Elevated expression of polymorphonuclear leukocyte elastase in breast cancer tissue is associated with tamoxifen failure in patients with advanced disease

Besides a variety of other proteases, polymorphonuclear leukocyte elastase (PMN-E) is also suggested to play a role in the processes of tumour cell invasion and metastasis. Yet, there is only limited data available on the relation between the tumour level of PMN-E and prognosis in patients with primary breast cancer, and no published information exists on its relation with the efficacy of response to systemic therapy in patients with advanced breast cancer. In the present study, we have measured with enzyme-linked immunosorbent assay the levels of total PMN-E in cytosolic extracts of 463 primary breast tumours, and have correlated their levels with the rate and duration of response on first-line tamoxifen therapy (387 patients) or chemotherapy (76 patients) in patients with locally advanced and/or distant metastatic breast cancer. Furthermore, the probabilities of progression-free survival and postrelapse survival were studied in relation to the tumour levels of PMN-E. Our results show that in logistic regression analysis for response to tamoxifen treatment in patients with advanced disease, high PMN-E tumour levels were associated with a poor rate of response compared with those with low PMN-E levels (odds ratio: OR, 0.40; 95% CI, 0.22-0.73; P = 0.003). After correction for the contribution of the traditional predictive factors in multivariate analysis, the tumour PMN-E status was an independent predictor of response (P = 0.01). Furthermore, a high tumour PMN-E level was related with a poor progression-free survival (P<0.001) and postrelapse survival (P = 0.002) in a time-dependent analysis. In contrast, the tumour level of PMN-E was not significantly related with the efficacy of response to first-line chemotherapy in patients with advanced breast cancer. Our present results suggest that PMN-E is an independent predictive marker for the efficacy of tamoxifen treatment in patients with advanced breast cancer

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+ SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (X-2 (3) = 63.704, p < 0.001) as well as casecontrol differences in age-related cortical thinning (X-2 (3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (X-2 (3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.Stress-related psychiatric disorders across the life spa