Increased PD-1 Expression on Circulating T Cells Correlates with Inferior Outcome after Autologous Stem Cell Transplantation.

High-dose chemotherapy followed by autologous stem cell transplantation (auto-SCT) is a well-established treatment option for multiple myeloma and malignant lymphoma patients. It is able to induce long-term progression-free survival (PFS) in both patient groups and even provide a cure in patients with aggressive lymphoma. However, relapse is common and has been associated with the pace and quality of immunologic reconstitution after transplantation, as well as with immune cell exhaustion and immunometabolic defects. We aimed to analyze the dynamics of the prototypical exhaustion marker PD-1 on immune cells during reconstitution on high-dose chemotherapy followed by auto-SCT and its impact on PFS. We performed a comprehensive analysis of exhaustion and metabolic markers on immune cells from myeloma and lymphoma patients undergoing auto-SCT using flow cytometry and NanoString technologies. The expression levels of PD-1 were increased during early reconstitution after transplantation on T cells and natural killer (NK) cells, as well as on monocytes. However, while PD-1 expression in NK cells and monocytes normalized over time, PD-1 expression on T cells demonstrated a variable course. Of note, lymphoma patients with continuously increasing PD-1 expression on T cells after auto-SCT had an inferior median PFS of only 146 days, whereas the median PFS was not reached in the lymphoma patients without such a PD-1 expression pattern. T cells from patients with increased PD-1 expression after auto-SCT exhibited an immunometabolic (over)activation and exhausted phenotype compared to T cells from patients with a low PD-1 expression after transplantation, including higher levels of the glycolytic pacemaker enzyme hexokinase 2 and the inhibitory receptor CTLA-4. In addition, proliferating Ki-67+ T cells were more abundant in patients with high PD-1 expression on T cells compared to those with low expression after auto-SCT (11.9% versus 4.2%). PD-1 expression on T cells might serve as an adverse biomarker for lymphoma patients undergoing auto-SCT; however, further validation by larger prospective studies is required.

Reconstructing Clonal Evolution-A Systematic Evaluation of Current Bioinformatics Approaches.

The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim-an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.

The metabolic profile of reconstituting T-cells, NK-cells, and monocytes following autologous stem cell transplantation and its impact on outcome.

Previous studies indicated a role of the reconstituting immune system for disease outcome upon high-dose chemotherapy (HDCT) and autologous stem cell transplantation (auto-SCT) in multiple myeloma (MM) and lymphoma patients. Since immune cell metabolism and function are closely interconnected, we used flow-cytometry techniques to analyze key components and functions of the metabolic machinery in reconstituting immune cells upon HDCT/auto-SCT. We observed increased proliferative activity and an upregulation of the glycolytic and fatty acid oxidation (FAO) machinery in immune cells during engraftment. Metabolic activation was more pronounced in T-cells of advanced differentiation stages, in CD56bright NK-cells, and CD14++CD16+ intermediate monocytes. Next, we investigated a potential correlation between the immune cells' metabolic profile and early progression or relapse in lymphoma patients within the first twelve months following auto-SCT. Here, persistently increased metabolic parameters correlated with a rather poor disease course. Taken together, reconstituting immune cells display an upregulated bioenergetic machinery following auto-SCT. Interestingly, a persistently enhanced metabolic immune cell phenotype correlated with reduced PFS. However, it remains to be elucidated, if the clinical data can be confirmed within a larger set of patients and if residual malignant cells not detected by conventional means possibly caused the metabolic activation.

Exploring Current Challenges and Perspectives for Automatic Reconstruction of Clonal Evolution.

BACKGROUND/AIM: In the field of cancer research, reconstructing clonal evolution is of major interest. The technique provides new insights for analysis and prediction of tumor development. However, reconstruction based on mutational data is characterized by several challenges. MATERIALS AND METHODS: By performing extensive literature research, we identified 51 currently available tools for reconstructing clonal evolution. By analyzing two cancer data sets (n=21), we investigated the applicability and performance of each tool. RESULTS: Seventeen out of 51 tools could be applied to our data. Correct clustering of variants can be observed for 4 patients in the presence of ≤3 clusters and ≥5 time points. Correct phylogenetic trees are determined for 10 patients. Accurate visualization is possible, by applying adjustments to the original algorithms. CONCLUSION: Despite bearing considerable potential, automatic reconstruction of clonal evolution remains challenging. To replace tedious manual reconstruction, further research including systematic error analyses using simulation tools needs to be conducted.