A meta-inference framework to integrate multiple external models into a current study.

It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of relying on a modestly sized data set collected internally. With some new predictors only available internally, we aim to build improved regression models based on individual-level data from an "internal" study while incorporating summary-level information from "external" models. We propose a meta-analysis framework along with two weighted estimators as the composite of empirical Bayes estimators, which combines the estimates from different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naïve analysis of the internal data and other naïve combinations of external estimators.

Aspirin use and head and neck cancer survival and recurrence.

BACKGROUND: Head and neck cancer (HNC) has low 5-year survival, and evidence-based recommendations for tertiary prevention are lacking. Aspirin improves outcomes for cancers at other sites, but its role in HNC tertiary prevention remains understudied. METHODS: HNC patients were recruited in the University of Michigan Head and Neck Cancer Specialized Program of Research Excellence (SPORE) from 2003 to 2014. Aspirin data were collected through medical record review; outcomes (overall mortality, HNC-specific mortality, and recurrence) were collected through medical record review, Social Security Death Index, or LexisNexis. Cox proportional hazards models were used to evaluate the associations between aspirin use at diagnosis (yes/no) and HNC outcomes. RESULTS: We observed no statistically significant associations between aspirin and cancer outcome in our HNC patient cohort (n = 1161) (HNC-specific mortality: HR = 0.91, 95% CI = 0.68-1.21; recurrence: HR = 0.94, 95% CI = 0.73-1.19). In analyses stratified by anatomic site, HPV status, and disease stage, we observed no association in any strata examined with the possible exception of a lower risk of recurrence in oropharynx patients (HR = 0.60, 95% CI 0.35-1.04). CONCLUSIONS: Our findings do not support a protective association between aspirin use and cancer-specific death or recurrence in HNC patients, with the possible exception of a lower risk of recurrence in oropharynx patients.

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017.

PURPOSE: Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period. METHODS: Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks. RESULTS: Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival. CONCLUSION: These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Robust data integration from multiple external sources for generalized linear models with binary outcomes.

We aim to estimate parameters in a generalized linear model (GLM) for a binary outcome when, in addition to the raw data from the internal study, more than 1 external study provides summary information in the form of parameter estimates from fitting GLMs with varying subsets of the internal study covariates. We propose an adaptive penalization method that exploits the external summary information and gains efficiency for estimation, and that is both robust and computationally efficient. The robust property comes from exploiting the relationship between parameters of a GLM and parameters of a GLM with omitted covariates and from downweighting external summary information that is less compatible with the internal data through a penalization. The computational burden associated with searching for the optimal tuning parameter for the penalization is reduced by using adaptive weights and by using an information criterion when searching for the optimal tuning parameter. Simulation studies show that the proposed estimator is robust against various types of population distribution heterogeneity and also gains efficiency compared to direct maximum likelihood estimation. The method is applied to improve a logistic regression model that predicts high-grade prostate cancer making use of parameter estimates from 2 external models.

Optimizing dynamic predictions from joint models using super learning.

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.

Modeling basal body temperature data using horseshoe process regression.

Biomedical data often exhibit jumps or abrupt changes. For example, women's basal body temperature may jump at ovulation, menstruation, implantation, and miscarriage. These sudden changes make these data challenging to model: many methods will oversmooth the sharp changes or overfit in response to measurement error. We develop horseshoe process regression (HPR) to address this problem. We define a horseshoe process as a stochastic process in which each increment is horseshoe-distributed. We use the horseshoe process as a nonparametric Bayesian prior for modeling a potentially nonlinear association between an outcome and its continuous predictor, which we implement via Stan and in the R package HPR. We provide guidance and extensions to advance HPR's use in applied practice: we introduce a Bayesian imputation scheme to allow for interpolation at unobserved values of the predictor within the HPR; include additional covariates via a partial linear model framework; and allow for monotonicity constraints. We find that HPR performs well when fitting functions that have sharp changes. We apply HPR to model women's basal body temperatures over the course of the menstrual cycle.

Surrogacy validation for time-to-event outcomes with illness-death frailty models.

A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the causal association paradigm assesses the relationship of the treatment effect on the surrogate outcome with the treatment effect on the true, primary outcome. In particular, we propose illness-death models to accommodate the censored and semicompeting risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multistate models, we characterize what a valid surrogate would look like using a causal effect predictiveness plot. We evaluate the estimation properties of a Bayesian method using Markov chain Monte Carlo and assess the sensitivity of our model assumptions. Our motivating data source is a localized prostate cancer clinical trial where the two survival outcomes are time to distant metastasis and time to death.

A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.

There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based methodology, where the goal is to fit a target regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The method allows for heterogeneity of covariate effects across the external populations. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression. This flexible and unified approach can improve statistical efficiency of the estimated coefficients in the internal study, improve predictions by utilizing even partial information available from models that use a subset of the full set of covariates used in the internal study, and provide statistical inference for the external population with potentially different covariate effects from the internal population.

Solutions for surrogacy validation with longitudinal outcomes for a gene therapy.

Valid surrogate endpoints S can be used as a substitute for a true outcome of interest T to measure treatment efficacy in a clinical trial. We propose a causal inference approach to validate a surrogate by incorporating longitudinal measurements of the true outcomes using a mixed modeling approach, and we define models and quantities for validation that may vary across the study period using principal surrogacy criteria. We consider a surrogate-dependent treatment efficacy curve that allows us to validate the surrogate at different time points. We extend these methods to accommodate a delayed-start treatment design where all patients eventually receive the treatment. Not all parameters are identified in the general setting. We apply a Bayesian approach for estimation and inference, utilizing more informative prior distributions for selected parameters. We consider the sensitivity of these prior assumptions as well as assumptions of independence among certain counterfactual quantities conditional on pretreatment covariates to improve identifiability. We examine the frequentist properties (bias of point and variance estimates, credible interval coverage) of a Bayesian imputation method. Our work is motivated by a clinical trial of a gene therapy where the functional outcomes are measured repeatedly throughout the trial.

Integrating Information from Existing Risk Prediction Models with No Model Details.

Consider the setting where (i) individual-level data are collected to build a regression model for the association between an event of interest and certain covariates, and (ii) some risk calculators predicting the risk of the event using less detailed covariates are available, possibly as algorithmic black boxes with little information available about how they were built. We propose a general empirical-likelihood-based framework to integrate the rich auxiliary information contained in the calculators into fitting the regression model, to make the estimation of regression parameters more efficient. Two methods are developed, one using working models to extract the calculator information and one making a direct use of calculator predictions without working models. Theoretical and numerical investigations show that the calculator information can substantially reduce the variance of regression parameter estimation. As an application, we study the dependence of the risk of high grade prostate cancer on both conventional risk factors and newly identified molecular biomarkers by integrating information from the Prostate Biopsy Collaborative Group (PBCG) risk calculator, which was built based on conventional risk factors alone.

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Spike-wave discharges in Sprague-Dawley rats reflect precise intra- and interhemispheric synchronization of somatosensory cortex.

Spike-wave discharges (SWDs) are among the most prominent electrical signals recordable from the rat cerebrum. Increased by inbreeding, SWDs have served as an animal model of human genetic absence seizures. Yet, SWDs are ubiquitous in inbred and outbred rats, suggesting they reflect normal brain function. We hypothesized that SWDs represent oscillatory neural ensemble activity underlying sensory encoding. To test this hypothesis, we simultaneously mapped SWDs from wide areas (8 × 8 mm) of both hemispheres in anesthetized rats, using 256-electrode epicortical arrays that covered primary and secondary somatosensory, auditory and visual cortex bilaterally. We also recorded the laminar pattern of SWDs with linear microelectrode arrays. We compared the spatial and temporal organization of SWDs to somatosensory-evoked potentials (SEPs), as well as auditory- and visual-evoked potentials (AEPs and VEPs) to examine similarities and/or differences between sensory-evoked and spontaneous oscillations in the same animals. We discovered that SWDs are confined to the facial representation of primary and secondary somatosensory cortex (SI and SII, respectively), areas that are preferentially engaged during environmental exploration in the rat. Furthermore, these oscillations exhibit highly synchronized bilateral traveling waves in SI and SII, simultaneously forming closely matched spread patterns in both hemispheres. We propose that SWDs could reflect a previously unappreciated capacity for rat somatosensory cortex to perform precise spatial and temporal analysis of rapidly changing sensory input at the level of large neural ensembles synchronized both within and between the cerebral hemispheres.NEW & NOTEWORTHY We simultaneously mapped electrocortical SWDs from both cerebral hemispheres of Sprague-Dawley rats and discovered that they reflect systematic activation of the facial representation of somatosensory cortex. SWDs form mirror spatiotemporal patterns in both hemispheres that are precisely aligned in both space and time. Our data suggest that SWDs may reflect a substrate by which large neural ensembles perform precise spatiotemporal processing of rapidly changing somatosensory input.

A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker.

Dynamic prediction uses patient information collected during follow-up to produce individualized survival predictions at given time points beyond treatment or diagnosis. This allows clinicians to obtain updated predictions of a patient's prognosis that can be used in making personalized treatment decisions. Two commonly used approaches for dynamic prediction are landmarking and joint modeling. Landmarking does not constitute a comprehensive probability model, and joint modeling often requires strong distributional assumptions and computationally intensive methods for estimation. We introduce an alternative approximate approach for dynamic prediction that aims to overcome the limitations of both methods while achieving good predictive performance. We separately specify the marker and failure time distributions conditional on surviving up to a prediction time of interest and use standard variable selection and goodness-of-fit techniques to identify the best-fitting models. Taking advantage of its analytic tractability and easy two-stage estimation, we use a Gaussian copula to link the marginal distributions smoothly at each prediction time with an association function. With simulation studies, we examine the proposed method's performance. We illustrate its use for dynamic prediction in an application to predicting death for heart valve transplant patients using longitudinal left ventricular mass index information.

Utility based approach in individualized optimal dose selection using machine learning methods.

The goal in personalized medicine is to individualize treatment using patient characteristics and improve health outcomes. Selection of optimal dose must balance the effect of dose on both treatment efficacy and toxicity outcomes. We consider a setting with one binary efficacy and one binary toxicity outcome. The goal is to find the optimal dose for each patient using clinical features and biomarkers from available dataset. We propose to use flexible machine learning methods such as random forest and Gaussian process models to build models for efficacy and toxicity depending on dose and biomarkers. A copula is used to model the joint distribution of the two outcomes and the estimates are constrained to have non-decreasing dose-efficacy and dose-toxicity relationships. Numerical utilities are elicited from clinicians for each potential bivariate outcome. For each patient, the optimal dose is chosen to maximize the posterior mean of the utility function. We also propose alternative approaches to optimal dose selection by adding additional toxicity based constraints and an approach taking into account the uncertainty in the estimation of the utility function. The proposed methods are evaluated in a simulation study to compare expected utility outcomes under various estimated optimal dose rules. Gaussian process models tended to have better performance than random forest. Enforcing monotonicity during modeling provided small benefits. Whether and how, correlation between efficacy and toxicity, was modeled, had little effect on performance. The proposed methods are illustrated with a study of patients with liver cancer treated with stereotactic body radiation therapy.

Introgression, admixture, and selection facilitate genetic adaptation to high-altitude environments in cattle.

Domestication and subsequent selection of cattle to form breeds and biological types that can adapt to different environments partitioned ancestral genetic diversity into distinct modern lineages. Genome-wide selection particularly for adaptation to extreme environments left detectable signatures genome-wide. We used high-density genotype data for 42 cattle breeds and identified the influence of Bos grunniens and Bos javanicus on the formation of Chinese indicine breeds that led to their divergence from India-origin zebu. We also found evidence for introgression, admixture, and migration in most of the Chinese breeds. Selection signature analyses between high-altitude (≥1800 m) and low-altitude adapted breeds (<1500 m) revealed candidate genes (ACSS2, ALDOC, EPAS1, EGLN1, NUCB2) and pathways that are putatively involved in hypoxia adaptation. Immunohistochemical, real-time PCR and CRISPR/cas9 ACSS2-knockout analyses suggest that the up-regulation of ACSS2 expression in the liver promotes the metabolic adaptation of cells to hypoxia via the hypoxia-inducible factor pathway. High altitude adaptation involved the introgression of alleles from high-altitude adapted yaks into Chinese Bos taurus taurus prior to their formation into recognized breeds and followed by selection. In addition to selection, adaptation to high altitude environments has been facilitated by admixture and introgression with locally adapted cattle populations.

Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients.

Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.

ATAC-Seq identifies regions of open chromatin in the bronchial lymph nodes of dairy calves experimentally challenged with bovine respiratory syncytial virus.

BACKGROUND: Bovine Respiratory Syncytial Virus (BRSV) is a cause of Bovine Respiratory Disease (BRD). DNA-based biomarkers contributing to BRD resistance are potentially present in non-protein-coding regulatory regions of the genome, which can be determined using ATAC-Seq. The objectives of this study were to: (i) identify regions of open chromatin in DNA extracted from bronchial lymph nodes (BLN) of healthy dairy calves experimentally challenged with BRSV and compare them with those from non-challenged healthy control calves, (ii) elucidate the chromatin regions that were differentially or uniquely open in the BRSV challenged relative to control calves, and (iii) compare the genes found in regions proximal to the differentially open regions to the genes previously found to be differentially expressed in the BLN in response to BRSV and to previously identified BRD susceptibility loci. This was achieved by challenging clinically healthy Holstein-Friesian calves (mean age 143 ± 14 days) with either BRSV inoculum (n = 12) or with sterile phosphate buffered saline (PBS) (n = 6) and preparing and sequencing ATAC-Seq libraries from fresh BLN tissues. RESULTS: Using Diffbind, 9,144 and 5,096 differentially accessible regions (P < 0.05, FDR < 0.05) were identified between BRSV challenged and control calves employing DeSeq2 and EdgeR, respectively. Additionally, 8,791 chromatin regions were found to be uniquely open in BRSV challenged calves. Seventy-six and 150 of the genes that were previously found to be differentially expressed using RNA-Seq, were located within 2 kb downstream of the differentially accessible regions, and of the regions uniquely open in BRSV challenged calves, respectively. Pathway analyses within ClusterProfiler indicated that these genes were involved in immune responses to infection and participated in the Th1 and Th2 pathways, pathogen recognition and the anti-viral response. There were 237 differentially accessible regions positioned within 40 previously identified BRD susceptibility loci. CONCLUSIONS: The identified open chromatin regions are likely to be involved in the regulatory response of gene transcription induced by infection with BRSV. Consequently, they may contain variants which impact resistance to BRD that could be used in breeding programmes to select healthier, more robust cattle.

Predicting subclinical psychotic-like experiences on a continuum using machine learning.

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants: A feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters; Regularisation of spatiotemporal maps of event-related potentials. Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.

Sensitivity of unilateral- versus bilateral-onset spike-wave discharges to ethosuximide and carbamazepine in the fluid percussion injury rat model of traumatic brain injury.

Unilateral-onset spike-wave discharges (SWDs) following fluid percussion injury (FPI) in rats have been used for nearly two decades as a model for complex partial seizures in human posttraumatic epilepsy (PTE). This study determined if SWDs with a unilateral versus bilateral cortical onset differed. In this experiment, 2-mo-old rats received severe FPI (3 atm) or sham surgery and were instrumented for chronic video-electrocorticography (ECoG) recording (up to 9 mo). The antiseizure drug, carbamazepine (CBZ), and the antiabsence drug, ethosuximide (ETX), were administered separately to determine if they selectively suppressed unilateral- versus bilateral-onset SWDs, respectively. SWDs did not significantly differ between FPI and sham rats on any measured parameter (wave-shape, frequency spectrum, duration, or age-related progression), including unilateral (∼17%) versus bilateral (∼83%) onsets. SWDs with a unilateral onset preferentially originated ipsilateral to the craniotomy in both FPI and sham rats, suggesting that the unilateral-onset SWDs were related to surgical injury and not specifically to FPI. ETX profoundly suppressed SWDs with either unilateral or bilateral onsets, and CBZ had no effect on either type of SWD. These results suggest that SWDs with either a unilateral or bilateral onset have a pharmacosensitivity similar to absence seizures and are very different from the complex partial seizures of PTE. Therefore, SWDs with a unilateral onset after FPI are not a model of the complex partial seizures that occur in PTE, and their use for finding new treatments for PTE could be counterproductive, particularly if their close similarity to normal brain oscillations is not acknowledged.NEW & NOTEWORTHY Unilateral-onset spike-wave discharges (SWDs) in rats have been used to model complex partial seizures in human posttraumatic epilepsy (PTE), compared to bilateral-onset SWDs thought to reflect human absence seizures. Here, we show that both unilateral- and bilateral-onset SWDs following traumatic brain injury are suppressed by the antiabsence drug ethosuximide and are unaffected by the antiseizure drug carbamazepine. We propose that unilateral-onset SWDs are not useful for studying mechanisms of, or treatments for, PTE.

Statin use and head and neck squamous cell carcinoma outcomes.

Head and neck squamous cell carcinoma (HNSCC) is a morbid cancer with poor outcomes. Statins possess anticancer properties such as immunomodulatory and anti-inflammatory effects. The objective of our study is to identify the association between statin use among untreated HNSCC patients and overall death, disease-specific death and recurrence. HNSCC patients were recruited to participate in the University of Michigan Head and Neck Cancer Specialized Program of Research Excellence (SPORE) from 2003 to 2014. Statin use data were collected through medical record review. Participants were considered a statin user if they used a statin at or after diagnosis. Outcome data were collected through medical record review, Social Security Death Index or LexisNexis. Our analytic cohort included 1638 participants. Cox proportional hazard models were used to estimate the association between ever statin use and HNSCC outcomes. Statin use was seen in 36.0% of participants. We observed a statistically significant inverse association between ever using a statin and overall death (HR = 0.75, 95% CI = 0.63-0.88) and HNSCC-specific death (HR = 0.79, 95% CI = 0.63-0.99) and a nonstatistically significant inverse association for recurrence (HR = 0.85, 95% CI = 0.70-1.04). When investigating the association between statin use and HNSCC outcomes utilizing interaction terms between statin use and human papillomavirus (HPV), statistically significant interactions for HNSCC-specific death and recurrence were identified (HNSCC-specific death: HPV-positive HR = 0.41, 95% CI = 0.21-0.84; HPV-negative HR = 1.04, 95% CI = 0.71-1.51; p-int=0.02; recurrence: HPV-positive HR = 0.49, 95% CI = 0.29-0.84; HPV-negative HR = 1.03, 95% CI = 0.74-1.43; p=int-0.02). Statin use may be protective for adverse outcomes in HNSCC patients, particularly those with HPV-positive disease. If true, these findings could have a meaningful impact on tertiary prevention for this cancer.

Pretreatment Dietary Patterns, Serum Carotenoids and Tocopherols Influence Tumor Immune Response in Head and Neck Squamous Cell Carcinoma.

BACKGROUND: Tumor infiltrating lymphocytes (TILs) aid in informing treatment for head and neck squamous cell carcinoma (HNSCC). Nevertheless, little is known about the role of diet on TILs. METHODS: Immunohistologic expression of CD4, CD8, CD68, CD103, CD104 and FOXP3 were assessed in tissue microarrays from 233 previously untreated HNSCC patients. Associations between these markers and pretreatment dietary patterns were evaluated using linear regression. Associations between baseline serum carotenoids, tocopherols and TILs were assessed using logistic regression. Cox models evaluated the association between diet and TILs on overall and recurrence-free survival. RESULTS: Consumption of a Western dietary pattern was associated with lower CD8+ and FOXP3+ infiltrates (p-value:0.03 and 0.02, respectively). Multivariable logistic regression models demonstrated significantly higher CD8+ (OR:2.21;p-value:0.001) and FOXP3+ (OR:4.26;p-value:<0.0001) among patients with high gamma tocopherol. Conversely, high levels of xanthophylls (OR:0.12;p-value:<0.0001), lycopene (OR:0.36;p-value:0.0001) and total carotenoids(OR:0.31;p-value: <0.0001) were associated with significantly lower CD68+. Among those with high CD4+ (HR:1.77;p-value:0.03), CD68+ (HR:2.42;p-value:0.004), CD103+ (HR:3.64;p-value:0.03) and FOXP3+ (HR:3.09;p-value:0.05), having a high Western dietary pattern increased the risk of overall mortality when compared to a low Western dietary pattern. CONCLUSION: Dietary patterns and serum carotenoids may play an important role in modifying TILs, and ultimately, outcome after diagnosis with HNSCC.