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Current strategies for early detection of breast and other cancers are limited in part because some lesions identified as potentially malignant do not develop into aggressive tumors. Acid pH has been suggested as a key characteristic of aggressive tumors that might distinguish aggressive lesions from more indolent pathology. We therefore investigated the novel class of molecules, pH low insertion peptides (pHLIPs), as markers of low pH in tumor allografts and of malignant lesions in a mouse model of spontaneous breast cancer, BALB/neu-T. pHLIP Variant 3 (Var3) conjugated with fluorescent Alexa546 was shown to insert into tumor spheroids in a sequence-specific manner. Its signal reflected pH in murine tumors. It was induced by carbonic anhydrase IX (CAIX) overexpression and inhibited by acetazolamide (AZA) administration. By using (31)P magnetic resonance spectroscopy (MRS), we demonstrated that pHLIP Var3 was retained in tumors of pH equal to or less than 6.7 but not in tissues of higher pH. In BALB/neu-T mice at different stages of the disease, the fluorescent signal from pHLIP Var3 marked cancerous lesions with a very low false-positive rate. However, only ∼60% of the smallest lesions retained a pHLIP Var3 signal, suggesting heterogeneity in pH. Taken together, these results show that pHLIP can identify regions of lower pH, allowing for its development as a theranostic tool for clinical applications.

Original publication




Journal article


Proceedings of the National Academy of Sciences of the United States of America

Publication Date





9710 - 9715


Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology and Biology, Department of Oncology, University of Oxford, Oxford OX3 7DQ, United Kingdom;


Cell Line, Tumor, Spheroids, Cellular, Animals, Mice, Inbred BALB C, Humans, Neoplasms, Neoplasm Invasiveness, Disease Models, Animal, Acids, Membrane Proteins, Magnetic Resonance Spectroscopy, Sensitivity and Specificity, ROC Curve, Amino Acid Sequence, Hydrogen-Ion Concentration, Molecular Sequence Data, Mutant Proteins, Biomarkers, Tumor