NK Cell Phenotype Is Associated With Response and Resistance to Daratumumab in Relapsed/Refractory Multiple Myeloma.

The CD38-targeting antibody daratumumab has marked activity in multiple myeloma (MM). Natural killer (NK) cells play an important role during daratumumab therapy by mediating antibody-dependent cellular cytotoxicity via their FcγRIII receptor (CD16), but they are also rapidly decreased following initiation of daratumumab treatment. We characterized the NK cell phenotype at baseline and during daratumumab monotherapy by flow cytometry and cytometry by time of flight to assess its impact on response and development of resistance (DARA-ATRA study; NCT02751255). At baseline, nonresponding patients had a significantly lower proportion of CD16+ and granzyme B+ NK cells, and higher frequency of TIM-3+ and HLA-DR+ NK cells, consistent with a more activated/exhausted phenotype. These NK cell characteristics were also predictive of inferior progression-free survival and overall survival. Upon initiation of daratumumab treatment, NK cells were rapidly depleted. Persisting NK cells exhibited an activated and exhausted phenotype with reduced expression of CD16 and granzyme B, and increased expression of TIM-3 and HLA-DR. We observed that addition of healthy donor-derived purified NK cells to BM samples from patients with either primary or acquired daratumumab-resistance improved daratumumab-mediated MM cell killing. In conclusion, NK cell dysfunction plays a role in primary and acquired daratumumab resistance. This study supports the clinical evaluation of daratumumab combined with adoptive transfer of NK cells.

A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data.

RATIONALE: Non-negative matrix factorization (NMF) has been used extensively for the analysis of mass spectrometry imaging (MSI) data, visualizing simultaneously the spatial and spectral distributions present in a slice of tissue. The statistical framework offers two related NMF methods: probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), which is a generative model. This work offers a mathematical comparison between NMF, PLSA, and LDA, and includes a detailed evaluation of Kullback-Leibler NMF (KL-NMF) for MSI for the first time. We will inspect the results for MSI data analysis as these different mathematical approaches impose different characteristics on the data and the resulting decomposition. METHODS: The four methods (NMF, KL-NMF, PLSA, and LDA) are compared on seven different samples: three originated from mice pancreas and four from human-lymph-node tissues, all obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). RESULTS: Where matrix factorization methods are often used for the analysis of MSI data, we find that each method has different implications on the exactness and interpretability of the results. We have discovered promising results using KL-NMF, which has only rarely been used for MSI so far, improving both NMF and PLSA, and have shown that the hitherto stated equivalent KL-NMF and PLSA algorithms do differ in the case of MSI data analysis. LDA, assumed to be the better method in the field of text mining, is shown to be outperformed by PLSA in the setting of MALDI-MSI. Additionally, the molecular results of the human-lymph-node data have been thoroughly analyzed for better assessment of the methods under investigation. CONCLUSIONS: We present an in-depth comparison of multiple NMF-related factorization methods for MSI. We aim to provide fellow researchers in the field of MSI a clear understanding of the mathematical implications using each of these analytical techniques, which might affect the exactness and interpretation of the results.

Correspondence-Aware Manifold Learning for Microscopic and Spatial Omics Imaging: A Novel Data Fusion Method Bringing Mass Spectrometry Imaging to a Cellular Resolution.

High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While hematoxylin and eosin (H&E) stainings are still the gold standard to diagnose diseases, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualize the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as mass spectrometry imaging (MSI) to the cellular resolution.

Deep immune profiling of patients treated with lenalidomide and dexamethasone with or without daratumumab.

CD38-targeted antibody, daratumumab, is approved for the treatment of multiple myeloma (MM). Phase 1/2 studies GEN501/SIRIUS revealed a novel immunomodulatory mechanism of action (MOA) of daratumumab that enhanced the immune response, reducing natural killer (NK) cells without affecting efficacy or safety. We further evaluated daratumumab's effects on immune cells in whole blood samples of relapsed/refractory MM patients from both treatment arms of the phase 3 POLLUX study (lenalidomide/dexamethasone [Rd] or daratumumab plus Rd [D-Rd]) at baseline (D-Rd, 40; Rd, 45) and after 2 months on treatment (D-Rd, 31; Rd, 33) using cytometry by time-of-flight. We confirmed previous reports of NK cell reduction with D-Rd. Persisting NK cells were phenotypically distinct, with increased expression of HLA-DR, CD69, CD127, and CD27. The proportion of T cells increased preferentially in deep responders to D-Rd, with a higher proportion of CD8+ versus CD4+ T cells. The expansion of CD8+ T cells correlated with clonality, indicating generation of adaptive immune response with D-Rd. D-Rd resulted in a higher proportion of effector memory T cells versus Rd. D-Rd reduced immunosuppressive CD38+ regulatory T cells. This study confirms daratumumab's immunomodulatory MOA in combination with immunomodulatory drugs and provides further insight into immune cell changes and activation status following daratumumab-based therapy.

Prioritization of m/z-Values in Mass Spectrometry Imaging Profiles Obtained Using Uniform Manifold Approximation and Projection for Dimensionality Reduction.

Mass spectrometry imaging (MSI) is a promising technique to assess the spatial distribution of molecules in a tissue sample. Nonlinear dimensionality reduction methods such as Uniform Manifold Approximation and Projection (UMAP) can be very valuable for the visualization of the massive data sets produced by MSI. These visualizations can offer us good initial insights regarding the heterogeneity and variety of molecular patterns present in the data, but they do not discern which molecules might be driving these observations. To prioritize the m/z-values associated with these biochemical profiles, we apply a bidirectional dimensionality reduction approach taking into account both the spectral and spatial information. The results show that both sources of information are instrumental to get a more comprehensive view on the relevant m/z-values and can support the reliability of the results obtained using UMAP. We illustrate our approach on heterogeneous pancreas tissues obtained from healthy mice.

Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data.

In this work, uniform manifold approximation and projection (UMAP) is applied for nonlinear dimensionality reduction and visualization of mass spectrometry imaging (MSI) data. We evaluate the performance of the UMAP algorithm on MSI data sets acquired in mouse pancreas and human lymphoma samples and compare it to those of principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and the Barnes-Hut (BH) approximation of t-SNE. Furthermore, we compare different distance metrics in (BH) t-SNE and UMAP and propose the use of spatial autocorrelation as a means of comparing the resulting low-dimensional embeddings. The results indicate that UMAP is competitive with t-SNE in terms of visualization and is well-suited for the dimensionality reduction of large (>100 000 pixels) MSI data sets. With an almost fourfold decrease in runtime, it is more scalable in comparison with the current state-of-the-art: t-SNE or the Barnes-Hut approximation of t-SNE. In what seems to be the first application of UMAP to MSI data, we assess the value of applying alternative distance metrics, such as the correlation, cosine, and the Chebyshev metric, in contrast to the traditionally used Euclidean distance metric. Furthermore, we propose "histomatch" as an additional custom distance metric for the analysis of MSI data.

Somatic Tumor Variant Filtration Strategies to Optimize Tumor-Only Molecular Profiling Using Targeted Next-Generation Sequencing Panels.

A common approach in clinical diagnostic laboratories to variant assessment from tumor molecular profiling is sequencing of genomic DNA extracted from both tumor (somatic) and normal (germline) tissue, with subsequent variant comparison to identify true somatic variants with potential impact on patient treatment or prognosis. However, challenges exist in paired tumor-normal testing, including increased cost of dual sample testing and identification of germline cancer predisposing variants. Alternatively, somatic variants can be identified by in silico tumor-only variant filtration precluding the need for matched normal testing. The barrier to tumor-only variant filtration is defining a reliable approach, with high sensitivity and specificity to identify somatic variants. In this study, we used retrospective data sets from paired tumor-normal samples tested on small (48 gene) and large (555 gene) targeted next-generation sequencing panels, to model algorithms for tumor-only variants classification. The optimal algorithm required an ordinal filtering approach using information from variant population databases (1000 Genomes Phase 3, ESP6500, ExAC), clinical mutation databases (ClinVar), and information on recurring clinically relevant somatic variants. Overall the tumor-only variant filtration strategy described in this study can define clinically relevant somatic variants from tumor-only analysis with sensitivity of 97% to 99% and specificity of 87% to 94%, and with significant potential utility for clinical laboratories implementing tumor-only molecular profiling.

High-Parameter Mass Cytometry Evaluation of Relapsed/Refractory Multiple Myeloma Patients Treated with Daratumumab Demonstrates Immune Modulation as a Novel Mechanism of Action.

Daratumumab is a CD38-targeted human monoclonal antibody with direct anti-myeloma cell mechanisms of action. Flow cytometry in relapsed and/or refractory multiple myeloma (RRMM) patients treated with daratumumab revealed cytotoxic T-cell expansion and reduction of immune-suppressive populations, suggesting immune modulation as an additional mechanism of action. Here, we performed an in-depth analysis of the effects of daratumumab on immune-cell subpopulations using high-dimensional mass cytometry. Whole-blood and bone-marrow baseline and on-treatment samples from RRMM patients who participated in daratumumab monotherapy studies (SIRIUS and GEN501) were evaluated with high-throughput immunophenotyping. In daratumumab-treated patients, the intensity of CD38 marker expression decreased on many immune cells in SIRIUS whole-blood samples. Natural killer (NK) cells were depleted with daratumumab, with remaining NK cells showing increased CD69 and CD127, decreased CD45RA, and trends for increased CD25, CD27, and CD137 and decreased granzyme B. Immune-suppressive population depletion paralleled previous findings, and a newly observed reduction in CD38+ basophils was seen in patients who received monotherapy. After 2 months of daratumumab, the T-cell population in whole-blood samples from responders shifted to a CD8 prevalence with higher granzyme B positivity (P = 0.017), suggesting increased killing capacity and supporting monotherapy-induced CD8+ T-cell activation. High-throughput cytometry immune profiling confirms and builds upon previous flow cytometry data, including comparable CD38 marker intensity on plasma cells, NK cells, monocytes, and B/T cells. Interestingly, a shift toward cytolytic granzyme B+ T cells was also observed and supports adaptive responses in patients that may contribute to depth of response. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Deep Profiling of the Immune System of Multiple Myeloma Patients Using Cytometry by Time-of-Flight (CyTOF).

Mass cytometry has emerged as a new state-of-the-art technology that enables in-depth characterization of cellular populations and functions at a single cell resolution. We describe the application of this technology to deeply phenotype the blood and bone marrow components of multiple myeloma patients in a clinical setting. This technology allows for simultaneous quantification of more than 40 markers, overcoming the challenges of traditional fluorescence-based flow cytometry.

Ageing with elegans: a research proposal to map healthspan pathways.

Human longevity continues to increase world-wide, often accompanied by decreasing birth rates. As a larger fraction of the population thus gets older, the number of people suffering from disease or disability increases dramatically, presenting a major societal challenge. Healthy ageing has therefore been selected by EU policy makers as an important priority ( http://www.healthyageing.eu/european-policies-and-initiatives ); it benefits not only the elderly but also their direct environment and broader society, as well as the economy. The theme of healthy ageing figures prominently in the Horizon 2020 programme ( https://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing ), which has launched several research and innovation actions (RIA), like "Understanding health, ageing and disease: determinants, risk factors and pathways" in the work programme on "Personalising healthcare" ( https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/693-phc-01-2014.html ). Here we present our research proposal entitled "ageing with elegans" (AwE) ( http://www.h2020awe.eu/ ), funded by this RIA, which aims for better understanding of the factors causing health and disease in ageing, and to develop evidence-based prevention, diagnostic, therapeutic and other strategies. The aim of this article, authored by the principal investigators of the 17 collaborating teams, is to describe briefly the rationale, aims, strategies and work packages of AwE for the purposes of sharing our ideas and plans with the biogerontological community in order to invite scientific feedback, suggestions, and criticism.