In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.
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Question: Most anticancer chemotherapies are immunosuppressive and induce nonimmunogenic tumor cell death. Bortezomib, a specific inhibitor of 26S proteasome, has shown clinical activity in several human tumors, including myeloma. Here we show that the uptake of human myeloma cells by dendritic cells (DCs) after tumor cell death by bortezomib, but not gamma irradiation or steroids, leads to the induction of antitumor immunity, including against primary tumor cells, without the need for any additional adjuvants. The delivery of activating signal from bortezomib-killed tumor cells to DCs depends on cell-cell contact between DCs and dying tumor cells and is mediated by bortezomib-induced exposure of heat shock protein 90 (hsp90) on the surface of dying cells. The combination of bortezomib and geldanamycin (an hsp90 inhibitor) leads to greater apoptosis of tumor cells but abrogates their immunogenicity. These data identify drug-induced exposure of endogenous heat shock proteins on the surface of dying cells as a mechanism of immunogenic death of human tumors. Specific targeting of bortezomib to tumors may enhance their immunogenicity and the induction of antitumor immunity.

Answer: Bortezomib enhances dendritic cell (DC)-mediated induction of immunity to human myeloma via exposure of cell surface heat shock protein 90 on dying tumor cells: therapeutic implications.

Question: BACKGROUND Clonal competition in cancer describes the process in which the progeny of a cell clone supersedes or succumbs to other competing clones due to differences in their functional characteristics, mostly based on subsequently acquired mutations. Even though the patterns of those mutations are well explored in many tumors, the dynamical process of clonal selection is underexposed.
METHODS We studied the dynamics of clonal competition in a BcrAbl-induced leukemia using a -retroviral vector library encoding the oncogene in conjunction with genetic barcodes. To this end, we studied the growth dynamics of transduced cells on the clonal level both in vitro and in vivo in transplanted mice.
RESULTS While we detected moderate changes in clonal abundancies in vitro, we observed monoclonal leukemias in 6/30 mice after transplantation, which intriguingly were caused by only two different BcrAbl clones. To analyze the success of these clones, we applied a mathematical model of hematopoietic tissue maintenance, which indicated that a differential engraftment capacity of these two dominant clones provides a possible explanation of our observations. These findings were further supported by additional transplantation experiments and increased BcrAbl transcript levels in both clones.
CONCLUSION Our findings show that clonal competition is not an absolute process based on mutations, but highly dependent on selection mechanisms in a given environmental context.

Answer: Clonal competition in BcrAbl-driven leukemia: how transplantations can accelerate clonal conversion

Question: Purpose. To demonstrate how correlations among predictor variables in a population pharmacokinetic model affect the ability to discern which covariates should enter into the structural pharmacokinetic model. Methods. Monte Carlo simulation was used to generate multiple-dose concentration-time data similar to that seen in a Phase III clinical trial. The drugs' pharmacokinetics were dependent on two covariates. Five data sets were simulated with increasing correlation between the two covariates. All data sets were analyzed using NONMEM both with and without inclusion of the covariates in the structural pharmacokinetic model. Summary measures for ill-conditioning and sensitivity analysis were used to examine how increasing correlation among covariates affects the accuracy and precision of the parameter estimates. Results. When covariates were included in the structural pharmacokinetic model and the correlation between covariates increased, the standard error of the parameter estimates increased and the value of parameter estimates themselves became increasingly biased. When the correlation between predictor variables was 0.75, the standard errors of the parameter estimates were too large to declare statistical significance. Conclusions. Correlations among predictor variables greater than 0.5 when entered into the model simultaneously should be a warning to researchers because the (1) the accuracy of the parameter estimates themselves may be biased and (2) the precision of the estimates may be inflated due to ill-conditioning.

Answer:
The Effect of Collinearity on Parameter Estimates in Nonlinear Mixed Effect Models