Postoperative serum proteomic profiles may predict metastatic relapse in high-risk primary breast cancer patients receiving adjuvant chemotherapy.

A total of 30-50% of early breast cancer (EBC) patients considered as high risk using standard prognostic factors develop metastatic recurrence despite standard adjuvant systemic treatment. A means to better predict clinical outcome is needed to optimize and individualize therapeutic decisions. To identify a protein signature correlating with metastatic relapse, we performed surface-enhanced laser desorption/ionization-time of flight mass spectrometry profiling of early postoperative serum from 81 high-risk EBC patients. Denatured and fractionated serum samples were incubated with IMAC30 and CM10 ProteinChip arrays. Several protein peaks were differentially expressed according to clinical outcome. By combining partial least squares and logistic regression methods, we built a multiprotein model that correctly predicted outcome in 83% of patients. The 5-year metastasis-free survival in 'good prognosis' and 'poor prognosis' patients as defined using the multiprotein index were strikingly different (83 and 22%, respectively; P<0.0001, log-rank test). In a multivariate Cox regression including conventional pathological factors and multiprotein index, the latter retained the strongest independent prognostic significance for metastatic relapse. Major components of the multiprotein index included haptoglobin, C3a complement fraction, transferrin, apolipoprotein C1 and apolipoprotein A1. Therefore, postoperative serum protein pattern may have an important prognostic value in high-risk EBC.

Protein biochips for differential profiling.

Progress has been made in utilizing ProteinChip technology to profile and compare protein expression in normal and diseased states, particularly in the areas of cancer, infectious disease and toxicology. The past year has also seen the development of several novel chip types designed to analyze proteins in a fashion analogous to the array-based format of DNA microarrays. Some of these platforms may be used for differential profiling.

Proteomic strategies for biomarker identification: progress and challenges.

The simplest definition of a biomarker is a molecule that indicates an alteration in physiology from normal. A more practical definition of a biomarker would require clinical utility of this molecule. In this sense, the biomarker would specifically and sensitively reflect a disease state and could be used for diagnosis as well as for disease monitoring during and following therapy. The need for such biomarkers in all clinical fields is urgent, since the current arsenal of biomarkers is sadly deficient and, in most cases, non-specific. In this review, we discuss strategies that use a proteomics approach to identify novel biomarkers and give examples of recent studies employing these strategies.

Identification of a dynein molecular motor component in Torpedo electroplax; binding and phosphorylation of Tctex-1 by Fyn.

The microtubule protein Tctex-1 was cloned from Torpedo electroplax, a biochemical model of the neuromuscular junction, using the unique domain of Fyn in the yeast two hybrid system. Binding of Tctex-1 and Fyn also occurred in vitro. Torpedo Tctex-1 was contained within the molecular motor protein dynein. A Src class kinase was also complexed with dynein. Tctex-1 was enriched in electric organ vs. skeletal muscle, was present in the postsynaptic membrane, and coprecipitated with the acetylcholine receptor. The sequence of Tctex-1 contained a tyrosine phosphorylation motif and Tctex-1 could be phosphorylated by Fyn in vitro and in vivo. These data demonstrated that Tctex-1-containing dynein is a cytoskeletal element at the acetylcholine receptor-enriched postsynaptic membrane and suggested that Tctex-1 may be a substrate for Fyn.