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Updated: Feb 13, 2026

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Proteomic profiling of rectal cancer reveals acid ceramidase is implicated in radiation response
D L Bowden1, P A Sutton1, M A Wall2
1The University of Liverpool, Department of Molecular and Clinical Pharmacology, Ashton Street, Liverpool L69 3GE, United Kingdom.
Background:
Neoadjuvant chemoradiotherapy (CRT) is used in locally advanced rectal cancer when tumours threaten the circumferential resection margin, with varying response to treatment. This experimental study aimed to identify significantly differentially expressed proteins between patients responding and not responding to CRT, and to validate any proteins of interest.
Methods:
Mass spectrometry (with isobaric tagging for relative quantification) analysis of rectal cancers pre- and post-CRT, and at resection. Validation of proteins of interest was performed by assessing tissue microarray (TMA) immunohistochemistry expression in a further 111 patients with rectal cancer.
Results:
Proteomic data are available via ProteomeXchange with identifier PXD008436. Reduced abundance of contributing peptide ions for acid ceramidase (AC) (log fold change -1.526, p = 1.17E-02) was observed in CRT responders. Differential expression of AC was confirmed upon analysis of the TMAs. Cancer site expression of AC in stromal cells from post-CRT resection specimens was observed to be relatively low in pathological complete response (p = 0.003), and relatively high with no response to CRT (p = 0.017).
Conclusion:
AC may be implicated in the response of rectal cancer to CRT. We propose its further assessment as a novel potential biomarker and therapeutic target.
Significance:
There is a need for biomarkers to guide the use of chemoradiotherapy in rectal cancer, as none are in routine clinical use. We have determined acid ceramidase may have a role in radiation response, based on novel proteomic profiling and validation in a wider dataset using tissue microarrays. The ability to predict or improve response would positively select those patients who will derive benefit, prevent delays in the local and systemic management of disease in non-responders, and reduce morbidity associated with chemoradiotherapy.
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