Search

Pulmonary tuberculosis (PTB) is characterized by lung granulomas, inflammation and tissue destruction. Here we used within-subject peripheral blood gene expression over time to correlate with the within-subject lung metabolic activity, as measured by positron emission tomography (PET) to identify biological processes and pathways underlying overall resolution of lung inflammation. We used next-generation RNA sequencing and [18F]FDG PET-CT data, collected at diagnosis, week 4, and week 24, from 75 successfully cured PTB patients, with the [18F]FDG activity as a surrogate for lung inflammation. Our linear mixed-effects models required that for each individual the slope of the line of [18F]FDG data in the outcome and the slope of the peripheral blood transcript expression data correlate, i.e., the slopes of the outcome and explanatory variables had to be similar. Of 10,295 genes that changed as a function of time, we identified 639 genes whose expression profiles correlated with decreasing [18F]FDG uptake levels in the lungs. Gene enrichment over-representation analysis revealed that numerous biological processes were significantly enriched in the 639 genes, including several well known in TB transcriptomics such as platelet degranulation and response to interferon gamma, thus validating our novel approach. Others not previously associated with TB pathobiology included smooth muscle contraction, a set of pathways related to mitochondrial function and cell death, as well as a set of pathways connecting transcription, translation and vesicle formation. We observed up-regulation in genes associated with B cells, and down-regulation in genes associated with platelet activation. We found 254 transcription factor binding sites to be enriched among the 639 gene promoters. In conclusion, we demonstrated that of the 10,295 gene expression changes in peripheral blood, only a subset of 639 genes correlated with inflammation in the lungs, and the enriched pathways provide a description of the biology of resolution of lung inflammation as detectable in peripheral blood. Surprisingly, resolution of PTB inflammation is positively correlated with smooth muscle contraction and, extending our previous observation on mitochondrial genes, shows the presence of mitochondrial stress. We focused on pathway analysis which can enable therapeutic target discovery and potential modulation of the host response to TB.

SUMMARY Basic leucine zipper transcription factor 2 (Batf2) activation is detrimental in Type 1-controlled infectious diseases, demonstrated during infection with Mycobacterium tuberculosis (Mtb) and Listeria monocytogenes Lm. In Batf2-deficient mice (Batf2−/−), infected with Mtb or Lm, mice survived and displayed reduced tissue pathology compared to infected control mice. Indeed, pulmonary inflammatory macrophage recruitment, pro-inflammatory cytokines and immune effectors were also decreased during tuberculosis. This explains that batf2 mRNA predictive early biomarker found in active TB patients is increased in peripheral blood. Similarly, Lm infection in human macrophages and mouse spleen and liver also increased Batf2 expression. In striking contrast, Type 2-controlled schistosomiasis exacerbates during infected Batf2−/− mice with increased intestinal fibro-granulomatous inflammation, pro-fibrotic immune cells, and elevated cytokine production leading to wasting disease and early death. Together, these data strongly indicate that Batf2 differentially regulates Type 1 and Type 2 immunity in infectious diseases.

The RTS,S/AS01 vaccine provides partial protection against Plasmodium falciparum infection but determinants of protection and/or disease are unclear. Previously, anti-circumsporozoite protein (CSP) antibody titers and blood RNA signatures were associated with RTS,S/AS01 efficacy against controlled human malaria infection (CHMI). By analyzing host blood transcriptomes from five RTS,S vaccination CHMI studies, we demonstrate that the transcript ratio MX2/GPR183, measured 1 day after third immunization, discriminates protected from non-protected individuals. This ratiometric signature provides information that is complementary to anti-CSP titer levels for identifying RTS,S/AS01 immunized people who developed protective immunity and suggests a role for interferon and oxysterol signaling in the RTS,S mode of action.

Despite widespread use of the bacille Calmette-Guerin (BCG) vaccine, tuberculosis (TB) remains a leading cause of global mortality from a single infectious agent (Mycobacterium tuberculosis or Mtb). Here, over two independent Mtb challenge studies, we demonstrate that subcutaneous vaccination of rhesus macaques (RMs) with rhesus cytomegalovirus vectors encoding Mtb antigen inserts (hereafter referred to as RhCMV/TB)-which elicit and maintain highly effector-differentiated, circulating and tissue-resident Mtb-specific CD4+ and CD8+ memory T cell responses-can reduce the overall (pulmonary and extrapulmonary) extent of Mtb infection and disease by 68%, as compared to that in unvaccinated controls, after intrabronchial challenge with the Erdman strain of Mtb at ∼1 year after the first vaccination. Fourteen of 34 RhCMV/TB-vaccinated RMs (41%) across both studies showed no TB disease by computed tomography scans or at necropsy after challenge (as compared to 0 of 17 unvaccinated controls), and ten of these RMs were Mtb-culture-negative for all tissues, an exceptional long-term vaccine effect in the RM challenge model with the Erdman strain of Mtb. These results suggest that complete vaccine-mediated immune control of highly pathogenic Mtb is possible if immune effector responses can intercept Mtb infection at its earliest stages.

Tuberculosis (TB) is a highly heterogeneous disease that develops in a subset of individuals infected with aerosolized Mycobacterium tuberculosis (Mtb). We sought to develop an improved experimental model by infecting mice with an ultra-low dose (ULD, 1-3 founding Mtb bacteria), which is believed to reflect a physiologic inoculum. ULD-infected mice exhibited bacterial burdens ranging from

Improved tuberculosis diagnostics and tools for monitoring treatment response are urgently needed. We developed a robust and simple, PCR-based host-blood transcriptomic signature, RISK6, for multiple applications: identifying individuals at risk of incident disease, as a screening test for subclinical or clinical tuberculosis, and for monitoring tuberculosis treatment. RISK6 utility was validated by blind prediction using quantitative real-time (qRT) PCR in seven independent cohorts.Prognostic performance significantly exceeded that of previous signatures discovered in the same cohort. Performance for diagnosing subclinical and clinical disease in HIV-uninfected and HIV-infected persons, assessed by area under the receiver-operating characteristic curve, exceeded 85%. As a screening test for tuberculosis, the sensitivity at 90% specificity met or approached the benchmarks set out in World Health Organization target product profiles for non-sputum-based tests. RISK6 scores correlated with lung immunopathology activity, measured by positron emission tomography, and tracked treatment response, demonstrating utility as treatment response biomarker, while predicting treatment failure prior to treatment initiation. Performance of the test in capillary blood samples collected by finger-prick was noninferior to venous blood collected in PAXgene tubes. These results support incorporation of RISK6 into rapid, capillary blood-based point-of-care PCR devices for prospective assessment in field studies.

HIV-infected individuals are at high risk of tuberculosis disease and those with prior tuberculosis episodes are at even higher risk of disease recurrence. A non-sputum biomarker that identifies individuals at highest tuberculosis risk would allow targeted microbiological testing and appropriate treatment and also guide need for prolonged therapy. We determined the utility of a previously developed whole blood transcriptomic correlate of risk (COR) signature for (1) predicting incident recurrent tuberculosis, (2) tuberculosis diagnosis and (3) its potential utility for tuberculosis treatment monitoring in HIV-infected individuals. We retrieved cryopreserved blood specimens from three previously completed clinical studies and measured the COR signature by quantitative microfluidic real-time-PCR. The signature differentiated recurrent tuberculosis progressors from non-progressors within 3 months of diagnosis with an area under the Receiver-operating characteristic (ROC) curve (AUC) of 0.72 (95% confidence interval (CI), 0.58–0.85) amongst HIV-infected individuals on antiretroviral therapy (ART). Twenty-five of 43 progressors (58%) were asymptomatic at microbiological diagnosis and thus had subclinical disease. The signature showed excellent diagnostic discrimination between HIV-uninfected tuberculosis cases and controls (AUC 0.97; 95%CI 0.94–1). Performance was lower in HIV-infected individuals (AUC 0.83; 95%CI 0.81–0.96) and signature scores were directly associated with HIV viral loads. Tuberculosis treatment response in HIV-infected individuals on ART with a new recurrent tuberculosis diagnosis was also assessed. Signature scores decreased significantly during treatment. However, pre-treatment scores could not differentiate between those who became sputum negative before and after 2 months. Direct application of the unmodified blood transcriptomic COR signature detected subclinical and active tuberculosis by blind validation in HIV-infected individuals. However, prognostic performance for recurrent tuberculosis, and performance as diagnostic and as treatment monitoring tool in HIV-infected persons was inferior to published results from HIV-negative cohorts. Our results suggest that performance of transcriptomic signatures comprising interferon stimulated genes are negatively affected in HIV-infected individuals, especially in those with incompletely suppressed viral loads.

Tuberculosis (TB) is a heterogeneous disease manifesting in a subset of individuals infected with aerosolized Mycobacterium tuberculosis (Mtb). Unlike human TB, murine infection results in uniformly high lung bacterial burdens and poorly organized granulomas. To develop a TB model that more closely resembles human disease, we infected mice with an ultra-low dose (ULD) of between 1–3 founding bacteria, reflecting a physiologic inoculum. ULD-infected mice exhibited highly heterogeneous bacterial burdens, well-circumscribed granulomas that shared features with human granulomas, and prolonged Mtb containment with unilateral pulmonary infection in some mice. We identified blood RNA signatures in mice infected with an ULD or a conventional Mtb dose (50–100 CFU) that correlated with lung bacterial burdens and predicted Mtb infection outcomes across species, including risk of progression to active TB in humans. Overall, these findings highlight the potential of the murine TB model and show that ULD infection recapitulates key features of human TB.

There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.

Tuberculosis (TB) is a highly heterogeneous disease that develops in a subset of individuals infected with aerosolized Mycobacterium tuberculosis (Mtb). To improve upon the standard experimental mouse model, we infected mice with an ultra-low Mtb dose (ULD), consisting of 1-3 founding bacteria, which reflects the physiologic inoculum of humans. These mice exhibited a broad range of outcomes, with bacterial burdens ranging from

BackgroundCurrent diagnostics are inadequate to meet the challenges presented by coinfection with Mycobacterium tuberculosis (Mtb) and HIV, the leading cause of death for HIV-infected individuals. Improved characterisation of Mtb/HIV coinfection as a distinct disease state may lead to better identification and treatment of affected individuals.MethodsFour previously published TB and HIV co-infection related datasets were used to train and validate multinomial machine learning classifiers that simultaneously predict TB and HIV status. Classifier predictive performance was measured using leave-one-out cross validation on the training set and blind predictive performance on multiple test sets using area under the ROC curve (AUC) as the performance metric. Linear modelling of signature gene expression was applied to systematically classify genes as TB-only, HIV-only or combined TB/HIV.ResultsThe optimal signature discovered was a single 10-gene random forest multinomial signature that robustly discriminates active tuberculosis (TB) from other non-TB disease states with improved performance compared with previously published signatures (AUC: 0. 87), and specifically discriminates active TB/HIV co-infection from all other conditions (AUC: 0.88). Signature genes exhibited a variety of transcriptional patterns including both TB-only and HIV-only response genes and genes with expression patterns driven by interactions between HIV and TB infection states, including the CD8+ T-cell receptor LAG3 and the apoptosis-related gene CERKL.ConclusionsBy explicitly including distinct disease states within the machine learning analysis framework, we developed a compact and highly diagnostic signature that simultaneously discriminates multiple disease states associated with Mtb/HIV co-infection. Examination of the expression patterns of signature genes suggests mechanisms underlying the unique inflammatory conditions associated with active TB in the presence of HIV. In particular, we observed that disregulation of CD8+ effector T-cell and NK-cell associated genes may be an important feature of Mtb/HIV co-infection.

New biomarkers of tuberculosis (TB) risk and disease are critical for the urgently needed control of the ongoing TB pandemic. In a prospective multisite study across Subsaharan Africa, we analyzed metabolic profiles in serum and plasma from HIV-negative, TB-exposed individuals who either progressed to TB 3–24 months post-exposure (progressors) or remained healthy (controls). We generated a trans-African metabolic biosignature for TB, which identifies future progressors both on blinded test samples and in external data sets and shows a performance of 69% sensitivity at 75% specificity in samples within 5 months of diagnosis. These prognostic metabolic signatures are consistent with development of subclinical disease prior to manifestation of active TB. Metabolic changes associated with pre-symptomatic disease are observed as early as 12 months prior to TB diagnosis, thus enabling timely interventions to prevent disease progression and transmission., The tuberculosis pandemic requires new methods for diagnosing and containing infections prior to active disease. Here, the authors performed a multi-site observational study within sub-Saharan Africa and present serum and plasma metabolic signatures that can predict the onset of active TB with a high degree of sensitivity and specificity.

Biomarkers that predict who among recently Mycobacterium tuberculosis (MTB)-exposed individuals will progress to active tuberculosis are urgently needed. Intracellular microRNAs (miRNAs) regulate the host response to MTB and circulating miRNAs (c-miRNAs) have been developed as biomarkers for other diseases. We performed machine-learning analysis of c-miRNA measurements in the serum of adult household contacts (HHCs) of TB index cases from South Africa and Uganda and developed a c-miRNA-based signature of risk for progression to active TB. This c-miRNA-based signature significantly discriminated HHCs within 6 months of progression to active disease from HHCs that remained healthy in an independent test set [ROC area under the ROC curve (AUC) 0.74, progressors

The pathogens causing AIDS, malaria, and tuberculosis have proven too complex to be overcome by classical approaches to vaccination. The complexities of human immunology and pathogen-induced modulation of the immune system mandate new approaches to vaccine discovery and design. A new field, systems vaccinology, weds holistic analysis of innate and adaptive immunity within a quantitative framework to enable rational design of new vaccines that elicit tailored protective immune responses. A key step in the approach is to discover relationships between the earliest innate inflammatory responses to vaccination and the subsequent vaccine-induced adaptive immune responses and efficacy. Analysis of these responses in clinical studies is complicated by the inaccessibility of relevant tissue compartments (such as the lymph node), necessitating reliance upon peripheral blood responses as surrogates. Blood transcriptomes, although indirect to vaccine mechanisms, have proven very informative in systems vaccinology studies. The approach is most powerful when innate and adaptive immune responses are integrated with vaccine efficacy, which is possible for malaria with the advent of a robust human challenge model. This is more difficult for AIDS and tuberculosis, given that human challenge models are lacking and efficacy observed in clinical trials has been low or highly variable. This challenge can be met by appropriate clinical trial design for partially efficacious vaccines and by analysis of natural infection cohorts. Ultimately, systems vaccinology is an iterative approach in which mechanistic hypotheses-derived from analysis of clinical studies-are evaluated in model systems, and then used to guide the development of new vaccine strategies. In this review, we will illustrate the above facets of the systems vaccinology approach with case studies.

Rationale: Contacts of patients with tuberculosis (TB) constitute an important target population for preventive measures because they are at high risk of infection with Mycobacterium tuberculosis and progression to disease. Objectives: We investigated bio-signatures with predictive ability for incident TB. Methods: In a case-control study nested within the Grand Challenges 6-74 longitudinal HIV-negative African cohort of exposed household contacts, we employed RNA sequencing, PCR, and the pair ratio algorithm in a training/test set approach. Overall, 79 progressors who developed TB between 3 and 24 months after diagnosis of index case and 328 matched nonprogressors who remained healthy during 24 months of follow-up were investigated. Measurements and Main Results: A four-transcript signature derived from samples in a South African and Gambian training set predicted progression up to two years before onset of disease in blinded test set samples from South Africa, the Gambia, and Ethiopia with little population-associated variability, and it was also validated in an external cohort of South African adolescents with latent M. tuberculosis infection. By contrast, published diagnostic or prognostic TB signatures were predicted in samples from some but not all three countries, indicating site-specific variability. Post hoc meta-analysis identified a single gene pair, C1QC/TRAV27 (complement C1q C-chain / T-cell receptor-a variable gene 27) that would consistently predict TB progression in household contacts from multiple African sites but not in infected adolescents without known recent exposure events. Conclusions: Collectively, we developed a simple whole blood-based PCR test to predict TB in recently exposed household contacts from diverse African populations. This test has potential for implementation in national TB contact investigation programs.

Recombinant adenoviral vectors (rAds) are lead vaccine candidates for protection against a variety of pathogens, including Ebola, HIV, tuberculosis, and malaria, due to their ability to potently induce T cell immunity in humans. However, the ability to induce protective cellular immunity varies among rAds. Here, we assessed the mechanisms that control the potency of CD8 T cell responses in murine models following vaccination with human-, chimpanzee-, and simian-derived rAds encoding SIV-Gag antigen (Ag). After rAd vaccination, we quantified Ag expression and performed expression profiling of innate immune response genes in the draining lymph node. Human-derived rAd5 and chimpanzee-derived chAd3 were the most potent rAds and induced high and persistent Ag expression with low innate gene activation, while less potent rAds induced less Ag expression and robustly induced innate immunity genes that were primarily associated with IFN signaling. Abrogation of type I IFN or stimulator of IFN genes (STING) signaling increased Ag expression and accelerated CD8 T cell response kinetics but did not alter memory responses or protection. These findings reveal that the magnitude of rAd-induced memory CD8 T cell immune responses correlates with Ag expression but is independent of IFN and STING and provide criteria for optimizing protective CD8 T cell immunity with rAd vaccines.

The cynomolgus macaque model of low-dose Mycobacterium tuberculosis infection recapitulates clinical aspects of human tuberculosis pathology, but it is unknown whether the 2 systems are sufficiently similar that host-based signatures of tuberculosis will be predictive across species. By blind prediction, we demonstrate that a subset of genes comprising a human signature for tuberculosis risk is simultaneously predictive in humans and macaques and prospectively discriminates progressor from controller animals 3-6 weeks after infection. Further analysis yielded a 3-gene signature involving PRDX2 that predicts tuberculosis progression in macaques 10 days after challenge, suggesting novel pathways that define protective responses to M. tuberculosis.

Biomarkers for tuberculosis treatment outcome will assist in guiding individualized treatment and evaluation of new therapies. To identify candidate biomarkers, RNA sequencing of whole blood from a well-characterized TB treatment cohort was performed. Application of a validated transcriptional correlate of risk for TB revealed symmetry in host gene expression during progression from latent TB infection to active TB disease and resolution of disease during treatment, including return to control levels after drug therapy. The symmetry was also seen in a TB disease signature, constructed from the TB treatment cohort, that also functioned as a strong correlate of risk. Both signatures identified patients at risk of treatment failure 1–4 weeks after start of therapy. Further mining of the transcriptomes revealed an association between treatment failure and suppressed expression of mitochondrial genes before treatment initiation, leading to development of a novel baseline (pre-treatment) signature of treatment failure. These novel host responses to TB treatment were integrated into a five-gene real-time PCR-based signature that captures the clinically relevant responses to TB treatment and provides a convenient platform for stratifying patients according to their risk of treatment failure. Furthermore, this 5-gene signature is shown to correlate with the pulmonary inflammatory state (as measured by PET-CT) and can complement sputum-based Gene Xpert for patient stratification, providing a rapid and accurate alternative to current methods.

Introduction Brentuximab vedotin (BV) targets CD30, a receptor expressed on the Reed-Sternberg cells of classic Hodgkin lymphoma (cHL). Nivolumab (Nivo) restores antitumor immunity by blocking the PD-1 receptor on activated T-cells. In this phase 1/2 study, combination treatment (tx) with BV + Nivo demonstrated durable efficacy in patients (pts) with relapsed/refractory (R/R) cHL (Herrera, Blood 2018; NCT02572167). Biomarkers consistent with innate and adaptive immune activation were observed in the peripheral blood of pts treated with the combination regimen. Herein, we associate biomarkers in the peripheral blood with clinical response, and present follow-up results for pts who received BV + Nivo under staggered and concurrent dosing schedules. Methods Enrolled pts had cHL that relapsed or was refractory to frontline chemotherapy. In Parts 1 and 2 (staggered dosing), pts received up to 4 cycles of BV 1.8 mg/kg and Nivo 3.0 mg/kg given on Days 1 and 8 of Cycle 1, respectively, and together on Day 1 of Cycles 2-4. Pts in Part 3 (concurrent dosing) received the same dose of both agents on Day 1 of all 4 cycles. After investigators assessed response (Cheson 2014, with the incorporation of LYRIC [Cheson 2016] for pts in Part 3), pts could undergo ASCT. Results Demographics and baseline characteristics were similar across all treated pts (N=91) in the staggered and concurrent dosing cohorts; median age 34 years (yrs, range; 18-69), 42% with primary refractory disease, and 30% with relapse within 1 yr of frontline therapy. All 91 pts are off-tx and have been observed through the 100-day safety reporting period. A total of 86 pts (92%) completed all 4 cycles of BV + Nivo. Early tx discontinuations were due to; AE (peripheral neuropathy and increased GGT [1 pt each]), progressive disease (PD), investigator decision, and pt decision (1 pt each). Most common AEs prior to ASCT or additional salvage therapy were nausea (52%) and infusion-related reactions (IRRs, 43%). Excluding IRRs, 14% of pts had immune-related AEs requiring tx with systemic steroids, including rash (8%), pneumonitis (4%), and AST increased, diarrhea, and Guillain-Barre syndrome (1% each). The ORR for all-treated pts was 85%, with 67% complete response (CR). A total of 67 pts (74%) underwent ASCT after tx with BV + Nivo. There were 22 pts who received additional salvage therapy after BV + Nivo (7 PD, 6 partial response, 5 stable disease, and 4 CR at EOT), 17 of whom later underwent ASCT. At a median of 22.6 months (range; 1.2, 41.2) from the start of tx, the estimated 2-yr PFS rate in all treated pts was 78%, and for pts who underwent ASCT after tx with BV + Nivo was 91% (Figure 1). At 2 years, the estimated OS rate for all treated pts was 93%. Staggered and concurrent dosing of BV + Nivo resulted in increased levels of activated and dividing CD4+ T cells, activated and dividing CD8+ T cells (concurrent dosing-only), regulatory T cells (Tregs), and circulating plasmablasts in blood. We did not observe any associations between the magnitude of these changes and clinical response. Pts with CR exhibited trends for higher pre-tx blood levels of CD30+ Tregs and CD30+ Th subsets compared to pts without CR, suggesting BV depletion of these populations may have a role in the clinical mode of action of BV + Nivo. Although pre-tx levels of cytotoxic lymphocytes (CTLs) in blood did not differentiate pts with CR from other pts, pts in the lower quartile of pre-tx CTL levels showed significantly shorter PFS than other pts, suggesting a potential association between CTLs and disease control. Changes in blood cytokine and chemokine levels were observed after BV + Nivo, including increased levels of IL-18, IP-10, I-TAC, and sCD30, and decreased levels of TARC, IL-2Ra, and IL-6. Our analyses support strong correlations between pre-tx cytokine/chemokine levels and clinical benefit including trends linking lower pre-tx levels of IL-18, I-TAC, and IL-2RA to achieving CR and longer PFS. Conclusion BV + Nivo, both staggered and current dosing, showed tolerability and high CR rates with durable remissions among pts with R/R cHL. Analysis of blood biomarkers identified trends potentially linking baseline levels of CD30+ immune cells and the baseline pt inflammatory state with the activity of BV + Nivo. Together, these encouraging results support further investigation of BV + Nivo as initial salvage therapy in pts with R/R cHL. Figure 1 Disclosures Moskowitz: Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Erytech Pharma: Consultancy; Erytech Pharma: Consultancy; ADC Therapeutics: Consultancy; Merck: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Merck: Research Funding; Cell Medica: Consultancy; Takeda Pharmaceuticals: Consultancy; Merck: Research Funding; ADC Therapeutics: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Erytech Pharma: Consultancy; Takeda Pharmaceuticals: Consultancy; Incyte: Research Funding; Merck: Research Funding; Merck: Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Cell Medica: Consultancy; Takeda Pharmaceuticals: Consultancy; Cell Medica: Consultancy; ADC Therapeutics: Consultancy; Incyte: Research Funding; Takeda Pharmaceuticals: Consultancy; Takeda Pharmaceuticals: Consultancy; Merck: Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Cell Medica: Consultancy; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Takeda Pharmaceuticals: Consultancy; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Cell Medica: Consultancy; miRagen Therapeutics Inc: Consultancy, Research Funding; Incyte: Research Funding; Takeda Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy, Honoraria, Research Funding; Takeda Pharmaceuticals: Consultancy; Cell Medica: Consultancy; ADC Therapeutics: Consultancy; Erytech Pharma: Consultancy; Erytech Pharma: Consultancy; Erytech Pharma: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Erytech Pharma: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Incyte: Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Incyte: Research Funding; Cell Medica: Consultancy; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Erytech Pharma: Consultancy; Takeda Pharmaceuticals: Consultancy; Takeda Pharmaceuticals: Consultancy; Merck: Research Funding; Takeda Pharmaceuticals: Consultancy; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Incyte: Research Funding; Cell Medica: Consultancy; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Incyte: Research Funding; ADC Therapeutics: Consultancy; Merck: Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; ADC Therapeutics: Consultancy; Incyte: Research Funding; Incyte: Research Funding; Cell Medica: Consultancy; Incyte: Research Funding; ADC Therapeutics: Consultancy; Erytech Pharma: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Erytech Pharma: Consultancy; Takeda Pharmaceuticals: Consultancy; Takeda Pharmaceuticals: Consultancy; Cell Medica: Consultancy; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Takeda Pharmaceuticals: Consultancy; Cell Medica: Consultancy; Erytech Pharma: Consultancy; Seattle Genetics: Consultancy, Honoraria, Research Funding; Erytech Pharma: Consultancy; Seattle Genetics: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Merck: Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Merck: Research Funding; Cell Medica: Consultancy; Seattle Genetics: Consultancy, Honoraria, Research Funding; Incyte: Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Incyte: Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Kyowa Hakko Kirin Pharma: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Incyte: Research Funding; ADC Therapeutics: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Erytech Pharma: Consultancy; Erytech Pharma: Consultancy; ADC Therapeutics: Consultancy; Incyte: Research Funding; ADC Therapeutics: Consultancy; miRagen Therapeutics Inc: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; ADC Therapeutics: Consultancy; ADC Therapeutics: Consultancy; ADC Therapeutics: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; miRagen Therapeutics Inc: Consultancy, Research Funding; Cell Medica: Consultancy; Cell Medica: Consultancy; Seattle Genetics: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Incyte: Research Funding; Merck: Research Funding; Merck: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Erytech Pharma: Consultancy; Takeda Pharmaceuticals: Consultancy; ADC Therapeutics: Consultancy; Cell Medica: Consultancy; Seattle Genetics: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; ADC Therapeutics: Consultancy; Seattle Genetics: Consultancy, Honoraria, Research Funding. Advani:Celmed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Forty-Seven: Research Funding; Infinity Pharma: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead Sciences, Inc./Kite Pharma, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Regeneron: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kyowa Kirin Pharmaceutical Developments, Inc.: Consultancy; Merck: Research Funding; Kura: Research Funding; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agensys: Research Funding; Cell Medica, Ltd: Consultancy; Stanford University: Employment, Equity Ownership; Seattle Genetics: Consultancy, Research Funding; Millennium: Research Funding; Janssen: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Bartlett:Affimed Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Research Funding; Dynavax: Research Funding; Forty-Seven: Research Funding; Genentech: Research Funding; Gilead: Research Funding; Immune Design: Research Funding; Janssen: Research Funding; Kite Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Medimmune: Research Funding; Merck: Research Funding; Millennium: Research Funding; Novartis: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Seattle Genetics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Vose:Acerta Pharma: Honoraria, Other: Grants, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Celgene Corporation: Research Funding; Incyte Corporation: Research Funding; Kite Pharma: Honoraria, Other: Grants, Research Funding; Novartis: Research Funding; Seattle Genetics: Research Funding; AbbVie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Legend Pharmaceuticals: Honoraria. Ramchandren:Genentech: Research Funding; Seattle Genetics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Pharmacyclics LLC, an Abbvie company: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Sandoz-Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Feldman:Takeda: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Other: Travel expenses, Speakers Bureau; AbbVie: Honoraria, Other: Travel expenses, Speakers Bureau; Pharmacyclics: Honoraria, Other: Travel expenses, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Kite Pharma: Honoraria, Other: Travel expenses, Speakers Bureau; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Cell Medica: Research Funding; Roche: Research Funding; Corvus: Research Funding; Eisai: Research Funding; Kyowa Hakko Kirin: Research Funding; Pfizer: Research Funding; Portola Pharma: Research Funding; Roche: Research Funding; Trillium: Research Funding; Viracta: Research Funding. LaCasce:BMS: Consultancy; Research to Practice: Speakers Bureau; Humanigen: Consultancy; Seattle Genetics: Consultancy, Research Funding. Christian:Bristol-Myers Squibb: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cephalon: Research Funding; Janssen: Research Funding; Immunomedics: Research Funding; Celgene: Research Funding; Acerta: Research Funding; Triphase: Research Funding; Millennium Pharmaceuticals Inc: Research Funding; Merck: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Moskowitz:ADC Therapeutics: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Celgene: Consultancy; Pharmacyclics: Research Funding. Brown:Seattle Genetics, Inc.: Employment, Equity Ownership. Taft:Seattle Genetics, Inc.: Employment, Equity Ownership. Ansari:Seattle Genetics, Inc.: Employment, Equity Ownership. Zak:Seattle Genetics, Inc.: Employment, Equity Ownership. Sacchi:Seattle Genetics, Inc.: Research Funding. Manley:Seattle Genetics: Employment, Equity Ownership. Herrera:Merck: Consultancy, Research Funding; AstraZeneca: Research Funding; Adaptive Biotechnologies: Consultancy; Pharmacyclics: Research Funding; Immune Design: Research Funding; Kite Pharma: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Gilead Sciences: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding.

RTS,S is an advanced malaria vaccine candidate and confers significant protection against Plasmodium falciparum infection in humans. Little is known about the molecular mechanisms driving vaccine immunity. Here, we applied a systems biology approach to study immune responses in subjects receiving three consecutive immunizations with RTS,S (RRR), or in those receiving two immunizations of RTS,S/AS01 following a primary immunization with adenovirus 35 (Ad35) (ARR) vector expressing circumsporozoite protein. Subsequent controlled human malaria challenge (CHMI) of the vaccinees with Plasmodium-infected mosquitoes, 3 wk after the final immunization, resulted in ∼50% protection in both groups of vaccinees. Circumsporozoite protein (CSP)-specific antibody titers, prechallenge, were associated with protection in the RRR group. In contrast, ARR-induced lower antibody responses, and protection was associated with polyfunctional CD4+ T-cell responses 2 wk after priming with Ad35. Molecular signatures of B and plasma cells detected in PBMCs were highly correlated with antibody titers prechallenge and protection in the RRR cohort. In contrast, early signatures of innate immunity and dendritic cell activation were highly associated with protection in the ARR cohort. For both vaccine regimens, natural killer (NK) cell signatures negatively correlated with and predicted protection. These results suggest that protective immunity against P. falciparum can be achieved via multiple mechanisms and highlight the utility of systems approaches in defining molecular correlates of protection to vaccination.

Caspase-11–Dependent Pyroptosis Inflammasomes are multiprotein complexes that assemble to initiate immunity to a variety of microorganisms, as well as to sterile tissue injury. Although a role for caspase-1 downstream of inflammasomes is well characterized, the discovery that caspase-1 knockout mice were also deficient in caspase-11 has led to a reassessment of the function of caspase-11. Aachoui et al. (p. 975 , published online 24 January; see the Perspective by Cemma and Brumell ) now demonstrate that caspase-11 is required for immunity against cytosolic bacteria in mice. Only bacteria that were able to access cytosol-activated caspase-11–dependent pyroptosis, an inflammatory type of cell death. This function of caspase-11 appeared to be independent of canonical inflammasomes.

There remains a pressing need for an efficacious vaccine to combat HIV. The burgeoning fields of systems biology and innate immunity, as harnessed in systems vaccinology, promise to accelerate the discovery process and meet this need.The tools of systems biology are increasingly employed to define innate immune responses to vaccination and thereby unmask early signaling events that control induced adaptive immunity. These studies involve a wide array of measurements, including transcriptomics and proteomics, and a wide array of biological systems, from in-vitro stimulated murine innate immune cells to whole blood collected from vaccinated human donors. Each measurement and each system offers unique insights as well as special limitations and challenges.A holistic consideration of the models available for intensive HIV systems vaccinology analysis identifies a suite of interlocking opportunities and constraints. Although the murine system enables detailed mechanistic analysis, vaccine efficacy cannot be assessed in this model. Systems analysis of blood donated by vaccinated humans permits identification of immunogenicity signatures and biomarkers, but deriving direct mechanisms from these indirect measurements is precarious. The goals of HIV systems vaccinology may be best met by judicious integration of in vitro, in vivo (murine and nonhuman primate), and human clinical analyses.

Precise control of the innate immune response is essential to ensure host defense against infection while avoiding inflammatory disease. Systems-level analyses of Toll-like receptor (TLR)-stimulated macrophages suggested that SHANK-associated RH domain-interacting protein (SHARPIN) might play a role in the TLR pathway. This hypothesis was supported by the observation that macrophages derived from chronic proliferative dermatitis mutation ( cpdm ) mice, which harbor a spontaneous null mutation in the Sharpin gene, exhibited impaired IL-12 production in response to TLR activation. Systems biology approaches were used to define the SHARPIN-regulated networks. Promoter analysis identified NF-κB and AP-1 as candidate transcription factors downstream of SHARPIN, and network analysis suggested selective attenuation of these pathways. We found that the effects of SHARPIN deficiency on the TLR2-induced transcriptome were strikingly correlated with the effects of the recently described hypomorphic L153P/ panr2 point mutation in Ikbkg [ N F-κB E ssential Mo dulator (NEMO)], suggesting that SHARPIN and NEMO interact. We confirmed this interaction by co-immunoprecipitation analysis and furthermore found it to be abrogated by panr2. NEMO-dependent signaling was affected by SHARPIN deficiency in a manner similar to the panr2 mutation, including impaired p105 and ERK phosphorylation and p65 nuclear localization. Interestingly, SHARPIN deficiency had no effect on IκBα degradation and on p38 and JNK phosphorylation. Taken together, these results demonstrate that SHARPIN is an essential adaptor downstream of the branch point defined by the panr2 mutation in NEMO.

Introduction: In recently-reported interim results from a phase 1/2, open-label, multicenter study (NCT02572167), we showed that 61% of relapsed/refractory Hodgkin Lymphoma (HL) patients achieved complete responses (CRs) after treatment with Brentuximab Vedotin (BV) in combination with Nivolumab (Nivo) (Herrera et al., 2018). To gain insight into tumor microenvironment driven disease characteristics associated with CRs after combined BV+Nivo therapy, we performed RNA sequencing (RNA-Seq) transcriptome analysis of formalin-fixed paraffin-embedded tumor biopsies obtained from 37 study participants (23 CR, 14 PR/SD/PD) prior to the start of treatment. Results: Within a set of 132 candidate genes comprised of BV+Nivo therapeutic targets (CD30, PD-1, PD-L1), the markers for immune cells, inflammatory response factors, and potential resistance mechanism factors within the tumor microenvironment, the gene most strongly associated with CRs was the BV target CD30 (TNFRSF8) (p=4x10-4, FDR=5%). Baseline tumor expression of CD30 above 16 counts per million mapped reads (CPM) was strongly associated with achieving CR after BV+Nivo (65% sensitivity and 93% specificity). CRs were associated with several other factors, including higher baseline tumor expression of macrophage markers CD14 and CD163 and lower expression of the Nivo target PD-1 (PDCD1) and multidrug resistance-associated protein 2 (MRP2/ABCC2) in the tumor microenvironment. Both PD-1 and MRP2 were significantly complementary to CD30 for discriminating CRs from the other patients on the basis of baseline gene expression profiling in tumor microenvironment (p Conclusions: The CR rate for combination therapy of BV+Nivo in relapsed/refractory HL is nearly double the rate reported for this patient population for either agent individually, suggesting the possibility of complementary mechanisms between BV-targeted and Nivo-targeted pathways. By performing RNA-Seq analysis of patient baseline tumor biopsies from the BV+Nivo trial (NCT02572167), we were able to identify specific tumor characteristics that are associated with favorable responses to treatment. In particular, the gene expression ratio of CD30/PD-1 was an accurate discriminator for patients that would achieve CRs. This result suggests that BV+Nivo efficacy may be observed in tumors in which the potential for BV-driven immunogenic cell death (ICD) - as indicated by CD30 expression - exceeds a threshold set by the immunosuppressive potential of the tumor (as indicated by PD-1 expression). This hypothesis is supported by the deconvolution analysis results that implicated a favorable role for macrophages (which may become activated in response to BV-induced ICD) in the tumor and a detrimental role for immunosuppressive tumor T cells. Immunohistochemical analyses of biopsies from the study are underway to quantify target abundance and to further characterize the TMEs. These data will be integrated with the transcriptomics to validate and refine the model for BV+Nivo efficacy in relapsed/refractory HL. Disclosures Zak: Seattle Genetics: Employment, Equity Ownership. Ogden:Seattle Genetics: Employment, Equity Ownership. Herrera:Pharmacyclics: Consultancy, Research Funding; Seattle Genetics: Research Funding; Immune Design: Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Merck, Inc.: Consultancy, Research Funding; AstraZeneca: Research Funding; Genentech: Consultancy, Research Funding; KiTE Pharma: Consultancy, Research Funding; Gilead Sciences: Research Funding. Sacchi:Bristol-Myers Squibb: Employment, Equity Ownership. Onsum:Seattle Genetics: Employment, Equity Ownership.

Introduction This phase 1/2 study evaluated the safety and antitumor activity of brentuximab vedotin (BV) administered in combination with nivolumab (Nivo) in adult patients (pts) with relapsed/refractory classic Hodgkin lymphoma (R/R cHL) who have failed frontline therapy (NCT02572167). Results from Parts 1 & 2 have been previously reported, wherein safety, efficacy, and biomarkers consistent with immune activation were observed in pts with R/R cHL (Herrera et al., Blood 2018). In Part 3, patients were treated with BV + Nivo on Day 1 of each cycle. In contrast, pts in Parts 1 & 2 received BV on C1D1 and Nivo on C1D8 with concurrent admin on subsequent cycles. Herein we present safety, efficacy, and biomarker results for Part 3 and updated progression free survival (PFS) from Parts 1 & 2. Methods Pts in Parts 1 & 2 received up to four 21-day cycles of staggered dosing (day 1 BV 1.8 mg/kg, day 8 Nivo 3 mg/kg in Cycle 1) and concurrent thereafter, with steroid and antihistamine premedication. Pts in Part 3 received up to four 21-day cycles of concurrent BV + Nivo on Day 1 with antihistamine premedication. Following Cycle 4 response assessment (Lugano Classification Revised Staging System with the incorporation of the Lymphoma Response to Immunomodulatory Therapy Criteria [LYRIC, 2016]) responding pts were eligible to undergo autologous stem cell transplant (ASCT). Results 30 pts were treated in Part 3 and all were evaluable for efficacy. Pt characteristics included the following: median age 31.5 yrs (range; 20 - 66), 63% female, 93% prior ABVD, 37% primary refractory HL, 30% relapsed within 1year of frontline therapy, 30% with extranodal disease and 17% with bulky disease at enrollment. 28 pts completed all 4 cycles. 1 pt discontinued treatment (tx) due to an adverse event (AE; Grade 3 [G3] elevated Gamma-Glutamyltransferase) and 1 pt due to progressive disease (PD). The latter pt eventually died due to PD. All pts are off tx and have been observed through the safety reporting period. 37% of pts experienced a G3 or higher tx emergent AE prior to ASCT. 30% of the pts experienced infusion related reactions (IRRs), which occurred most frequently during Cycle 2. Potential G3 or higher immune-related AEs (IRAEs), excluding IRRs, occurred in 2 pts, one of whom required steroids for G4 pneumonitis, which subsequently resolved. Among the 30 efficacy evaluable pts, the complete response (CR) rate was 80% (24/30), with an objective response rate (ORR) of 93%. 47% of pts (14) had a Deauville ≤2, and 17% (5) had Deauville 3. 5 pts with CR had a Deauville >3 and met LYRIC criteria for IR-2. 4/5 pts had a negative follow-up biopsy that confirmed CR and in 1 pt the PET finding was considered to be a false positive as contrast enhanced CT did not reveal any abnormality. All 5 pts proceeded directly to ASCT and remain in CR at follow-up. Among all treated patients, 25 went directly to ASCT after completing BV + Nivo tx with a median 6.8 x 106 CD34+ cells/kg (range 1-20) collected. Median times to neutrophil and platelet engraftment were 12 and 14 days, respectively. 4 pts required additional salvage therapy subsequent to study tx. Patients were followed for a median of 6 months from ASCT (N=25, range 0.8-8.8) and 10 months from first dose (N=30, range 1.8-12.7). The estimated 9-month PFS rate in all-treated pts was 88%. This is comparable to all-treated pts in Parts 1 & 2, whose estimated 9 and 15 month PFS were 86% and 82%, respectively (Fig 2). Biomarker testing was performed on peripheral blood samples, included immunophenotyping, serum cytokine analysis, and TCRβ sequencing. Concurrent dosing of BV+ Nivo resulted in increased levels of both activated and dividing CD4+ and CD8+ T cells, as well as increased regulatory T-cells and circulating plasmablasts. Cytokines and chemokines associated with innate and adaptive immune activation, including Type I and Type II interferons, IL-18, and IP-10, were significantly upregulated following BV + Nivo, while TARC levels were significantly diminished following therapy. TCRβ sequencing revealed clonal expansion in the periphery following BV + Nivo. Conclusion A concurrent dosing schedule of BV + Nivo was well tolerated with a high CR rate of 80%. Biomarkers evaluated in Part 3 indicate immune activation in the periphery following BV + Nivo. Cumulatively, the results in Part 3, along with the durable remissions noted in Parts 1 & 2, support BV + Nivo combination as an encouraging first salvage therapy prior to ASCT in pts with R/R cHL. Disclosures Advani: Forty Seven: Research Funding; Infinity: Research Funding; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Gilead/Kite: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Merck: Research Funding; Agensys: Research Funding; Millenium: Research Funding; Janssen: Research Funding; Celgene: Research Funding; Autolus: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kura: Research Funding; Kyowa: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Bayer: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Regeneron: Research Funding; Astra Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Cell Medica: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding. Moskowitz:Bristol Myers-Squibb: Consultancy, Research Funding; Incyte: Research Funding; Takeda: Honoraria; Merck: Research Funding; ADC Therapeutics: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding. Bartlett:Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Vose:Celgene: Research Funding; Legend Pharmaceuticals: Honoraria; Bristol Myers Squibb: Research Funding; Merck Sharp & Dohme Corp.: Research Funding; Incyte Corp.: Research Funding; Roche: Honoraria; Novartis: Honoraria, Research Funding; Seattle Genetics, Inc.: Research Funding; Acerta Pharma: Research Funding; Abbvie: Honoraria; Epizyme: Honoraria; Kite Pharma: Research Funding. Ramchandren:Merck: Research Funding; Bristol-Myers Squibb: Consultancy; Seattle Genetics: Consultancy, Research Funding; Pharmacyclics LLC an AbbVie Company: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Feldman:Johnson and Johnson: Speakers Bureau; Celgene: Speakers Bureau; Pharmacyclics: Speakers Bureau; Seattle Genetics: Research Funding, Speakers Bureau; Portola: Research Funding; Janssen: Speakers Bureau; KITE: Speakers Bureau. LaCasce:Seattle Genetics: Consultancy, Honoraria; Bristol-Myers Squibb: Other: Data safety and monitoring board; Research to Practice: Speakers Bureau; Humanigen: Consultancy, Honoraria. Christian:Acerta: Research Funding; Merck: Research Funding; Bristol-Myers Squibb: Research Funding; Immunomedics: Research Funding; Celgene: Research Funding; Seattle Genetics: Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ansell:LAM Therapeutics: Research Funding; Pfizer: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding; Celldex: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Takeda: Research Funding. Moskowitz:Celgene: Consultancy; Merck & Co: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Consultancy, Research Funding. Fenton:Seattle Genetics, Inc.: Employment, Equity Ownership. Ogden:Seattle Genetics, Inc: Employment, Equity Ownership. Taft:Seattle Genetics: Employment, Equity Ownership. Zak:Seattle Genetics: Employment, Equity Ownership. Sacchi:Bristol-Myers Squibb: Employment, Equity Ownership. Galderisi:Seattle Genetics: Employment, Equity Ownership. Herrera:Immune Design: Research Funding; Merck, Inc.: Consultancy, Research Funding; KiTE Pharma: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Seattle Genetics: Research Funding; AstraZeneca: Research Funding; Pharmacyclics: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Gilead Sciences: Research Funding.

Motivation: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation. Results: We tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by ∼50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01. Availability: The data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac. Contact: aderem@systemsbiology.org; ishmulevich@systemsbiology.org Supplementary information: Supplementary data are available at Bioinformatics online.

Tuberculosis (TB) is a highly heterogeneous human disease that develops in a subset of individuals who inhale Mycobacterium tuberculosis (Mtb). Using advanced machine-learning algorithms, we discovered a blood transcriptional signature that identifies Mtb-exposed individuals in the process of progressing to active TB up to 18 months before they exhibit clinical symptoms. This signature may help prevent TB disease by identifying individuals who could benefit from early intervention. However, it fails to identify ~30% of progressors and does not reveal insights into the diverse outcome-governing pathways within the infected lung that underlie TB progression. To develop a tractable system that enables blood-based signatures to be linked to mechanistic pathways within the lung, we pioneered a novel murine TB model that recapitulates key aspects of human Mtb infection. We find that mice infected with a physiologic, ultra-low dose (i.e., 1–3 CFUs) of aerosolized Mtb exhibit a broad range of outcomes, with bacterial burdens ranging from less than 10 to ~106 CFUs within individual lungs. Mice that contain Mtb exhibit well-circumscribed granulomatous structures that share many features with human Mtb granulomas. In addition, we have identified a blood transcriptional signature that distinguishes “controller” and “progressor” mice. Remarkably, this mouse-derived signature is equally as effective as our previously identified human-derived signature at predicting TB risk in humans, confirming the model’s relevance to human disease. This finding enables us to use the tools of the tractable mouse system to address questions central to TB pathogenesis in a clinically relevant model.

The innate immune system is like a double-edged sword: it is absolutely required for host defense against infection, but when uncontrolled, it can trigger a plethora of inflammatory diseases. Here we use systems-biology approaches to predict and confirm the existence of a gene-regulatory network involving dynamic interaction among the transcription factors NF-kappaB, C/EBPdelta and ATF3 that controls inflammatory responses. We mathematically modeled transcriptional regulation of the genes encoding interleukin 6 and C/EBPdelta and experimentally confirmed the prediction that the combination of an initiator (NF-kappaB), an amplifier (C/EBPdelta) and an attenuator (ATF3) forms a regulatory circuit that discriminates between transient and persistent Toll-like receptor 4-induced signals. Our results suggest a mechanism that enables the innate immune system to detect the duration of infection and to respond appropriately.

Systems biology is the comprehensive and quantitative analysis of the interactions between all of the components of biological systems over time. Systems biology involves an iterative cycle, in which emerging biological problems drive the development of new technologies and computational tools. These technologies and tools then open new frontiers that revolutionize biology. Innate immunity is well suited for systems analysis, because the relevant cells can be isolated in various functional states and their interactions can be reconstituted in a biologically meaningful manner. Application of the tools of systems biology to the innate immune system will enable comprehensive analysis of the complex interactions that maintain the difficult balance between host defense and inflammatory disease. In this review, we discuss innate immunity in the context of the systems biology concepts, emergence, robustness, and modularity, and we describe emerging technologies we are applying in our systems-level analyses. These technologies include genomics, proteomics, computational analysis, forward genetics screens, and analyses that link human genetic polymorphisms to disease resistance.

SummaryDifferentiation of memory cells involves DNA-sequence changes in B lymphocytes but is less clearly defined in T cells. RNA rearrangement is identified here as a key event in memory T cell differentiation by analysis of a mouse mutation that altered the proportions of naive and memory T cells and crippled the process of Ptprc exon silencing needed to generate CD45RO in memory T cells. A single substitution in a memory-induced RNA-binding protein, hnRNPLL, destabilized an RNA-recognition domain that bound with micromolar affinity to RNA containing the Ptprc exon-silencing sequence. Hnrpll mutation selectively diminished T cell accumulation in peripheral lymphoid tissues but not proliferation. Exon-array analysis of Hnrpll mutant naive and memory T cells revealed an extensive program of alternative mRNA splicing in memory T cells, coordinated by hnRNPLL. A remarkable overlap with alternative splicing in neural tissues may reflect a co-opted strategy for diversifying memory T cells.

The dynamics and molecular mechanisms underlying vaccine immunity in early childhood remain poorly understood. Here we applied systems approaches to investigate the innate and adaptive responses to trivalent inactivated influenza vaccine (TIV) and MF59-adjuvanted TIV (ATIV) in 90 14- to 24-mo-old healthy children. MF59 enhanced the magnitude and kinetics of serum antibody titers following vaccination, and induced a greater frequency of vaccine specific, multicytokine-producing CD4(+) T cells. Compared with transcriptional responses to TIV vaccination previously reported in adults, responses to TIV in infants were markedly attenuated, limited to genes regulating antiviral and antigen presentation pathways, and observed only in a subset of vaccinees. In contrast, transcriptional responses to ATIV boost were more homogenous and robust. Interestingly, a day 1 gene signature characteristic of the innate response (antiviral IFN genes, dendritic cell, and monocyte responses) correlated with hemagglutination at day 28. These findings demonstrate that MF59 enhances the magnitude, kinetics, and consistency of the innate and adaptive response to vaccination with the seasonal influenza vaccine during early childhood, and identify potential molecular correlates of antibody responses.

One objective of systems biology is to create predictive, quantitative models of the transcriptional regulation networks that govern numerous cellular processes. Gene expression measurements, as provided by microarrays, are commonly used in studies that attempt to infer the regulation underlying these processes. At present, most gene expression models that have been derived from microarray data are based in discrete-time, which have limited applicability to common biological data sets, and may impede the integration of gene expression models with other models of biological processes that are formulated as ordinary differential equations (ODEs). To overcome these difficulties, a continuous-time approach for process identification to identify gene expression models based in ODEs was developed. The approach utilizes the modulating functions method of parameter identification. The method was applied to three simulated systems: (1) a linear gene expression model, (2) an autoregulatory gene expression model, and (3) simulated microarray data from a nonlinear transcriptional network. In general, the approach was well suited for identifying models of gene expression dynamics, capable of accurately identifying parameters for small numbers of data samples in the presence of modest experimental noise. Additionally, numerous insights about gene expression modeling were revealed by the case studies.

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Loss of IL-27R on T cells results in increased protection from Mycobacterium tuberculosis. Torrado et al. demonstrate that IL-27R−/− T cells show improved fitness that is associated with decreased expression of cell death molecules, maintenance of IL-2 production, and preferential accumulation in the lung parenchyma and around infected macrophages., CD4+ T cells mediate protection against Mycobacterium tuberculosis (Mtb); however, the phenotype of protective T cells is undefined, thereby confounding vaccination efforts. IL-27 is highly expressed during human tuberculosis (TB), and absence of IL-27R (Il27ra) specifically on T cells results in increased protection. IL-27R deficiency during chronic Mtb infection does not impact antigen-specific CD4+ T cell number but maintains programmed death-1 (PD-1), CD69, and CD127 expression while reducing T-bet and killer cell lectin-like receptor G1 (KLRG1) expression. Furthermore, T-bet haploinsufficiency results in failure to generate KLRG1+, antigen-specific CD4+ T cells, and in improved protection. T cells in Il27ra−/− mice accumulate preferentially in the lung parenchyma within close proximity to Mtb, and antigen-specific CD4+ T cells lacking IL-27R are intrinsically more fit than intact T cells and maintain IL-2 production. Improved fitness of IL-27R–deficient T cells is not associated with increased proliferation but with decreased expression of cell death–associated markers. Therefore, during Mtb infection, IL-27R acts intrinsically on T cells to limit protection and reduce fitness, whereas the IL-27R–deficient environment alters the phenotype and location of T cells. The significant expression of IL-27 in TB and the negative influence of IL-27R on T cell function demonstrate the pathway by which this cytokine/receptor pair is detrimental in TB.

Systems-level analysis of biological processes strives to comprehensively and quantitatively evaluate the interactions between the relevant molecular components over time, thereby enabling development of models that can be employed to ultimately predict behavior. Rapid development in measurement technologies (omics), when combined with the accessible nature of the cellular constituents themselves, is allowing the field of innate immunity to take significant strides toward this lofty goal. In this review, we survey exciting results derived from systems biology analyses of the immune system, ranging from gene regulatory networks to influenza pathogenesis and systems vaccinology.

The NAIP/NLRC4 inflammasomes activate caspase-1 in response to bacterial type III secretion systems (T3SSs). Inadvertent injection of the T3SS rod protein and flagellin into the cytosol is detected through murine NAIP2 and NAIP5/6, respectively. In this study, we identify the agonist for the orphan murine NAIP1 receptor as the T3SS needle protein. NAIP1 is poorly expressed in resting mouse bone marrow–derived macrophages; however, priming with polyinosinic-polycytidylic acid induces it and confers needle protein sensitivity. Further, overexpression of NAIP1 in immortalized bone marrow–derived macrophages by retroviral transduction enabled needle detection. In contrast, peritoneal cavity macrophages basally express NAIP1 and respond to needle protein robustly, independent of priming. Human macrophages are known to express only one NAIP gene, which detects the needle protein, but not rod or flagellin. Thus, murine NAIP1 is functionally analogous to human NAIP.