Information on Genetic Variants Does Not Increase Identification of Individuals at Risk of Esophageal Adenocarcinoma Compared to Clinical Risk Factors

Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

Healthy dietary patterns, genetic risk, and gastrointestinal cancer incident risk: a large-scale prospective cohort study

BACKGROUND

Dietary patterns have been associated with several cancers, especially gastrointestinal cancer (GIC). However, whether a healthy dietary pattern could modify the risk of GIC among people with different genetic backgrounds is not clear.

OBJECTIVE

The objective of the study was to investigate how dietary patterns and genetic susceptibility contribute to the risk of GIC independently and jointly.

METHODS

This large-scale prospective cohort study included 105,463 participants in UK Biobank who were aged 40-72 y and cancer-free at baseline. Dietary intake (Oxford WebQ) was used to calculate dietary pattern scores including dietary approach to stop hypertension (DASH) score and healthful plant-based diet index (hPDI). Genetic risk was quantified by a polygenic risk score (PRS) comprising 129 known GIC-associated loci. Cox proportional hazards regression was performed to estimate the associations of dietary patterns and PRS with GIC incidence after adjusting for potential confounders.

RESULTS

Over a median follow-up of 11.70 y, 1,661 participants were diagnosed with GIC. DASH and hPDI were associated with 20% and 36% reductions, respectively, in GIC risk. Low PRS was associated with a 30 % decrease in GIC risk (HR: 0.70; 95% CI: 0.62, 0.79). Participants with healthy dietary scores at high-genetic risk had a lower GIC risk with HR of 0.77 (95% CI: 0.60, 0.98) for DASH and 0.66 (95% CI: 0.52, 0.84) for hPDI than those with unhealthy dietary score. Participants with both high-dietary score and low-genetic risk showed the lowest risk of GIC, with HR of 0.58 (95% CI: 0.45, 0.75) for DASH and 0.45 (95% CI: 0.34, 0.58) for hPDI.

CONCLUSIONS

Adherence to DASH and hPDI were associated with a lower risk of some gastrointestinal cancers, and these 2 dietary patterns may partly compensate for genetic predispositions to cancer. Our results advance the development of precision medicine strategies that consider both dietary patterns and genetics to improve gastrointestinal health.

Albuminuria-Related Genetic Biomarkers: Replication and Predictive Evaluation in Individuals with and without Diabetes from the UK Biobank

Increased albuminuria indicates underlying glomerular pathology and is associated with worse renal disease outcomes, especially in diabetic kidney disease. Many single nucleotide polymorphisms (SNPs), associated with albuminuria, could be potentially useful to construct polygenic risk scores (PRSs) for kidney disease. We investigated the diagnostic accuracy of SNPs, previously associated with albuminuria-related traits, on albuminuria and renal injury in the UK Biobank population, with a particular interest in diabetes. Multivariable logistic regression was used to evaluate the influence of 91 SNPs on urine albumin-to-creatinine ratio (UACR)-related traits and kidney damage (any pathology indicating renal injury), stratifying by diabetes. Weighted PRSs for microalbuminuria and UACR from previous studies were used to calculate the area under the receiver operating characteristic curve (AUROC). CUBN-rs1801239 and DDR1-rs116772905 were associated with all the UACR-derived phenotypes, in both the overall and non-diabetic cohorts, but not with kidney damage. Several SNPs demonstrated different effects in individuals with diabetes compared to those without. SNPs did not improve the AUROC over currently used clinical variables. Many SNPs are associated with UACR or renal injury, suggesting a role in kidney dysfunction, dependent on the presence of diabetes in some cases. However, individual SNPs or PRSs did not improve the diagnostic accuracy for albuminuria or renal injury compared to standard clinical variables.

Healthy Diet, Polygenic Risk Score, and Upper Gastrointestinal Cancer Risk: A Prospective Study from UK Biobank

Dietary and genetic factors are considered to be associated with UGI cancer risk. However, examinations of the effect of healthy diet on UGI cancer risk and the extent to which healthy diet modifies the impact of genetic susceptibility on UGI cancer remains limited. Associations were analyzed through Cox regression of the UK Biobank data (n = 415,589). Healthy diet, based on “healthy diet score,” was determined according to fruit, vegetables, grains, fish, and meat consumption. We compared adherence to healthy diet and the risk of UGI cancer. We also constructed a UGI polygenic risk score (UGI-PRS) to assess the combined effect of genetic risk and healthy diet. For the results high adherence to healthy diet reduced 24% UGI cancer risk (HR high-quality diet: 0.76 (0.62–0.93), p = 0.009). A combined effect of high genetic risk and unhealthy diet on UGI cancer risk was observed, with HR reaching 1.60 (1.20–2.13, p = 0.001). Among participants with high genetic risk, the absolute five-year incidence risk of UGI cancer was significantly reduced, from 0.16% to 0.10%, by having a healthy diet. In summary, healthy diet decreased UGI cancer risk, and individuals with high genetic risk can attenuate UGI cancer risk by adopting a healthy diet.

Genetic risk, metabolic syndrome, and gastrointestinal cancer risk: A prospective cohort study

BACKGROUND

Gastrointestinal (GI) cancer risk has been associated with metabolic syndrome (MetS), a surrogate indicator for unhealthy lifestyles, and a number of genetic loci, but the combined effect of MetS and genetic variants on GI cancer risk is uncertain.

METHODS

We included 430,036 participants with available MetS and genotype data from UK Biobank. During the follow-up time, 5494 incident GI cancer cases, including esophageal cancer, gastric cancer, and colorectal cancer, were identified. We created a GI polygenic risk score (GI-PRS) for overall GI cancer derived from three site-specific cancer PRSs. Cox proportional hazards regression was used to estimate the associations of MetS and GI-PRS with the risk of GI cancer.

RESULTS

MetS was significantly associated with 28% increment in GI cancer risk (hazard ratio [HR]_{MetS vs. non-MetS} : 1.28, 95% confidence interval [CI]: 1.21-1.35, p < 0.0001), whereas a high GI-PRS (top quintile) was associated with 2.28-fold increase in risk (HR_{high vs. low} : 2.28, 95% CI: 2.09-2.49, p < 0.0001). Compared with participants without MetS and at low genetic risk (bottom quintile of GI-PRS), those with MetS and at high genetic risk had 2.75-fold increase in GI cancer risk (HR: 2.75, 95% CI: 2.43-3.12, p < 0.0001). Additionally, MetS in comparison with no MetS had 1.49‰, 2.75‰, and 3.37‰ absolute risk increases in 5 years among participants at low, intermediate (quintiles 2-4 of GI-PRS) and high genetic risk, respectively, representing the number of subjects diagnosed as MetS causing a new GI cancer case in 5 years were 669, 364, and 296, respectively.

CONCLUSIONS

Metabolic and genetic factors may jointly contribute to GI cancer risk and may serve as predictors by quantitative measurements to identify high-risk populations of GI cancer for precise prevention.

Toward Targeted Therapies in Oesophageal Cancers: An Overview

Oesophageal cancer is one of the leading causes of cancer-related death worldwide. Oesophageal cancer occurs as squamous cell carcinoma (ESCC) or adenocarcinoma (EAC). Prognosis for patients with either ESCC or EAC is poor, with less than 20% of patients surviving more than 5 years after diagnosis. A major progress has been made in the development of biomarker-driven targeted therapies against breast and lung cancers, as well as melanoma. However, precision oncology for patients with oesophageal cancer is still virtually non-existent. In this review, we outline the recent advances in oesophageal cancer profiling and clinical trials based on targeted therapies in this disease.

Risk prediction models for esophageal cancer: A systematic review and critical appraisal

BACKGROUND AND AIMS

Esophageal cancer risk prediction models allow for risk-stratified endoscopic screening. We aimed to assess the quality of these models developed in the general population.

METHODS

A systematic search of the PubMed and Embase databases from January 2000 through May 2021 was performed. Studies that developed or validated a model of esophageal cancer in the general population were included. Screening, data extraction, and risk of bias (ROB) assessment by the Prediction model Risk Of Bias Assessment Tool (PROBAST) were performed independently by two reviewers.

RESULTS

Of the 13 models included in the qualitative analysis, 8 were developed for esophageal squamous cell carcinoma (ESCC) and the other 5 were developed for esophageal adenocarcinoma (EAC). Only two models conducted external validation. In the ESCC models, cigarette smoking was included in each model, followed by age, sex, and alcohol consumption. For EAC models, cigarette smoking and body mass index were included in each model, and gastroesophageal reflux disease, uses of acid-suppressant medicine, and nonsteroidal anti-inflammatory drug were exclusively included. The discriminative performance was reported in all studies, with C statistics ranging from 0.71 to 0.88, whereas only six models reported calibration. For ROB, all the models had a low risk in participant and outcome, but all models showed high risk in analysis, and 60% of models showed a high risk in predictors, which resulted in all models being classified as having overall high ROB. For model applicability, about 60% of these models had an overall low risk, with 30% of models of high risk and 10% of models of unclear risk, concerning the assessment of participants, predictors, and outcomes.

CONCLUSIONS

Most current risk prediction models of esophageal cancer have a high ROB. Prediction models need further improvement in their quality and applicability to benefit esophageal cancer screening.

Global burden and epidemiology of Barrett oesophagus and oesophageal cancer

Oesophageal cancer is a global health problem; in 2018 there were more than 572,000 people newly diagnosed with oesophageal cancer worldwide. There are two main histological subtypes of oesophageal cancer, oesophageal adenocarcinoma (EAC) and oesophageal squamous cell carcinoma (ESCC), and there has been a dramatic shift in its epidemiology. While the incidence of EAC and its precursor lesion, Barrett oesophagus, has increased in Western populations over the past four decades, the incidence of ESCC has declined in most parts of the world over the same period. ESCC still accounts for the vast majority of all oesophageal cancer cases diagnosed worldwide each year. Prognosis for patients with oesophageal cancer is strongly related to stage at diagnosis. As most patients are diagnosed with late-stage disease, overall 5-year survival for oesophageal cancer remains <20%. Knowledge of epidemiology and risk factors for oesophageal cancer is essential for public health and clinical decisions about risk stratification, screening and prevention. The goal of this Review is to establish the current epidemiology of oesophageal cancer, with a particular focus on the Western world and the increasing incidence of EAC and Barrett oesophagus.

Epidemiology of Barrett's Esophagus and Esophageal Adenocarcinoma: Implications for Screening and Surveillance

In the United States, the incidence of esophageal adenocarcinoma increased markedly since the 1970s with a recent stabilization. Despite evolving screening and surveillance strategies to diagnose, risk triage, and intervene in Barrett's esophagus patients to prevent esophageal adenocarcinoma, most cases present with advanced disease and poor resultant survival. Epidemiologic studies have identified the main risk factors for these conditions, including increasing age, male sex, white race, gastroesophageal reflux disease, abdominal obesity, cigarette smoking, and lack of infection with Helicobacter pylori. This review summarizes the current epidemiologic evidence with implications for screening and surveillance in Barrett's esophagus and esophageal adenocarcinoma.

Risk Prediction Models for Barrett's Esophagus Discriminate Well and Are Generalizable in an External Validation Study

BACKGROUND

Barrett's esophagus is the precursor to the highly lethal esophageal adenocarcinoma. Risk prediction models have been developed to assist in its detection, potentially improving early identification and treatment of esophageal adenocarcinoma. Six models have been developed.

AIMS

To externally validate three models (Rubenstein, Thrift, and Baldwin-Hunter models) and compare them to a fourth risk prediction model (Ireland model) for Barrett's esophagus.

METHODS

Data from 120 Barrett's cases and 235 population controls were available to externally validate the three models. Discriminatory ability of these models was assessed by the area under the receiver operating characteristic curve. Calibration was assessed with the calibration slope, Hosmer-Lemeshow test, and Lowess smoother calibration plot. Following external validation, diagnostic accuracy of the three models was compared to that of the Ireland model.

RESULTS

On external validation, the Rubenstein model had an area under the receiver operating characteristic curve of 0.71 and was well calibrated (Hosmer-Lemeshow test, p = 0.67). Likewise, the Thrift and Baldwin-Hunter models had similar discrimination (0.71 and 0.70, respectively) and were also well calibrated (p = 0.69 and p = 0.28). Our previous external validation of the Ireland model provided an area under the receiver operating characteristic curve of 0.83 and was well calibrated (p = 0.14). The Ireland model demonstrated a statistically significantly greater area under the receiver operating characteristic curve than the Rubenstein (p = 0.02), Thrift (p = 0.001), and Baldwin-Hunter (p = 0.002) models.

CONCLUSION

We externally validated the Rubenstein, Thrift, and Baldwin-Hunter risk prediction models and compared them to the Ireland model. The Ireland model demonstrated improved accuracy, albeit with slightly poorer calibration.

Circulating Levels of Inflammatory and Metabolic Biomarkers and Risk of Esophageal Adenocarcinoma and Barrett Esophagus: Systematic Review and Meta-analysis

Associations between circulating levels of obesity-related biomarkers and risk of esophageal adenocarcinoma and Barrett esophagus have been reported, but the results are inconsistent. A literature search until October 2018 in MEDLINE and EMBASE was performed. Pooled ORs with 95% confidence intervals (CI) were estimated for associations between 13 obesity-related inflammatory and metabolic biomarkers and risk of esophageal adenocarcinoma or Barrett esophagus using random effect meta-analyses. Among 7,641 studies, 19 were eligible for inclusion (12 cross-sectional, two nested case-control, and five cohort studies). Comparing the highest versus lowest categories of circulating biomarker levels, the pooled ORs were increased for leptin (OR, 1.68; 95% CI, 0.95-2.97 for Barrett esophagus), glucose (OR, 1.12; 95% CI, 1.03-1.22 for esophageal adenocarcinoma), insulin (OR, 1.47; 95% CI, 1.06-2.00 for Barrett esophagus), C-reactive protein (CRP; OR, 2.06; 95% CI, 1.28-3.31 for esophageal adenocarcinoma), IL6 (OR, 1.50; 95% CI, 1.03-2.19 for esophageal adenocarcinoma), and soluble TNF receptor 2 (sTNFR-2; OR, 3.16; 95% CI, 1.76-5.65 for esophageal adenocarcinoma). No associations were identified for adiponectin, ghrelin, insulin-like growth factor 1, insulin-like growth factor-binding protein 3, triglycerides, IL8, or TNFα. Higher circulating levels of leptin, glucose, insulin, CRP, IL6, and sTNFR-2 may be associated with an increased risk of esophageal adenocarcinoma or Barrett esophagus. More prospective studies are required to identify biomarkers that can help select high-risk individuals for targeted prevention and early detection.

Identification of an Individualized Prognostic Signature Based on the RWSR Model in Early-Stage Bladder Carcinoma

Bladder cancer (BLCA) is the fourth common cancer among males in the United States, which is also the fourth leading cause of cancer-related death in old males. BLCA has a high recurrence rate, with over 50% of patients which has at least one recurrence within five years. Due to the complexity of the molecular mechanisms and heterogeneous cancer feature, BLCA clinicians find it hard to make an efficient management decision as they lack reliable assessment of mortality risk. Meanwhile, there is currently no screening suitable prognostic signature or method recommended for early detection, which is significantly important to early-stage detection and prognosis. In this study, a novel model, named the risk-weighted sparse regression (RWSR) model, is constructed to identify a robust signature for patients of early-stage BLCA. The 17-gene signature is generated and then validated as an independent prognostic factor in BLCA cohorts from GSE13507 and TCGA_BLCA datasets. Meanwhile, a risk score model is developed and validated among the 17-gene signature. The risk score is also considered an independent factor for prognosis prediction, which is confirmed through prognosis analysis. The Kaplan-Meier with the log-rank test is used to assess survival difference. Furthermore, the predictive capacity of the signature is proved through stratification analysis. Finally, an effective patient classification is completed by a combination of the 17-gene signature and stage information, which is for better survival prediction and treatment decisions. Besides, 11 genes in the signature, such as coiled-coil domain containing 73 (CCDC73) and protein kinase, DNA-activated, and catalytic subunit (PRKDC), are proved to be prognosis marker genes or strongly associated with prognosis and progress of other types of cancer in published literature already. As a result, this paper would more accurately predict a patient's prognosis and improve surveillance in the clinical setting, which may provide a quantitative and reliable decision-making basis for the treatment plan.

External validation of a model to determine risk of progression of Barrett's oesophagus to neoplasia

BACKGROUND

A risk prediction model containing sex, smoking history, Barrett's oesophagus length and presence of low-grade dysplasia was found to identify individuals at a higher risk of progression to oesophageal adenocarcinoma or high-grade dysplasia.

AIM

To externally validate the model predicting risk of progression from Barrett's oesophagus to neoplasia and assess the predictive utility of additional factors.

METHODS

We conducted a retrospective cohort study among individuals from the population-based Northern Ireland Barrett's register with a histologically confirmed diagnosis of Barrett's oesophagus (with intestinal metaplasia) between 1993 and 2005. The association between a points based model and risk of progression to high-grade dysplasia or oesophageal adenocarcinoma until 2010 was assessed using Cox Proportional Hazards model. Model performance was assessed using area under the receiver operating characteristics curves (AUROC), sensitivity and specificity.

RESULTS

We identified 1198 individuals with Barrett's oesophagus of whom 54 progressed. The model discriminated reasonably well between progressors and nonprogressors, with an AUROC of 0.70 (95% CI 0.63-0.78). When categorised into low, intermediate and high risk groups, the AUROC was 0.68 (95% CI 0.61-0.74). Compared to using data on dysplasia and segment length for risk stratification, the model resulted in a net reclassification improvement of 20.9%.

CONCLUSIONS

This external validation provides further evidence that a model based on sex, smoking, Barrett's segment length and baseline low-grade dysplasia may help to risk stratify patients after an initial diagnosis of Barrett's oesophagus. The model also performed better than the use of low-grade dysplasia status alone for risk-stratification.

Barrett's Esophagus and Esophageal Carcinoma: Can Biomarkers Guide Clinical Practice?

PURPOSE OF REVIEW

Despite gastrointestinal societal recommendations for endoscopic screening and surveillance of Barrett's esophagus, the rates of esophageal adenocarcinoma continue to rise. Furthermore, this current practice is costly to patients and the medical system without clear evidence of reduction in cancer mortality. The use of biomarkers to guide screening, surveillance, and treatment strategies might alleviate some of these issues.

RECENT FINDINGS

Incredible advances in biomarker identification, biomarker assays, and minimally-invasive modalities to acquire biomarkers have shown promising results. We will highlight recently published, key studies demonstrating where we are with using biomarkers for screening and surveillance in clinical practice, and what is on the horizon regarding novel non-invasive and minimally invasive methods to acquire biomarkers. Proof-of principle studies using in silico models demonstrate that biomarker-guided screening, surveillance, and therapeutic intervention strategies can be cost-effective and can reduce cancer deaths in patients with Barrett's esophagus.