Central tolerance to tumor-associated Ags is an immune-escape mechanism that significantly limits the TCR repertoires available for tumor eradication. The repertoires expanded in wild-type BALB/c and rat-HER-2/neu (rHER-2) transgenic BALB-neuT mice following DNA immunization against rHER-2 were compared by spectratyping the variable (V)β and the joining (J)β CDR 3. Following immunization, BALB/c mice raised a strong response. Every mouse used one or more CD8+ T cell rearrangements of the Vβ9-Jβ1.2 segments characterized by distinct length of the CDR3 and specific for 63-71 or 1206-1214 rHER-2 peptides. In addition, two CD4+ T cell rearrangements recurred in >50% of mice. Instead, BALB-neuT mice displayed a limited response to rHER-2. Their repertoire is smaller and uses different rearrangements confined to CD4+ T cells. Thus, central tolerance in BALB-neuT mice acts by silencing the BALB/c mice self-reactive repertoire and reducing the size of the CD8+ T cell component. CD8+ and CD4+ T cells from both wild-type and transgenic mice home to tumors. This definition of the T cell repertoires available is critical to the designing of immunological maneuvers able to elicit an effective immune reaction against HER-2-driven carcinogenesis.
PURPOSE For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse. METHODS The data we used for our model consisted of 642 patients with 21 clinicopathologic variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter α) and dissemination (parameter μ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of five covariates with the best predictive power. These were further considered to individually predict the model parameters by using a backward selection approach. Predictive performances were compared with classic Cox regression and machine learning algorithms. RESULTS The mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with α ( P = .001) and EGFR expression with μ ( P = .009). The model achieved a c-index of 0.65 (95% CI, 0.60 to 0.71) in cross-validation and had predictive performance similar to that of random survival forest (95% CI, 0.66 to 0.69) and Cox regression (95% CI, 0.62 to 0.67) as well as machine learning classification algorithms. CONCLUSION By providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool for routine management of patients with breast cancer.
Ceramides are intramembrane diffusible mediators involved in transducing signals originated from a variety of cell surface receptors. Different adaptive and differentiative cellular responses, including apoptotic cell death, use ceramide-mediated pathways as an essential part of the program. Here, we show that human dendritic cells respond to CD40 ligand, as well as to tumor necrosis factor-α and IL-1β, with intracellular ceramide accumulation, as they are induced to differentiate. Dendritic cells down-modulate their capacity to take up soluble antigens in response to exogenously added or endogenously produced ceramides. This is followed by an impairment in presenting soluble antigens to specific T cell clones, while cell viability and the capacity to stimulate allogeneic responses or to present immunogenic peptides is fully preserved. Thus, ceramide-mediated pathways initiated by different cytokines can actively modulate professional antigen-presenting cell function and antigen-specific immune responses.
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