BACKGROUND:Solid Organ Transplant (SOT) provides a survival advantage for individuals with end organ failure. Little is known about the specific effects of immunosuppression on the competence of the immune system during the post-transplant period especially in the pediatric population, and current immunosuppression and prophylaxis practices are not well informed by immune data. METHODS:Blood samples from pediatric patients < 18 years old were collected prior to and 6- and 12 months after liver transplant. Peripheral blood mononuclear cells (PBMCs) were stimulated with PMA/Ionomycin and the phenotype and function of T cell subsets and NK cells were systematically characterized using cytometry by time-of-flight (CyTOF). RESULTS:In a cohort of 4 pediatric liver transplant patients, levels of CD4+ and CD8+ T cells, Tregs and NK cells at 6- and 12 months following liver transplant were similar to levels before transplant. Upon stimulation, pro-inflammatory, antiviral, and inhibitory T cell cytokines and enzymes were not reduced at 6 months post-transplant and remained stable at 12 months. IL-2 production, a tacrolimus target, from CD4+ and CD8+ T cells did not correlate with tacrolimus levels. CONCLUSION:T cell function was not reduced by 6 months following liver transplant. This suggests that it might be safe to discontinue viral prophylaxis or initiate vaccinations sooner than previously suspected. Tregs functional marker expression was intact by 6 months post-transplant suggesting lower risk of rejection in this cohort. Tacrolimus level may not be a good indication for immunocompetence and T cell functional assays may be more predictive of susceptibility to infection.

Chronic viral infections are ubiquitous in humans, with individuals carrying multiple viruses that can reactivate during physiological stress, including severe illness1. Notably, SARS-CoV-2 infection has been shown to reactivate chronic viruses such as Epstein-Barr virus and cytomegalovirus, yet the full extent, temporal dynamics and immunological impact of viral reactivation in COVID-19 remain incompletely understood2-7. Here, leveraging multi-omic longitudinal data from 1,154 hospitalized patients with COVID-19 from the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study, we reveal significant reactivation of Herpesviridae and Anelloviridae during acute COVID-19, with distinct temporal dynamics for different viruses, and demonstrate that reactivation correlates with disease severity, host immune effects and clinical outcomes. Although our results do not establish causation between virus reactivation and clinical outcomes, we highlight the prevalence of chronic viral reactivation during acute COVID-19 and long COVID. Our findings challenge the prevailing view that chronic viral reactivation is primarily a consequence of immunosuppression, demonstrating that reactivations occur frequently in immunocompetent individuals during severe illness and in association with increased systemic inflammation. Additionally, we demonstrate persistence of viral reactivation in convalescence, and report an association of Anelloviridae with long COVID. This study provides immune, transcriptomic and metabolomic signatures of viral reactivation that could inform future strategies to prognosticate and treat acute COVID-19 and long COVID.

The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will develop long COVID is challenging due to the absence of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models may address this gap by leveraging clinical data to enhance diagnostic precision. Clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, are used to predict the likelihood of acute COVID-19 progressing to long COVID. Machine learning models are trained and evaluated for predictive performance. Feature importance analysis is performed to identify the most influential predictors. The machine learning models achieve median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating predictive capabilities. Low antibody titers and high viral loads at hospital admission emerge as the strongest predictors of long COVID outcomes. Comorbidities—such as chronic respiratory, cardiac, and neurologic diseases—and female sex are also identified as significant risk factors. Machine learning models identify patients at risk for developing long COVID based on baseline clinical characteristics. These models guide early interventions, improve patient outcomes, and mitigate the long-term public health impacts of SARS-CoV-2. Long COVID, or post-acute sequelae of SARS-CoV-2, is a prolonged health condition that can occur after acute COVID-19 infection. However, the ability to predict who will develop long COVID remains limited due to the absence of clear tests or biomarkers. We looked at patients’ medical information, including the amount of virus in their body at hospital admission, and how strong their immune response was. Using computer programs that can find hidden patterns in large sets of data, we discovered that people with a weaker immune response, higher amounts of virus, certain long term health problems and women are more likely to develop long COVID. This study highlights that computer-based tools could help doctors identify high-risk patients early and provide care that may prevent long-term complications. Jayavelu, Samaha et al., apply machine learning models on hospital admission data, including antibody titers and viral load, to identify patients at high risk for Long COVID. Low antibody levels, high viral loads, chronic diseases, and female sex are key predictors, supporting early, targeted interventions.

Azithromycin is a widely used antibiotic and was frequently used to treat hospitalized patients during the COVID-19 pandemic. The impact of empiric azithromycin use on the respiratory microbiome in patients with viral respiratory infections is unclear. Here we used longitudinal metatranscriptomics on nasal swabs from a prospective multicentre cohort of 1,164 patients hospitalized for COVID-19. We compared the upper respiratory microbiome, resistome and systemic immune response in patients treated with azithromycin (n = 366) with those who received no antibiotics (n = 474) or other antibiotics (n = 324). We found that azithromycin altered microbiome composition and increased the expression and relative proportion of macrolide/lincosamide/streptogramin (MLS) resistance genes. These changes occurred after 1 day of exposure and persisted for over a week. MLS resistance gene expression was associated with commensals and potential pathogens, while there were no differences in host inflammatory gene expression in blood and airways. This demonstrates that empiric azithromycin treatment impacts the upper respiratory microbiome and resistome without apparent anti-inflammatory benefit.

Abstract Introduction Mycobacterium tuberculosis (Mtb) remains a leading cause of death worldwide. IFNγ Release Assay (IGRA) is widely used to diagnose Mtb infection by measuring the IFNγ response to Mtb antigens. However, healthy IGRA- individuals with high Mtb exposure (“resisters”) have shown evidence of infection and make Mtb-specific responses that differ from IGRA+ individuals. Human CD4 T cells are critical in controlling Mtb. IFNγ-expressing Th1 cells are largely believed to be the major protective subset. Resisters’ negative response to IGRA suggests alternative protective mechanisms of CD4 T cells. This study aims to profile the antigen-specific CD4+ T cell responses in Mtb-exposed IGRA- individuals. Methods We performed TCR sequencing on Ugandan resisters and IGRA+ individuals. TCR repertoires were analyzed with GLIPH3 algorithm we recently developed to identify resister-specific TCRs. Mtb antigenic ligands were discovered by a new T cell epitope discovery platform. Antigen-specific CD4+ T cells were then isolated using peptide-MHC multimers covering the discovered antigenic peptide, and characterized by single-cell multi-omics and Flow Cytometry. Results We identified 24 TCR specificity groups uniquely enriched in resisters. Two ligand peptides were decoded from Mtb antigens Rv2140c and ESAT6. In a parallel South African cohort, in IGRA- individuals, we detected T cell responses to ESAT6, the antigen used in IGRA test, and a robust response to Rv2140c. Notably, Rv2140c-specific CD4 T cells were predominantly follicular helper cells (Tfh), which correlated with protection from Mtb in mice, whereas IGRA+ individuals showed primarily Th1 responses. Conclusion We identified a novel Mtb antigen associated with protection, highlighting its potential as vaccine candidate. ESAT6-specific responses in IGRA- individuals confirmed underlying Mtb infection, indicating the limitations of IGRA tests. The predominance of Tfh cells reveals new protective human T cell mechanism against Mtb beyond the classic Th1 response. Funding Source Bill & Melinda Gates Foundation Topic Categories Microbial, Parasitic, and Fungal Immunology (MPF)

Type I interferon (IFN) signalling promotes development of type 1 regulatory (Tr1) CD4+ T cells, which suppress inflammation but may limit protective immunity in malaria by constraining effector Th1 cells and antibody-promoting T follicular helper (Tfh) cells. We tested whether transiently blocking IFN-driven JAK/STAT signalling with ruxolitinib would reduce Tr1 cell development and enhance protective Th1 and Tfh cell responses. Using controlled human malaria infection, we investigated the impact of ruxolitinib on CD4+ T cell activation and antigen-specific immunity. Whole blood stimulation with IFNβ, IL-2, or PMA/Ionomycin revealed that ruxolitinib suppressed pSTAT1, pSTAT3, and pSTAT5 in CD4+ T cells following treatment. However, antigen-specific CD4+ T cell responses were preserved. Further, ruxolitinib enhanced recall CD4+ T cell responses during a second infection, with increased frequencies of antigen-specific Th1, Tfh, and Tr1 cell subsets. Transcriptional analysis revealed overlapping gene signatures and clonal sharing between Th1, Tfh and Tr1 cells. These findings show transient JAK inhibition can modulate development of Th1, Tfh and Tr1 cell subsets in malaria, suppressing inhibitory signalling while enhancing the magnitude and durability of the CD4+ T cell recall response following a second infection. Targeting IFN-driven Tr1 cell activity therefore represents a promising host-directed strategy to improve malaria immunity.

Background Antigen-specific CD4 T cells are essential but not sufficient for controlling Mycobacterium tuberculosis ( Mtb ) infection. We characterized changes in Mtb- specific CD4, and CD8 and γδ T cell frequencies and phenotypes during tuberculosis progression. Methods Mtb-infected adolescents, who remained healthy or progressed to tuberculosis, were followed longitudinally. Bulk and Mtb- specific T cells were profiled by intracellular cytokine staining and mass cytometry. Systemic inflammation was profiled by blood transcriptomics and proteomics. Features associated with disease progression or systemic inflammation were identified by mixed-effects modelling. Results Among many phenotypes of bulk CD4, CD8 and γδ T cells, only CD4 T cell activation (HLA-DR+) increased significantly during progression. However, levels of systemic inflammatory biomarkers of tuberculosis were strongly associated with changes in many phenotypic subsets of CD4, CD8 and γδ T cells. Similarly, among hundreds of Mtb- specific functional and phenotypic CD4 and CD8 T cell subsets, only two early-differentiated CD4 subsets (IL-2 + TNF + CD7 + CD27 + and IFNγ + IL-2 + TNF + CD7 + CD27 + ) significantly decreased, while Mtb -specific CD4 T cell activation increased during progression. These changes correlated with systemic inflammation. Conclusion Our findings suggest that peripheral blood frequencies and functions of Mtb-specific T cell subsets do not associate with tuberculosis progression, but that inflammation drives marked changes in T cell activation and differentiation.

Study Objectives:Onsets of Narcolepsy type-1 (NT1) increased following A/H1N1 vaccination with Pandemrix® in Europe and with A/H1N1pdm2009 infections in China and other countries. To test if other strains could trigger narcolepsy, we measured strain-specific antibodies in patients with recent onset NT1 compared to controls. Methods:Antibodies against hemagglutinin (HA) and neuraminidase (NA) were tested in 62 patients with very recent onset (onset and blood collection following a single flu season, mean ± SEM: 0.44 ± 0.06 years since onset) and 100 controls matched by age, sex, season and year of collection (2000-2025). Results were next extended to 181 recent onset patients (mean± SEM: 1.00 ± 0.05 years) versus 260 controls, matched by sex, season and year, but having a slightly higher mean age. HA inhibition (HAI) and NA inhibition (NAI) assays were conducted using flu strains known to circulate during the corresponding flu seasons. HAI results are shown as % positive (titers ≥ 40) and NAI results as geometric mean titers. Odds ratio (OR) and β coefficient were used to compare antibody titers in NT1 versus controls. The contribution of each assay to prediction was finally quantified in the larger sample set using Shapley decomposition. Results:NT1 patients had increased anti-HA and anti-NA antibodies against A/H1N1pdm2009 (anti-HA OR= 3.86, anti-NA β= 0.35) and B/Victoria (anti-HA OR=1.90, anti-NA β=0.22), but not A/H1N1pre2009, A/H3N2, or B/Yamagata, independent of HLA-DQB1*06:02 status, age, sex, and flu season. Correlations between anti-HA and anti-NA antibodies titers were weak to moderate but significant (r 2 =-0.10 to 0.34). Multivariable model outperformed age-only baseline (McFadden R 2 = 0.19 vs. 0.03; AUC = 0.79 vs. 0.64; likelihood-ratio test χ 2 = 51, p<10 -9 ), with anti-HA against A/H1N1pdm2009 (β = 0.78, p < 10 -6 ) and anti-NA against B/Victoria (β = 0.69, p < 10 -5 ) emerging as the strongest independent predictors. Conclusions:A/H1N1pdm2009 and B/Victoria, but not other strains can trigger the autoimmune process leading to orexin cell loss in narcolepsy.

Neuroinflammation, along with amyloid beta (Aβ) deposition, phospho-tau (ptau) accumulation, blood-brain barrier (BBB) disruption, and cognitive decline are recognized components of Alzheimer's disease (AD). However, the timing and nature of peripheral immune changes across AD biological and clinical stages remain poorly understood. Here we performed mass cytometry profiling of whole blood and cerebrospinal fluid (CSF) immune cells from 351 human samples across two independent clinical cohorts spanning the AD continuum. We identify coordinated peripheral immune signaling signatures that emerge during preclinical stage of AD and precede significant elevation of plasma ptau217, CSF ptau181 and BBB disruption measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). AD-enriched immune features, including increased phospho-Akt signaling in naï ve T killer cells and phospho-PLCγ2 signaling in granulocytes, were not observed in patients with Frontotemporal lobar degeneration or treatment-naï ve multiple sclerosis. Furthermore, these immune signaling states could be induced in healthy donor immune cells following exposure to plasma or CSF from individuals with AD, indicating that circulating factors can drive these peripheral immune alterations. Together, our findings demonstrate that dynamic peripheral immune state changes arise early in AD and precede canonical biomarker and vascular changes, highlighting immune signaling pathways as potential targets for early therapeutic intervention.

Objectives:Monocytes and dendritic cells (DCs) are essential players in the immune response to infections, involved in shaping innate and adaptive immunity. However, a complete understanding of their specific roles in respiratory infections, including SARS-CoV-2, remains elusive. Methods:To investigate the dynamics of monocytes and DCs in blood as well as the upper and lower airways, we sampled 147 patients with varying degree of COVID-19 severity longitudinally during the spring of 2020. Results:Using flow cytometry, proteomics and in vitro TLR stimulation, we found differences in the distribution and function of monocytes and DCs in patients compared with controls, and importantly, reduced levels of DCs in both blood and airways. In fact, lower frequencies of cDC2s (Lin- HLA-DR+ CD1c+) early after symptom onset predicted subsequent severe disease, and depletion of DC subsets lasted longer in patients with more severe disease. In contrast, severe COVID-19 was associated with increased frequencies of activated monocytes in the lower, but not the upper, airways. Proteomic analysis showed that monocyte and DC-related cytokines in plasma and airways associated with disease severity. During convalescence, cell frequencies and responses to TLR ligands normalised in blood, except for persistently low plasmacytoid DCs. Conclusion:Our study reveals a distinct pattern of recruitment of monocytes but not DCs to the airways during severe COVID-19. Instead, decreased levels of DCs in both blood and airways were found, possibly contributing to more severe COVID-19. The connection between low blood DCs early in disease course and more severe outcomes provides insight into COVID-19 immunopathology, with possible therapeutic implications.

Aging-related immune dysfunction is linked to cancer, atherosclerosis, and neurodegenerative diseases. This 6-week randomized controlled trial evaluated whether 2'-fucosyllactose (2'-FL), a human breast milk oligosaccharide with established benefits in infants and animal models, could improve gut microbiota and immune function in 89 healthy older adults (mean age 67.3 years). While the primary endpoint of cytokine response change was not met, 2'-FL supplementation increased gut Bifidobacterium levels and elevated serum insulin, high-density lipoprotein (HDL) cholesterol, and FGF21 hormone. Bifidobacterium "responders" experienced additional metabolic and proteomic changes and also performed better on a cognitive test of visual memory. Nonresponders were more likely to lack Bifidobacterium in their gut microbiota at the start of the intervention. Multi-omics analysis indicated a systemic response to 2'-FL, which could be detected in blood and urine, showcasing the potential of this prebiotic to provide diverse benefits for healthy aging. This trial was registered at ClinicalTrials.gov (NCT03690999).

BACKGROUND:Immune-related changes impact clinical outcomes in people with Alzheimer's disease (AD). However, these changes have yet to translate into robust blood biomarkers of AD, in part due to high inter-individual variation, small effect sizes, and the indirect relationship between peripheral blood and central nervous system changes. METHOD:In this prospective cohort study of 55 subjects, 19 healthy controls (11 female and 8 male), 19 mild cognitive impairment (MCI) stage of AD (13 female and 6 male), 8 AD dementia (4 female and 4 male) and 9 frontotemporal lobar degeneration (FTD) (7 female and 2 male) individuals, we used mass cytometry to identify a sex-specific immune cell signature in AD peripheral blood and cerebrospinal fluid (CSF). Dynamic contrast enhanced magnetic resonance (DCE-MRI) imaging was used to assess regional blood-brain barrier (BBB) permeability in subjects. Five patients contributed blood and CSF samples after one-year longitudinal follow-up. RESULT:Peripheral blood immune cell responsiveness is more robust than the CSF immune cell signature and peaks earlier in the peripheral blood in the MCI stage among females and later in males at the AD dementia stage. To demonstrate clinical utility of this immune cell signature to identify the MCI stage of AD, we show its effect size exceeded that of Amyloid β42/40, phospho-tau181, plasma phospho-tau217, and cytokine signatures both in the plasma and CSF and was significantly elevated relative to FTD. The immune cell activation signature also correlated with MRI measures of hippocampal BBB permeability and cognitive outcomes. CONCLUSION:The immune cell changes identified here were distinct among cellular phenotypes in peripheral blood and CSF in their responsiveness to AD pathological changes and show promise for novel biomarkers and future immune related therapeutics in AD.

Elevated circulating endothelial cells (CECs), released from monolayers after insult, have been implicated in worse outcomes in ARDS and COVID-19, however there is no consensus proteomic phenotype that define CECs. We queried whether a transcriptomic approach would alternatively support the presence of endothelial cells in circulation and correlate with worsening respiratory failure. To test whether elevated endothelial cell signatures (ECS) in circulation plays a role in worse respiratory outcomes, we used unsupervised bulk-transcriptome deconvolution to quantify ECS

Inhibiting the inflammatory response to malaria offers a promising strategy to improve clinical outcomes and overcome immunoregulatory barriers that hinder development of antiparasitic immunity. We conducted a double-blind, randomized, placebo-controlled trial assessing whether ruxolitinib, a Janus-activated kinase (JAK) 1/2 inhibitor, can reduce inflammatory responses and enhance antiparasitic immunity in malaria-naïve volunteers inoculated with blood-stage Plasmodium falciparum . Twenty participants were inoculated and, on day 8, randomized to receive artemether-lumefantrine with either ruxolitinib or placebo. Ninety days later, participants underwent a second inoculation. Ruxolitinib was safe and well tolerated; moreover, it attenuated inflammatory responses to the initial infection, with reduced posttreatment increases in C-reactive protein and markers of disease severity, including angiopoietin-2 and intercellular adhesion molecule-1. Ruxolitinib also enhanced immune memory after the second infection, with elevated human leukocyte antigen–DRA and 4-1BB, consistent with increased T cell activation. These data support the further evaluation of ruxolitinib as an adjunctive treatment to improve clinical outcomes and boost antiparasitic immunity in clinical malaria.

Immunotherapy of cancer is now an essential pillar of treatment for patients with many individual tumor types. Novel immune targets and technical advances are driving a rapid exploration of new treatment strategies incorporating immune agents in cancer clinical practice. Immunotherapies perturb a complex system of interactions among genomically unstable tumor cells, diverse cells within the tumor microenvironment including the systemic adaptive and innate immune cells. The drive to develop increasingly effective immunotherapy regimens is tempered by the risk of immune-related adverse events. Evidence-based biomarkers that measure the potential for therapeutic response and/or toxicity are critical to guide optimal patient care and contextualize the results of immunotherapy clinical trials. Responding to the lack of guidance on biomarker testing in early-phase immunotherapy clinical trials, we propose a definition and listing of essential biomarkers recommended for inclusion in all such protocols. These recommendations are based on consensus provided by the Society for Immunotherapy of Cancer (SITC) Clinical Immuno-Oncology Network (SCION) faculty with input from the SITC Pathology and Biomarker Committees and the Journal for ImmunoTherapy of Cancer readership. A consensus-based selection of essential biomarkers was conducted using a Delphi survey of SCION faculty. Regular updates to these recommendations are planned. The inaugural list of essential biomarkers includes complete blood count with differential to generate a neutrophil-to-lymphocyte ratio or systemic immune-inflammation index, serum lactate dehydrogenase and albumin, programmed death-ligand 1 immunohistochemistry, microsatellite stability assessment, and tumor mutational burden. Inclusion of these biomarkers across early-phase immunotherapy clinical trials will capture variation among trials, provide deeper insight into the novel and established therapies, and support improved patient selection and stratification for later-phase clinical trials.

BACKGROUNDFollowing SARS-CoV-2 infection, approximately 10%-35% of patients with COVID-19 experience long COVID (LC), in which debilitating symptoms persist for at least 3 months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities.METHODSWe utilized machine learning methods on biologic analytes provided over 12 months after hospital discharge from more than 500 patients with COVID-19 in the IMPACC cohort to identify a multiomics "recovery factor," trained on patient-reported physical function survey scores. Immune profiling data included PBMC transcriptomics, serum O-link and plasma proteomics, plasma metabolomics, and blood mass cytometry by time of flight (CyTOF) protein levels. Recovery factor scores were tested for association with LC, disease severity, clinical parameters, and immune subset frequencies. Enrichment analyses identified biologic pathways associated with recovery factor scores.RESULTSParticipants with LC had lower recovery factor scores compared with recovered participants. Recovery factor scores predicted LC as early as hospital admission, irrespective of acute COVID-19 severity. Biologic characterization revealed increased inflammatory mediators, elevated signatures of heme metabolism, and decreased androgenic steroids as predictive and ongoing biomarkers of LC. Lower recovery factor scores were associated with reduced lymphocyte and increased myeloid cell frequencies. The observed signatures are consistent with persistent inflammation driving anemia and stress erythropoiesis as major biologic underpinnings of LC.CONCLUSIONThe multiomics recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNational Institute of Allergy and Infectious Diseases (NIAID), NIH (3U01AI167892-03S2, 3U01AI167892-01S2, 5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI057229-18, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07S1, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-1, 3U19AI1289130, U19AI128913-04S1, R01AI122220); NIH (UM1TR004528); and National Science Foundation (NSF) (DMS2310836).

PURPOSE:SWOG S1609 Dual Anti-CTLA-4 and anti-PD-1 blockade in Rare Tumors (DART) studied the efficacy of ipilimumab combined with nivolumab across multiple rare tumor types. We report the results of the pancreatic neuroendocrine neoplasm (PNEN) cohort. EXPERIMENTAL DESIGN:Treatment consisted of ipilimumab 1 mg/kg intravenously every 6 weeks with nivolumab 240 mg intravenously every 2 weeks. The primary endpoint was overall response rate (ORR) (Response Evaluation Criteria In Solid TumorsRECIST V.1.1). Secondary endpoints include progression-free survival (PFS), overall survival (OS), and toxicity. Clinical benefit rate (includes ORR plus stable disease (SD)>6 months was examined. Correlative studies were performed. The trial was conducted by the National Cancer Institute/Southwest Oncology Group Early Therapeutics and Rare Cancers Committee and opened at >1,000 sites. RESULTS:19 patients with PNEN were enrolled. The median number of lines of prior therapy was 2 (range: 0-4). The ORR was 11% (2/19 patients); the clinical benefit rate (CBR; stable disease >6 months+partial response+complete response), 26% (5/19). The median PFS was 3 months; median OS, 24 months. The longest PFSs were 26 (intermediate grade PNEN), 31 (low grade) and 39+months (intermediate grade). The most common toxicities were fatigue (47% of patients) and aspartate aminotransferase (AST) elevation (32%); the most common grade 3/4 immune-related adverse event (AE) was AST (32%) and bilirubin elevation (26%), with no grade 5 events. Programmed death-ligand 1 expression by chromogenic immunohistochemistry (N=12 patients assessed) did not associate with ORR; tumor mutation burden (TMB) was high in three patients; one of the two patients with partial remission (PFS=26 months) had high TMB (150 mutations/mb). Peripheral effector memory T-cell activation (N=11 patients assessed by cytometry by time-of-flight with 5 having longitudinal analysis) was associated with response, though the number of patients evaluated was limited. CONCLUSIONS:Low-dose ipilimumab plus nivolumab demonstrated an 11% ORR and 26% CBR (includes SD>6 months) in patients with refractory PNEN, with durable benefit (>2 years) in 3 (16%) patients. TRIAL REGISTRATION NUMBER:NCT02834013.

Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor". IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids in LC patients. The recovery factor was also associated with altered circulating immune cell frequencies. Notably, recovery factor scores were predictive of LC occurrence in patients as early as hospital admission, irrespective of acute disease severity. Thus, the recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.

Chikungunya (CHIKV) and dengue (DENV) are mosquito-borne viruses that cause severe epidemics, often in remote regions. A limitation to our understanding of these pathogens is the difficulty of performing assays of the cellular immune response. To fill this gap, we developed a novel miniaturized automated system capable of processing 250 μl of whole blood for high-throughput cellular analysis. In a field study with a pediatric cohort in Msambweni, Kenya, known for previous exposure to CHIKV and/or DENV, we processed 133 whole blood samples using our system under three conditions: no stimulation, and stimulation with CHIKV or DENV peptide pools. These samples underwent CyTOF or flow cytometry analysis to evaluate virus-specific memory T cell responses and phenotypes. CyTOF analysis of 81 participant samples revealed significant cytokine responses to CHIKV and DENV, particularly IFNγ (P < 0.01 and P < 0.0001, respectively) and TNF-α (P < 0.0001) by γδ T cells. Additionally, a significant TNF-α response was observed in the CD8+ TEMRA memory subset to DENV, albeit to a lesser degree than in γδ T cells. To confirm our CyTOF findings, we employed flow cytometry on the remaining 40 samples using a targeted panel, validating significant TNF-α (P < 0.0001 and P < 0.01) and IFN-γ (P < 0.05) responses by γδ T cells to CHIKV and DENV, respectively. Our study demonstrates that our innovative automated system enables detailed assessment of immune function, particularly beneficial in pediatric populations and resource-limited settings with limited sample volumes. This approach holds promise for advancing our understanding of cellular immune responses to various viral and infectious diseases.