CD19 occupancy with tafasitamab increases therapeutic index of CART19 cell therapy and diminishes severity of CRS.

ABSTRACT: In the development of various strategies of anti-CD19 immunotherapy for the treatment of B-cell malignancies, it remains unclear whether CD19 monoclonal antibody therapy impairs subsequent CD19-targeted chimeric antigen receptor T-cell (CART19) therapy. We evaluated the potential interference between the CD19-targeting monoclonal antibody tafasitamab and CART19 treatment in preclinical models. Concomitant treatment with tafasitamab and CART19 showed major CD19 binding competition, which led to CART19 functional impairment. However, when CD19+ cell lines were pretreated with tafasitamab overnight and the unbound antibody was subsequently removed from the culture, CART19 function was not affected. In preclinical in vivo models, tafasitamab pretreatment demonstrated reduced incidence and severity of cytokine release syndrome and exhibited superior antitumor effects and overall survival compared with CART19 alone. This was associated with transient CD19 occupancy with tafasitamab, which in turn resulted in the inhibition of CART19 overactivation, leading to diminished CAR T apoptosis and pyroptosis of tumor cells.

Activity of tafasitamab in combination with rituximab in subtypes of aggressive lymphoma.

Background: Despite recent advances in the treatment of aggressive lymphomas, a significant fraction of patients still succumbs to their disease. Thus, novel therapies are urgently needed. As the anti-CD20 antibody rituximab and the CD19-targeting antibody tafasitamab share distinct modes of actions, we investigated if dual-targeting of aggressive lymphoma B-cells by combining rituximab and tafasitamab might increase cytotoxic effects. Methods: Antibody single and combination efficacy was determined investigating different modes of action including direct cytotoxicity, antibody-dependent cell-mediated cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) in in vitro and in vivo models of aggressive B-cell lymphoma comprising diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL). Results: Three different sensitivity profiles to antibody monotherapy or combination treatment were observed in in vitro models: while 1/11 cell lines was primarily sensitive to tafasitamab and 2/11 to rituximab, the combination resulted in enhanced cell death in 8/11 cell lines in at least one mode of action. Treatment with either antibody or the combination resulted in decreased expression of the oncogenic transcription factor MYC and inhibition of AKT signaling, which mirrored the cell line-specific sensitivities to direct cytotoxicity. At last, the combination resulted in a synergistic survival benefit in a PBMC-humanized Ramos NOD/SCID mouse model. Conclusion: This study demonstrates that the combination of tafasitamab and rituximab improves efficacy compared to single-agent treatments in models of aggressive B-cell lymphoma in vitro and in vivo.

Tafasitamab mediates killing of B-cell non-Hodgkin's lymphoma in combination with γδ T cell or allogeneic NK cell therapy.

Tafasitamab is an Fc-modified monoclonal antibody that binds to CD19, a cell-surface antigen that is broadly expressed on various types of B-cell non-Hodgkin's lymphoma (NHL). Antibody-dependent cellular cytotoxicity (ADCC), a key mode of action of tafasitamab, is mediated through the binding of tafasitamab's Fc region to FcγRIIIa receptors on immune effector cells and results in antitumor activity. Despite the proven clinical activity of tafasitamab in combination with lenalidomide in the treatment of diffuse large B-cell lymphoma (DLBCL), a higher number of immune cells in cancer patients may improve the activity of tafasitamab. Here, we characterized two ex vivo-expanded FcγRIIIa receptor-expressing cell types-γδ T and MG4101 natural killer (NK) cells-as effector cells for tafasitamab in vitro, and found that in the presence of these cells tafasitamab was able to induce ADCC against a range of NHL cell lines and patient-derived cells. We also explored the concept of effector cell supplementation during tafasitamab treatment in vivo by coadministering MG4101 NK cells in Raji and Ramos xenograft models of NHL. Combination treatment of tafasitamab and allogeneic MG4101 NK cells in these models demonstrated a survival benefit compared with tafasitamab or MG4101 monotherapy (Raji: 1.7- to 1.9-fold increase in lifespan; Ramos: 2.0- to 4.1-fold increase in lifespan). In conclusion, adoptive cell transfer of ex vivo-expanded allogeneic NK or autologous γδ T cells in combination with tafasitamab treatment may potentially be a promising novel approach to increase the number of immune effector cells and enhance the antitumor effect of tafasitamab.

A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems.

For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype's user interface (UI) features and segmentation methodologies.

Neutralization of IL-17C Reduces Skin Inflammation in Mouse Models of Psoriasis and Atopic Dermatitis.

IL-17C is a functionally distinct member of the IL-17 family that was believed to play a role in the pathogenesis of psoriasis. Here we confirmed that IL-17C is involved in psoriasis and explored potential roles for IL-17C in atopic dermatitis (AD). An anti-IL-17C antibody, MOR106, was generated that potently and selectively binds to human and mouse IL-17C, thereby inhibiting the binding of IL-17C to its IL-17RE receptor. The antibody inhibited cutaneous inflammation in an IL-23-induced psoriatic-like skin inflammation model. In lesional skin of patients with AD, IL-17C expression levels were increased and localized to keratinocytes and infiltrating immune cells. To determine the contribution of IL-17C to AD pathogenesis, MOR106 was tested in two distinct in vivo models. In the calcipotriol-induced AD model, ear skin inflammation, TSLP, and IL-33 protein production in ears was suppressed by MOR106. Consistently, in the flaky tail strain mouse model, spontaneous development of AD-like skin inflammation was reduced by MOR106. Moreover, serum IgE levels, number of mast cells in skin and T helper type 2-related cytokines IL-4 and CCL17 in serum were all reduced. Overall, our results indicate that IL-17C is a central mediator of skin inflammation beyond psoriasis and is relevant in particular in AD.

A self-taught artificial agent for multi-physics computational model personalization.

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.

Molecular basis of in vitro affinity maturation and functional evolution of a neutralizing anti-human GM-CSF antibody.

X-ray structure analysis of 4 antibody Fab fragments, each in complex with human granulocyte macrophage colony stimulating factor (GM-CSF), was performed to investigate the changes at the protein-protein binding interface during the course of in vitro affinity maturation by phage display selection. The parental antibody MOR03929 was compared to its derivatives MOR04252 (CDR-H2 optimized), MOR04302 (CDR-L3 optimized) and MOR04357 (CDR-H2 and CDR-L3 optimized). All antibodies bind to a conformational epitope that can be divided into 3 sub-epitopes. Specifically, MOR04357 binds to a region close to the GM-CSF N-terminus (residues 11-24), a short second sub-epitope (residues 83-89) and a third at the C-terminus (residues 112-123). Modifications introduced during affinity maturation in CDR-H2 and CDR-L3 led to the establishment of additional hydrogen bonds and van der Waals contacts, respectively, providing a rationale for the observed improvement in binding affinity and neutralization potency. Once GM-CSF is complexed to the antibodies, modeling predicts a sterical clash with GM-CSF binding to GM-CSF receptor α and ß chain. This predicted mutually exclusive binding was confirmed by a GM-CSF receptor α chain ligand binding inhibition assay. Finally, high throughput sequencing of clones obtained after affinity maturation phage display pannings revealed highly selected consensus sequences for CDR-H2 as well for CDR-L3, which are in accordance with the sequence of the highest affinity antibody MOR04357. The resolved crystal structures highlight the criticality of these strongly selected residues for high affinity interaction with GM-CSF.

Randomized phase 1b trial of MOR103, a human antibody to GM-CSF, in multiple sclerosis.

OBJECTIVES: To determine the safety, pharmacokinetics (PK), and immunogenicity of the recombinant human monoclonal antibody MOR103 to granulocyte-macrophage colony-stimulating factor (GM-CSF) in patients with multiple sclerosis (MS) with clinical or MRI activity. METHODS: In this 20-week, randomized, double-blind, placebo-controlled phase 1b dose-escalation trial (registration number NCT01517282), adults with relapsing-remitting MS (RRMS) or secondary progressive MS (SPMS) received an IV infusion of placebo (n = 6) or MOR103 0.5 (n = 8), 1.0 (n = 8), or 2.0 (n = 9) mg/kg every 2 weeks for 10 weeks. Patients had to have ≤10 gadolinium (Gd)-enhancing brain lesions on T1-weighted MRI at baseline. The primary objective was safety. RESULTS: Most treatment-emergent adverse events (TEAEs) were mild to moderate in severity. The most frequent was nasopharyngitis. Between-group differences in TEAE numbers were small. There were no TEAE-related trial discontinuations, infusion-related reactions, or deaths. Nine patients experienced MS exacerbations: 3, 5, 1, and 0 patient(s) in the placebo, 0.5, 1.0, and 2.0 mg/kg groups, respectively. A few T1 Gd-enhancing lesions and/or new or enlarging T2 lesions indicative of inflammation were observed in all treatment groups. No clinically significant changes were observed in other clinical assessments or laboratory safety assessments. No anti-MOR103 antibodies were detected. PK evaluations indicated dose linearity with low/no drug accumulation over time. CONCLUSIONS: MOR103 was generally well-tolerated in patients with RRMS or SPMS. No evidence of immunogenicity was found. CLASSIFICATION OF EVIDENCE: This phase 1b study provides Class I evidence that MOR103 has acceptable tolerability in patients with MS.

A Gauss-Seidel iteration scheme for reference-free 3-D histological image reconstruction.

Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after six iterations of our proposed method.

Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm.

We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.

Using the low-pass monogenic signal framework for cell/background classification on multiple cell lines in bright-field microscope images.

PURPOSE: Several cell detection approaches which deal with bright-field microscope images utilize defocusing to increase image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a low-pass monogenic signal framework. The purpose of this paper is to show that using the local phase of the aforementioned monogenic signal instead of the defocused image improves the cell/background classification accuracy. MATERIALS AND METHODS: The paper statement was tested on an image database composed of three cell lines: adherent CHO, adherent L929, and Sf21 in suspension. Local phase and local energy images were generated using the low-pass monogenic signal framework with axial derivative images as input. Machine learning was then employed to investigate the discriminative power of the local phase. Three classifier models were utilized: random forest (RF), support vector machine (SVM) with a linear kernel, and SVM with a radial basis function (RBF) kernel. RESULTS: The improvement, averaged over cell lines, of classifying 5×5 sized patches extracted from the local phase image instead of the defocused image was 7.3% using the RF, 11.6% using the linear SVM, and 10.2% when a RBF kernel was employed instead of the linear one. Furthermore, the feature images can be sorted by increasing discriminative power as follows: at-focus signal, local energy, defocused signal, local phase. The only exception to this order was the superiority of local energy over defocused signal for suspended cells. CONCLUSIONS: Local phase computed using the low-pass monogenic signal framework considerably outperforms the defocused image for the purpose of pixel-patch cell/background classification in bright-field microscopy.

Granulocyte-macrophage colony-stimulating factor is a key mediator in inflammatory and arthritic pain.

OBJECTIVES: Better therapies are needed for inflammatory pain. In arthritis the relationship between joint pain, inflammation and damage is unclear. Granulocyte-macrophage colony-stimulating factor (GM-CSF) is important for the progression of a number of inflammatory/autoimmune conditions including arthritis; clinical trials targeting its action in rheumatoid arthritis are underway. However, its contribution to inflammatory and arthritic pain is unknown. The aims of this study were to determine whether GM-CSF controls inflammatory and/or arthritic pain. METHODS: A model of inflammatory pain (complete Freund's adjuvant footpad), as well as two inflammatory arthritis models, were induced in GM-CSF(-/-) mice and development of pain (assessment of weight distribution) and arthritic disease (histology) was assessed. Pain was further assessed in a GM-CSF-driven arthritis (methylated bovine serum albumin/GM-CSF) model and the cyclooxygenase-dependence determined using indomethacin. RESULTS: GM-CSF was absolutely required for pain development in both the inflammatory pain and arthritis models, including for IL-1-dependent arthritic pain. Pain in a GM-CSF-driven arthritis model, but not the disease itself, was abolished by the cyclooxygenase inhibitor, indomethacin, indicating separate pathways downstream of GM-CSF for pain and arthritis control. CONCLUSIONS: GM-CSF is key to the development of inflammatory and arthritic pain, suggesting that pain alleviation could result from trials evaluating its role in inflammatory/autoimmune conditions.

Granulocyte-macrophage colony-stimulating factor is a key mediator in experimental osteoarthritis pain and disease development.

INTRODUCTION: Granulocyte-macrophage colony-stimulating factor (GM-CSF) has been shown to be important in the development of inflammatory models of rheumatoid arthritis and there is encouraging data that its blockade may have clinical relevance in patients with rheumatoid arthritis. The aims of the current study were to determine whether GM-CSF may also be important for disease and pain development in a model of osteoarthritis. METHODS: The role of GM-CSF was investigated using the collagenase-induced instability model of osteoarthritis. We studied both GM-CSF-/- mice and wild-type (C57BL/6) mice treated prophylactically or therapeutically with a monoclonal antibody to GM-CSF. Disease development (both early and late) was evaluated by histology and knee pain development was measured by assessment of weight distribution. RESULTS: In the absence of GM-CSF, there was less synovitis and matrix metalloproteinase-mediated neoepitope expression at week 2 post disease induction, and less cartilage damage at week 6. GM-CSF was absolutely required for pain development. Therapeutic neutralization of GM-CSF not only abolished the pain within 3 days but also led to significantly reduced cartilage damage. CONCLUSIONS: GM-CSF is key to the development of experimental osteoarthritis and its associated pain. Importantly, GM-CSF neutralization by a therapeutic monoclonal antibody-based protocol rapidly and completely abolished existing arthritic pain and suppressed the degree of arthritis development. Our results suggest that it would be worth exploring the importance of GM-CSF for pain and disease in other osteoarthritis models and perhaps clinically for this form of arthritis.

In vitro affinity maturation of HuCAL antibodies: complementarity determining region exchange and RapMAT technology.

Monoclonal antibodies gain ever-increasing importance in the treatment of human diseases across a broad range of indications. Diverse technologies currently exist, which are used to generate recombinant therapeutic antibodies that are basically indistinguishable from naturally occurring human immunoglobulins. We describe how human combinatorial antibody libraries are used together with unique optimization techniques to produce such therapeutically relevant proteins, for instance in the areas of oncology and inflammation.

In vitro affinity maturation of human GM-CSF antibodies by targeted CDR-diversification.

The mammalian immune system applies somatic hypermutation to select for antibodies with improved dissociation rates in vivo up to an intrinsic limit, previously termed as affinity ceiling. However, for certain therapeutic applications it may be desirable to further improve antibody affinities beyond that limit. In this study the selection of antibodies specific for the pro-inflammatory cytokine granulocyte-macrophage colony-stimulating factor (GM-CSF) from the HuCAL GOLD human antibody library is described. In order to increase affinity and also functional activity, in vitro affinity maturation of a pool of lead Fab candidates was carried out. CDR-L3 and parallel CDR-H2 diversification using trinucleotide consensus cassettes were followed by the combination of optimized CDR-L3 and CDR-H2 leading to a 5000-fold improved affinity finally reaching a K(D) of 400 fM. Cytokine neutralizing potential of MOR04357 was evaluated in a TF-1 proliferation assay. Along with affinity optimization a 2000-fold increase in potency was observed compared to the parental antibody. Due to species cross-reactivity MOR04357 also blocks rat GM-CSF induced proliferation of FDCP-1 cells. Receptor inhibition studies showed that MOR04357 prevents the interaction of GM-CSF with the GM-CSF receptor alpha chain. As a consequence this leads to a blockade in signal transduction as measured by abolished STAT5 phosphorylation in the presence of GM-CSF and antibody. Due to its pro-inflammatory role GM-CSF has been implicated in the pathophysiology of inflammatory diseases like rheumatoid arthritis or asthma. Based on the mode of action described herein MOR04357 shows favourable antibody features as a potential drug candidate.

Nuclear translocation of the heterotrimeric CCAAT binding factor of Aspergillus oryzae is dependent on two redundant localising signals in a single subunit.

The CCAAT-binding complex in the Aspergillus species, also known as the Hap complex, consists of at least three subunits, namely HapB, HapC and HapE. Each Hap subunit contains an evolutionary conserved core domain. Recently, we have found that the HapC and HapE subunits do not carry a nuclear localisation signal. Furthermore, when in complex with HapB, they are transported into the nucleus via a 'piggy back mechanism' in A. nidulans. To extend our findings to other filamentous fungi, we examined the nuclear localisation of the A. oryzae Hap subunits by analysing several GFP fusion proteins with these Hap subunits in the hap deletion strains of A. nidulans. The nuclear translocation of the A. oryzae complex was found to be dependent on two redundant localising signals in HapB.

A single subunit of a heterotrimeric CCAAT-binding complex carries a nuclear localization signal: piggy back transport of the pre-assembled complex to the nucleus.

An unresolved question concerns the nuclear localization of the heterotrimeric CCAAT-binding complex, which is evolutionarily conserved in eukaryotic organisms including fungi, plants and mammals. All three subunits are necessary for DNA binding. In the filamentous fungus Aspergillus nidulans the corresponding complex was designated AnCF (A.nidulans CCAAT-binding factor). AnCF consists of the HapB, HapC and HapE subunits. Here, by using various green fluorescent protein constructs, a nuclear localization signal sequence (NLS) of the HapB protein was identified, outside of the evolutionarily conserved domain. HapB-EGFP was transported into the nucleus in both DeltahapC and DeltahapE strains, indicating that its NLS interacts with the import machinery independently of the other Hap subunits. In contrast, HapC-EGFP did not enter the nucleus in the absence of HapE or HapB. A similar finding was made for HapE-EGFP, which did not localize to the nucleus in the absence of HapC or HapB. Addition of the HapB-NLS to either HapC or HapE led to nuclear localization of the respective protein fusions, indicating that both HapC and HapE lack a functional NLS. Furthermore, these data strongly suggest that HapC and HapE have first to form a heterodimer and can be transported only as a heterodimer via the HapB protein into the nucleus. Therefore, the HapB subunit is the primary cargo for the import machinery, while HapC and HapE are transported to the nucleus only as a heterodimer and in complex with HapB via a piggy back mechanism. This enables the cell to provide equimolar concentrations of all subunits to the nucleus.