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Updated: Mar 14, 2026
Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
Published on: March 27, 2020
CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from
Elana J Fertig1, Hiroyuki Ozawa1,2, Manjusha Thakar1
1Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.
Abstract:
Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance.
Insights
EGFR inhibitors can paradoxically increase EGFR expression through AP-2 transcription factors, potentially driving resistance in cancer cells with specific genetic alterations. This feedback loop impacts key cellular pathways.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Targeted therapies, including Epidermal Growth Factor Receptor (EGFR) inhibitors, are crucial for treating oncogene-driven tumors.
- Genomic data reveals frequent alterations in EGFR, MAPK, and PI3K pathways in untreated cancers.
Purpose of the Study:
- To investigate the complex interactions and gene expression changes resulting from EGFR inhibitor treatment in cancer cells with specific genetic alterations.
- To identify molecular mechanisms underlying EGFR inhibitor response and potential resistance.
Main Methods:
- Utilized bioinformatics algorithms and modified HaCaT keratinocyte cell lines with activated EGFR, HRAS, and PI3K.
- Measured gene expression changes following treatment with gefitinib, afatinib, and cetuximab.
- Employed the CoGAPS algorithm to distinguish gene expression signatures and infer feedback mechanisms.
Main Results:
- Identified a gene expression signature indicating EGFR network silencing and an unexpected feedback signature with increased EGFR expression in responsive cells.
- Observed increased expression of growth factor receptors regulated by AP-2 transcription factors in the feedback signature.
- Correlated AP-2alpha gene expression signatures with cetuximab sensitivity, EGFR expression changes in head and neck squamous cell carcinoma (HNSCC), and pathway inhibition in LINCS data.
Conclusions:
- AP-2 transcription factors are activated as a feedback response to EGFR network inhibition.
- This AP-2 activation may play a significant role in mediating resistance to EGFR inhibitors.
- Findings suggest AP-2 factors as potential therapeutic targets for overcoming EGFR inhibitor resistance.
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