Expression of angiogenic markers in the peripheral blood of docetaxel-treated advanced breast cancer patients: a Hellenic Cooperative Oncology Group (HeCOG) study.

It is well known that low-dose metronomic chemotherapy has antiangiogenic activity. The aim of the present trial was to investigate the antiangiogenic properties of weekly docetaxel in patients with metastatic breast cancer. In total, 157 metastatic breast cancer patients received 35 mg/m2 docetaxel weekly as a recommended treatment. Blood samples were collected before the start of chemotherapy (baseline) and during treatment. Nitric oxide (NO) and vascular endothelial growth factor A (VEGF-A) plasma levels were measured at baseline and during treatment, while VEGF-A, endothelial nitric oxide synthase (eNOS) and thrombospondin-1 (THBS-1) peripheral blood mRNA levels were measured at baseline, in 127 patients and 39 female healthy controls. In general, the treatment was well-tolerated. Sixty-one patients (38%) achieved an objective response (4% complete and 34% partial response), while 52 (33%) had stable disease and 27 (17%) progressed. At a median follow-up of 33.5 months (range 2.8-45.0), 118 patients (74%) demonstrated disease progression and 94 (59%) had died. Median progression-free survival (PFS) was 8.8 months and median overall survival (OS) was 27.7 months. Median baseline level of plasma NO was significantly lower in patients than in healthy controls (p=0.010), while none of the plasma markers significantly changed upon docetaxel treatment. In addition, the median relative quantification value for THBS-1 mRNA was significantly higher (p<0.001) in patients as compared to healthy controls. NO plasma levels were positively associated with the number of organs involved (p=0.008). In multivariate analysis, low eNOS mRNA levels showed adverse prognostic significance for OS and high THBS-1 mRNA levels were found to be associated with shorter OS and PFS, independently from established clinical prognostic factors. Although an antiangiogenic activity of weekly docetaxel was not demonstrated in the present study, some interesting observations regarding the prognostic role of a number of blood angiogenic markers could be made.

Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer.

BACKGROUND: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. METHODOLOGY/PRINCIPAL FINDINGS: Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set. CONCLUSIONS/SIGNIFICANCE: Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.