Leukemia immunotherapy frequently does not meet expectation, one of the handicaps being tumor exosome (TEX)-promoted immunosuppression. We here asked, using the mouse myeloid leukemia WEHI3B and the renal cell carcinoma line RENCA, whether dendritic cell (DC) vaccination suffices to counterregulate TEX-induced immunosuppression and whether TEX could serve as tumor antigen for DC-loading. DC-vaccination significantly prolonged the survival time of WEHI3B-bearing mice, TEXloaded DC (DC-TEX) being superior to lysate-loaded DC (DC-lys), even an excess of TEX not interfering with immune response induction. The superior response to DC-TEX was accompanied by an increase in WEHI3B-specific CD41 T cells, evaluated by trogocytosis and proliferation. Similar findings accounted for DC loaded with RENCA TEX. TEX was efficiently taken-up by DC and TEX uptake supported CD11c, MHCII and IL12 upregulation in DC. Importantly, TEX was partly recruited into the MHCIIloading compartment such that "TEX" presentation time and recovery in T cells significantly exceeded that of tumor-lysate. Thus, TEX did not drive DC into a suppressive phenotype and were a superior antigen due to higher efficacy of TEXpresentation that is supported by prolonged persistence, preferential processing in the MHCII-loading compartment and pronounced trogocytosis by T helper cells. TEX is present in tumor patients' sera. TEX, recovered and enriched from patients' sera, might well provide an optimized, individual-specific antigen source for DC-loading and vaccination.Cancer therapy can profit from supportive immunotherapy, less burdened by side effects than radio-or chemotherapy.

In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg [1,2] is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

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