Better cancer biomarker discovery through better study design

Tasks and Experiences of the Prospective, Longitudinal, Multicenter MoMar (Molecular Markers) Study for the Early Detection of Mesothelioma in Individuals Formerly Exposed to Asbestos Using Liquid Biopsies

Mesothelioma is an aggressive cancer, strongly associated with prior exposure to asbestos. Commonly, tumors are detected at late stages of the disease. Detection at early stages might be meaningful, because therapies might be more effective when the tumor burden is relatively low and the tumor has not spread to distant sites. Circulating biomarkers in blood might be a promising tool to improve the early detection of mesothelioma, but for screening in asymptomatic subjects, candidate biomarkers need to be validated in appropriate studies. This study was conducted to assess the performance of biomarkers in liquid biopsies to detect mesothelioma at early stages. Over a period of 10 years, 2769 volunteers formerly exposed to asbestos were annually examined and liquid biopsies were collected. A follow-up was completed 17 months after the last blood collection. The article provides a detailed overview of our lessons learned and experiences of conducting a prospective, longitudinal, multicenter study. The existing cohort of individuals at risk is highly suitable for the validation of blood-based biomarkers for the early detection of mesothelioma as well as lung cancer.

A flexible matching strategy for matched nested case-control studies

PURPOSE

Individual matching in case-control studies improves statistical efficiency over random selection of controls but can lead to selection bias if cases are excluded due to the lack of appropriate controls or residual confounding with less strict matching criteria. We introduce flex matching, an algorithm using multiple rounds of control selection with successively relaxed matching criteria to select controls for cases.

METHODS

We simulated exposure-disease relationships in multiple cohort data sets with a range of confounding scenarios and conducted 16,800,000 nested case-control studies, comparing random selection of controls, strict matching, and flex matching. We computed average bias and statistical efficiency in estimates of exposure-disease relationships under each matching strategy.

RESULTS

On average, flex matching produced the least biased estimates of exposure-disease associations with the smallest standard errors. Strict matching algorithms that excluded cases for whom matched controls could not be identified produced biased estimates with larger standard errors. Estimates from studies with random assignment of controls were relatively unbiased, but the standard errors were larger than from studies using flex matching.

CONCLUSIONS

Flex matching should be considered for case-control designs, especially for biomarker studies where matching on technical artifacts is necessary and maximizing efficiency is a priority.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Metabolomic Biomarkers for the Detection of Obesity-Driven Endometrial Cancer

Simple Summary

Endometrial cancer is the commonest cancer of the female genital tract and obesity is its main modifiable risk factor. Over 80% of endometrial cancers develop in the context of obesity-induced metabolic changes. This study focuses on the potential of plasma-based metabolites to enable the early detection of endometrial cancer in a cohort of women with body mass index (BMI) ≥ 30 kg/m². Specific lipid metabolites including phospholipids and sphingolipids (sphingomyelins) demonstrated good accuracy for the detection of endometrial cancer, especially when combined in a diagnostic model. This study advances our knowledge of the role of metabolomics in endometrial cancer and provides a basis for the minimally invasive screening of women with elevated BMI.

Abstract

Endometrial cancer is the most common malignancy of the female genital tract and a major cause of morbidity and mortality in women. Early detection is key to ensuring good outcomes but a lack of minimally invasive screening tools is a significant barrier. Most endometrial cancers are obesity-driven and develop in the context of severe metabolomic dysfunction. Blood-derived metabolites may therefore provide clinically relevant biomarkers for endometrial cancer detection. In this study, we analysed plasma samples of women with body mass index (BMI) ≥ 30 kg/m² and endometrioid endometrial cancer (cases, n = 67) or histologically normal endometrium (controls, n = 69), using a mass spectrometry-based metabolomics approach. Eighty percent of the samples were randomly selected to serve as a training set and the remaining 20% were used to qualify test performance. Robust predictive models (AUC > 0.9) for endometrial cancer detection based on artificial intelligence algorithms were developed and validated. Phospholipids were of significance as biomarkers of endometrial cancer, with sphingolipids (sphingomyelins) discriminatory in post-menopausal women. An algorithm combining the top ten performing metabolites showed 92.6% prediction accuracy (AUC of 0.95) for endometrial cancer detection. These results suggest that a simple blood test could enable the early detection of endometrial cancer and provide the basis for a minimally invasive screening tool for women with a BMI ≥ 30 kg/m².

Residential neighborhood features associated with objectively measured walking near home: Revisiting walkability using the Automatic Context Measurement Tool (ACMT)

Many distinct characteristics of the social, natural, and built neighborhood environment have been included in walkability measures, and it is unclear which measures best describe the features of a place that support walking. We developed the Automatic Context Measurement Tool, which measures neighborhood environment characteristics from public data for any point location in the United States. We explored these characteristics in home neighborhood environments in relation to walking identified from integrated GPS, accelerometer, and travel log data from 681 residents of King Country, WA. Of 146 neighborhood characteristics, 92 (63%) were associated with walking bout counts after adjustment for individual characteristics and correction for false discovery. The strongest built environment predictor of walking bout count was housing unit count. Models using data-driven and a priori defined walkability measures exhibited similar fit statistics. Walkability measures consisting of different neighborhood characteristic measurements may capture the same underlying variation in neighborhood conditions.

Study Design Considerations for Cancer Biomarker Discoveries

Background

Biomarker discovery studies have generated an array of omic data, however few novel biomarkers have reached clinical use. Guidelines for rigorous study designs are needed.

Content

Biases frequently occur in sample selection, outcome ascertainment, or unblinded sample handling and assaying process. The principles of a prospective-specimen collection and retrospective-blinded-evaluation (PRoBE) design can be adapted to mitigate various sources of biases in discovery. We recommend establishing quality biospecimen repositories using matched two-phase designs to minimize biases and maximize efficiency. We also highlight the importance of taking the clinical context into consideration in both sample selection and power calculation for discovery studies.

Summary

Biomarker discovery research should follow rigorous design principles in sample se- lection to avoid biases. Consideration of clinical application and the corresponding biomarker performance characteristics in study designs will lead to a more fruitful discovery study.

Impact

Appropriate study designs will improve the quality and clinical rigor of biomarker discovery studies.

Nested case-control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach

BACKGROUND

A nested case-control (NCC) design within a prospective cohort study can realize substantial benefits for biomarker studies. In this context, it is natural to consider the sample availability in the selection of controls to minimize data loss when implementing the design. However, this violates the randomness required for selection, and it leads to biased analyses. An inverse probability weighting may improve the analysis, but the current approach using weighted Cox regression fails to maintain the benefits of NCC design.

METHODS

This paper introduces weighted conditional logistic regression. We illustrate our proposed analysis using data recently investigated in The Environmental Determinants of Diabetes in the Young (TEDDY). Considering the potential data loss, the TEDDY NCC design was moderately selective in its selection of controls. A data-driven simulation study was performed to present the bias correction when a nonrandom control selection was ignored in the analysis.

RESULTS

The TEDDY data analysis showed that the standard analysis using conditional logistic regression estimated the parameter: -0.015 (-0.023, -0.007). The biased estimate using Cox regression was -0.011 (95% confidence interval: -0.019, -0.003). Weighted Cox regression estimated -0.013 (-0.026, 0.0004). The proposed weighted conditional logistic regression estimated -0.020 (-0.033, -0.007), showing a stronger negative effect size than the one using conditional logistic regression. The simulation study also showed that the standard estimate of β ignoring the nonrandom control selection tends to be greater than the true β (ie, positive relative biases).

CONCLUSION

Weighted conditional logistic regression can enhance the analysis by offering flexibility in the selection of controls, while maintaining the matching.

A Critical Evaluation of Wet Biomarkers for Huntington's Disease: Current Status and Ways Forward

There is an unmet clinical need for objective biomarkers to monitor disease progression and treatment response in Huntington's disease (HD). The aim of this review is, therefore, to provide practical advice for biomarker discovery and to summarise studies on biofluid markers for HD. A PubMed search was performed to review literature with regard to candidate saliva, urine, blood and cerebrospinal fluid biomarkers for HD. Information has been organised into tables to allow a pragmatic approach to the discussion of the evidence and generation of practical recommendations for future studies. Many of the markers published converge on metabolic and inflammatory pathways, although changes in other analytes representing antioxidant and growth factor pathways have also been found. The most promising markers reflect neuronal and glial degeneration, particularly neurofilament light chain. International collaboration to standardise assays and study protocols, as well as to recruit sufficiently large cohorts, will facilitate future biomarker discovery and development.

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

Unbiased data analytic strategies to improve biomarker discovery in precision medicine

Omics technologies promised improved biomarker discovery for precision medicine. The foremost problem of discovered biomarkers is irreproducibility between patient cohorts. From a data analytics perspective, the main reason for these failures is bias in statistical approaches and overfitting resulting from batch effects and confounding factors. The keys to reproducible biomarker discovery are: proper study design, unbiased data preprocessing and quality control analyses, and a knowledgeable application of statistics and machine learning algorithms. In this review, we discuss study design and analysis considerations and suggest standards from an expert point-of-view to promote unbiased decision-making in biomarker discovery in precision medicine.

Prognostic Value of Combined C-Reactive Protein, Body Mass Index, and Left Ventricular Ejection Fraction in Predicting Cardiovascular Events in Patients ≥80 Years of Age With Acute Myocardial Infarction

Elevated high-sensitivity C-reactive protein (hsCRP) and low body mass index (BMI) are linked to increased mortality in the elderly population. However, the combined value for predicting adverse cardiovascular events in the oldest-old (≥80 years old) with acute myocardial infarction (AMI) remains undetermined. A total of 463 AMI patients, who were ≥80 years old, were enrolled in this study between January 2012 and June 2017. A nested case-control study was implemented in 106 deaths and 212 controls, who were matched for age, gender, time of inclusion, and myocardial infarction type. Furthermore, the individual and additive values of hsCRP, BMI, and left ventricular ejection fraction (LVEF) were assessed using adjusted hazard ratio, unadjusted Kaplan-Meier analysis, and receiver-operating characteristic curve models. The median follow-up time was 19.15 months, and there were 106 deaths (33.3%). Furthermore, HsCRP, BMI, and LVEF were significantly associated with all-cause mortality (p <0.05, respectively). In addition, a negative correlation between BMI and LVEF, and the positive association of hsCRP with all-cause mortality in the fully adjusted Cox proportional hazards model were detected. The combination of hsCRP, BMI, and LVEF was found to exhibit an enhanced predictive value for all-cause mortality (0.733 in jointly vs 0.623 in cardiovascular risk factors, p = 0.0007) in these oldest-old AMI patients. HsCRP, BMI, and LVEF are the independent risk factors for all-cause mortality for the oldest-old patients with AMI, and this combination offers more appreciable and reliable predictive value for all-cause mortality.

Statistical Methods for Identifying Biomarkers from miRNA Profiles of Cancers

Biomarkers play important roles in early diagnosis and treatment plan for cancer patients and the importance is growing. With advances in high-throughput molecular profiling technology for various types of molecules such as DNA, RNA, proteins, or metabolites, it is now possible to perform massive profiling analysis that allows accelerating discovery of novel biomolecules. Because no single marker is sufficiently accurate for clinical use, the cancer biomarker is developed in the form of multiple biomarker panels. No single marker is sufficiently accurate for clinical use, and thus cancer biomarkers are developed in the form of multiple biomarker panels. Of various types of molecular biomarkers, microRNA (miRNA) has emerged as a class of promising cancer biomarker recently. MiRNAs are small noncoding RNAs that regulate gene expression. The chapter overviews the process of identification of biomarker panels from miRNA profiles focusing on statistical methods. Introduction to molecular cancer biomarkers is touched first. From sample design to miRNA profiling process is reviewed in the method section.Statistical methods for biomarker development are introduced according to three typical purposes of molecular biomarkers: tumor subtype classification, early detection, and prediction of treatment response or prognosis of patients. Example codes for R program are provided as well for selected methods.

A new framework for prediction and variable selection for uncommon events in a large prospective cohort study

When prediction is a goal, validation utilizing data outside of the prediction effort is desirable. Typically, data is split into two parts: one for a development and one for validation. But this approach becomes less attractive when predicting uncommon events, as it substantially reduces power. When predicting uncommon events within a large prospective cohort study, we propose the use of a nested case-control design, which is an alternative to the full cohort analysis. By including all cases but only a subset of the non-cases, this design is expected to produce a result similar to the full cohort analysis. In our framework, variable selection is conducted and a prediction model is fit on those selected variables in the case-control cohort. Then, the fraction of true negative predictions (specificity) of the fitted prediction model in the case-control cohort is compared to that in the rest of the cohort (non-cases) for validation. In addition, we propose an iterative variable selection using random forest for missing data imputation, as well as a strategy for a valid classification. Our framework is illustrated with an application featuring high-dimensional variable selection in a large prospective cohort study.

Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)

Background: Few older adults achieve recommended physical activity levels. We conducted a "neighborhood environment-wide association study (NE-WAS)" of neighborhood influences on physical activity among older adults, analogous, in a genetic context, to a genome-wide association study.Methods: Physical Activity Scale for the Elderly (PASE) and sociodemographic data were collected via telephone survey of 3,497 residents of New York City aged 65 to 75 years. Using Geographic Information Systems, we created 337 variables describing each participant's residential neighborhood's built, social, and economic context. We used survey-weighted regression models adjusting for individual-level covariates to test for associations between each neighborhood variable and (i) total PASE score, (ii) gardening activity, (iii) walking, and (iv) housework (as a negative control). We also applied two "Big Data" analytic techniques, LASSO regression, and Random Forests, to algorithmically select neighborhood variables predictive of these four physical activity measures.Results: Of all 337 measures, proportion of residents living in extreme poverty was most strongly associated with total physical activity [-0.85; (95% confidence interval, -1.14 to -0.56) PASE units per 1% increase in proportion of residents living with household incomes less than half the federal poverty line]. Only neighborhood socioeconomic status and disorder measures were associated with total activity and gardening, whereas a broader range of measures was associated with walking. As expected, no neighborhood meaZsures were associated with housework after accounting for multiple comparisons.Conclusions: This systematic approach revealed patterns in the domains of neighborhood measures associated with physical activity.Impact: The NE-WAS approach appears to be a promising exploratory technique. Cancer Epidemiol Biomarkers Prev; 26(4); 495-504. ©2017 AACRSee all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences."

The current deconstruction of paradoxes: one sign of the ongoing methodological "revolution"

The current deconstruction of paradoxes is one among several signs that a profound renewal of methods for clinical and epidemiological research is taking place; perhaps for some basic life sciences as well. The new methodological approaches have already deconstructed and explained long puzzling apparent paradoxes, including the (non-existent) benefits of obesity in diabetics, or of smoking in low birth weight. Achievements of the new methods also comprise the elucidation of the causal structure of long-disputed and highly complex questions, as Berkson's bias and Simpson's paradox, and clarifying reasons for deep controversies, as those on estrogens and endometrial cancer, or on adverse effects of hormone replacement therapy. These are signs that the new methods can go deeper and beyond the methods in current use. A major example of a highly relevant idea is: when we condition on a common effect of a pair of variables, then a spurious association between such pair is likely. The implications of these ideas are potentially vast. A substantial number of apparent paradoxes may simply be the result of collider biases, a source of selection bias that is common not just in epidemiologic research, but in many types of research in the health, life, and social sciences. The new approaches develop a new framework of concepts and methods, as collider, instrumental variables, d-separation, backdoor path and, notably, Directed Acyclic Graphs (DAGs). The current theoretical and methodological renewal-or, perhaps, "revolution"-may be changing deeply how clinical and epidemiological research is conceived and performed, how we assess the validity and relevance of findings, and how causal inferences are made. Clinical and basic researchers, among others, should get acquainted with DAGs and related concepts.

Leveraging biospecimen resources for discovery or validation of markers for early cancer detection

Validation of early detection cancer biomarkers has proven to be disappointing when initial promising claims have often not been reproducible in diagnostic samples or did not extend to prediagnostic samples. The previously reported lack of rigorous internal validity (systematic differences between compared groups) and external validity (lack of generalizability beyond compared groups) may be effectively addressed by utilizing blood specimens and data collected within well-conducted cohort studies. Cohort studies with prediagnostic specimens (eg, blood specimens collected prior to development of clinical symptoms) and clinical data have recently been used to assess the validity of some early detection biomarkers. With this background, the Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) of the National Cancer Institute (NCI) held a joint workshop in August 2013. The goal was to advance early detection cancer research by considering how the infrastructure of cohort studies that already exist or are being developed might be leveraged to include appropriate blood specimens, including prediagnostic specimens, ideally collected at periodic intervals, along with clinical data about symptom status and cancer diagnosis. Three overarching recommendations emerged from the discussions: 1) facilitate sharing of existing specimens and data, 2) encourage collaboration among scientists developing biomarkers and those conducting observational cohort studies or managing healthcare systems with cohorts followed over time, and 3) conduct pilot projects that identify and address key logistic and feasibility issues regarding how appropriate specimens and clinical data might be collected at reasonable effort and cost within existing or future cohorts.

Biomarker discovery study design for type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study

AIMS

The Environmental Determinants of Diabetes in the Young planned biomarker discovery studies on longitudinal samples for persistent confirmed islet cell autoantibodies and type 1 diabetes using dietary biomarkers, metabolomics, microbiome/viral metagenomics and gene expression.

METHODS

This article describes the details of planning The Environmental Determinants of Diabetes in the Young biomarker discovery studies using a nested case-control design that was chosen as an alternative to the full cohort analysis. In the frame of a nested case-control design, it guides the choice of matching factors, selection of controls, preparation of external quality control samples and reduction of batch effects along with proper sample allocation.

RESULTS AND CONCLUSION

Our design is to reduce potential bias and retain study power while reducing the costs by limiting the numbers of samples requiring laboratory analyses. It also covers two primary end points (the occurrence of diabetes-related autoantibodies and the diagnosis of type 1 diabetes). The resulting list of case-control matched samples for each laboratory was augmented with external quality control samples.

Biomarker research with prospective study designs for the early detection of cancer

This article describes the principles of marker research with prospective studies along with examples for diagnostic tumor markers. A plethora of biomarkers have been claimed as useful for the early detection of cancer. However, disappointingly few biomarkers were approved for the detection of unrecognized disease, and even approved markers may lack a sound validation phase. Prospective studies aimed at the early detection of cancer are costly and long-lasting and therefore the bottleneck in marker research. They enroll a large number of clinically asymptomatic subjects and follow-up on incident cases. As invasive procedures cannot be applied to collect tissue samples from the target organ, biomarkers can only be determined in easily accessible body fluids. Marker levels increase during cancer development, with samples collected closer to the occurrence of symptoms or a clinical diagnosis being more informative than earlier samples. Only prospective designs allow the serial collection of pre-diagnostic samples. Their storage in a biobank upgrades cohort studies to serve for both, marker discovery and validation. Population-based cohort studies, which may collect a wealth of data, are commonly conducted with just one baseline investigation lacking serial samples. However, they can provide valuable information about factors that influence the marker level. Screening programs can be employed to archive serial samples but require significant efforts to collect samples and auxiliary data for marker research. Randomized controlled trials have the highest level of evidence in assessing a biomarker's benefit against usual care and present the most stringent design for the validation of promising markers as well as for the discovery of new markers. In summary, all kinds of prospective studies can benefit from a biobank as they can serve as a platform for biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

New training tools for new epidemiologists

Molecular epidemiology is moving from an era of single marker studies to omics-driven population-based studies, made possible by recent rapid technological advances in high-throughput laboratory assays. The use of these assays in large, observational, epidemiologic studies poses major methodological challenges. The traditional, reductionist approach that most epidemiologists have been taught is no longer sufficient with the introduction of omics data into epidemiology. However, basic epidemiological principles, such as confounding, must still be addressed. In order to successfully implement these tools into molecular epidemiology, scientists in the required disciplines must be able to effectively communicate. We propose an interdisciplinary, three-level, problem-based approach to training in molecular epidemiology. This approach requires interdisciplinary teaching teams that appeal to students coming from diverse backgrounds. First, concepts from each field necessary for molecular epidemiology must be addressed at a basic level, with a specific focus on biological, epidemiological, and statistical concepts. Second, state-of the art methods applied in molecular epidemiology should be covered (e.g., omics technologies, smart study designs, statistical modeling, and biological interpretation). Third, the application of omics technologies in population studies calls for awareness of issues related to biobanking, ethics, legislation, and public health. Problem-based learning is a useful tool for teaching when students need to apply complex concepts to complex situations as is the case in molecular epidemiology. Using this approach, students from diverse backgrounds work together to analyze problems, discuss, and exchange knowledge in modern molecular epidemiology. Training tools characterized by this approach will shape the molecular epidemiologists of the future.

Association between Helicobacter pylori infection and pancreatic cancer development: a meta-analysis

BACKGROUND

Pancreatic cancer is one of the most troublesome malignancies with dismal prognosis. H. pylori has been recognized as a type I carcinogen. Several studies have evaluated the association between H. pylori infection and pancreatic cancer development, however, the conclusions are inconsistent.

METHODS

Literature search was carried out in PubMed, EMBASE, Cochrane Library and CNKI databases to identify eligible researches. We performed overall meta-analysis of all studies included and subgroup analysis based on regional distribution. Quality of the studies (assessed by Newcastle-Ottawa quality assessment scale for case-control studies) and CagA+ strains of H. pylori were taken into consideration, and we conducted additional analyses including high-quality researches and those concerning CagA+ H. pylori respectively.

RESULTS

9 studies involving 3033 subjects (1083 pancreatic cancer cases, 1950 controls) were included. Summary OR and 95%CI of the overall meta-analysis of all included studies were 1.47 and 1.22-1.77, pooled data of the 4 high-quality studies were OR 1.28, 95%CI 1.01-1.63. OR of the 5 studies examined CagA+ strains was 1.42, corresponding 95%CI was 0.79 to 2.57. Summary estimates of subgroup analysis based on regional distribution are as follows, Europe group: OR 1.56, 95%CI 1.15-2.10; East Asia group: OR 2.01, 95%CI 1.33-3.02; North America group: OR 1.17, 95%CI 0.87-1.58. There was not obvious heterogeneity across the 9 studies. No publication bias was detected.

CONCLUSION

H. pylori infection is significantly, albeit weakly, associated with pancreatic cancer development. The association is prominent in Europe and East Asia, but not in North America. CagA+ H. pylori strains appear not to be associated with pancreatic cancer. However, more studies, especially prospective studies, are needed to validate our results.