Langerhans cell histiocytosis: NACHO update on progress, chaos, and opportunity on the path to rational cures.

Langerhans cell histiocytosis (LCH) is a myeloid neoplastic disorder characterized by lesions with CD1a-positive/Langerin (CD207)-positive histiocytes and inflammatory infiltrate that can cause local tissue damage and systemic inflammation. Clinical presentations range from single lesions with minimal impact to life-threatening disseminated disease. Therapy for systemic LCH has been established through serial trials empirically testing different chemotherapy agents and durations of therapy. However, fewer than 50% of patients who have disseminated disease are cured with the current standard-of-care vinblastine/prednisone/(mercaptopurine), and treatment failure is associated with long-term morbidity, including the risk of LCH-associated neurodegeneration. Historically, the nature of LCH-whether a reactive condition versus a neoplastic/malignant condition-was uncertain. Over the past 15 years, seminal discoveries have broadly defined LCH pathogenesis; specifically, activating mitogen-activated protein kinase pathway mutations (most frequently, BRAFV600E) in myeloid precursors drive lesion formation. LCH therefore is a clonal neoplastic disorder, although secondary inflammatory features contribute to the disease. These paradigm-changing insights offer a promise of rational cures for patients based on individual mutations, clonal reservoirs, and extent of disease. However, the pace of clinical trial development behind lags the kinetics of translational discovery. In this review, the authors discuss the current understanding of LCH biology, clinical characteristics, therapeutic strategies, and opportunities to improve outcomes for every patient through coordinated agent prioritization and clinical trial efforts.

T-cell activation profiles distinguish hemophagocytic lymphohistiocytosis and early sepsis.

Hemophagocytic lymphohistiocytosis (HLH) is a fatal disorder of immune hyperactivation that has been described as a cytokine storm. Sepsis due to known or suspected infection has also been viewed as a cytokine storm. Although clinical similarities between these syndromes suggest similar immunopathology and may create diagnostic uncertainty, distinguishing them is critical as treatments are widely divergent. We examined T-cell profiles from children with either HLH or sepsis and found that HLH is characterized by acute T-cell activation, in clear contrast to sepsis. Activated T cells in patients with HLH were characterized as CD38high/HLA-DR+ effector cells, with activation of CD8+ T cells being most pronounced. Activated T cells were type 1 polarized, proliferative, and displayed evidence of recent and persistent activation. Circulating activated T cells appeared to be broadly characteristic of HLH, as they were seen in children with and without genetic lesions or identifiable infections and resolved with conventional treatment of HLH. Furthermore, we observed even greater activation and type 1 polarization in tissue-infiltrating T cells, described here for the first time in a series of patients with HLH. Finally, we observed that a threshold of >7% CD38high/HLA-DR+ cells among CD8+ T cells had strong positive and negative predictive value for distinguishing HLH from early sepsis or healthy controls. We conclude that the cytokine storm of HLH is marked by distinctive T-cell activation whereas early sepsis is not, and that these 2 syndromes can be readily distinguished by T-cell phenotypes.

Neuroinflammatory Disease as an Isolated Manifestation of Hemophagocytic Lymphohistiocytosis.

Isolated neuroinflammatory disease has been described in case reports of familial hemophagocytic lymphohistiocytosis (FHL), but the clinical spectrum of disease manifestations, response to therapy and prognosis remain poorly defined. We combined an international survey with a literature search to identify FHL patients with (i) initial presentation with isolated neurological symptoms; (ii) absence of cytopenia and splenomegaly at presentation; and (iii) systemic HLH features no earlier than 3 months after neurological presentation. Thirty-eight (20 unreported) patients were identified with initial diagnoses including acute demyelinating encephalopathy, leukoencephalopathy, CNS vasculitis, multiple sclerosis, and encephalitis. Median age at presentation was 6.5 years, most commonly with ataxia/gait disturbance (75%) and seizures (53%). Diffuse multifocal white matter changes (79%) and cerebellar involvement (61%) were common MRI findings. CSF cell count and protein were increased in 22/29 and 15/29 patients, respectively. Fourteen patients progressed to systemic inflammatory disease fulfilling HLH-2004 criteria at a mean of 36.9 months after initial neurological presentation. Mutations were detected in PRF1 in 23 patients (61%), RAB27A in 10 (26%), UNC13D in 3 (8%), LYST in 1 (3%), and STXBP2 in 1 (3%) with a mean interval to diagnosis of 28.3 months. Among 19 patients who underwent HSCT, 11 neurologically improved, 4 were stable, one relapsed, and 3 died. Among 14 non-transplanted patients, only 3 improved or had stable disease, one relapsed, and 10 died. Isolated CNS-HLH is a rare and often overlooked cause of inflammatory brain disease. HLH-directed therapy followed by HSCT seems to improve survival and outcome.

Manipulating DNA damage-response signaling for the treatment of immune-mediated diseases.

Antigen-activated lymphocytes undergo extraordinarily rapid cell division in the course of immune responses. We hypothesized that this unique aspect of lymphocyte biology leads to unusual genomic stress in recently antigen-activated lymphocytes and that targeted manipulation of DNA damage-response (DDR) signaling pathways would allow for selective therapeutic targeting of pathological T cells in disease contexts. Consistent with these hypotheses, we found that activated mouse and human T cells display a pronounced DDR in vitro and in vivo. Upon screening a variety of small-molecule compounds, we found that potentiation of p53 (via inhibition of MDM2) or impairment of cell cycle checkpoints (via inhibition of CHK1/2 or WEE1) led to the selective elimination of activated, pathological T cells in vivo. The combination of these strategies [which we termed "p53 potentiation with checkpoint abrogation" (PPCA)] displayed therapeutic benefits in preclinical disease models of hemophagocytic lymphohistiocytosis and multiple sclerosis, which are driven by foreign antigens or self-antigens, respectively. PPCA therapy targeted pathological T cells but did not compromise naive, regulatory, or quiescent memory T-cell pools, and had a modest nonimmune toxicity profile. Thus, PPCA is a therapeutic modality for selective, antigen-specific immune modulation with significant translational potential for diverse immune-mediated diseases.

Clinical resistance associated with a novel MAP2K1 mutation in a patient with Langerhans cell histiocytosis.

Patients with Langerhans cell histiocytosis (LCH) harbor BRAF V600E and activating mutations of MAP2K1/MEK1 in 50% and 25% of cases, respectively. We evaluated a patient with treatment-refractory LCH for mutations in the RAS-RAF-MEK-ERK pathway and identified a novel mutation in the MAP2K1 gene resulting in a p.L98_K104 > Q deletion and predicted to be auto-activating. During treatment with the MEK inhibitor trametinib, the patient's disease showed significant progression. In vitro characterization of the MAP2K1 p.L98_K104 > Q deletion confirmed its effect on cellular activation of the ERK pathway and drug resistance.

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Real-world treatment patterns and outcomes in patients with primary hemophagocytic lymphohistiocytosis treated with emapalumab.

ABSTRACT: Hemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening, hyperinflammatory syndrome. Emapalumab, a fully human monoclonal antibody that neutralizes the proinflammatory cytokine interferon gamma, is approved in the United States to treat primary HLH (pHLH) in patients with refractory, recurrent, or progressive disease, or intolerance with conventional HLH treatments. REAL-HLH, a retrospective study, conducted across 33 US hospitals, evaluated real-world treatment patterns and outcomes in patients treated with ≥1 dose of emapalumab between 20 November 2018 and 31 October 2021. In total, 46 patients met the pHLH classification criteria. Median age at diagnosis was 1.0 year (range, 0.3-21.0). Emapalumab was initiated for treating refractory (19/46), recurrent (14/46), or progressive (7/46) pHLH. At initiation, 15 of 46 patients were in the intensive care unit, and 35 of 46 had received prior HLH-related therapies. Emapalumab treatment resulted in normalization of key laboratory parameters, including chemokine ligand 9 (24/33, 72.7%), ferritin (20/45, 44.4%), fibrinogen (37/38, 97.4%), platelets (39/46, 84.8%), and absolute neutrophil count (40/45, 88.9%). Forty-two (91.3%) patients were considered eligible for transplant. Pretransplant survival was 38 of 42 (90.5%). Thirty-one (73.8%) transplant-eligible patients proceeded to transplant, and 23 of 31 (74.2%) of those who received transplant were alive at the end of the follow-up period. Twelve-month survival probability from emapalumab initiation for the entire cohort (N = 46) was 73.1%. There were no discontinuations because of adverse events. In conclusion, results from the REAL-HLH study, which describes treatment patterns, effectiveness, and outcomes in patients with pHLH treated with emapalumab in real-world settings, are consistent with the emapalumab pivotal phase 2/3 pHLH trial.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.

Successful treatment of recurrent pediatric inflammatory myofibroblastic tumor in a single patient with a novel chemotherapeutic regimen containing celecoxib.

Inflammatory myofibroblastic tumors are rare tumors characterized as low-to-intermediate grade sarcomas. This is a case of a 7-year-old male with a 5-cm lung mass, which recurred 11 months after complete resection. The recurrence manifested as multifocal metastatic disease involving the ipsilateral parietal and visceral pleura. A novel chemotherapeutic regimen, which included vincristine, ifosfamide, doxorubicin, and celecoxib was utilized for the disease recurrence. The patient had complete and durable remission of the disease and has been disease-free for >4 years. This novel regimen including a cyclooxygenase 2 inhibitor may be an effective regimen for metastatic inflammatory myofibroblastic tumors.

Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex.

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex.

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.

Autologous peripheral blood stem cell transplantation in children with refractory or relapsed lymphoma: results of Children's Oncology Group study A5962.

This prospective study was designed to determine the safety and efficacy of cyclophosphamide, BCNU, and etoposide (CBV) conditioning and autologous peripheral blood stem cell transplant (PBSCT) in children with relapsed or refractory Hodgkin and non-Hodgkin lymphoma (HL and NHL). Patients achieving complete remission (CR) or partial remission (PR) after 2 to 4 courses of reinduction underwent a granulocyte-colony stimulating factor (G-CSF) mobilized PBSC apheresis with a target collection dose of 5 × 106 CD34(+)/kg. Those eligible to proceed received autologous PBSCT after CBV (7200 mg/m², 450-300 mg/m², 2400 mg/m²). Forty-three of 69 patients (30/39 HL, 13/30 NHL) achieved a CR/PR after reinduction. Thirty-eight patients (28 HL, 10 NHL) underwent PBSCT. All initial 6 patients who received BCNU at 450 mg/m² experienced grade III or IV pulmonary toxicity compared to none of the subsequent 32 receiving 300 mg/m² (P < .0001). The probability of overall survival (OS) at 3 years for all patients is 51% and for transplanted patients is 64%. The 3-year event-free survival (EFS) is 38% (45% for HL; 30% NHL). The 3-year EFS in transplanted patients is 66% (65% HL; 70% NHL). Initial duration of remission of ≥12 versus <12 months was associated with a significant increase in OS (3 years OS 70% versus 34%) (P = .003). BCNU at 300 mg/m(2) in a CBV regimen prior to PBSCT is well tolerated in relapsed or refractory pediatric lymphoma patients. A short duration (<12 months) of initial remission is associated with a poorer prognosis. Last, a high percentage of patients achieving a CR/PR after reinduction therapy can be salvaged with CBV and autologlous PBSCT.

Distinct, gene-specific effect of heat shock on heat shock factor-1 recruitment and gene expression of CXC chemokine genes.

The heat shock (HS) response, a phylogenetically conserved ubiquitous response to stress, is generally characterized by the induced expression of heat shock protein (HSP) genes. Our earlier studies showed that the stress-activated transcription factor, heat shock factor-1 (HSF1), activated at febrile range or HS temperatures also modified expression of non-HSP genes including cytokine and chemokine genes. We also showed by in silico analysis that 28 among 29 human and mouse CXC chemokine genes had multiple putative heat shock response elements (HSEs) present in their gene promoters. To further determine whether these potential HSEs were functional and bound HSF1, we analyzed the recruitment of HSF1 to promoters of 5 human CXC chemokine genes (CXCL-1, 2, 3, 5 and 8) by chromatin immunoprecipitation (ChIP) assay and analyzed the effect of HS exposure on tumor necrosis factor-α (TNFα)-induced expression of these genes in human lung epithelial-like A549 cells. HSF1 ChIP analysis showed that HSF1 was recruited to all but one of these CXC chemokine genes (CXCL-3) and HS caused a significant increase in recruitment of HSF1 to one or multiple HSEs present in the promoters of CXCL-1, 2, 5 and 8 genes. However, the effect of HS exposure on expression of these genes showed a variable gene-specific effect. For example, CXCL8 expression was markedly enhanced (p<0.05) whereas CXCL5 expression was significantly repressed (p<0.05) in cells exposed to HS coincident with TNFα stimulation. In contrast, expression of CXCL1 and CXCL2, despite HSF1 recruitment to their promoters, was not affected by HS exposure. Our results indicate that some, if not all, putative HSEs present in the CXC chemokine gene promoters are functional and recruit HSF1 in vivo but the effects on gene expression are variable and gene specific. We speculate, the physical proximity and interactions of other transcription factors and co-regulators with HSF1 could be critical to determining the effects of HS on the expression of these genes.

IFN-γ signature in the plasma proteome distinguishes pediatric hemophagocytic lymphohistiocytosis from sepsis and SIRS.

Hemophagocytic lymphohistiocytosis (HLH) is a syndrome characterized by pathologic immune activation in which prompt recognition and initiation of immune suppression is essential for survival. Children with HLH have many overlapping clinical features with critically ill children with sepsis and systemic inflammatory response syndrome (SIRS) in whom alternative therapies are indicated. To determine whether plasma biomarkers could differentiate HLH from other inflammatory conditions and to better define a core inflammatory signature of HLH, concentrations of inflammatory plasma proteins were compared in 40 patients with HLH to 47 pediatric patients with severe sepsis or SIRS. Fifteen of 135 analytes were significantly different in HLH plasma compared with SIRS/sepsis, including increased interferon-γ (IFN-γ)-regulated chemokines CXCL9, CXCL10, and CXCL11. Furthermore, a 2-analyte plasma protein classifier including CXCL9 and interleukin-6 was able to differentiate HLH from SIRS/sepsis. Gene expression in CD8+ T cells and activated monocytes from blood were also enriched for IFN-γ pathway signatures in peripheral blood cells from patients with HLH compared with SIRS/sepsis. This study identifies differential expression of inflammatory proteins as a diagnostic strategy to identify critically ill children with HLH, and comprehensive unbiased analysis of inflammatory plasma proteins and global gene expression demonstrates that IFN-γ signaling is uniquely elevated in HLH. In addition to demonstrating the ability of diagnostic criteria for HLH and sepsis or SIRS to identify groups with distinct inflammatory patterns, results from this study support the potential for prospective evaluation of inflammatory biomarkers to aid in diagnosis of and optimizing therapeutic strategies for children with distinctive hyperinflammatory syndromes.

General-Purpose Coarse-Grained Toughened Thermoset Model for 44DDS/DGEBA/PES.

The objective of this work is to predict the morphology and material properties of crosslinking polymers used in aerospace applications. We extend the open-source dybond plugin for HOOMD-Blue to implement a new coarse-grained model of reacting epoxy thermosets and use the 44DDS/DGEBA/PES system as a case study for calibration and validation. We parameterize the coarse-grained model from atomistic solubility data, calibrate reaction dynamics against experiments, and check for size-dependent artifacts. We validate model predictions by comparing glass transition temperatures measurements at arbitrary degree of cure, gel-points, and morphology predictions against experiments. We demonstrate for the first time in molecular simulations the cure-path dependence of toughened thermoset morphologies.