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Jia K, Kundrot S, Palchuk MB, Warnick J, Haapala K, Kaplan ID, Rinard M, Appelbaum L. A pancreatic cancer risk prediction model (Prism) developed and validated on large-scale US clinical data. EBioMedicine 2023; 98:104888. [PMID: 38007948 PMCID: PMC10755107 DOI: 10.1016/j.ebiom.2023.104888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND Pancreatic Duct Adenocarcinoma (PDAC) screening can enable early-stage disease detection and long-term survival. Current guidelines use inherited predisposition, with about 10% of PDAC cases eligible for screening. Using Electronic Health Record (EHR) data from a multi-institutional federated network, we developed and validated a PDAC RISk Model (Prism) for the general US population to extend early PDAC detection. METHODS Neural Network (PrismNN) and Logistic Regression (PrismLR) were developed using EHR data from 55 US Health Care Organisations (HCOs) to predict PDAC risk 6-18 months before diagnosis for patients 40 years or older. Model performance was assessed using Area Under the Curve (AUC) and calibration plots. Models were internal-externally validated by geographic location, race, and time. Simulated model deployment evaluated Standardised Incidence Ratio (SIR) and other metrics. FINDINGS With 35,387 PDAC cases, 1,500,081 controls, and 87 features per patient, PrismNN obtained a test AUC of 0.826 (95% CI: 0.824-0.828) (PrismLR: 0.800 (95% CI: 0.798-0.802)). PrismNN's average internal-external validation AUCs were 0.740 for locations, 0.828 for races, and 0.789 (95% CI: 0.762-0.816) for time. At SIR = 5.10 (exceeding the current screening inclusion threshold) in simulated model deployment, PrismNN sensitivity was 35.9% (specificity 95.3%). INTERPRETATION Prism models demonstrated good accuracy and generalizability across diverse populations. PrismNN could find 3.5 times more cases at comparable risk than current screening guidelines. The small number of features provided a basis for model interpretation. Integration with the federated network provided data from a large, heterogeneous patient population and a pathway to future clinical deployment. FUNDING Prevent Cancer Foundation, TriNetX, Boeing, DARPA, NSF, and Aarno Labs.
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Affiliation(s)
- Kai Jia
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | | | | | | | | | - Irving D Kaplan
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
| | - Martin Rinard
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Limor Appelbaum
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
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Ke TM, Lophatananon A, Muir KR. An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank. Biomedicines 2023; 11:3206. [PMID: 38137427 PMCID: PMC10740416 DOI: 10.3390/biomedicines11123206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/24/2023] Open
Abstract
Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.
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Affiliation(s)
| | | | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK; (T.-M.K.); (A.L.)
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Placido D, Yuan B, Hjaltelin JX, Zheng C, Haue AD, Chmura PJ, Yuan C, Kim J, Umeton R, Antell G, Chowdhury A, Franz A, Brais L, Andrews E, Marks DS, Regev A, Ayandeh S, Brophy MT, Do NV, Kraft P, Wolpin BM, Rosenthal MH, Fillmore NR, Brunak S, Sander C. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med 2023; 29:1113-1122. [PMID: 37156936 PMCID: PMC10202814 DOI: 10.1038/s41591-023-02332-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 03/31/2023] [Indexed: 05/10/2023]
Abstract
Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.
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Affiliation(s)
- Davide Placido
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bo Yuan
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Jessica X Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chunlei Zheng
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Amalie D Haue
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Piotr J Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chen Yuan
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jihye Kim
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Renato Umeton
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Weill Cornell Medicine, New York City, NY, USA
| | | | | | - Alexandra Franz
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | | | | | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Boston, MA, USA
- Genentech, Inc., South San Francisco, CA, USA
| | | | - Mary T Brophy
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Nhan V Do
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Peter Kraft
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brian M Wolpin
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Michael H Rosenthal
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Nathanael R Fillmore
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
| | - Chris Sander
- Harvard Medical School, Boston, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Boston, MA, USA.
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Chen W, Zhou Y, Xie F, Butler RK, Jeon CY, Luong TQ, Zhou B, Lin YC, Lustigova E, Pisegna JR, Kim S, Wu BU. Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer. Am J Gastroenterol 2023; 118:157-167. [PMID: 36227806 PMCID: PMC9822857 DOI: 10.14309/ajg.0000000000002050] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 10/04/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records. METHODS This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry. RESULTS The Kaiser Permanente Southern California cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PDAC cases. The 18-month incidence rate of PDAC was 0.77 (95% confidence interval 0.73-0.80)/1,000 person-years. The final main model contained age, abdominal pain, weight change, HbA1c, and alanine transaminase change (c-index: mean = 0.77, SD = 0.02; calibration test: P value 0.4, SD 0.3). The final early detection model comprised the same features as those selected by the main model except for abdominal pain (c-index: 0.77 and SD 0.4; calibration test: P value 0.3 and SD 0.3). The VA testing cohort consisted of 2.7 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30)/1,000 person-years. The recalibrated main and early detection models based on VA testing data sets achieved a mean c-index of 0.71 (SD 0.002) and 0.68 (SD 0.003), respectively. DISCUSSION Using widely available parameters in electronic health records, we developed and externally validated parsimonious machine learning-based models for detection of PC. These models may be suitable for real-time clinical application.
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Affiliation(s)
- Wansu Chen
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Yichen Zhou
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Fagen Xie
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Rebecca K. Butler
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | | | - Tiffany Q. Luong
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Botao Zhou
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Yu-Chen Lin
- Cedars-Sinai Medical Center, Los Angeles, CA
| | - Eva Lustigova
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Joseph R. Pisegna
- Division of Gastroenterology and Hepatology, VA Greater Los Angeles Healthcare System, Los Angeles, CA and Departments of Medicine and Human Genetics David Geffen School of Medicine at UCLA
| | - Sungjin Kim
- Cedars-Sinai Medical Center, Los Angeles, CA
| | - Bechien U. Wu
- Center for Pancreatic Care, Department of Gastroenterology, Los Angeles Medical Center, Southern California Permanente Medical Group, Los Angeles, CA
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Clinical Prediction Models for Pancreatic Cancer in General and At-Risk Populations: A Systematic Review. Am J Gastroenterol 2023; 118:26-40. [PMID: 36148840 DOI: 10.14309/ajg.0000000000002022] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/16/2022] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Identifying high-risk individuals using a risk prediction model could be a crucial first stage of screening pathways to improve the early detection of pancreatic cancer. A systematic review was conducted to critically evaluate the published primary literature on the development or validation of clinical risk prediction models for pancreatic cancer risk. METHODS MEDLINE, Embase, and Web of Science were searched for relevant articles from the inception of each database up to November 2021. Study selection and data extraction were conducted by 2 independent reviewers. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to assess risk of bias. RESULTS In total, 33 studies were included, describing 38 risk prediction models. Excluding studies with an overlapping population, this study consist of 15,848,100 participants, of which 58,313 were diagnosed with pancreatic cancer. Eight studies externally validated their model, and 13 performed internal validation. The studies described risk prediction models for pancreatic cancer in the general population (n = 14), patients with diabetes (n = 8), and individuals with gastrointestinal (and other) symptoms (symptoms included abdominal pain, unexplained weight loss, jaundice, and change in bowel habits and indigestion; n = 11). The commonly used clinical risk factors in the model were cigarette smoking (n = 27), age (n = 25), diabetes history (n = 22), chronic pancreatitis (n = 18), and body mass index (n = 14). In the 25 studies that assessed model performance, C-statistics ranged from 0.61 to 0.98. Of the 33 studies included, 6 were rated as being at a low risk of bias based on PROBAST. DISCUSSION Many clinical risk prediction models for pancreatic cancer had been developed for different target populations. Although low risk-of-bias studies were identified, these require external validation and implementation studies to ensure that these will benefit clinical decision making.
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Yuan C, Kim J, Wang QL, Lee AA, Babic A, Amundadottir LT, Klein AP, Li D, McCullough ML, Petersen GM, Risch HA, Stolzenberg-Solomon RZ, Perez K, Ng K, Giovannucci EL, Stampfer MJ, Kraft P, Wolpin BM. The age-dependent association of risk factors with pancreatic cancer. Ann Oncol 2022; 33:693-701. [PMID: 35398288 PMCID: PMC9233063 DOI: 10.1016/j.annonc.2022.03.276] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/04/2022] [Accepted: 03/31/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Pancreatic cancer presents as advanced disease in >80% of patients; yet, appropriate ages to consider prevention and early detection strategies are poorly defined. We investigated age-specific associations and attributable risks of pancreatic cancer for established modifiable and non-modifiable risk factors. PATIENTS AND METHODS We included 167 483 participants from two prospective US cohort studies with 1190 incident cases of pancreatic cancer during >30 years of follow-up; 5107 pancreatic cancer cases and 8845 control participants of European ancestry from a completed multicenter genome-wide association study (GWAS); and 248 893 pancreatic cancer cases documented in the US Surveillance, Epidemiology, and End Results (SEER) Program. Across different age categories, we investigated cigarette smoking, obesity, diabetes, height, and non-O blood group in the prospective cohorts; weighted polygenic risk score of 22 previously identified single nucleotide polymorphisms in the GWAS; and male sex and black race in the SEER Program. RESULTS In the prospective cohorts, all five risk factors were more strongly associated with pancreatic cancer risk among younger participants, with associations attenuated among those aged >70 years. The hazard ratios comparing participants with three to five risk factors with those with no risk factors were 9.24 [95% confidence interval (CI) 4.11-20.77] among those aged ≤60 years, 3.00 (95% CI 1.85-4.86) among those aged 61-70 years, and 1.46 (95% CI 1.10-1.94) among those aged >70 years (Pheterogeneity = 3×10-5). These factors together were related to 65.6%, 49.7%, and 17.2% of incident pancreatic cancers in these age groups, respectively. In the GWAS and the SEER Program, the associations with the polygenic risk score, male sex, and black race were all stronger among younger individuals (Pheterogeneity ≤0.01). CONCLUSIONS Established risk factors are more strongly associated with earlier-onset pancreatic cancer, emphasizing the importance of age at initiation for cancer prevention and control programs targeting this highly lethal malignancy.
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Affiliation(s)
- C Yuan
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, USA.
| | - J Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Q L Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, USA
| | - A A Lee
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - A Babic
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, USA
| | - L T Amundadottir
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, USA
| | - A P Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, USA; Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, USA
| | - D Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - M L McCullough
- Department of Population Science, American Cancer Society, Atlanta, USA
| | - G M Petersen
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine, Rochester, USA
| | - H A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, USA
| | | | - K Perez
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, USA
| | - K Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, USA
| | - E L Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - M J Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - P Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA
| | - B M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, USA
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Casolino R, Corbo V, Beer P, Hwang CI, Paiella S, Silvestri V, Ottini L, Biankin AV. Germline Aberrations in Pancreatic Cancer: Implications for Clinical Care. Cancers (Basel) 2022; 14:3239. [PMID: 35805011 PMCID: PMC9265115 DOI: 10.3390/cancers14133239] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 12/13/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis and represents a major public health issue, as both its incidence and mortality are expecting to increase steeply over the next years. Effective screening strategies are lacking, and most patients are diagnosed with unresectable disease precluding the only chance of cure. Therapeutic options for advanced disease are limited, and the treatment paradigm is still based on chemotherapy, with a few rare exceptions to targeted therapies. Germline variants in cancer susceptibility genes-particularly those involved in mechanisms of DNA repair-are emerging as promising targets for PDAC treatment and prevention. Hereditary PDAC is part of the spectrum of several syndromic disorders, and germline testing of PDAC patients has relevant implications for broad cancer prevention. Germline aberrations in BRCA1 and BRCA2 genes are predictive biomarkers of response to poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitor olaparib and platinum-based chemotherapy in PDAC, while mutations in mismatch repair genes identify patients suitable for immune checkpoint inhibitors. This review provides a timely and comprehensive overview of germline aberrations in PDAC and their implications for clinical care. It also discusses the need for optimal approaches to better select patients for PARP inhibitor therapy, novel therapeutic opportunities under clinical investigation, and preclinical models for cancer susceptibility and drug discovery.
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Affiliation(s)
- Raffaella Casolino
- Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, UK; (P.B.); (A.V.B.)
- Beatson West of Scotland Cancer Centre, Glasgow G12 0YN, UK
- NHS Greater Glasgow and Clyde, Glasgow G4 0SF, UK
| | - Vincenzo Corbo
- Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy;
| | - Philip Beer
- Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, UK; (P.B.); (A.V.B.)
| | - Chang-il Hwang
- Department of Microbiology and Molecular Genetics, College of Biological Sciences, University of California Davis, Davis, CA 95616, USA;
- Comprehensive Cancer Center, University of California Davis, Sacramento, CA 95817, USA
| | - Salvatore Paiella
- General and Pancreatic Surgery Unit, Pancreas Institute, University of Verona Hospital Trust, 37134 Verona, Italy;
| | - Valentina Silvestri
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy; (V.S.); (L.O.)
| | - Laura Ottini
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy; (V.S.); (L.O.)
| | - Andrew V. Biankin
- Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, UK; (P.B.); (A.V.B.)
- West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow G31 2ER, UK
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
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Abstract
Background It is estimated that about 10% of pancreatic cancer cases have a genetic background. People with a familial predisposition to pancreatic cancer can be divided into 2 groups. The first is termed hereditary pancreatic cancer, which occurs in individuals with a known hereditary cancer syndrome caused by germline single gene mutations (e.g., BRCA1/2, CDKN2A). The second is considered as familial pancreatic cancer, which is associated with several genetic factors responsible for the more common development of pancreatic cancer in certain families, but the precise single gene mutation has not been found. Aim This review summarizes the current state of knowledge regarding the risk of pancreatic cancer development in hereditary pancreatic cancer and familial pancreatic cancer patients. Furthermore, it gathers the latest recommendations from the three major organizations dealing with the prevention of pancreatic cancer in high-risk groups and explores recent guidelines of scientific societies on screening for pancreatic cancers in individuals at risk for hereditary or familial pancreatic cancer. Conclusions In order to improve patients’ outcomes, authors of current guidelines recommend early and intensive screening in patients with pancreatic cancer resulting from genetic background. The screening should be performed in excellence centers. The scope, extent and cost-effectiveness of such interventions requires further studies.
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Kumar S, Santos RJ, McGuigan AJ, Singh U, Johnson P, Kunzmann AT, Turkington RC. The Role of Circulating Protein and Metabolite Biomarkers in the Development of Pancreatic Ductal Adenocarcinoma (PDAC): A Systematic Review and Meta-analysis. Cancer Epidemiol Biomarkers Prev 2022; 31:1090-1102. [PMID: 34810209 PMCID: PMC9377754 DOI: 10.1158/1055-9965.epi-21-0616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/19/2021] [Accepted: 11/08/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and this is attributed to it being diagnosed at an advanced stage. Understanding the pathways involved in initial development may improve early detection strategies. This systematic review assessed the association between circulating protein and metabolite biomarkers and PDAC development. METHODS A literature search until August 2020 in MEDLINE, EMBASE, and Web of Science was performed. Studies were included if they assessed circulating blood, urine, or salivary biomarkers and their association with PDAC risk. Quality was assessed using the Newcastle-Ottawa scale for cohort studies. Random-effects meta-analyses were used to calculate pooled relative risk. RESULTS A total of 65 studies were included. Higher levels of glucose were found to be positively associated with risk of developing PDAC [n = 4 studies; pooled relative risk (RR): 1.61; 95% CI: 1.16-2.22]. Additionally, an inverse association was seen with pyridoxal 5'-phosphate (PLP) levels (n = 4 studies; RR: 0.62; 95% CI: 0.44-0.87). Meta-analyses showed no association between levels of C-peptide, members of the insulin growth factor signaling pathway, C-reactive protein, adiponectin, 25-hydroxyvitamin D, and folate/homocysteine and PDAC risk. Four individual studies also reported a suggestive positive association of branched-chain amino acids with PDAC risk, but due to differences in measures reported, a meta-analysis could not be performed. CONCLUSIONS Our pooled analysis demonstrates that higher serum glucose levels and lower levels of PLP are associated with risk of PDAC. IMPACT Glucose and PLP levels are associated with PDAC risk. More prospective studies are required to identify biomarkers for early detection.
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Affiliation(s)
- Swati Kumar
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Ralph J. Santos
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Andrew J. McGuigan
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Urvashi Singh
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Peter Johnson
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Andrew T. Kunzmann
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Richard C. Turkington
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
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Thomas J, Liao LM, Sinha R, Patel T, Antwi SO. Hepatocellular Carcinoma Risk Prediction in the NIH-AARP Diet and Health Study Cohort: A Machine Learning Approach. J Hepatocell Carcinoma 2022; 9:69-81. [PMID: 35211426 PMCID: PMC8858015 DOI: 10.2147/jhc.s341045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/21/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Prediction of hepatocellular carcinoma (HCC) development in persons with known risk factors remain a challenge and is an urgent unmet need, considering projected increases in HCC incidence and mortality in the US. We aimed to use machine learning techniques to identify a set of demographic, lifestyle, and health history information that can be used simultaneously for population-level HCC risk prediction. METHODS Data from 377,065 participants of the NIH-AARP Diet and Health Study, among whom 647 developed HCC over 16 years of follow-up, were analyzed. The sample was randomly divided into independent training (60%) and validation (40%) sets. We evaluated 123 participant characteristics and tested 15 different machine learning algorithms for robustness in predicting HCC risk. Separately, we evaluated variables selected from multivariable logistic regression for risk prediction. RESULTS The random under-sampling boosting (RUSBoost) algorithm performed best during model testing. Fourteen participant characteristics were selected for risk prediction based on differences between cases and controls (Bonferroni-corrected p-values <0.0004) and from the most frequently used variables in the initial two decision trees of the RUSBoost learner trees. A predictive model based on the 14 variables had an AUC of 0.72 (sensitivity=0.68, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.68, specificity=0.63). A subset of 9 variables identified through logistic regression also had an AUC of 0.72 (sensitivity=0.67, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.70, specificity=0.61). CONCLUSION Population-level HCC risk prediction can be performed with a machine learning-based algorithm and could inform strategies for improving HCC risk reduction in at-risk groups.
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Affiliation(s)
- Jonathan Thomas
- Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA
| | - Linda M Liao
- Division of Cancer Epidemiology and Genetics, The National Cancer Institute, Bethesda, MD, USA
| | - Rashmi Sinha
- Division of Cancer Epidemiology and Genetics, The National Cancer Institute, Bethesda, MD, USA
| | - Tushar Patel
- Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA
| | - Samuel O Antwi
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA,Correspondence: Samuel O Antwi, Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Road South, Vincent Stabile Building 756N, Jacksonville, FL, 32224, USA, Tel +1-904-953-0310, Fax +1-904-953-1447, Email
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11
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Lu Y, Gentiluomo M, Macauda A, Gioffreda D, Gazouli M, Petrone MC, Kelemen D, Ginocchi L, Morelli L, Papiris K, Greenhalf W, Izbicki JR, Kiudelis V, Mohelníková-Duchoňová B, Bueno-de-Mesquita B, Vodicka P, Brenner H, Diener MK, Pezzilli R, Ivanauskas A, Salvia R, Szentesi A, Aoki MN, Németh BC, Sperti C, Jamroziak K, Chammas R, Oliverius M, Archibugi L, Ermini S, Novák J, Kupcinskas J, Strouhal O, Souček P, Cavestro GM, Milanetto AC, Vanella G, Neoptolemos JP, Theodoropoulos GE, van Laarhoven HWM, Mambrini A, Moz S, Kala Z, Loveček M, Basso D, Uzunoglu FG, Hackert T, Testoni SGG, Hlaváč V, Andriulli A, Lucchesi M, Tavano F, Carrara S, Hegyi P, Arcidiacono PG, Busch OR, Lawlor RT, Puzzono M, Boggi U, Guo F, Małecka-Panas E, Capurso G, Landi S, Talar-Wojnarowska R, Strobel O, Gao X, Vashist Y, Campa D, Canzian F. Identification of Recessively Inherited Genetic Variants Potentially Linked to Pancreatic Cancer Risk. Front Oncol 2021; 11:771312. [PMID: 34926279 PMCID: PMC8678088 DOI: 10.3389/fonc.2021.771312] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/16/2021] [Indexed: 02/05/2023] Open
Abstract
Although 21 pancreatic cancer susceptibility loci have been identified in individuals of European ancestry through genome-wide association studies (GWASs), much of the heritability of pancreatic cancer risk remains unidentified. A recessive genetic model could be a powerful tool for identifying additional risk variants. To discover recessively inherited pancreatic cancer risk loci, we performed a re-analysis of the largest pancreatic cancer GWAS, the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including 8,769 cases and 7,055 controls of European ancestry. Six single nucleotide polymorphisms (SNPs) showed associations with pancreatic cancer risk according to a recessive model of inheritance. We replicated these variants in 3,212 cases and 3,470 controls collected from the PANcreatic Disease ReseArch (PANDoRA) consortium. The results of the meta-analyses confirmed that rs4626538 (7q32.2), rs7008921 (8p23.2) and rs147904962 (17q21.31) showed specific recessive effects (p<10-5) compared with the additive effects (p>10-3), although none of the six SNPs reached the conventional threshold for genome-wide significance (p < 5×10-8). Additional bioinformatic analysis explored the functional annotations of the SNPs and indicated a possible relationship between rs36018702 and expression of the BCL2L11 and BUB1 genes, which are known to be involved in pancreatic biology. Our findings, while not conclusive, indicate the importance of considering non-additive genetic models when performing GWAS analysis. The SNPs associated with pancreatic cancer in this study could be used for further meta-analysis for recessive association of SNPs and pancreatic cancer risk and might be a useful addiction to improve the performance of polygenic risk scores.
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Affiliation(s)
- Ye Lu
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany
| | | | - Angelica Macauda
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Domenica Gioffreda
- Division of Gastroenterology and Research Laboratory, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Maria Gazouli
- Department of Basic Medical Sciences, Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Maria C. Petrone
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Dezső Kelemen
- Department of Surgery, Medical School, University of Pécs, Pécs, Hungary
| | - Laura Ginocchi
- Oncological Department, Oncological Unit of Massa Carrara, Azienda Unità Sanitaria Locale (USL) Toscana Nord Ovest, Carrara, Italy
| | - Luca Morelli
- General Surgery, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Konstantinos Papiris
- Endoscopic Surgery Department, Hippocratio General Hospital of Athens, Athens, Greece
| | - William Greenhalf
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Jakob R. Izbicki
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Vytautas Kiudelis
- Department of Gastroenterology, Institute for Digestive Research, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Beatrice Mohelníková-Duchoňová
- Department of Oncology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Czechia
| | - Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czechia
- First Faculty of Medicine, Institute of Biology and Medical Genetics, Prague, Czechia
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Markus K. Diener
- Department of General Surgery, University of Heidelberg, Heidelberg, Germany
| | | | - Audrius Ivanauskas
- Department of Gastroenterology, Institute for Digestive Research, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Roberto Salvia
- Department of General and Pancreatic Surgery, The Pancreas Institute, University and Hospital Trust of Verona, Verona, Italy
| | - Andrea Szentesi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - Mateus Nóbrega Aoki
- Laboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Curitiba, Brazil
| | - Balázs C. Németh
- First Department of Medicine, University of Szeged, Szeged, Hungary
| | - Cosimo Sperti
- Department of Surgery-Dipartimento di Scienze Chirurgiche Oncologiche e Gastroenterologiche (DiSCOG), Padua University Hospital, Padua, Italy
| | - Krzysztof Jamroziak
- Department of Hematology, Transplantation and Internal Medicine, Medical University of Warsaw, Warsaw, Poland
| | - Roger Chammas
- Department of Radiology and Oncology, Institute of Cancer of São Paulo (ICESP), São Paulo, Brazil
- Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Martin Oliverius
- Department of Surgery, Faculty Hospital Kralovske Vinohrady and Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Livia Archibugi
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Digestive and Liver Disease Unit, Sant’ Andrea Hospital, Rome, Italy
- Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Stefano Ermini
- Blood Transfusion Service, Children’s Hospital, Azienda Ospedaliero-Universitaria Meyer, Florence, Italy
| | - János Novák
- Pándy Kálmán Hospital of Békés County, Gyula, Hungary
| | - Juozas Kupcinskas
- Department of Gastroenterology, Institute for Digestive Research, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Ondřej Strouhal
- Department of Oncology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Czechia
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czechia
| | - Pavel Souček
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czechia
| | - Giulia M. Cavestro
- Division of Experimental Oncology, Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Anna C. Milanetto
- Department of Surgery-Dipartimento di Scienze Chirurgiche Oncologiche e Gastroenterologiche (DiSCOG), Padua University Hospital, Padua, Italy
| | - Giuseppe Vanella
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Digestive and Liver Disease Unit, Sant’ Andrea Hospital, Rome, Italy
- Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - John P. Neoptolemos
- Department of General Surgery, University of Heidelberg, Heidelberg, Germany
| | - George E. Theodoropoulos
- First Propaedeutic University Surgery Clinic, Hippocratio General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Hanneke W. M. van Laarhoven
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Andrea Mambrini
- Oncological Department, Oncological Unit of Massa Carrara, Azienda Unità Sanitaria Locale (USL) Toscana Nord Ovest, Carrara, Italy
| | - Stefania Moz
- Department of Medicine (DIMED), Padua University Hospital, Padua, Italy
| | - Zdenek Kala
- Department of Surgery, University Hospital Brno Bohunice, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Martin Loveček
- Department of Surgery I, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Czechia
| | - Daniela Basso
- Department of Medicine (DIMED), Padua University Hospital, Padua, Italy
| | - Faik G. Uzunoglu
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thilo Hackert
- Department of General Surgery, University of Heidelberg, Heidelberg, Germany
| | - Sabrina G. G. Testoni
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Viktor Hlaváč
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czechia
| | - Angelo Andriulli
- Division of Gastroenterology and Research Laboratory, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Maurizio Lucchesi
- Oncological Department, Oncological Unit of Massa Carrara, Azienda Unità Sanitaria Locale (USL) Toscana Nord Ovest, Carrara, Italy
| | - Francesca Tavano
- Division of Gastroenterology and Research Laboratory, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Silvia Carrara
- Division of Gastroenterology and Digestive Endoscopy, Department of Gastroenterology, Humanitas Clinical and Research Center Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Péter Hegyi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - Paolo G. Arcidiacono
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Olivier R. Busch
- Department of Surgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Rita T. Lawlor
- Applied Research on Cancer (ARC)-Net Research Center, University and Hospital Trust of Verona, Verona, Italy
| | - Marta Puzzono
- Division of Experimental Oncology, Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Ugo Boggi
- Division of General and Transplant Surgery, Pisa University Hospital, Pisa, Italy
| | - Feng Guo
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ewa Małecka-Panas
- Department of Digestive Tract Diseases, Medical University of Lodz, Lodz, Poland
| | - Gabriele Capurso
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Digestive and Liver Disease Unit, Sant’ Andrea Hospital, Rome, Italy
- Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Stefano Landi
- Department of Biology, University of Pisa, Pisa, Italy
| | | | - Oliver Strobel
- Department of General Surgery, University of Heidelberg, Heidelberg, Germany
| | - Xin Gao
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yogesh Vashist
- Centre for Surgical Oncology, Medias Klinikum Burghausen, Burghausen, Germany
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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12
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Biller LH, Wolpin BM, Goggins M. Inherited Pancreatic Cancer Syndromes and High-Risk Screening. Surg Oncol Clin N Am 2021; 30:773-786. [PMID: 34511196 DOI: 10.1016/j.soc.2021.06.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Pancreatic cancer is the third leading cause of cancer death in the United States, with a 5-year survival rate of 9%. Individuals with inherited pancreatic cancer syndromes are at an increased risk for developing pancreatic cancer and may benefit from pancreatic cancer surveillance with the goal to detect and intervene on early-stage cancer or high-risk precursor lesions. Given the screening implications for family members and therapeutic implications for probands, all patients diagnosed with pancreatic cancer are recommended to undergo germline genetic testing.
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Affiliation(s)
- Leah H Biller
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Avenue, Boston, MA, USA. https://twitter.com/leahbillermd
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Avenue, Boston, MA, USA.
| | - Michael Goggins
- Johns Hopkins University, 1550 Orleans Street, Baltimore, MD, USA.
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13
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Chen Q, Cherry DR, Nalawade V, Qiao EM, Kumar A, Lowy AM, Simpson DR, Murphy JD. Clinical Data Prediction Model to Identify Patients With Early-Stage Pancreatic Cancer. JCO Clin Cancer Inform 2021; 5:279-287. [PMID: 33739856 DOI: 10.1200/cci.20.00137] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Pancreatic cancer is an aggressive malignancy with patients often experiencing nonspecific symptoms before diagnosis. This study evaluates a machine learning approach to help identify patients with early-stage pancreatic cancer from clinical data within electronic health records (EHRs). MATERIALS AND METHODS From the Optum deidentified EHR data set, we identified early-stage (n = 3,322) and late-stage (n = 25,908) pancreatic cancer cases over 40 years of age diagnosed between 2009 and 2017. Patients with early-stage pancreatic cancer were matched to noncancer controls (1:16 match). We constructed a prediction model using eXtreme Gradient Boosting (XGBoost) to identify early-stage patients on the basis of 18,220 features within the EHR including diagnoses, procedures, information within clinical notes, and medications. Model accuracy was assessed with sensitivity, specificity, positive predictive value, and the area under the curve. RESULTS The final predictive model included 582 predictive features from the EHR, including 248 (42.5%) physician note elements, 146 (25.0%) procedure codes, 91 (15.6%) diagnosis codes, 89 (15.3%) medications, and 9 (1.5%) demographic features. The final model area under the curve was 0.84. Choosing a model cut point with a sensitivity of 60% and specificity of 90% would enable early detection of 58% late-stage patients with a median of 24 months before their actual diagnosis. CONCLUSION Prediction models using EHR data show promise in the early detection of pancreatic cancer. Although widespread use of this approach on an unselected population would produce high rates of false-positive tests, this technique may be rapidly impactful if deployed among high-risk patients or paired with other imaging or biomarker screening tools.
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Affiliation(s)
- Qinyu Chen
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA
| | - Daniel R Cherry
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA.,School of Medicine, University of California San Diego, La Jolla, CA
| | - Vinit Nalawade
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA
| | - Edmund M Qiao
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA.,School of Medicine, University of California San Diego, La Jolla, CA
| | - Abhishek Kumar
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA.,School of Medicine, University of California San Diego, La Jolla, CA
| | - Andrew M Lowy
- Department of Surgery, University of California San Diego, La Jolla, CA
| | - Daniel R Simpson
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA
| | - James D Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA.,School of Medicine, University of California San Diego, La Jolla, CA
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14
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Fahrmann JF, Schmidt CM, Mao X, Irajizad E, Loftus M, Zhang J, Patel N, Vykoukal J, Dennison JB, Long JP, Do KA, Zhang J, Chabot JA, Kluger MD, Kastrinos F, Brais L, Babic A, Jajoo K, Lee LS, Clancy TE, Ng K, Bullock A, Genkinger J, Yip-Schneider MT, Maitra A, Wolpin BM, Hanash S. Lead-Time Trajectory of CA19-9 as an Anchor Marker for Pancreatic Cancer Early Detection. Gastroenterology 2021; 160:1373-1383.e6. [PMID: 33333055 PMCID: PMC8783758 DOI: 10.1053/j.gastro.2020.11.052] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/20/2020] [Accepted: 11/29/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND & AIMS There is substantial interest in liquid biopsy approaches for cancer early detection among subjects at risk, using multi-marker panels. CA19-9 is an established circulating biomarker for pancreatic cancer; however, its relevance for pancreatic cancer early detection or for monitoring subjects at risk has not been established. METHODS CA19-9 levels were assessed in blinded sera from 175 subjects collected up to 5 years before diagnosis of pancreatic cancer and from 875 matched controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. For comparison of performance, CA19-9 was assayed in blinded independent sets of samples collected at diagnosis from 129 subjects with resectable pancreatic cancer and 275 controls (100 healthy subjects; 50 with chronic pancreatitis; and 125 with noncancerous pancreatic cysts). The complementary value of 2 additional protein markers, TIMP1 and LRG1, was determined. RESULTS In the PLCO cohort, levels of CA19-9 increased exponentially starting at 2 years before diagnosis with sensitivities reaching 60% at 99% specificity within 0 to 6 months before diagnosis for all cases and 50% at 99% specificity for cases diagnosed with early-stage disease. Performance was comparable for distinguishing newly diagnosed cases with resectable pancreatic cancer from healthy controls (64% sensitivity at 99% specificity). Comparison of resectable pancreatic cancer cases to subjects with chronic pancreatitis yielded 46% sensitivity at 99% specificity and for subjects with noncancerous cysts, 30% sensitivity at 99% specificity. For prediagnostic cases below cutoff value for CA19-9, the combination with LRG1 and TIMP1 yielded an increment of 13.2% in sensitivity at 99% specificity (P = .031) in identifying cases diagnosed within 1 year of blood collection. CONCLUSION CA19-9 can serve as an anchor marker for pancreatic cancer early detection applications.
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Affiliation(s)
- Johannes F. Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - C. Max Schmidt
- Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana
| | - Xiangying Mao
- Department of Clinical Cancer Prevention, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Ehsan Irajizad
- Department of Clinical Cancer Prevention, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Maureen Loftus
- Dana-Farber Brigham and Women’s Cancer Center, Division of Gastrointestinal Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Jinming Zhang
- Dana-Farber Brigham and Women’s Cancer Center, Division of Gastrointestinal Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Nikul Patel
- Department of Clinical Cancer Prevention, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Jody Vykoukal
- Department of Clinical Cancer Prevention, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Jennifer B. Dennison
- Department of Clinical Cancer Prevention, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - James P. Long
- Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Kim-Anh Do
- Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Jianjun Zhang
- Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana
| | - John A. Chabot
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, Department of Epidemiology, Columbia Mailman School of Public Health, New York, New York
| | - Michael D. Kluger
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, Department of Epidemiology, Columbia Mailman School of Public Health, New York, New York
| | - Fay Kastrinos
- Division of Digestive and Liver Diseases, Columbia University Irving Medical Cancer and the Vagelos College of Physicians and Surgeons, New York, New York, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, Department of Surgery, New York, New York
| | - Lauren Brais
- Dana-Farber Brigham and Women’s Cancer Center, Division of Gastrointestinal Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Ana Babic
- Dana-Farber Brigham and Women’s Cancer Center, Division of Gastrointestinal Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Kunal Jajoo
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Linda S. Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Thomas E. Clancy
- Dana-Farber Brigham and Women’s Cancer Center, Division of Surgical Oncology, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kimmie Ng
- Dana-Farber Brigham and Women’s Cancer Center, Division of Gastrointestinal Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Andrea Bullock
- Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Jeanine Genkinger
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, Department of Epidemiology, Columbia Mailman School of Public Health, New York, New York
| | | | - Anirban Maitra
- Division of Digestive and Liver Diseases, Columbia University Irving Medical Cancer and the Vagelos College of Physicians and Surgeons, New York, New York, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Brian M. Wolpin
- Dana-Farber Brigham and Women’s Cancer Center, Division of Gastrointestinal Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Samir Hanash
- Department of Clinical Cancer Prevention, The University of Texas M. D. Anderson Cancer Center, Houston, Texas.
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Tang H, Jiang L, Stolzenberg-Solomon RZ, Arslan AA, Beane Freeman LE, Bracci PM, Brennan P, Canzian F, Du M, Gallinger S, Giles GG, Goodman PJ, Kooperberg C, Le Marchand L, Neale RE, Shu XO, Visvanathan K, White E, Zheng W, Albanes D, Andreotti G, Babic A, Bamlet WR, Berndt SI, Blackford A, Bueno-de-Mesquita B, Buring JE, Campa D, Chanock SJ, Childs E, Duell EJ, Fuchs C, Gaziano JM, Goggins M, Hartge P, Hassam MH, Holly EA, Hoover RN, Hung RJ, Kurtz RC, Lee IM, Malats N, Milne RL, Ng K, Oberg AL, Orlow I, Peters U, Porta M, Rabe KG, Rothman N, Scelo G, Sesso HD, Silverman DT, Thompson IM, Tjønneland A, Trichopoulou A, Wactawski-Wende J, Wentzensen N, Wilkens LR, Yu H, Zeleniuch-Jacquotte A, Amundadottir LT, Jacobs EJ, Petersen GM, Wolpin BM, Risch HA, Chatterjee N, Klein AP, Li D, Kraft P, Wei P. Genome-Wide Gene-Diabetes and Gene-Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia. Cancer Epidemiol Biomarkers Prev 2020; 29:1784-1791. [PMID: 32546605 PMCID: PMC7483330 DOI: 10.1158/1055-9965.epi-20-0275] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/06/2020] [Accepted: 06/09/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. METHODS We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. RESULTS No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7). CONCLUSIONS This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. IMPACT This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.
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Affiliation(s)
- Hongwei Tang
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lai Jiang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | | | - Alan A Arslan
- Department of Obstetrics and Gynecology, New York University School of Medicine, New York, New York
- Department of Population Health, New York University School of Medicine, New York, New York
- Department of Environmental Medicine, New York University School of Medicine, New York, New York
| | | | - Paige M Bracci
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mengmeng Du
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Steven Gallinger
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, Ontario, Canada
| | - Graham G Giles
- Division of Cancer Epidemiology, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Phyllis J Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Loïc Le Marchand
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Rachel E Neale
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Emily White
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | | | - Ana Babic
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - William R Bamlet
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Amanda Blackford
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
- Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, the Netherlands
- Department of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom
- Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Julie E Buring
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Erica Childs
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Eric J Duell
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Charles Fuchs
- Yale Cancer Center, New Haven, Connecticut
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Smilow Cancer Hospital, New Haven, Connecticut
| | - J Michael Gaziano
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Boston Veteran Affairs Healthcare System, Boston, Massachusetts
| | - Michael Goggins
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Patricia Hartge
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Manal H Hassam
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Elizabeth A Holly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, Ontario, Canada
| | - Robert C Kurtz
- Gastroenterology, Hepatology, and Nutrition Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - I-Min Lee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre, Madrid, Spain
| | - Roger L Milne
- Division of Cancer Epidemiology, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ann L Oberg
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Irene Orlow
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ulrike Peters
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Miquel Porta
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Hospital del Mar Institute of Medical Research (IMIM), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Kari G Rabe
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | | | - Howard D Sesso
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Debra T Silverman
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Ian M Thompson
- CHRISTUS Santa Rosa Hospital - Medical Center, San Antonio, Texas
| | - Anne Tjønneland
- Department of Public Health, University of Copenhagen and Danish Cancer Society Research Center Diet, Genes and Environment, Copenhagen, Denmark
| | - Antonia Trichopoulou
- Hellenic Health Foundation, World Health Organization Collaborating Center of Nutrition, Medical School, University of Athens, Athens, Greece
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University of Buffalo, Buffalo, New York
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Lynne R Wilkens
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Herbert Yu
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Anne Zeleniuch-Jacquotte
- Department of Population Health, New York University School of Medicine, New York, New York
- Department of Environmental Medicine, New York University School of Medicine, New York, New York
| | | | - Eric J Jacobs
- Department of Public Health, University of Copenhagen and Danish Cancer Society Research Center Diet, Genes and Environment, Copenhagen, Denmark
| | - Gloria M Petersen
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison P Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Gentiluomo M, Canzian F, Nicolini A, Gemignani F, Landi S, Campa D. Germline genetic variability in pancreatic cancer risk and prognosis. Semin Cancer Biol 2020; 79:105-131. [DOI: 10.1016/j.semcancer.2020.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 08/04/2020] [Accepted: 08/06/2020] [Indexed: 02/07/2023]
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