Citations

Abstract Multiple factors linked to host genetics/inherent biology play a role in interindividual variability in immune response outcomes after rubella vaccination. In order to identify these factors, we conducted a study of rubella‐specific humoral immunity before (Baseline) and after (Day 28) a third dose of MMR‐II vaccine in a cohort of 109 women of childbearing age. We performed mRNA‐Seq profiling of PBMCs after rubella virus in vitro stimulation to delineate genes associated with post‐vaccination rubella humoral immunity and to define genes mediating the association between prior immune response status (high or low antibody) and subsequent immune response outcome. Our study identified novel genes that mediated the association between prior immune response and neutralizing antibody titer after a third MMR vaccine dose. These genes included the following: CDC34 ; CSNK1D ; APOBEC3F ; RAD18 ; AAAS ; SLC37A1 ; FAS ; and JAK2 . The encoded proteins are involved in innate antiviral response, IFN/cytokine signaling, B cell repertoire generation, the clonal selection of B lymphocytes in germinal centers, and somatic hypermutation/antibody affinity maturation to promote optimal antigen‐specific B cell immune function. These data advance our understanding of how subjects’ prior immune status and/or genetic propensity to respond to rubella/MMR vaccination ultimately affects innate immunity and humoral immune outcomes after vaccination.

Introduction: In 2017, a two-dose recombinant zoster vaccine (RZV) containing glycoprotein E (gE) and a novel adjuvant was approved to prevent herpes zoster and its complications in adults ≥50 years of age in the US. There are limited data on the immunogenicity and safety of RZV in patients with chronic lymphocytic leukemia (CLL). Methods: Between 8/15/2018 and 11/4/2019, RZV-naïve patients with CLL and monoclonal B-cell lymphocytosis (MBL) seen at Mayo Clinic, Rochester, MN were enrolled in this IRB-approved study. All participants received two doses of vaccine, at 0 and 2 months after enrollment. Cell-mediated immunity was measured by dual-color interferon γ (IFN- γ) and interleukin 2 (IL-2) FLUOROSpot in peripheral blood mononuclear cells (PBMC) after depletion of ≥50% leukemic B cells. Results were reported in spot forming cells (SFC)/106 PBMC in wells stimulated with gE overlapping peptides or varicella zoster virus (VZV) inactivated cell lysate. Response rate was defined as the proportion of participants who achieved a ≥2-fold increase in IFN- γ or IL-2 SFC/106 PBMC one month after the second dose of vaccine compared with pre-vaccination baseline levels. We also determined the geometric mean fold rise (GMFR) and geometric mean count (GMC) of SFC /106 PBMC at baseline and 90 days after the first dose of vaccine. Two cohorts were enrolled: Cohort 1 consisted of individuals with previously untreated MBL and CLL; and Cohort 2 consisted of CLL participants who were on Bruton tyrosine kinase inhibitor (BTKi) therapy. We compared the response data to historic age-and sex-matched healthy controls who received RZV. Chi-square or Fisher's exact tests measured differences in response rates and Kruskall-Wallis tested for differences in continuous variables. Among CLL participants, multivariable regression analysis was used to investigate the association between day 90 SFC and prior BTKi treatment, accounting for age at RZV, age at CLL diagnosis, sex, and day 0 SFC. Results: Sixty-one participants were enrolled. Paired samples were available for 41 participants (22 in Cohort 1 and 19 [18 ibrutinib and 1 acalabrutinib] in Cohort 2), and 68 age- and sex-matched healthy controls. Baseline characteristics of MBL/CLL participants are shown in Table 1. Compared to healthy controls, participants with MBL/CLL had a significantly lower IFN-γ VZV-specific response rate (41% vs. 63%, p=0.03) and IFN-γ gE-specific response (56% vs. 96%, p<0.001); although there were no differences in IL-2 response rates to VZV (51% vs 68%, p=0.09) or gE (95% vs. 100%, p=0.14, Table 2). There were differences in GMFR between participants with MBL/CLL and controls that reached statistical significance for IFN-γ and IL-2 gE-specific responses and borderline significance for IFN-γ VZV-specific responses (Table 2). VZV-specific response rates did not significantly differ between individuals with untreated MBL/CLL and those receiving BTKi (52% vs. 28%, p=0.12 for IFN- γ; (62% vs. 39%, p=0.15 for IL-2), nor did IL-2 gE-specific responses (96% vs. 95%, p=1.0). However, untreated MBL/CLL participants had significantly higher IFN- γ gE-specific response rate compared to BTKi-treated individuals (73% vs. 37%, p=0.02, Table 2). IFN- γ and IL-2 VZV- and gE-specific SFC GMC was significantly higher at Day 90 compared to Day 0 in all vaccinees, although the magnitude of response was significantly lower in MBL/CLL participants compared to healthy controls (Table 3). Among participants with MBL/CLL, age at diagnosis, age at receipt of RZV, and sex did not predict response to vaccination after accounting for prevaccination values. No serious adverse events were noted among MBL/CLL participants while on this study. Additional analyses including determination of humoral immunogenicity and long-term follow up to ascertain risk of herpes zoster infection are ongoing at this time. Conclusion: RZV was associated with lower cell mediated immunogenicity in MBL/CLL participants compared to healthy older adults. Among individuals with CLL, BTKi treatment was associated with decreased cell mediated immunogenicity of the vaccine. Given the inferior response rates, an additional dose of RZV may be considered to boost responses and should be studied in this patient population. Disclosures Johnson: GSK: Research Funding. Ding:Abbvie: Research Funding; Astra Zeneca: Research Funding; DTRM: Research Funding; alexion: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; Octapharma: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Membership on an entity's Board of Directors or advisory committees. Kenderian:Humanigen: Consultancy, Patents & Royalties, Research Funding; Torque: Consultancy; Novartis: Patents & Royalties, Research Funding; Kite: Research Funding; Gilead: Research Funding; Juno: Research Funding; BMS: Research Funding; Tolero: Research Funding; Mettaforge: Patents & Royalties; Lentigen: Research Funding; MorphoSys: Research Funding; Sunesis: Research Funding. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Kay:Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding. Levin:GSK: Research Funding. Kennedy:ICW Ventures: Research Funding. Weinberg:GSK: Research Funding. Parikh:GlaxoSmithKline: Honoraria; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; TG Therapeutics: Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; MorphoSys: Research Funding; Verastem Oncology: Honoraria; Pharmacyclics: Honoraria, Research Funding; Genentech: Honoraria; Ascentage Pharma: Research Funding.

Only 50% to 70% of patients with mesothelioma report asbestos exposure. Other exposures (eg, radiation) play a role in some cases, but some patients have no obvious cause. We describe a series of patients with long-standing indwelling intra-abdominal shunt catheters who developed malignant peritoneal mesothelioma, suggesting a novel association. We identified 7 patients who had shunts and subsequently developed mesothelioma (5 women; median age: 31 y, range: 18 to 45 y). Clinical history and pathology materials were reviewed, and RNA sequencing was performed. Clinical presentations varied; 6 patients had hydrocephalus and a ventriculoperitoneal shunt, and 1 patient had portal hypertension and a portoatrial shunt. The median duration of shunt therapy in 5 cases was 29 years (range: 12 to 35 y); the remaining 2 patients also had shunts for many years, but specific details were unavailable. Two patients had radiotherapy for malignancies in childhood. One had an alleged exposure to asbestos and 1 had prior exposure to talc. The rest had no known risk factors. Histologically, all tumors were purely epithelioid. Treatments included surgical debulking, chemotherapy, and palliative care. All 7 died of disease (median survival: 7 mo, range: 1 to 18 mo). Molecular testing showed loss of NF2 and CDKN2A/B and a BAP1 mutation in 1 case, and no genomic alterations associated with mesothelioma in 2 cases. Peritoneal mesothelioma may represent a complication of long-standing indwelling shunt catheters. The mechanism is unknown, but chronic peritoneal irritation may play a role. Albeit rare, mesothelioma should be considered in patients with a shunt who present with new ascites.

AbstractThis article provides a review of studies evaluating the role of host (and viral) genetics (including variation in HLA genes) in the immune response to coronaviruses, as well as the clinical outcome of coronavirus‐mediated disease. The initial sections focus on seasonal coronaviruses, SARS‐CoV, and MERS‐CoV. We then examine the state of the knowledge regarding genetic polymorphisms and SARS‐CoV‐2 and COVID‐19. The article concludes by discussing research areas with current knowledge gaps and proposes several avenues for future scientific exploration in order to develop new insights into the immunology of SARS‐CoV‐2.

TPS11120 Background: Currently there is no biomarker to predict specific benefit from DNA-damaging anthracycline and cyclophosphamide-based chemotherapy in the clinic. Loss of the Fanconi anemia/BRCA (FA/BRCA) DNA-damage response pathway occurs in approximately 25% of breast cancer and results in sensitivity to DNA-damaging agents. We therefore developed an assay to detect loss of the FA/BRCA pathway, for the purpose of predicting benefit from chemotherapy. Methods: 21 FA patient samples were analyzed to identify genetic processes associated with loss of the FA/BRCA pathway. Unsupervised hierarchical clustering was then performed using 60 BRCA1/2 mutant and 47 sporadic tumor samples and a molecular subgroup was identified that was defined by the molecular processes representing loss of the FA/BRCA pathway. A 44-gene DNA Damage response deficient (DDRD) assay was developed that could identify this subgroup from formalin fixed, paraffin embedded (FFPE) samples in the clinic. Results: In a publicly available independent cohort of 204 patients, the assay predicted response to neoadjuvant DNA-damaging chemotherapy (5-fluorouracil, anthracycline and cyclophosphamide) with an odds ratio of 4.01, (95% Cl:1.69-9.54). We also analysed samples from an independent cohort of 114 node-negative breast cancer patients treated with adjuvant 5-fluorouracil, epirubicin and cyclophosphamide treatment at the Northern Ireland Cancer Centre. The DDRD assay significantly predicted 5-year relapse free survival with a hazard ratio of 0.27 (95% Cl:0.10-0.83). The assay was not predictive of survival in patients who did not receive chemotherapy. Conclusions: An FFPE tissue-based assay that detects loss of the FA/BRCA pathway has been developed and independently validated as a predictor of response and prognosis following DNA damaging anthracycline/cyclophosphamide-based chemotherapy in the neoadjuvant and adjuvant settings.

Background Identifying the biologic functions of groups of genes identified in high-throughput studies currently requires considerable time and/or bioinformatics experience. This is due in part to each resource housed within separate databases, requiring users to know about them, and integrate across them. Time consuming and often repeated for each study, integrating across resources and merging with data under study is an increasingly common bioinformatics task. Methods We developed an open-source R software package for assisting researchers in annotating their genesets with functions, pathways, and their interconnectivity across a diversity of network resources. Results We present rapid integration of term annotation and network resources (RITAN) for the rapid and comprehensive annotation of a list of genes using functional term and pathway resources and their relationships among each other using multiple network biology resources. Currently, and to comply with data redistribution policies, RITAN allows rapid access to 16 term annotations spanning gene ontology, biologic pathways, and immunologic modules, and nine network biology resources, with support for user-supplied resources; we provide recommendations for additional resources and scripts to facilitate their addition to RITAN. Having the resources together in the same system allows users to derive novel combinations. RITAN has a growing set of tools to explore the relationships within resources themselves. These tools allow users to merge resources together such that the merged annotations have a minimal overlap with one another. Because we index both function annotation and network interactions, the combination allows users to expand small groups of genes using links from biologic networks—either by adding all neighboring genes or by identifying genes that efficiently connect among input genes—followed by term enrichment to identify functions. That is, users can start from a core set of genes, identify interacting genes from biologic networks, and then identify the functions to which the expanded list of genes contribute. Conclusion We believe RITAN fills the important niche of bridging the results of high-throughput experiments with the ever-growing corpus of functional annotations and network biology resources. Availability Rapid integration of term annotation and network resources is available as an R package at github.com/MTZimmer/RITAN and BioConductor.org .

Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who are at higher risk for infection despite influenza vaccination. We developed a multi-level machine learning strategy to build a predictive model of vaccine response using pre−vaccination antibody titers and network interactions between pre−vaccination gene expression levels. The first-level baseline−antibody model explains a significant amount of variation in post-vaccination response, especially for subjects with large pre−existing antibody titers. In the second level, we clustered individuals based on pre−vaccination antibody titers to focus gene−based modeling on individuals with lower baseline HAI where additional response variation may be predicted by baseline gene expression levels. In the third level, we used a gene−association interaction network (GAIN) feature selection algorithm to find the best pairs of genes that interact to influence antibody response within each baseline titer cluster. We used ratios of the top interacting genes as predictors to stabilize machine learning model generalizability. We trained and tested the multi-level approach on data with young and older individuals immunized against influenza vaccine in multiple cohorts. Our results indicate that the GAIN feature selection approach improves model generalizability and identifies genes enriched for immunologically relevant pathways, including B Cell Receptor signaling and antigen processing. Using a multi-level approach, starting with a baseline HAI model and stratifying on baseline HAI, allows for more targeted gene−based modeling. We provide an interactive tool that may be extended to other vaccine studies.