Volume 12 Supplement 1
Identification of proteins and signalling pathways involved in neoadjuvant chemotherapy responsiveness of breast tumours using proteomics
© BioMed Central Ltd. 2010
Published: 18 May 2010
A significant proportion of patients presenting with large locally advanced breast tumours, treated with neoadjuvant chemotherapy (NAC), show a poor or partial response to the treatment. The aim of this study is to identify novel phospho-proteins that may predict responsiveness to NAC treatment as there are no such biological markers available in the clinic to date.
Materials and methods
Frozen tissues collected before (core biopsies) and after NAC (surgical tissues) were categorised by pathological response (complete response, no response and progressive disease). Lysates were enriched for tyrosine phospho (pY) proteins and separated in two dimensions by IEF and PAGE. Proteins showing consistent differences in tyrosine phosphorylation from the different response groups were identified by mass spectrometry (MALDI-TOF) and using the NCBInr sequence database (ProFound). Functional and pathway analysis was performed using Ingenuity Pathway Analysis http://www.ingenuity.com.
Phospho-protein expression profiles were successfully established from core biopsies and surgical tissues. Proteins involved in cell division, polarisation and microtubule formation such as PAR6D and Kif3 were identified in core biopsies from the complete response group. In the no response/progressive disease group, proteins such as CHIMP, ZAP70, and serologically defined breast cancer antigen (NY-BR-15) were distinguished. Further network pathway analysis in this disease group suggested that two main signalling pathways, TP53 and TNF, may be involved in NAC nonresponsiveness.
Proteins and pathways were identified that showed scientific and clinical relevance to NAC responsiveness. Our data support the largely conflicting evidence that suggests P53 and TNF may be possible predictive markers for NAC responsiveness. These findings may lead to the accurate prediction of chemotherapy responsiveness in breast cancer patients and to the development of personalised treatment plans for future patients.