., Coffman, E. J., Benes, C. H., Gómez, H. L., Baselga, J., Arteaga, C. L., Rivera, M. N., Dias-Santagata, D., Jain, R. K., & Engelman, J. A.
Affiliations: Harvard University (US)
Authors' Affiliations: 1Pathology and 2Radiology, Massachusetts General Hospital, Boston; 3Massachusetts General Hospital Cancer Center, Boston; 4Department of Medicine, Harvard Medical School, Boston; 5Radiation Oncology, Steele Lab for Tumor Biology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; 6Department of Medicine, 7Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee; 8Instituto Nacional de Enfermedades Neoplásicas, Lima, Perú
Corresponding author: Engelman, Jeffrey A.
ORCID: Faber, Anthony C.
Corcoran, Ryan B.
Ebi, Hiromichi
Sequist, Lecia V.
Chung, Euiheon
Digumarthy, Subba R.
Song, Youngchul
Muzikansky, Alona
Roberge, Sylvie
Benes, Cyril H.
Gómez, Henry L.
Arteaga, Carlos L.
Rivera, Miguel N.
Dias-Santagata, Dora
Engelman, Jeffrey A.
Venue: Cancer Discovery
Publisher: American Association for Cancer Research (AACR)
Published: 2011-09-01
Volume: 1
Issue: 4
Pages: 352-365
ISSN: 2159-8274 (ISSN-L)
2159-8290
DOI: 10.1158/2159-8290.cd-11-0106
PMC: PMC3229203
OpenAlex: W2117986690
Cited by: 283
References: 50
Citations by year: 2026: 9 · 2025: 6 · 2024: 4 · 2023: 11 · 2022: 13 · 2021: 15 · 2020: 15 · 2019: 8 · 2018: 27 · 2017: 26 · 2016: 27 · 2015: 31 · 2014: 34 · 2013: 33 · 2012: 22
FWCI: 24.81
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Cancer Discovery (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: Vanderbilt University
Vanderbilt-Ingram Cancer Center
National Cancer Institute (P50CA127003, R01CA080195, R01CA140594, P50CA090578, P01CA080124, P20CA090578, R01CA135257, K08CA120060, P50CA098131, R01CA137008)
Topics: Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Cell death mechanisms and regulation
Health Sciences › Medicine › Oncology › HER2/EGFR in Cancer Research
Keywords: building information modeling, BH3-only proteins, kinase inhibitors, predictive biomarkers, apoptosis, EGFR inhibitors, HER2 inhibitors, PI3K inhibitors, oncogene addiction, EGFR-mutant lung cancer, PIK3CA mutation, treatment-naive tumors, drug response prediction
MeSH terms: Bcl-2-Like Protein 11, Phosphoinositide-3 Kinase Inhibitors, Animals, Female, Humans, Lung Neoplasms, Membrane Proteins, Mice, Nude, Oncogenes, Proto-Oncogene Proteins, Retrospective Studies, Cohort Studies, Signal Transduction, Apoptosis, Receptor, ErbB-2, Erb-b2 Receptor Tyrosine Kinases, Phosphatidylinositol 3-Kinases, Cell Line, Tumor, Protein Kinase Inhibitors, Apoptosis Regulatory Proteins, BH3 Interacting Domain Death Agonist Protein, Mice, ErbB Receptors

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BIM expression in treatment-naive cancers predicts responsiveness to kinase inhibitors

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