Magnetic moment quenching in small Pd clusters in solution

Small palladium clusters in vacuum show pronounced magnetic moments. With the help of Born–Oppenheimer molecular dynamics simulations based on density functional theory, we investigate for the paradigmatic examples of the Pd$$_{13}$$ and the Pd$$_8$$ cluster whether these magnetic moments prevail when the clusters are solvated. Our results show that the interaction with acetophenone quenches the magnetic moment. The reduction of the magnetic moment is a direct consequence of the electronic interaction between the Pd clusters and the solvent molecules, and not an indirect effect due to a different cluster geometry being stabilized by the solvation shell

Combined Metabolic Targeting With Metformin and the NSAIDs Diflunisal and Diclofenac Induces Apoptosis in Acute Myeloid Leukemia Cells

The accelerated metabolism of tumor cells, inevitable for maintaining high proliferation rates, is an emerging target for tumor therapy. Increased glucose and lipid metabolism as well as mitochondrial activity have been shown in solid tumors but also in leukemic cells. As tumor cells are able to escape the blockade of one metabolic pathway by a compensatory increase in other pathways, treatment strategies simultaneously targeting metabolism at different sites are currently developed. However, the number of clinically applicable anti-metabolic drugs is still limited. Here, we analyzed the impact of the anti-diabetic drug metformin alone or in combination with two non-steroidal anti-inflammatory drugs (NSAIDs) diclofenac and diflunisal on acute myeloid leukemia (AML) cell lines and primary patient blasts. Diclofenac but not diflunisal reduced lactate secretion in different AML cell lines (THP-1, U937, and KG-1) and both drugs increased respiration at low concentrations. Despite these metabolic effects, both NSAIDs showed a limited effect on tumor cell proliferation and viability up to a concentration of 0.2 mM. In higher concentrations of 0.4–0.8 mM diflunisal alone exerted a clear effect on proliferation of AML cell lines and blocked respiration. Single treatment with the anti-diabetic drug metformin blocked mitochondrial respiration, but proliferation and viability were not affected. However, combining all three drugs exerted a strong cytostatic and cytotoxic effect on THP-1 cells. Comparable to the results obtained with THP-1 cells, the combination of all three drugs significantly reduced proliferation of primary leukemic blasts and induced apoptosis. Furthermore, NSAIDs supported the effect of low dose chemotherapy with cytarabine and reduced proliferation of primary AML blasts. Taken together we show that low concentrations of metformin and the two NSAIDs diclofenac and diflunisal exert a synergistic inhibitory effect on AML proliferation and induce apoptosis most likely by blocking tumor cell metabolism. Our results underline the feasibility of applying anti-metabolic drugs for AML therapy

HLA-DPB1 Reactive T Cell Receptors for Adoptive Immunotherapy in Allogeneic Stem Cell Transplantation

HLA-DPB1 antigens are mismatched in about 80% of allogeneic hematopoietic stem cell transplantations from HLA 10/10 matched unrelated donors and were shown to be associated with a decreased risk of leukemia relapse. We recently developed a reliable in vitro method to generate HLA-DPB1 mismatch-reactive CD4 T-cell clones from allogeneic donors. Here, we isolated HLA-DPB1 specific T cell receptors (TCR DP) and used them either as wild-type or genetically optimized receptors to analyze in detail the reactivity of transduced CD4 and CD8 T cells toward primary AML blasts. While both CD4 and CD8 T cells showed strong AML reactivity in vitro, only CD4 T cells were able to effectively eliminate leukemia blasts in AML engrafted NOD/SCID/IL2Rγc−/− (NSG) mice. Further analysis showed that optimized TCR DP and under some conditions wild-type TCR DP also mediated reactivity to non-hematopoietic cells like fibroblasts or tumor cell lines after HLA-DP upregulation. In conclusion, T cells engineered with selected allo-HLA-DPB1 specific TCRs might be powerful off-the-shelf reagents in allogeneic T-cell therapy of leukemia. However, because of frequent (common) cross-reactivity to non-hematopoietic cells with optimized TCR DP T cells, safety mechanisms are mandatory

Breast MRI texture analysis for prediction of BRCA-associated genetic risk

Background BRCA1/2 deleterious variants account for most of the hereditary breast and ovarian cancer cases. Prediction models and guidelines for the assessment of genetic risk rely heavily on criteria with high variability such as family cancer history. Here we investigated the efficacy of MRI (magnetic resonance imaging) texture features as a predictor for BRCA mutation status. Methods A total of 41 female breast cancer individuals at high genetic risk, sixteen with a BRCA1/2 pathogenic variant and twenty five controls were included. From each MRI 4225 computer-extracted voxels were analyzed. Non-imaging features including clinical, family cancer history variables and triple negative receptor status (TNBC) were complementarily used. Lasso-principal component regression (L-PCR) analysis was implemented to compare the predictive performance, assessed as area under the curve (AUC), when imaging features were used, and lasso logistic regression or conventional logistic regression for the remaining analyses. Results Lasso-selected imaging principal components showed the highest predictive value (AUC 0.86), surpassing family cancer history. Clinical variables comprising age at disease onset and bilateral breast cancer yielded a relatively poor AUC (~ 0.56). Combination of imaging with the non-imaging variables led to an improvement of predictive performance in all analyses, with TNBC along with the imaging components yielding the highest AUC (0.94). Replacing family history variables with imaging components yielded an improvement of classification performance of ~ 4%, suggesting that imaging compensates the predictive information arising from family cancer structure. Conclusions The L-PCR model uncovered evidence for the utility of MRI texture features in distinguishing between BRCA1/2 positive and negative high-risk breast cancer individuals, which may suggest value to diagnostic routine. Integration of computer-extracted texture analysis from MRI modalities in prediction models and inclusion criteria might play a role in reducing false positives or missed cases especially when established risk variables such as family history are missing