In vivo Bruton's tyrosine kinase inhibition attenuates alcohol-associated liver disease by regulating CD84-mediated granulopoiesis.

Severe alcohol-associated hepatitis (AH) is a life-threatening form of alcohol-associated liver disease. Liver neutrophil infiltration is a hallmark of AH, yet the effects of alcohol on neutrophil functions remain elusive. Identifying therapeutic targets to reduce neutrophil-mediated liver damage is essential. Bruton's tyrosine kinase (BTK) plays an important role in neutrophil development and function; however, the role of BTK in AH is unknown. Using RNA sequencing of circulating neutrophils, we found an increase in Btk expression (P = 0.05) and phosphorylated BTK (pBTK) in patients with AH compared with healthy controls. In vitro, physiologically relevant doses of alcohol resulted in a rapid, TLR4-mediated induction of pBTK in neutrophils. In a preclinical model of AH, administration of a small-molecule BTK inhibitor (evobrutinib) or myeloid-specific Btk knockout decreased proinflammatory cytokines and attenuated neutrophil-mediated liver damage. We found that pBTK was essential for alcohol-induced bone marrow granulopoiesis and liver neutrophil infiltration. In vivo, BTK inhibition or myeloid-specific Btk knockout reduced granulopoiesis, circulating neutrophils, liver neutrophil infiltration, and liver damage in a mouse model of AH. Mechanistically, using liquid chromatography-tandem mass spectrometry, we identified CD84 as a kinase target of BTK, which is involved in granulopoiesis. In vitro, CD84 promoted alcohol-induced interleukin-1ß and tumor necrosis factor-α in primary human neutrophils, which was inhibited by CD84-blocking antibody treatment. Our findings define the role of BTK and CD84 in regulating neutrophil inflammation and granulopoiesis, with potential therapeutic implications in AH.

A dynamic microRNA profile that tracks a chemotherapy resistance phenotype in osteosarcoma. Implications for novel therapeutics.

Osteosarcoma is a rare primary bone tumor for which no significant therapeutic advancement has been made since the late 1980s despite ongoing efforts. Overall, the five-year survival rate remains about 65%, and is much lower in patients with tumors unresponsive to methotrexate, doxorubicin, and cisplatin therapy. Genetic studies have not revealed actionable drug targets, but our group, and others, have reported that epigenomic biomarkers, including regulatory RNAs, may be useful prognostic tools for osteosarcoma. We tested if microRNA (miRNA) transcriptional patterns mark the transition from a chemotherapy sensitive to resistant tumor phenotype. Small RNA sequencing was performed using 14 patient matched pre-chemotherapy biopsy and post-chemotherapy resection high-grade osteosarcoma frozen tumor samples. Independently, small RNA sequencing was performed using 14 patient matched biopsy and resection samples from untreated tumors. Separately, miRNA specific Illumina DASL arrays were used to assay an independent cohort of 65 pre-chemotherapy biopsy and 26 patient matched post-chemotherapy resection formalin fixed paraffin embedded (FFPE) tumor samples. mRNA specific Illumina DASL arrays were used to profile 37 pre-chemotherapy biopsy and five post-chemotherapy resection FFPE samples, all of which were also used for Illumina DASL miRNA profiling. The National Cancer Institute Therapeutically Applicable Research to Generate Effective Treatments dataset, including PCR based miRNA profiling and RNA-seq data for 86 and 93 pre-chemotherapy tumor samples, respectively, was also used. Paired differential expression testing revealed a profile of 17 miRNAs with significantly different transcriptional levels following chemotherapy. Genes targeted by the miRNAs were differentially expressed following chemotherapy, suggesting the miRNAs may regulate transcriptional networks. Finally, an in vitro pharmacogenomic screen using miRNAs and their target transcripts predicted response to a set of candidate small molecule therapeutics which potentially reverse the chemotherapy resistance phenotype and synergize with chemotherapy in otherwise treatment resistant tumors. Importantly, these novel therapeutic targets are distinct from targets identified by a similar pharmacogenomic analysis of previously published prognostic miRNA profiles from pre chemotherapy biopsy specimens.

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Feasibility, Safety, and Performance of Full-Head Subscalp EEG Using Minimally Invasive Electrode Implantation.

BACKGROUND AND OBJECTIVES: Current practice in clinical neurophysiology is limited to short recordings with conventional EEG (days) that fail to capture a range of brain (dys)functions at longer timescales (months). The future ability to optimally manage chronic brain disorders, such as epilepsy, hinges upon finding methods to monitor electrical brain activity in daily life. We developed a device for full-head subscalp EEG (Epios) and tested here the feasibility to safely insert the electrode leads beneath the scalp by a minimally invasive technique (primary outcome). As secondary outcome, we verified the noninferiority of subscalp EEG in measuring physiologic brain oscillations and pathologic discharges compared with scalp EEG, the established standard of care. METHODS: Eight participants with pharmacoresistant epilepsy undergoing intracranial EEG received in the same surgery subscalp electrodes tunneled between the scalp and the skull with custom-made tools. Postoperative safety was monitored on an inpatient ward for up to 9 days. Sleep-wake, ictal, and interictal EEG signals from subscalp, scalp, and intracranial electrodes were compared quantitatively using windowed multitaper transforms and spectral coherence. Noninferiority was tested for pairs of neighboring subscalp and scalp electrodes with a Bland-Altman analysis for measurement bias and calculation of the interclass correlation coefficient (ICC). RESULTS: As primary outcome, up to 28 subscalp electrodes could be safely placed over the entire head through 1-cm scalp incisions in a ∼1-hour procedure. Five of 10 observed perioperative adverse events were linked to the investigational procedure, but none were serious, and all resolved. As a secondary outcome, subscalp electrodes advantageously recorded EEG percutaneously without requiring any maintenance and were noninferior to scalp electrodes for measuring (1) variably strong, stage-specific brain oscillations (alpha in wake, delta, sigma, and beta in sleep) and (2) interictal spikes peak-potentials and ictal signals coherent with seizure propagation in different brain regions (ICC >0.8 and absence of bias). DISCUSSION: Recording full-head subscalp EEG for localization and monitoring purposes is feasible up to 9 days in humans using minimally invasive techniques and noninferior to the current standard of care. A longer prospective ambulatory study of the full system will be necessary to establish the safety and utility of this innovative approach. TRIAL REGISTRATION INFORMATION: clinicaltrials.gov/study/NCT04796597.

Determinants of gastric cancer immune escape identified from non-coding immune-landscape quantitative trait loci.

The landscape of non-coding mutations in cancer progression and immune evasion is largely unexplored. Here, we identify transcrptome-wide somatic and germline 3' untranslated region (3'-UTR) variants from 375 gastric cancer patients from The Cancer Genome Atlas. By performing gene expression quantitative trait loci (eQTL) and immune landscape QTL (ilQTL) analysis, we discover 3'-UTR variants with cis effects on expression and immune landscape phenotypes, such as immune cell infiltration and T cell receptor diversity. Using a massively parallel reporter assay, we distinguish between causal and correlative effects of 3'-UTR eQTLs in immune-related genes. Our approach identifies numerous 3'-UTR eQTLs and ilQTLs, providing a unique resource for the identification of immunotherapeutic targets and biomarkers. A prioritized ilQTL variant signature predicts response to immunotherapy better than standard-of-care PD-L1 expression in independent patient cohorts, showcasing the untapped potential of non-coding mutations in cancer.

The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: clinician and healthcare informatician perspectives.

Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis. We conducted electronic surveys asking clinician and healthcare informaticians about their perspectives on AI/ML for VTE prevention and management. Of 101 respondents to the informatician survey, most were 40 years or older, male, clinicians and data scientists, and had performed research on AI/ML. Of the 607 US-based respondents to the clinician survey, most were 40 years or younger, female, physicians, and had never used AI to inform clinical practice. Most informaticians agreed that AI/ML can be used to manage VTE (56.0%). Over one-third were concerned that clinicians would not use the technology (38.9%), but the majority of clinicians believed that AI/ML probably or definitely can help with VTE prevention (70.1%). The most common concern in both groups was a perceived lack of transparency (informaticians 54.4%; clinicians 25.4%). These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation.

Inhibition of Cyp1a Protects Mice against Anthracycline Cardiomyopathy.

Background: Anthracyclines such as doxorubicin (Dox) are highly effective anti-tumor agents, but their use is limited by dose-dependent cardiomyopathy and heart failure. Our laboratory previously reported that induction of cytochrome P450 family 1 (Cyp1) enzymes contributes to acute Dox cardiotoxicity in zebrafish and in mice, and that potent Cyp1 inhibitors prevent cardiotoxicity. However, the role of Cyp1 enzymes in chronic Dox cardiomyopathy, as well as the mechanisms underlying cardioprotection associated with Cyp1 inhibition, have not been fully elucidated. Methods: The Cyp1 pathway was evaluated using a small molecule Cyp1 inhibitor in wild-type (WT) mice, or Cyp1-null mice ( Cyp1a1/1a2 -/- , Cyp1b1 -/- , and Cyp1a1/1a2/1b1 -/- ). Low-dose Dox was administered by serial intraperitoneal or intravenous injections, respectively. Expression of Cyp1 isoforms was measured by RT-qPCR, and myocardial tissue was isolated from the left ventricle for RNA sequencing. Cardiac function was evaluated by transthoracic echocardiography. Results: In WT mice, Dox treatment was associated with a decrease in Cyp1a2 and increase in Cyp1b1 expression in the heart and in the liver. Co-treatment of WT mice with Dox and the novel Cyp1 inhibitor YW-130 protected against cardiac dysfunction compared to Dox treatment alone. Cyp1a1/1a2 -/- and Cyp1a1/1a2/1b1 -/- mice were protected from Dox cardiomyopathy compared to WT mice. Male, but not female, Cyp1b1 -/- mice had increased cardiac dysfunction following Dox treatment compared to WT mice. RNA sequencing of myocardial tissue showed upregulation of Fundc1 and downregulation of Ccl21c in Cyp1a1/1a2 -/- mice treated with Dox, implicating changes in mitophagy and chemokine-mediated inflammation as possible mechanisms of Cyp1a-mediated cardioprotection. Conclusions: Taken together, this study highlights the potential therapeutic value of Cyp1a inhibition in mitigating anthracycline cardiomyopathy.

The causality measure of partial mutual information from mixed embedding (PMIME) revisited.

The measure of partial mutual information from mixed embedding (PMIME) is an information theory-based measure to accurately identify the direct and directional coupling, termed Granger causality or simply causality, between the observed variables or subsystems of a high-dimensional dynamical and complex system, without any a priori assumptions about the nature of the coupling relationship. In its core, it is a forward selection procedure that aims to iteratively identify the lag-dependence structure of a given observed variable (response) to all the other observed variables (candidate drivers). This model-free approach is capable of detecting nonlinear interactions, abundantly present in real-world complex systems, and it was shown to perform well on multivariate time series of moderately high dimension. However, the PMIME presents some inefficiencies in its performance mainly when applied on strongly stochastic (linear or nonlinear) systems as it may falsely detect non-existent relationships. Moreover, and by construction, the measure cannot extract purely synergetic relationships present in a system. In the current work, the issue of false detections is addressed by introducing an improved resampling significance test and a procedure of rechecking the identified drivers (backward revision). Regarding the inability to detect synergetic relationships, the PMIME is further enhanced by checking pairs as candidate drivers for the response variable after having considered all drivers individually. The effects of these modifications are investigated in a systematic simulation study on properly designed systems involving strong stochasticity, regressor terms with synergetic effects, and a system dimension ranging from 3 to 30. The overall results of the simulations indicate that these modifications indeed improve the performance of PMIME and alleviate to a significant degree the issues of the original algorithm. Guidelines for balancing between accuracy and computational efficiency are also given, particularly relevant for real-world applications. Finally, the measure performance is investigated in the study of futures of various government bonds and stock market indices in the period around COVID-19 pandemic.

Polycomb-mediated silencing of miR-8 is required for maintenance of intestinal stemness in Drosophila melanogaster.

Balancing maintenance of self-renewal and differentiation is a key property of adult stem cells. The epigenetic mechanisms controlling this balance remain largely unknown. Herein, we report that the Polycomb Repressive Complex 2 (PRC2) is required for maintenance of the intestinal stem cell (ISC) pool in the adult female Drosophila melanogaster. We show that loss of PRC2 activity in ISCs by RNAi-mediated knockdown or genetic ablation of the enzymatic subunit Enhancer of zeste, E(z), results in loss of stemness and precocious differentiation of enteroblasts to enterocytes. Mechanistically, we have identified the microRNA miR-8 as a critical target of E(z)/PRC2-mediated tri-methylation of histone H3 at Lys27 (H3K27me3) and uncovered a dynamic relationship between E(z), miR-8 and Notch signaling in controlling stemness versus differentiation of ISCs. Collectively, these findings uncover a hitherto unrecognized epigenetic layer in the regulation of stem cell specification that safeguards intestinal homeostasis.

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

ABSTRACT: Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism and/or deep vein thrombosis. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI, 0.881-0.962), specificity 0.984 (95% CI, 0.967-0.992), PPV 0.910 (95% CI, 0.865-0.941) and NPV 0.985 (95% CI, 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words and deep-learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies, and only 4 validated their model on an external data set. Further standardization of ML studies can help progress this novel technology toward real-world implementation.

Use of Serum Protein Measurements as Biomarkers that Can Predict the Outcome of Diabetic Foot Ulceration.

Objectives: To identify proteins that are prognostic for diabetic foot ulcer (DFU) healing and may serve as biomarkers for its management, serum samples were analyzed from diabetic mellitus (DM) patients. Approach: The serum specimens that were evaluated in this study were obtained from DM patients with DFU who participated in a prospective study and were seen biweekly until they healed their ulcer or the exit visit at 12 weeks. The group was divided into Healers (who healed their DFU during the study) and Non-Healers. Results: Interleukin (IL)-10, IL-4, IL-5, IL-6, and IL-13 and interferon-gamma were higher in the Healers while Fractalkine, IL-8, and TNFα were higher in the Non-Healers. The trajectory of IL-10 levels remained stable over time within and across groups, resulting in a strong prognostic ability for the prospective DFU healing course. Classification and Regression Tree analysis created an 11-node decision tree with healing status as the categorical response. Innovation: Consecutive measurements of proteins associated with wound healing can identify biomarkers that can predict DFU healing over a 12-week period. IL-10 was the strongest candidate for prediction. Conclusion: Measurement of serum proteins can serve as a successful strategy in guiding clinical management of DFU. The data also indicate likely superior performance of building a multiprotein biomarker score instead of relying on single biomarkers.

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs.

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

Venous thromboembolism prophylaxis for hospitalized adult patients: a survey of US health care providers on attitudes and practices.

Background: Venous thromboembolism (VTE) is a leading cause of preventable mortality among hospitalized patients, but appropriate risk assessment and thromboprophylaxis remain underutilized or misapplied. Objectives: We conducted an electronic survey of US health care providers to explore attitudes, practices, and barriers related to thromboprophylaxis in adult hospitalized patients and at discharge. Results: A total of 607 US respondents completed the survey: 63.1% reported working in an academic hospital, 70.7% identified as physicians, and hospital medicine was the most frequent specialty (52.1%). The majority of respondents agreed that VTE prophylaxis is important (98.8%; 95% CI: 97.6%-99.5%) and that current measures are safe (92.6%; 95% CI: 90.2%-94.5%) and effective (93.8%; 95% CI: 91.6%-95.6%), but only half (52.0%; 95% CI: 47.9%-56.0%) believed that hospitalized patients at their institution are on appropriate VTE prophylaxis almost all the time. One-third (35.4%) reported using a risk assessment model (RAM) to determine VTE prophylaxis need; 44.9% reported unfamiliarity with RAMs. The most common recommendation for improving rates of appropriate thromboprophylaxis was to leverage technology. A majority of respondents (84.5%) do not reassess a patient's need for VTE prophylaxis at discharge, and a minority educates patients about the risk (16.2%) or symptoms (18.9%) of VTE at discharge. Conclusion: Despite guideline recommendations to use RAMs, the majority of providers in our survey do not use them. A majority of respondents believed that technology could help improve VTE prophylaxis rates. A majority of respondents do not reassess the risk of VTE at discharge or educate patients about this risk of VTE at discharge.

MicroRNA-22 Is a Key Regulator of Lipid and Metabolic Homeostasis.

Obesity is a growing public health problem associated with increased risk of type 2 diabetes, cardiovascular disease, nonalcoholic fatty liver disease (NAFLD) and cancer. Here, we identify microRNA-22 (miR-22) as an essential rheostat involved in the control of lipid and energy homeostasis as well as the onset and maintenance of obesity. We demonstrate through knockout and transgenic mouse models that miR-22 loss-of-function protects against obesity and hepatic steatosis, while its overexpression promotes both phenotypes even when mice are fed a regular chow diet. Mechanistically, we show that miR-22 controls multiple pathways related to lipid biogenesis and differentiation. Importantly, genetic ablation of miR-22 favors metabolic rewiring towards higher energy expenditure and browning of white adipose tissue, suggesting that modulation of miR-22 could represent a viable therapeutic strategy for treatment of obesity and other metabolic disorders.

Identifying and profiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with Machaon.

Using protein structure to predict function, interactions, and evolutionary history is still an open challenge, with existing approaches relying extensively on protein homology and families. Here, we present Machaon, a data-driven method combining orientation invariant metrics on phi-psi angles, inter-residue contacts and surface complexity. It can be readily applied on whole structures or segments-such as domains and binding sites. Machaon was applied on SARS-CoV-2 Spike monomers of native, Delta and Omicron variants and identified correlations with a wide range of viral proteins from close to distant taxonomy ranks, as well as host proteins, such as ACE2 receptor. Machaon's meta-analysis of the results highlights structural, chemical and transcriptional similarities between the Spike monomer and human proteins, indicating a multi-level viral mimicry. This extended analysis also revealed relationships of the Spike protein with biological processes such as ubiquitination and angiogenesis and highlighted different patterns in virus attachment among the studied variants. Available at: https://machaonweb.com .

Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging.

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP).

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

Randomized Phase II Trial of Dendritic Cell/Myeloma Fusion Vaccine with Lenalidomide Maintenance after Upfront Autologous Hematopoietic Cell Transplantation for Multiple Myeloma: BMT CTN 1401.

PURPOSE: Vaccination with dendritic cell (DC)/multiple myeloma (MM) fusions has been shown to induce the expansion of circulating multiple myeloma-reactive lymphocytes and consolidation of clinical response following autologous hematopoietic cell transplant (auto-HCT). PATIENTS AND METHODS: In this randomized phase II trial (NCT02728102), we assessed the effect of DC/MM fusion vaccination, GM-CSF, and lenalidomide maintenance as compared with control arms of GM-CSF and lenalidomide or lenalidomide maintenance alone on clinical response rates and induction of multiple myeloma-specific immunity at 1-year posttransplant. RESULTS: The study enrolled 203 patients, with 140 randomized posttransplantation. Vaccine production was successful in 63 of 68 patients. At 1 year, rates of CR were 52.9% (vaccine) and 50% (control; P = 0.37, 80% CI 44.5%, 61.3%, and 41.6%, 58.4%, respectively), and rates of VGPR or better were 85.3% (vaccine) and 77.8% (control; P = 0.2). Conversion to CR at 1 year was 34.8% (vaccine) and 27.3% (control; P = 0.4). Vaccination induced a statistically significant expansion of multiple myeloma-reactive T cells at 1 year compared with before vaccination (P = 0.024) and in contrast to the nonvaccine arm (P = 0.026). Single-cell transcriptomics revealed clonotypic expansion of activated CD8 cells and shared dominant clonotypes between patients at 1-year posttransplant. CONCLUSIONS: DC/MM fusion vaccination with lenalidomide did not result in a statistically significant increase in CR rates at 1 year posttransplant but was associated with a significant increase in circulating multiple myeloma-reactive lymphocytes indicative of tumor-specific immunity. Site-specific production of a personalized cell therapy with centralized product characterization was effectively accomplished in the context of a multicenter cooperative group study. See related commentary by Qazilbash and Kwak, p. 4703.