Survival analysis of circulating tumor cells in triple-negative breast cancer

Background. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% (1.38 million) of the total new cancer cases and 14% (458,400) of the total cancer deaths in 2008. [1] Triple-negative breast cancer (TNBC) is an aggressive phenotype comprising 10–20% of all breast cancers (BCs). [2-4] TNBCs show absence of estrogen, progesterone and HER2/neu receptors on the tumor cells. Because of the absence of these receptors, TNBCs are not candidates for targeted therapies. Circulating tumor cells (CTCs) are observed in blood of breast cancer patients even at early stages (Stage I & II) of the disease. Immunological and molecular analysis can be used to detect the presence of tumor cells in the blood (Circulating tumor cells; CTCs) of many breast cancer patients. These cells may explain relapses in early stage breast cancer patients even after adequate local control. CTC detection may be useful in identifying patients at risk for disease progression, and therapies targeting CTCs may improve outcome in patients harboring them. Methods . In this study we evaluated 80 patients with TNBC who are enrolled in a larger prospective study conducted at M D Anderson Cancer Center in order to determine whether the presence of circulating tumor cells is a significant prognostic factor in relapse free and overall survival . Patients with metastatic disease at the time of presentation were excluded from the study. CTCs were assessed using CellSearch System™ (Veridex, Raritan, NJ). CTCs were defined as nucleated cells lacking the presence of CD45 but expressing cytokeratins 8, 18 or 19. The distribution of patient and tumor characteristics was analyzed using chi square test and Fisher\u27s exact test. Log rank test and Cox regression analysis was applied to establish the association of circulating tumor cells with relapse free and overall survival. Results. The median age of the study participants was 53years. The median duration of follow-up was 40 months. Eighty-eight percent (88%) of patients were newly diagnosed (without a previous history of breast cancer), and (60%) of patients were chemo naïve (had not received chemotherapy at the time of their blood draw for CTC analysis). Tumor characteristics such as stage (P=0.40), tumor size (P=69), sentinel nodal involvement (P=0.87), axillary lymph node involvement (P=0.13), adjuvant therapy (P=0.83), and high histological grade of tumor (P=0.26) did not predict the presence of CTCs. However, CTCs predicted worse relapse free survival (1 or more CTCs log rank P value = 0.04, at 2 or more CTCs P = 0.02 and at 3 or more CTCs P \u3c 0.0001) and overall survival (at 1 or more CTCs log rank P value = 0.08, at 2 or more CTCs P = 0.01 and at 3 or more CTCs P = 0.0001. Conclusions. The number of circulating tumor cells predicted worse relapse free survival and overall survival in TNBC patients

Exploration of Genetically Relevant Representations of Glucose Tolerance Tests Among Mexican Americans in Starr County, Texas

As obesity rates have risen, the prevalence of concomitant metabolic disorders has increased dramatically. Globally, around 415 million people have diabetes (9% of adults). Over the next decade, the number of affected cases is estimated to increase to 642 million people, making type 2 diabetes one of the fastest growing global epidemics. Rising prevalence, the severity of comorbidities, and relatively high estimates of heritability have lead type 2 diabetes to be the focus of decades of genetic research. Despite identifying \u3e80 loci associated with type 2 diabetes risk only ∼20% of its heritability is explained [1]. glucose dysregulation traits that are genetically enriched, may lead to the identification of newer loci associated with type 2 diabetes. We propose a novel application of canonical correlation analysis as a way to extract genetically enriched traits. Canonical correlation analysis identifies the best function of genetic variation that is maximally correlated with the function of phenotypic variation. Via simulation, we demonstrate that our implementation of canonical correlation analysis performs better than genome-wide analysis on each of the traits individually as well as the principal components of those traits. Our implementation of canonical correlation provides overall less power compared to the established methods of multivariate genome-wide analyses such as multi-trait meta-analysis, and multivariate-PLINK. However, our implementation provides more flexibility by allowing a search for genetic associations in the context of biological processes. We implemented canonical correlation analysis to explore genetically enriched oral glucose tolerance test data for genetic associations. We utilized data from the Starr County Health Studies. This dataset contains an array of traits and biological samples that have been collected over more than three decades. Analyses were performed using 484 Hispanic individuals who underwent oral glucose tolerance test from 2002-2006. We demonstrated a statistically significant canonical correlation between the traits of oral glucose tolerance test and previously documented genetic variants associated with type 2 diabetes. Additionally, we demonstrated a similar relationship with variants representing genes associated with maturity-onset diabetes of young (MODY). Although, in our analyses, no variant reached genome-wide statistical significance using our implementation of canonical correlation analysis, multivariate-PLINK, and multivariate meta-analysis method by Bolormaa et al.[2], our method provides a novel and flexible framework to identify genetically relevant variation while analyzing any set of correlated traits

Circulating tumor cells in breast cancer patients treated by neoadjuvant chemotherapy: A Meta-analysis

BACKGROUND: We conducted a meta-analysis in nonmetastatic breast cancer patients treated by neoadjuvant chemotherapy (NCT) to assess the clinical validity of circulating tumor cell (CTC) detection as a prognostic marker. METHODS: We collected individual patient data from 21 studies in which CTC detection by CellSearch was performed in early breast cancer patients treated with NCT. The primary end point was overall survival, analyzed according to CTC detection, using Cox regression models stratified by study. Secondary end points included distant disease-free survival, locoregional relapse-free interval, and pathological complete response. All statistical tests were two-sided. RESULTS: Data from patients were collected before NCT (n = 1574) and before surgery (n = 1200). CTC detection revealed one or more CTCs in 25.2% of patients before NCT; this was associated with tumor size (P < .001). The number of CTCs detected had a detrimental and decremental impact on overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P < .001), but not on pathological complete response. Patients with one, two, three to four, and five or more CTCs before NCT displayed hazard ratios of death of 1.09 (95% confidence interval [CI] = 0.65 to 1.69), 2.63 (95% CI = 1.42 to 4.54), 3.83 (95% CI = 2.08 to 6.66), and 6.25 (95% CI = 4.34 to 9.09), respectively. In 861 patients with full data available, adding CTC detection before NCT increased the prognostic ability of multivariable prognostic models for overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P = .008). CONCLUSIONS: CTC count is an independent and quantitative prognostic factor in early breast cancer patients treated by NCT. It complements current prognostic models based on tumor characteristics and response to therapy