Faculty Bibliography

Observational studies have shown higher folate consumption to be associated with lower risk of colorectal cancer (CRC). Understanding whether and how genetic risk factors interact with folate could further elucidate the underlying mechanism. Aggregating functionally relevant genetic variants in set-based variant testing has higher power to detect gene-environment (G × E) interactions and may provide information on the underlying biological pathway. We investigated interactions between folate consumption and predicted gene expression on colorectal cancer risk across the genome. We used variant weights from the PrediXcan models of colon tissue-specific gene expression as a priori variant information for a set-based G × E approach. We harmonized total folate intake (mcg/day) based on dietary intake and supplemental use across cohort and case-control studies and calculated sex and study specific quantiles. Analyses were performed using a mixed effects score tests for interactions between folate and genetically predicted expression of 4839 genes with available genetically predicted expression. We pooled results across 23 studies for a total of 13,498 cases with colorectal tumors and 13,918 controls of European ancestry. We used a false discovery rate of 0.2 to identify genes with suggestive evidence of an interaction. We found suggestive evidence of interaction with folate intake on CRC risk for genes including glutathione S-Transferase Alpha 1 (GSTA1; p = 4.3E-4), Tonsuko Like, DNA Repair Protein (TONSL; p = 4.3E-4), and Aspartylglucosaminidase (AGA: p = 4.5E-4). We identified three genes involved in preventing or repairing DNA damage that may interact with folate consumption to alter CRC risk. Glutathione is an antioxidant, preventing cellular damage and is a downstream metabolite of homocysteine and metabolized by GSTA1. TONSL is part of a complex that functions in the recovery of double strand breaks and AGA plays a role in lysosomal breakdown of glycoprotein.

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:The link between oral diseases and mortality remains under-explored. We aimed to evaluate the associations between tooth count, untreated caries and risk of all-cause and cause-specific mortality. METHODS:Data on 24 029 adults from the National Health and Nutrition Examination Survey 1988-94/1999-2010, with mortality linkage to the National Death Index to 31 December 2015, were analysed. Baseline total number of permanent teeth and any untreated caries were assessed by trained dental professionals. RESULTS:During up to 27 years of follow-up, 5270 deaths occurred. Fewer permanent teeth were associated with higher all-cause mortality, including heart disease and cancer mortality (all P <0.05 for trend) but not cerebrovascular disease mortality. For every 10 teeth missing, the multivariable-adjusted hazard ratios (HRs) were 1.13 (95% CI: 1.08 to 1.18) for all-cause, 1.16 (95% CI: 1.05, 1.29) for heart disease and 1.19 (95% CI: 1.09, 1.29) for cancer mortality. Untreated caries was associated with increased all-cause (HR: 1.26, 95% CI: 1.15, 1.39) and heart disease mortality (HR: 1.48, 95% CI: 1.17, 1.88) but not cerebrovascular disease/cancer mortality, after adjusting for tooth count, periodontitis and sociodemographic/lifestyle factors. Compared with those without untreated caries and with 25-28 teeth, individuals with untreated caries and 1-16 teeth had a 53% increased risk of all-cause mortality (HR: 1.53, 95% CI: 1.27, 1.85) and 96 % increased risk of heart disease mortality (HR: 1.96, 95% CI: 1.28, 3.01). CONCLUSIONS:In nationally representative cohorts, fewer permanent teeth and untreated caries were associated with all-cause and heart disease mortality. Fewer teeth were also associated with higher cancer mortality.

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.

BACKGROUND/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED:The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration, and provides great potential in understanding the microbiome's role in disease diagnosis and prognosis.

BACKGROUND:Incidence of colorectal cancer (CRC) among individuals aged less than 50 years has been increasing. As screening guidelines lower the recommended age of screening initiation, concerns including the burden on screening capacity and costs have been recognized, suggesting that an individualized approach may be warranted. We developed risk prediction models for early-onset CRC that incorporate an environmental risk score (ERS), including 16 lifestyle and environmental factors, and a polygenic risk score (PRS), of 141 variants. METHODS:Relying on risk score weights for ERS and PRS derived from studies of CRC at all ages, we evaluated risks for early-onset CRC in 3,486 cases and 3,890 controls aged less than 50 years. Relative and absolute risks for early-onset CRC were assessed according to values of the ERS and PRS. The discriminatory performance of these scores was estimated using the covariate-adjusted area under the receiver operating characteristic curve. RESULTS:Increasing values of ERS and PRS were associated with increasing relative risks for early-onset CRC (odds ratio per standard deviation of ERS = 1.14, 95% confidence interval [CI] = 1.08, 1.20; odds ratio per standard deviation of PRS = 1.59, 95% CI = 1.51, 1.68), both contributing to case-control discrimination (area under the curve = 0.631, 95% CI = 0.615, 0.647). Based on absolute risks, we can expect 26 excess cases per 10,000 men and 21 per 10,000 women, among those scoring at the 90th percentile for both risk scores. CONCLUSIONS:Personal risk scores have the potential to identify individuals at differential relative and absolute risk for early-onset CRC. Improved discrimination may aid in targeted CRC screening of younger, high-risk individuals, potentially improving outcomes.

OBJECTIVES/OBJECTIVE:Benzene is a known haematoxin and leukemogen that can cause benzene poisoning (BP), that is, a persistent reduction in white cell counts that is strongly associated with increased risk of lymphohaematopoietic malignancies. Data are needed on the exposure-response, particularly at low doses and susceptible populations for clinical and regulatory purposes. METHODS:In a case-cohort study among 110 631 Chinese workers first employed 1949-1987 and followed up during 1972-1999, we evaluated BP risk according to benzene exposure level and investigated risk modification by subject (sex, attained age) and exposure-related factors (latency, exposure windows, age at first benzene exposure, coexposure to toluene) using excess relative risk and excess absolute risk models. RESULTS:There were 538 BP cases and 909 benzene-exposed referents. The exposure metric with best model fit was cumulative benzene exposure during a 5-year risk window, followed by a 9-month lag period before BP diagnosis. Estimated excess absolute risk of BP at age 60 increased from 0.5% for subjects in the lowest benzene exposure category (>0 to 10 ppm-years) to 5.0% for those in the highest category (>100 ppm-years) compared with unexposed subjects. Increased risks were apparent at low cumulative exposure levels and for workers who were first exposed at <30 years of age. CONCLUSIONS:Our data show a clear association between benzene exposure and BP, beginning at low cumulative benzene exposure levels with no threshold, and with higher risks for workers exposed at younger ages. These findings are important because BP has been linked to a strongly increased development of lymphohaematopoietic malignancies.

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/UNASSIGNED:Smoking has been associated with colorectal cancer (CRC) incidence and mortality in previous studies, but current evidence on smoking in association with survival after CRC diagnosis is limited. Methods/UNASSIGNED:We pooled data from 12 345 patients with stage I-IV CRC from 11 epidemiologic studies in the International Survival Analysis in Colorectal Cancer Consortium. Cox proportional hazards regression models were used to evaluate the associations of prediagnostic smoking behavior with overall, CRC-specific, and non-CRC-specific survival. Results/UNASSIGNED:Among 12 345 patients with CRC, 4379 (35.5%) died (2515 from CRC) over a median follow-up time of 7.5 years. Smoking was strongly associated with worse survival in stage I-III patients, whereas no association was observed among stage IV patients. Among stage I-III patients, clear dose-response relationships with all survival outcomes were seen for current smokers. For example, current smokers with 40 or more pack-years had statistically significantly worse overall, CRC-specific, and non-CRC-specific survival compared with never smokers (hazard ratio [HR] =1.94, 95% confidence interval [CI] =1.68 to 2.25; HR = 1.41, 95% CI = 1.12 to 1.78; and HR = 2.67, 95% CI = 2.19 to 3.26, respectively). Similar associations with all survival outcomes were observed for former smokers who had quit for less than 10 years, but only a weak association with non-CRC-specific survival was seen among former smokers who had quit for more than 10 years. Conclusions/UNASSIGNED:This large consortium of CRC patient studies provides compelling evidence that smoking is strongly associated with worse survival of stage I-III CRC patients in a clear dose-response manner. The detrimental effect of smoking was primarily related to noncolorectal cancer events, but current heavy smoking also showed an association with CRC-specific survival.