Faculty Bibliography

UNLABELLED:< 0.05). These findings suggest that whole grain and dietary fiber are associated with overall gut microbiome structure, largely fiber-fermenting microbiota. Higher refined grain and gluten intakes may be associated with lower microbial diversity. SIGNIFICANCE:Regular consumption of whole grains and dietary fiber was associated with greater abundance of gut bacteria that may lower risk of colorectal cancer. Further research on the association of refined grains and gluten with gut microbial composition is needed to understand their roles in health and disease.

The human oral microbiome is associated with chronic diseases including cancer. However, our understanding of its relationship with diet is limited. We assessed the associations between carbohydrate and glycemic index (GI) with oral microbiome composition in 834 non-diabetic subjects from the NCI-PLCO and ACS-CPSII cohorts. The oral microbiome was characterized using 16Sv3-4 rRNA-sequencing from oral mouthwash samples. Daily carbohydrate and GI were assessed from food frequency questionnaires. We used linear regression, permutational MANOVA, and negative binomial Generalized Linear Models (GLM) to test associations of diet with α- and β-diversity and taxon abundance (adjusting for age, sex, cohort, BMI, smoking, caloric intake, and alcohol). A q-value (FDR-adjusted P-value) of <0.05 was considered significant. Oral bacterial α-diversity trended higher in participants in the highest quintiles of carbohydrate intake, with marginally increased richness and Shannon diversity (p-trend=0.06 and 0.07). Greater carbohydrate intake was associated with greater abundance of class Fusobacteriia (q=0.02) and genus Leptotrichia (q=0.01) and with lesser abundance of an Actinomyces OTU (q=4.7E-04). Higher GI was significantly related to greater abundance of genus Gemella (q=0.001). This large, nationwide study provides evidence that diets high in carbohydrates and GI may influence the oral microbiome.

BACKGROUND:Cancer recurrence after tumor resection in early-stage non-small cell lung cancer (NSCLC) is common, yet difficult to predict. The lung microbiota and systemic immunity may be important modulators of risk for lung cancer recurrence, yet biomarkers from the lung microbiome and peripheral immune environment are understudied. Such markers may hold promise for prediction as well as improved etiologic understanding of lung cancer recurrence. METHODS:In tumor and distant normal lung samples from 46 stage II NSCLC patients with curative resection (39 tumor samples, 41 normal lung samples), we conducted 16S rRNA gene sequencing. We also measured peripheral blood immune gene expression with nanoString®. We examined associations of lung microbiota and peripheral gene expression with recurrence-free survival (RFS) and disease-free survival (DFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression, and examined predictive accuracy using time-dependent receiver operating characteristic (ROC) curves. RESULTS:Over a median of 4.8 years of follow-up (range 0.2-12.2 years), 43% of patients experienced a recurrence, and 50% died. In normal lung tissue, a higher abundance of classes Bacteroidia and Clostridia, and orders Bacteroidales and Clostridiales, were associated with worse RFS, while a higher abundance of classes Alphaproteobacteria and Betaproteobacteria, and orders Burkholderiales and Neisseriales, were associated with better RFS. In tumor tissue, a higher abundance of orders Actinomycetales and Pseudomonadales were associated with worse DFS. Among these taxa, normal lung Clostridiales and Bacteroidales were also related to worse survival in a previous small pilot study and an additional independent validation cohort. In peripheral blood, higher expression of genes TAP1, TAPBP, CSF2RB, and IFITM2 were associated with better DFS. Analysis of ROC curves revealed that lung microbiome and peripheral gene expression biomarkers provided significant additional recurrence risk discrimination over standard demographic and clinical covariates, with microbiome biomarkers contributing more to short-term (1-year) prediction and gene biomarkers contributing to longer-term (2-5-year) prediction. CONCLUSIONS:We identified compelling biomarkers in under-explored data types, the lung microbiome, and peripheral blood gene expression, which may improve risk prediction of recurrence in early-stage NSCLC patients. These findings will require validation in a larger cohort.

BACKGROUND:With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction. METHODS:Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: (1) identifying a sub-community consisting of the signature microbial taxa associated with disease and (2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction. RESULTS:Through three comprehensive real-data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa, respectively, are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 (0.85-0.91) and 0.86 (0.78-0.95) in the discovery and validation cohorts, respectively. CONCLUSIONS:The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration and provides a great potential in understanding the microbiome's role in disease diagnosis and prognosis. Video Abstract.

Introduction: Diffuse large B cell lymphoma (DLBCL) is the most common form of malignant lymphoma and may arise de novo, or through transformation from a pre-existing low-grade B cell lymphoma such as follicular lymphoma (FL). However, the post-translational mechanisms and deregulated pathways underlying the pathogenesis of disease evolution are not fully understood.
Method(s): We employed integrated functional and structural genomics and mass spectrometry (MS)-driven proteomics which implicated a possible novel tumor suppressor role for a conserved E3 ubiquitin ligase FBXO45 in DLBCL pathogenesis. We generated conditional knockout mice targeting loss of Fbxo45 in germinal center (GC) B-cells using the Cg1-Cre-loxP system and an assortment of CRISPR-mediated knockouts of FBXO45 in B cell lymphoma cells (FL518, BJAB, U2932). We engineered B cell lines (BJAB, U2932) to inducibly express FLAG-tagged FBXO45 to identify candidate substrates of FBXO45 using liquid chromatography-tandem MS. In vitro biochemical and in vivo studies using a variety of genetically-modified lines in xenograft studies in immunodeficient mice were performed to validate observations from proteogenomic studies. Whole genome sequencing (WGS) and genomic copy number studies were interrogated to investigate structural alterations targeting FBXO45 in primary human lymphoma samples.
Result(s): Conditional targeting of Fbxo45 in GCB-cells in transgenic mice resulted in abnormal germinal center formation with increased number and size of germinal centers. Strikingly, targeted deletion of Fbxo45 in GCB-cells resulted in spontaneous B cell lymphomas with (22/22);100%) penetrance and none of the wild-type (WT) littermates (0/20; 0%) developed lymphoma at 24 months. Macroscopic examination revealed large tumor masses, splenomegaly, and lymphadenopathy at different anatomic locations including ileocecal junction, mesenteric, retroperitoneal and cervical lymph nodes and thymus. Next generation sequencing of immunoglobulin heavy chain genes revealed monoclonal or oligoclonal B cell populations. Using proteomic analysis of affinity-purified FBXO45-immunocomplexes and differential whole proteome analysis from GCB-cells of Fbxo45 ^wt/wt vs Fbxo45 ^fl/fl mice, we discovered that FBXO45 targets the RHO guanine exchange factor GEF-H1 for ubiquitin-mediated proteasomal degradation. FBXO45 exclusively interacts with GEF H1 among 8 F-box proteins investigated and silencing of FBXO45 using three independent shRNA and CRISPR-Cas9-mediated knockouts in B-cell lymphoma cell lines promotes RHOA and MAPK activation, B cell growth and enhances proliferation. GEF-H1 is stabilized by FBXO45 depletion and GEF-H1 ubiquitination by FBXO45 requires phosphorylation of GEF-H1. Importantly, FBXO45 depletion and expression of a GEF-H1 mutant that is unable to bind FBXO45 results in GEF-H1 stabilization, promotes hyperactivated RHO and MAPK signaling and B-cell oncogenicity in vitro and in vivo. Notably, this phenotype is reverted by co-silencing of GEF-H1. Inducible ectopic expression of FBXO45 triggers accelerated turnover of GEF H1 and decreased RHOA signaling. Genomic analyses revealed recurrent loss targeting FBXO45 in transformed DLBCL (25%), de novo DLBCL (6.6%) and FL (2.3%). In keeping with our observation of prolonged hyperactivation of pERK1/2 consequent to FBXO45 ablation, in vitro and in vivo studies using B-cell lymphoma cell lines and xenografts demonstrated increased sensitivity to pharmacologic blockade with the MAP2K1/2 (ERK1/2) inhibitor Trametinib.
Conclusion(s): Our findings define a novel FBXO45-GEF-H1-MAPK signalling axis, which plays an important role in DLBCL pathogenesis. Our studies carry implications for potential exploitation of this pathway for targeted therapies. Disclosures: Siebert: AstraZeneca: Speakers Bureau. Lim: EUSA Pharma: Honoraria.
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BACKGROUND:Immune checkpoint blockade (ICB) shows lasting benefits in advanced melanoma; however, not all patients respond to this treatment and many develop potentially life-threatening immune-related adverse events (irAEs). Identifying individuals who will develop irAEs is critical in order to improve the quality of care. Here, we prospectively demonstrate that the gut microbiome predicts irAEs in melanoma patients undergoing ICB. METHODS:Pre-, during, and post-treatment stool samples were collected from 27 patients with advanced stage melanoma treated with IPI (anti-CTLA-4) and NIVO (anti-PD1) ICB inhibitors at NYU Langone Health. We completed 16S rRNA gene amplicon sequencing, DNA deep shotgun metagenomic, and RNA-seq metatranscriptomic sequencing. The divisive amplicon denoising algorithm (DADA2) was used to process 16S data. Taxonomy for shotgun sequencing data was assigned using MetaPhlAn2, and gene pathways were assigned using HUMAnN 2.0. Compositionally aware differential expression analysis was performed using ANCOM. The Cox-proportional hazard model was used to assess the prospective role of the gut microbiome (GMB) in irAES, with adjustment for age, sex, BMI, immune ICB treatment type, and sequencing batch. RESULTS:= 0.88, p < 0.001). CONCLUSIONS:We identified two distinct fecal bacterial community clusters which are associated differentially with irAEs in ICB-treated advanced melanoma patients.

BACKGROUND:Increasing evidence suggests that tobacco smoking, a well-known driver of carcinogenesis, influences the gut microbiome; however, these relationships remain understudied in diverse populations. Thus, we performed an analysis of smoking and the gut microbiome in a subset of 803 adults from the multi-ethnic NYU FAMiLI study. METHODS:We assessed fecal microbiota using 16S rRNA gene sequencing, and clustered samples into Amplicon Sequence Variants using QIIME2. We evaluated inferred microbial pathway abundance using PICRUSt. We compared population beta diversity, and relative taxonomic and functional pathway abundance, between never smokers, former smokers, and current smokers. RESULTS:We found that the overall composition of the fecal microbiome in former and current smokers differs significantly from that of never smokers. The taxa Prevotella and Veillonellaceae were enriched in current and former smokers, while the taxa Lachnospira and Tenericutes were depleted, relative to never smokers. These shifts were consistent across racial and ethnic subgroups. Relative to never smokers, the abundance of taxa enriched in current smokers were positively correlated with the imputed abundance of pathways involving smoking-associated toxin breakdown and response to reactive oxygen species (ROS). CONCLUSIONS:Our findings suggest common mechanisms of smoking associated microbial change across racial subgroups, regardless of initial microbiome composition. The correlation of these differentials with ROS exposure pathways may suggest a role for these taxa in the known association between smoking, ROS and carcinogenesis. IMPACT/CONCLUSIONS:Smoking shifts in the microbiome may be independent of initial composition, stimulating further studies on the microbiome in carcinogenesis and cancer prevention.

PURPOSE/OBJECTIVE:While immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer by producing durable antitumor responses, only 10%-30% of treated patients respond and the ability to predict clinical benefit remains elusive. Several studies, small in size and using variable analytic methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations. EXPERIMENTAL DESIGN/METHODS:= 303 unique patients) using a uniform computational approach. RESULTS:= 105). CONCLUSIONS:Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.

B-cell non-Hodgkin lymphoma cell survival depends on poorly understood immune evasion mechanisms. In melanoma, the composition of the gut microbiota (GMB) is associated with immune system regulation and response to immunotherapy. We investigated the association of GMB composition and diversity with lymphoma biology and treatment outcome. Patients with diffuse large B-cell lymphoma (DLBCL), marginal zone (MZL), and follicular lymphoma (FL) were recruited at Mayo Clinic, Minnesota, and Perlmutter Cancer Center, NYU Langone Health. The pretreatment GMB was analyzed using 16S ribosomal RNA gene sequencing. We examined GMB compositions in 3 contexts: lymphoma patients (51) compared with healthy controls (58), aggressive (DLBCL) (8) compared with indolent (FL, MZL) (18), and the association of GMB with immunochemotherapy treatment outcomes (8 responders, 6 nonresponders). Respectively, we found that the pretreatment GMB in lymphoma patients had a distinct composition compared with healthy controls (P < .001); GMB compositions in DLBCL patients were significantly different than indolent patients (P = .01) with a trend toward reduced microbial diversity in DLBCL patients (P = .08); and pretreatment GMB diversity and composition were significant predictors of treatment responses (P = .01). The impact of these pilot results is limited by our small sample size, and should be considered a proof of principle. If validated, our results could lead toward improved treatment outcomes by improving medication stewardship and informing which GMB-targeted therapies should be tested to improve patient outcomes.

BACKGROUND:Ambient air pollution is a modifiable risk factor for cardiovascular disease, yet uncertainty remains about the size of risks at lower levels of fine particulate matter (PM2.5) exposure which now occur in the USA and elsewhere. METHODS:We investigated the relationship of ambient PM2.5 exposure with cause-specific cardiovascular disease mortality in 565 477 men and women, aged 50 to 71 years, from the National Institutes of Health-AARP Diet and Health Study. During 7.5 x 106 person-years of follow up, 41 286 cardiovascular disease deaths, including 23 328 ischaemic heart disease (IHD) and 5894 stroke deaths, were ascertained using the National Death Index. PM2.5 was estimated using a hybrid land use regression (LUR) geostatistical model. Multivariate Cox regression models were used to estimate relative risks (RRs) and 95% confidence intervals (CI). RESULTS:Each increase of 10  μg/m3 PM2.5 (overall range, 2.9-28.0  μg/m3) was associated, in fully adjusted models, with a 16% increase in mortality from ischaemic heart disease [hazard ratio (HR) 1.16; 95% CI 1.09-1.22] and a 14% increase in mortality from stroke (HR 1.14; CI 1.02-1.27). Compared with PM2.5 exposure <8  μg/m3 (referent), risks for CVD were increased in relation to PM2.5 exposures in the range of 8-12  μg/m3 (CVD: HR 1.04; 95% CI 1.00-1.08), in the range 12-20  μg/m3 (CVD: HR 1.08; 95% CI 1.03-1.13) and in the range 20+ μg/m3 (CVD: HR 1.19; 95% CI 1.10-1.28). Results were robust to alternative approaches to PM2.5 exposure assessment and statistical analysis. CONCLUSIONS:Long-term exposure to fine particulate air pollution is associated with ischaemic heart disease and stroke mortality, with excess risks occurring in the range of and below the present US long-term standard for ambient exposure to PM2.5 (12  µg/m3), indicating the need for continued improvements in air pollution abatement for CVD prevention.