Radiation Pneumonitis after breast cancer irradiation: analysis of the complication probability using the relative seriality model

Background: Toxicity of the respiratory system is quite common after radiotherapy of thoracic tumors; breast cancer patients represent one of the groups for which there is also a long expected survival. The quantification of lung tissue response to irradiation is important in designing treatments associated with a minimum of complications and maximum tumor control. Methods: The study population consisted of 68 patients who received irradiation for breast cancer at Stage II. radiation pneumonitis was retrospectively assessed on the basis of clinical symptoms and radiological findings. For each patient, a measure of the exposure (i.e., the lung dose-volume histogram [DVH]) and a measure of the outcome was available. Based on these data, a maximum likelihood fitting to the relative seriality model was performed. The uncertainties of the model parameters were calculated and their impact on the dose-response curve was studied. The optimum parameter set was then applied to 5 other patient groups treated for breast cancer, and the normal tissue complication probability (NTCP) was calculated. Each group was individuated by the radiotherapy treatment technique used; the dose distribution in the lung was described by a mean DVH and the incidence of radiation pneumonitis in each group was known. Lung radiosensitivity was assumed to be homogeneous through all of the calculations. Results: The relative seriality model could describe the dataset, The volume effect was found to be relevant in the description of radiation pneumonitis. Age was found to be associated with increased risk of radiation pneumonitis. Two distinct dose-response curves were obtained by splitting the group according to age, The impact of the parameter uncertainties on the dose-response curve was quite large, The parameter set determined could be used predictively on 3 of the 5 patient groups. Conclusion: The complication data could be modeled with the relative seriality model, However, further independent datasets, classified according to the same endpoint, must be analyzed before introducing NTCP modeling in clinical practice. (C) 2000 Elsevier Science Inc

Mammographic density and molecular subtypes of breast cancer.

BACKGROUND: Gene expression profiling has led to a subclassification of breast cancers independent of established clinical parameters, such as the Sorlie-Perou subtypes. Mammographic density (MD) is one of the strongest risk factors for breast cancer, but it is unknown if MD is associated with molecular subtypes of this carcinoma. METHODS: We investigated whether MD was associated with breast cancer subtypes in 110 women with breast cancer, operated in Stockholm, Sweden, during 1994 to 1996. Subtypes were defined using expression data from HGU133A+B chips. The MD of the unaffected breast was measured using the Cumulus software. We used multinomial logistic models to investigate the relationship between MD and Sorlie-Perou subtypes. RESULTS: Although the distribution of molecular subtypes differed in women with high vs low MD, this was statistically non-significant (P=0.249), and further analyses revealed no association between the MD and Sorlie-Perou subtypes as a whole, nor with individual subtypes. CONCLUSION: These findings suggest that although MD is one of the strongest risk factors for breast cancer, it does not seem to be differentially associated with breast cancer molecular subtypes. However, larger studies with more comprehensive covariate information are needed to confirm these results

Serum thymidine kinase activity compared with CA 15-3 in locally advanced and metastatic breast cancer within a randomized trial

The primary objective was to estimate serum thymidine kinase 1 (TK1) activity, reflecting total body cell proliferation rate including cancer cell proliferation, in women with loco regional inoperable or metastatic breast cancer participating in a prospective and randomized study. Secondary objectives were to analyze TK1 in relation to progression-free survival (PFS), overall survival (OS), therapy response and other tumour characteristics, including CA 15-3, widely used as a standard serum marker for disease progression. TK1 and CA 15-3 were analysed in 198 serum samples collected prospectively from women included in the randomized TEX trial between December 2002 and June 2007. TK1 activity was determined by the ELISA based DiviTum (TM) assay, and CA 15-3 analyses was generated with the electrochemiluminescence immunoassay Cobas Elecsys CA 15-3 II. High pre-treatment TK1 activity predicted shorter PFS (10 vs. 15 months p = 0.02) and OS (21 vs. 38 months, p < 0.0001), respectively. After adjustment for age, metastatic site and study treatment TK1 showed a trend as predictor of PFS (p = 0.059) and was an independent prognostic factor for OS, (HR 1.81, 95 % confidence interval (CI) 1.26-2.61, p = 0.001). There was a trend of shortened OS for women with high CA 15-3 (p = 0.054) in univariate analysis, but not after adjustment for the above mentioned covariates. Both TK1 (p = 0.0011) and CA 15-3 (p = 0.0004) predicted response to treatment. There were statistically different distributions of TK1 and CA 15-3 in relation to the site of metastases. TK1 activity measured by DiviTum (TM) predicted therapy response, PFS and OS in loco regional inoperable or disseminated breast cancer. These results suggest that this factor is a useful serum marker. In the present material, a prognostic value of CA 15-3 could not be proven

Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts

10.1186/bcr1325Breast cancer research : BCR76R953-R96