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Updated: Feb 19, 2026
Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
Published on: September 25, 2011
Pharmacoproteomic characterisation of human colon and rectal cancer
Martin Frejno1,2, Riccardo Zenezini Chiozzi2,3, Mathias Wilhelm2
1Department of Oncology, University of Oxford, Oxford, UK.
Abstract:
Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials.
Insights
This study profiles colorectal cancer (CRC) proteomes, linking them to drug responses and patient subtypes. MERTK protein is identified as a key marker for predicting drug resistance and patient survival in CRC.
Area of Science:
- Proteomics
- Cancer Biology
- Pharmacology
Background:
- Molecular cancer therapies target proteins, but proteome data for patient stratification and drug sensitivity prediction are limited.
- Understanding the proteome status of colorectal cancer (CRC) is crucial for developing targeted therapies.
Purpose of the Study:
- To create a comprehensive proteomic map of colorectal cancer (CRC) cell lines and patient tumors.
- To integrate proteomic and transcriptomic data for defining CRC subtypes and identifying representative cell line models.
- To build predictive models for drug sensitivity in CRC based on proteome profiles.
Main Methods:
- Mass spectrometry-based proteomic profiling of 65 colorectal cancer (CRC) cell lines (>10,000 proteins per line).
- Integration of proteomic data from 90 CRC patients and matched transcriptomics data.
- Pharmacological profiling of 52 CRC cell lines against 577 drugs, correlating responses with proteome data.
Main Results:
- Defined integrated CRC subtypes by combining proteomic and transcriptomic data.
- Identified cell lines that accurately represent distinct CRC tumor subtypes.
- Developed models predicting drug sensitivity in cell lines and patients based on proteomic profiles.
- Discovered MERTK as a predictive marker for resistance to MEK1/2 inhibitors.
- Confirmed MERTK as a prognostic survival marker in 1,074 CRC tumors via immunohistochemistry.
Conclusions:
- Proteomic profiling provides a valuable resource for understanding CRC heterogeneity and guiding therapeutic strategies.
- The identified MERTK marker offers potential for personalized medicine approaches in CRC treatment.
- The generated proteomic and drug response data can facilitate the design of future clinical trials.
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