Related Experiment Video
Updated: Apr 26, 2026
Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure
Published on: August 11, 2017
Current approaches for predicting a lack of response to anti-EGFR therapy in KRAS wild-type patients
Tze-Kiong Er1, Chih-Chieh Chen2, Luis Bujanda3
1Division of Molecular Diagnostics, Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan ; Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan.
Abstract:
Targeting epidermal growth factor receptor (EGFR) has been one of the most effective colorectal cancer strategies. Anti-EGFR antibodies function by binding to the extracellular domain of EGFR, preventing its activation, and ultimately providing clinical benefit. KRAS mutations in codons 12 and 13 are recognized prognostic and predictive biomarkers that should be analyzed at the clinic prior to the administration of anti-EGFR therapy. However, still an important fraction of KRAS wild-type patients do not respond to the treatment. The identification of additional genetic determinants of primary or secondary resistance to EGFR targeted therapy for further improving the selection of patients is urgent. Herein, we review the latest published literature highlighting the most important genes that may predict resistance to anti-EGFR monoclonal antibodies in colorectal cancer patients. According to the available findings, the evaluation of BRAF, NRAS, PIK3CA, and PTEN status could be the right strategy to select patients who are likely to respond to anti-EGFR therapies. In the future, the combination of those biomarkers will help establish consensus that can be introduced into clinical practice.
Insights
Identifying additional genetic markers beyond KRAS mutations is crucial for predicting colorectal cancer patient response to epidermal growth factor receptor (EGFR) targeted therapies. Evaluating BRAF, NRAS, PIK3CA, and PTEN status may improve patient selection for anti-EGFR antibody treatment.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Targeting the epidermal growth factor receptor (EGFR) is a key strategy in colorectal cancer (CRC) treatment.
- Anti-EGFR antibodies block receptor activation, offering clinical benefits to patients.
- KRAS mutations (codons 12 and 13) are established biomarkers for anti-EGFR therapy, but many KRAS wild-type patients still exhibit resistance.
Purpose of the Study:
- To review and identify genetic determinants of resistance to anti-EGFR monoclonal antibodies in colorectal cancer.
- To highlight genes that can improve patient selection for anti-EGFR targeted therapy.
- To discuss the potential of combined biomarkers for clinical practice.
Main Methods:
- Literature review of published studies on genetic markers and anti-EGFR therapy resistance in colorectal cancer.
- Analysis of findings related to genes predicting response or resistance to anti-EGFR antibodies.
- Synthesis of current knowledge on biomarkers for patient stratification.
Main Results:
- KRAS mutations are important predictive biomarkers for anti-EGFR therapy in colorectal cancer.
- A significant proportion of KRAS wild-type patients do not respond to anti-EGFR treatment, indicating the need for additional biomarkers.
- BRAF, NRAS, PIK3CA, and PTEN gene status are emerging as important factors in predicting resistance to anti-EGFR therapies.
Conclusions:
- Beyond KRAS, evaluating BRAF, NRAS, PIK3CA, and PTEN status is a promising strategy to select colorectal cancer patients likely to benefit from anti-EGFR therapies.
- These additional biomarkers could help overcome primary and secondary resistance to anti-EGFR monoclonal antibodies.
- Future research should focus on combining these biomarkers to establish clinical consensus for improved patient management.
More Related Videos
13:34A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
07:59Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
