Fatty acid synthase as a tumor marker: its extracellular expression in human breast cancer.

Overexpression of fatty acid synthase (FAS EC 2.3.1.85) is associated with certain cancers and therefore is a putative tumor marker. The presence of FAS in patients with breast, prostate, colon, ovarian, and other cancers has been reported. The mechanism of FAS overexpression in malignancies remains unknown. Here, we show that FAS is released into the extracellular space in cancer cells. The extracellular FAS are present in various immunoreactive forms, and show different expression patterns in various cancer cells. In serum of breast cancer patients, the FAS is a small molecule similar to the form in breast cancer cell lysate but not conditioned medium of cultured cells. The extracellular expression of FAS in breast cancer cells is time dependent and may be hormone independent. These results indicate that the FAS are an ordered cellular response of a living cell and actively exclude excess intracellular FAS molecules from the cell. This phenomenon is up-regulated in breast and may be in other cancer cells as well. Significant elevation of FAS was detected in serum of breast cancer patients compared to healthy subjects. In comparison with CA27.29, no correlation between these two tumor markers was found. Thus, the extracellular FAS may serve as a potential diagnostic and prognostic marker.

Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer.

Early detection remains the most promising approach to improve long-term survival of patients with ovarian cancer. In a five-center case-control study, serum proteomic expressions were analyzed on 153 patients with invasive epithelial ovarian cancer, 42 with other ovarian cancers, 166 with benign pelvic masses, and 142 healthy women. Data from patients with early stage ovarian cancer and healthy women at two centers were analyzed independently and the results cross-validated to discover potential biomarkers. The results were validated using the samples from two of the remaining centers. After protein identification, biomarkers for which an immunoassay was available were tested on samples from the fifth center, which included 41 healthy women, 41 patients with ovarian cancer, and 20 each with breast, colon, and prostate cancers. Three biomarkers were identified as follows: (a) apolipoprotein A1 (down-regulated in cancer); (b) a truncated form of transthyretin (down-regulated); and (c) a cleavage fragment of inter-alpha-trypsin inhibitor heavy chain H4 (up-regulated). In independent validation to detect early stage invasive epithelial ovarian cancer from healthy controls, the sensitivity of a multivariate model combining the three biomarkers and CA125 [74% (95% CI, 52-90%)] was higher than that of CA125 alone [65% (95% CI, 43-84%)] at a matched specificity of 97% (95% CI, 89-100%). When compared at a fixed sensitivity of 83% (95% CI, 61-95%), the specificity of the model [94% (95% CI, 85-98%)] was significantly better than that of CA125 alone [52% (95% CI, 39-65%)]. These biomarkers demonstrated the potential to improve the detection of early stage ovarian cancer.

A simple affinity spin tube filter method for removing high-abundant common proteins or enriching low-abundant biomarkers for serum proteomic analysis.

Although it is possible to identify new proteins from crude cell extracts using proteomics technology, it is often difficult to elucidate low-abundant biomarkers in the presence of a large amount of high-abundant proteins in serum. We have developed a simple and rapid method using an affinity spin tube filter to remove high-abundant common proteins and enrich the low-abundant biomarkers. The affinity spin tube filter contains protein G, coupled with antibodies against either high-abundant proteins or specific proteins of interest. After incubating with serum, the flow-through or the elute was collected and analyzed by two-dimensional gel electrophoresis. By using this affinity spin tube filter, the possibilities of identifying new biomarkers are shown. This technique could be used for large-scale sample preparation for high-throughput proteomic analysis.

A new model ELISA, based on two monoclonal antibodies, for quantification of fatty acid synthase.

A new model ELISA, based on two monoclonal antibodies, was developed for the quantification of fatty acid synthase (FAS). In this sandwich assay, a monoclonal antibody M6 was used as a capture on Nunc MaxiSorp ELISA/EIA Modules and another monoclonal antibody M3, labeled with biotin, was used as a detection antibody. More than 10 molecules of biotin were labeled on the anti-FAS monoclonal antibody using modified biotinylation conditions. The within- and between-run CVs were less than 10%, and the detection limit was 3.22 ng/mL. Recoveries were 98.54-121.95%, averaging 106.05%. The average FAS concentration obtained from the total 55 healthy volunteers blood was 4.07 +/- 1.81 ng/mL, 4.25 +/- 2.14 ng/mL in women (n = 37) and 3.70 +/- 0.74 ng/mL in men (n = 18). When compared with the previously developed polyclonal-monoclonal ELISA, a different pattern of FAS levels was observed in the supernatant of two cultured breast cancer cell lines in a time course study and there was no linear correlation between the two assays using 215 human blood samples. Thus, this new model FAS-ELISA could be used as an independent assay in measuring clinical samples. In summary, this monoclonal-monoclonal FAS-ELISA is sensitive, accurate, and precise in quantification of fatty acid synthase and has potential as a complementary tool in testing clinical samples.

Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer.

BACKGROUND: Surface-enhanced laser desorption/ionization (SELDI) is an affinity-based mass spectrometric method in which proteins of interest are selectively adsorbed to a chemically modified surface on a biochip, whereas impurities are removed by washing with buffer. This technology allows sensitive and high-throughput protein profiling of complex biological specimens. METHODS: We screened for potential tumor biomarkers in 169 serum samples, including samples from a cancer group of 103 breast cancer patients at different clinical stages [stage 0 (n = 4), stage I (n = 38), stage II (n = 37), and stage III (n = 24)], from a control group of 41 healthy women, and from 25 patients with benign breast diseases. Diluted serum samples were applied to immobilized metal affinity capture Ciphergen ProteinChip Arrays previously activated with Ni2+. Proteins bound to the chelated metal were analyzed on a ProteinChip Reader Model PBS II. Complex protein profiles of different diagnostic groups were compared and analyzed using the ProPeak software package. RESULTS: A panel of three biomarkers was selected based on their collective contribution to the optimal separation between stage 0-I breast cancer patients and noncancer controls. The same separation was observed using independent test data from stage II-III breast cancer patients. Bootstrap cross-validation demonstrated that a sensitivity of 93% for all cancer patients and a specificity of 91% for all controls were achieved by a composite index derived by multivariate logistic regression using the three selected biomarkers. CONCLUSIONS: Proteomics approaches such as SELDI mass spectrometry, in conjunction with bioinformatics tools, could greatly facilitate the discovery of new and better biomarkers. The high sensitivity and specificity achieved by the combined use of the selected biomarkers show great potential for the early detection of breast cancer.