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Irradiated breast cancer patients demonstrate subgroup-specific regularities in protein expression patterns of circulating leukocytes.

BACKGROUND
Breast cancer is one of the most frequent types of cancer with fatal outcome worldwide. The use of breast conserving lumpectomy followed by radiation therapy is common and has been shown to be a strategy competitive to mastectomy in preventing mortality caused by breast cancer. However, breast irradiation, particularly applied after pre-irradiation chemotherapy, frequently leads to serious short- and long-term side-effects, the prediction of which is highly desirable in terms of individual therapy planning. For these purposes, minimal-invasive molecular blood analysis is considered as a powerful diagnostic tool: molecular interplay in blood is highly informative and may predict individual side-effects of therapy.


MATERIALS AND METHODS
Ex vivo comparative protein expression profiling was performed in circulating leukocytes isolated from fresh blood samples of seven breast cancer patients before lumpectomy and consequently at several checkpoints under radiation treatment (0-60 Gy). Protein expression patterns were investigated by two-dimensional polyacrylamide gel electrophoresis followed by protein spot identification using matrix assisted laser desorption/ ionisation -- time of flight. Specific expression levels of highly affected differentially expressed proteins were quantified by Western blotting.


RESULTS
The radiation treatment caused individual extensive alterations in expression patterns of leukocytes in the patients tested. In particular, a key regulator of redox status, thioredoxin, and the free-radical detoxification cascade members, SOD-2 and catalase, were highly affected. In spite of the high diversity of individual expression levels, characteristic protein expression patterns were recognized and patients were grouped according to the similarities found.


CONCLUSION
Characteristic expression patterns in circulating leukocytes might provide novel molecular targets for prediction of therapy side-effects and improve individual therapy planning for breast cancer patients, thus avoiding unnecessary and excessive treatment-related toxicity. Molecular candidates and specific patterns are demonstrated in this work.