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Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data:a multi-phased study of prostate cancer

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cancer Communications 2021年第12期41卷 1387-1397页

作者： Chong Wu Jingjing Zhu Austin King Xiaoran Tong Qing Lu Jong Y.Park Liang Wang Guimin Gao Hong-Wen Deng Yaohua Yang Karen E.Knudsen Timothy R.Rebbeck Jirong Long Wei Zheng Wei Pan David V.Conti Christopher A Haiman Lang Wu Department of Statistics Florida State UniversityTallahasseeFL 32304USA Cancer Epidemiology Division Population Sciences in the Pacific ProgramUniversity of Hawaii Cancer CenterUniversity of Hawaii at ManoaHonoluluHI 96813USA Department of Epidemiology and Biostatistics Michigan State UniversityEast LansingMI 48824USA Department of Biostatistics University of FloridaGainesvilleFL 32603USA Department of Cancer Epidemiology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Tumor Biology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Public Health Sciences University of ChicagoChicagoIL 60637USA Center of Bioinformatics and Genomics Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLA 70112USA Division of Epidemiology Department of MedicineVanderbilt Epidemiology CenterVanderbilt-Ingram Cancer CenterVanderbilt University Medical CenterNashvilleTN 37203USA Department of Cancer Biology Sidney Kimmel Cancer CenterThomas Jefferson UniversityPhiladelphiaPA 19107USA Department of Medical Oncology Dana-Farber Cancer InstituteBostonMA 02215USA Department of Epidemiology Harvard TH Chan School of Public HealthBostonMA 02115USA Division of Biostatistics University of MinnesotaMinneapolisMN 55455USA Department of Preventive Medicine Keck School of MedicineUniversity of Southern California/Norris Comprehensive Cancer CenterLos AngelesCA 90033USA

Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative ***:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding *** the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best *** an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual ***,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and ***:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family *** overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family ***:We developed and validated a new PRS which may improve the utility in predicting the risk of developing *** innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

关键词： risk prediction polygenic risk scores predicted gene expression predicted DNA methylation integrative models prostate cancer

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Development and Evaluation of a Process to Link cancer Patients in the SEER Registries to National Medicaid Enrollment data

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Journal of the National cancer Institute - Monographs 2020年第55期2020卷 89-95页

作者： Warren, Joan L. Benner, Suzie Stevens, Jennifer Enewold, Lindsey Huang, Bin Zhao, Lirong Tilahun, Negussie Bradley, Cathy J. Healthcare Assessment Research Branch Healthcare Delivery Research Program Division of Cancer Control and Population Science National Cancer Institute Bethesda MD United States Fu Associates Ltd Arlington VA United States Information Management Services Calverton MD United States Department of Biostatistics College of Public Health Markey Cancer Center University of Kentucky Lexington KY United States Division of Data Research and Analytic Methods Center for Medicare and Medicaid Innovation Centers for Medicare and Medicaid Services Baltimore MD United States Department of Health Systems Management and Policy School of Public Health University of Colorado Aurora CO United States

cancer patients receiving Medicaid have worse prognosis. Patients in 14 Surveillance, Epidemiology, and End Results (SEER) cancer registries were linked to national Medicaid enrollment files, 2006-2013, to determine enrollment status during the year before and after diagnosis. A deterministic algorithm based on Social Security number, Medicare Health Insurance Claim number, sex, and date of birth was utilized. Results were compared with an independent linkage of Kentucky-based SEER and Medicaid data. A total 559 484 cancer cases were linked to national Medicaid enrollment files, representing 15-17% of persons with cancer yearly. About 60% of these cases were a complete match on all variables. There was 99% agreement on enrollment status compared with the Kentucky linked data. SEER data were successfully linked to national Medicaid enrollment data. NCI will make the linked data available to researchers, allowing for more detailed assessments of the impact Medicaid enrollment has on cancer diagnosis and outcomes. © 2020 Published by Oxford University Press. This work is written by US Government employees and is in the public domain in the US.

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Deep learning-based computational pathology predicts origins for cancers of unknown primary

arXiv

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arXiv 2020年

作者： Lu, Ming Y. Zhao, Melissa Shady, Maha Lipkova, Jana Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States

cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined1,2. This poses a significant challenge, since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor3. Patients with a CUP diagnosis routinely undergo an extensive diagnostic work-up of pathology, radiology, endoscopy, laboratory tests, and clinical correlation in an attempt to determine the primary origin. Such exploration is not only time and resource consuming, but it might significantly delay administration of the suitable treatment. Despite extensive diagnostic work-ups the primary may never be determined in many cases. Recent work has focused on using genomics and transcriptomics for identification of tumor origins4–6. However, genomic testing is not conducted for every patient and lacks clinical penetration in low resource settings. Herein, to overcome these challenges, we present a deep learning-based computational pathology algorithm-TOAD-that can provide a differential diagnosis for CUP using routinely acquired histology slides. We used 17,486 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to simultaneously identify the tumor as primary or metastatic and predict its site of origin. We tested our model on an internal test set of 4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 while on our external test set of 662 cases from 202 different hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a dataset of 717 CUP cases from 151 different medical centers and identified a subset of 290 cases for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 50% of cases (κ=0.4 when adjusted for agreement by chance) and a top-3 agreement of 75%. Our proposed method can be used

关键词： Diseases

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CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

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Nature Immunology 2025年 1-12页

作者： Bokai Zhu Alex K. Shalek Yunhao Bai Amy Y. Huang Sizun Jiang Sheng Gao Shuxiao Chen Yuchen Wang Jason Yeung Yao Yu Yeo Guanrui Liao Shulin Mao Ka-Chun Wong Zhenghui G. Jiang Scott J. Rodig Garry P. Nolan Zongming Ma Ragon Institute of MGH MIT and Harvard Cambridge MA USA Broad Institute of MIT and Harvard Cambridge MA USA Massachusetts Institute of Technology Cambridge MA USA Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA USA Dana-Farber Cancer Institute Boston MA USA Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Pathology Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Statistics and Data Science The Wharton School University of Pennsylvania Philadelphia PA USA Department of Computer Science City University of Hong Kong Hong Kong People’s Republic of China Center of Hepato-Pancreato-Biliary Surgery The First Affiliated Hospital of Sun Yat-sen University Guangzhou People’s Republic of China Division of Genetics and Genomics Boston Children’s Hospital Harvard Medical School Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Division of Gastroenterology/Liver Center Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Pathology Stanford University Stanford CA USA Department of Statistics and Data Science Yale University New Haven CT USA

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells’ molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions.

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Infection after CD19 chimeric antigen receptor T cell therapy for large B cell lymphoma: Real-world analysis from CIBMTR

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Blood Advances 2025年

作者： Kitsada Wudhikarn Megan M. Herr Min Chen Michael J. Martens John H. Baird Lohith Gowda Hemalatha G. Rangarajan Muhammad Bilal Abid Mohamed A. Kharfan-Dabaja Kirsten M. Williams Siddhartha Ganguly Jo-Anne H. Young Akshay Sharma Giancarlo Fatobene Tania Jain Christopher G. Kanakry Dipenkumar Modi Natalie S. Grover Baheyeldin Salem Marjorie Vieira Batista Jeffery J. Auletta Center of Excellence in Translational Hematology and Division of Hematology Faculty of Medicine Chulalongkorn University Bangkok Thailand Roswell Park Comprehensive Cancer Center Buffalo NY CIBMTR® (Center for International Blood and Marrow Transplant Research) Medical College of Wisconsin Milwaukee WI Division of Biostatistics Data Science Institute Medical College of Wisconsin Milwaukee WI Division of Lymphoma Department of Hematology & Hematopoietic Cell Transplantation City of Hope Duarte CA Yale Cancer Center and Yale School of Medicine New Haven CT Department of Pediatric Hematology Oncology Blood and Marrow Transplantation Nationwide Children’s hospital Columbus OH Divisions of Hematology/Oncology & Infectious Diseases BMT & Cellular Therapy Program Medical College of Wisconsin Milwaukee WI Division of Hematology-Oncology Blood and Marrow Transplantation Program Mayo Clinic Jacksonville FL Aflac Cancer & Blood Disorders Center at Children’s Healthcare of Atlanta Emory University Atlanta GA Houston Methodist Hospital and Neal Cancer Center Houston TX Division of Infectious Disease and International Medicine Department of Medicine University of Minnesota Minneapolis MN Department of Bone Marrow Transplantation and Cellular Therapy St. Jude Children's Research Hospital Memphis TN Hospital das Clínicas and LIM31 Faculty of Medicine University of São Paulo Brazil / Hospital Vila Nova Star - Rede D'Or Sao Paulo Brazil Division of Hematological Malignancies and Bone Marrow Transplantation Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD Center for Immuno-Oncology Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD Division of Oncology Karmanos Cancer Center Wayne State University Detroit MI Lineberger Comprehensive Cancer Center Division of Hematology Department of Medicine University of North Carolina Chapel Hill NC Center for Cell and Gene Therapy Texas Children's Hospital

Infection is increasingly recognized as a significant cause of morbidity and mortality in patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL) receiving CD19 chimeric antigen receptor (CAR) T-cell therapy. The current study analyzed the natural history, risk factors, and outcomes of infection in 3350 R/R LBCL patients receiving commercial CD19 CAR T-cell (n=2804 axicabtagene ciloleucel, n=546 tisagenlecleucel) from December 2017 to June 2022. Infection developed in 834 (24.9%) patients within 100 days post-infusion, resulting in an infection density of 0.43 per 100 patient-days and a 100-day cumulative incidence of 22%. Bacterial, viral, and fungal infections were recorded in 527 (15.7%), 374 (11.2%), and 108 (3.2%) patients, respectively, with corresponding infection densities of 0.23, 0.15, and 0.04 per 100 patient-days. After a 24-month median follow-up, 1482 (44%) patients had died, with infection as the primary cause in 173 cases (12%). The 100-day infection-related mortality (IRM) was 1.6% (95% confidence interval, 1.2-2.0%). Patients with Karnofsky score ≤80, infection history pre-CAR-T, axicabtagene ciloleucel therapy, severe cytokine release syndrome (grade ≥3), and severe immune effector cell-associated neurotoxicity syndrome (grade ≥3) had increased infection risk. Infections within 100 days were an independent risk factor for inferior overall survival beyond day 100 after CD19 CAR T-cell therapy. In conclusion, study results show a significant incidence of infection and IRM in patients with R/R LBCL treated with CD19 CAR T-cell. Furthermore, results identify patients at heightened risk for infection, offering insights to guide potential interventions aimed at mitigating infection and improving patient outcomes after CAR T-cell therapy.

关键词： Infection CD19 Chimeric Antigen Receptor T cell Lymphoma LBCL Relapse

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Molecular Mechanisms Promoting Long-Term Cytopenia after BCMA CAR-T Therapy in Multiple Myeloma

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BLOOD 2024年 144卷 4798-4799页

作者： [Anonymous] Hematology and Cell Therapy Department. Clinica Universidad de Navarra. IdiSNA. Pamplona Spain Hemato-Oncology Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Computational Biology Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Immunology and Immunotherapy Department. Clinica Universidad de Navarra. Pamplona Spain Centro de Investigación Biomedica en Red de Cancer (CIBERONC). Madrid Spain Cancer Center Clinica Universidad de Navarra (CCUN). Pamplona Spain Cancer Center Clinica Universidad de Navarra (CCUN) Pamplona Spain Immunology and Immunotherapy Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Center for Data Science and Artificial Intelligence Institute (DATAI) Universidad de Navarra Pamplona Pamplona Spain Hematology and Cell Therapy Department Clinica Universidad de Navarra IdiSNA Pamplona Spain Centro de Investigación Biomedica en Red de Cancer (CIBERONC) Madrid Spain

Hematologic toxicity, specifically prolonged cytopenia, is a common side effect associated with CAR-T therapies, being particularly severe in patients with relapsed/refractory Multiple Myeloma (MM) (1). However, to date, its etiology is poorly understood. Thus, the main objective of this study was to identify and understand the underlying mechanisms of prolonged cytopenias associated with CAR-T therapy targeting BCMA. In our work, we combined the retrospective analysis of a cohort of 48 patients treated with BCMA CAR-T cells at Clinica Universidad de Navarra, with ex vivo transcriptomic analysis using single-cell RNA sequencing, to characterize the kinetics of cytopenia, identify predictive factors and determine potential mechanism underlying these toxicities. The overall incidence of cytopenia was 95.7%, and grade>3 thrombocytopenia and neutropenia one month after infusion, was observed in 57% and 53% of the patients, being still present after one year in 4 and 3 patients respectively. Presence of cytopenia at baseline and high peak inflammatory markers highly correlated with cytopenia persisting up to three months. To determine potential mechanisms underpinning cytopenias, we evaluated the paracrine effect of BCMA CAR-T cells on HSPCs differentiation, using an ex vivo myeloid differentiation model (2). Thus, supernatants from CAR-T cells or control T lymphocytes, co-cultured with MM tumor cell line U266, were added to the differentiation process. Phenotypic analysis by flow cytometry showed that supernatants from activated CAR-T cells (spCAR) halted HSPCs differentiation, promoting more immature phenotypes, with reduced expression of granulocytic, monocytic, and erythroid maturation markers, suggesting a paracrine effect related to CAR-T cells activation. Interestingly, this immature phenotype could be prevented by a combination of IFNγ, TNFα/β, TGFβ, IL-6, and IL-17 inhibitors, indicating the essential role of these cytokines and providing potential targets to pr

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CAR-T and CAR-NK as cellular cancer immunotherapy for solid tumors

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Cellular & Molecular Immunology 2024年第10期21卷 1089-1108页

作者： Lei Peng Giacomo Sferruzza Luojia Yang Liqun Zhou Sidi Chen Department of Genetics Yale University School of MedicineNew HavenCTUSA System Biology Institute Yale UniversityWest HavenCTUSA Combined Program in the Biological and Biomedical Sciences Yale UniversityNew HavenCTUSA Molecular Cell Biology Geneticsand Development ProgramYale UniversityNew HavenCTUSA Immunobiology Program Yale UniversityNew HavenCTUSA Yale Comprehensive Cancer Center Yale University School of MedicineNew HavenCTUSA Department of Neurosurgery Yale University School of MedicineNew HavenCTUSA Yale Stem Cell Center Yale University School of MedicineNew HavenCTUSA Yale Liver Center Yale University School of MedicineNew HavenCTUSA Yale Center for Biomedical Data Science Yale University School of MedicineNew HavenCTUSA Yale Center for RNA Science and Medicine Yale University School of MedicineNew HavenCTUSA

In the past decade,chimeric antigen receptor(CAR)-T cell therapy has emerged as a promising immunotherapeutic approach for combating cancers,demonstrating remarkable efficacy in relapsed/refractory hematological malignancies in both pediatric and adult ***-natural killer(CAR-NK)cell complements CAR-T cell therapy by offering several distinct ***-NK cells do not require HLA compatibility and exhibit low safety ***,CAR-NK cells are conducive to“off-the-shelf”therapeutics,providing significant logistic advantages over CAR-T *** CAR-T and CAR-NK cells have shown consistent and promising results in hematological ***,their efficacy against solid tumors remains limited due to various obstacles including limited tumor trafficking and infiltration,as well as an immuno-suppressive tumor *** this review,we discuss the recent advances and current challenges of CAR-T and CAR-NK cell immunotherapies,with a specific focus on the obstacles to their application in solid *** also analyze in depth the advantages and drawbacks of CAR-NK cells compared to CAR-T cells and highlight CAR-NK CAR ***,we explore future perspectives of these adoptive immunotherapies,highlighting the increasing contribution of cutting-edge biotechnological tools in shaping the next generation of cellular immunotherapy.

关键词： CAR-T CAR-NK Cell therapy cancer immunotherapy Solid tumor

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Pan-cancer integrative histology-genomic analysis via interpretable multimodal deep learning

arXiv

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arXiv 2021年

作者： Chen, Richard J. Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Lipkova, Jana Shaban, Muhammad Shady, Maha Williams, Mane Joo, Bumjin Noor, Zahra Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Computer Science Harvard University CambridgeMA United States

The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are either based on histology or genomics alone and do not address how histology and genomics can be integrated to develop joint image-omic prognostic models. Additionally identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We used multimodal deep learning to integrate gigapixel whole slide pathology images, RNA-seq abundance, copy number variation, and mutation data from 5,720 patients across 14 major cancer types. Our interpretable, weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities for predicting outcomes and discover prognostic features from these modalities that corroborate with poor and favorable outcomes via multimodal interpretability. We compared our model with unimodal deep learning models trained on histology slides and molecular profiles alone, and demonstrate performance increase in risk stratification on 9 out of 14 cancers. In addition, we analyze morphologic and molecular markers responsible for prognostic predictions across all cancer types. All analyzed data, including morphological and molecular correlates of patient prognosis across the 14 cancer types at a disease and patient level are presented in an interactive open-access database (http://***) to allow for further exploration and prognostic biomarker discovery. To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied. Copyright © 2021, The Authors. All rights reserved.

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Disparities in stage at diagnosis for liver cancer in China

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Journal of the National cancer Center 2023年第1期3卷 7-13页

作者： Tianhao Shan Xianhui Ran Huizhang Li Guoshuang Feng Siwei Zhang Xuehong Zhang Lei Zhang Lingeng Lu Lan An Ruiying Fu Kexin Sun Shaoming Wang Ru Chen Li Li Wanqing Chen Wenqiang Wei Hongmei Zeng Jie He National Central Cancer Registry National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Cancer Hospital of University of Chinese Academy of Sciences/Zhejiang Cancer Hospital Department of Cancer PreventionInstitute of Cancer Research and Basic Medical SciencesChinese Academy of SciencesHangzhouChina Big Data Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Channing Division of Network Medicine Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA Department of Nutrition T.H.Chan School of Public HealthHarvard UniversityBostonUSA China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’anChina Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health CentreAlfred HealthMelbourneAustralia Central Clinical School Faculty of MedicineMonash UniversityMelbourneAustralia Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhouChina Department of Chronic Disease Epidemiology Yale School of Public HealthYale Cancer CenterNew HavenUSA Department of Thoracic Surgery National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

Background:The stage at diagnosis is a major factor in making treatment strategies and cancer control ***,the stage distribution for liver cancer in China was not well *** this multi-center hospital-based study,we aimed to identify the distribution and factors associated with stage at diagnosis for liver cancer in ***:We included patients diagnosed with primary liver cancer in 13 hospitals of 10 provinces covering various geographic and socioeconomic populations during 2016-2017 in *** stage distribution overall,and by sex and age at diagnosis were *** used logistic regression to identify the factors associated with stage Ⅲ-Ⅳ *** further compared these estimates with data from the ***:We included 2,991 patients with known stage at diagnosis in *** proportion of patients diagnosed with stageⅠ,Ⅱ,Ⅲ,and Ⅳ was 17.5%,25.6%,29.3%,and 27.6%,*** proportion of stage Ⅲ-Ⅳ cases was higher in women[65.1%vs 54.9%,adjusted odds ratio(OR)=1.5,95%CI:1.2,1.8]and those≥60 years(61.6%vs 52.8%,OR=1.4,95%CI:1.2,1.6).We found an increased risk of stage Ⅲ-Ⅳ among drinkers and those without a family history of *** to the USA,our study population had a substantially higher proportion of stage Ⅲ-Ⅳ cases(56.9%vs 45.6%).Conclusion:The disparities in liver cancer stage at diagnosis among different populations within China,and between China and the USA,imply the necessity for improving cancer awareness and early detection for liver cancer in China.

关键词： Liver cancer Stage at diagnosis Disparity China USA

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THE OVERSELLING OF GENETIC ANXIETY

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HASTINGS CENTER REPORT 1982年第5期12卷 20-27页

作者： GOODMAN, MJ GOODMAN, LE MadeleineJ. Goodman Ph.D.is Director Women's Studies Program and associate professor General Science and Women's Studies University of Hawaii. She is also principal data analyst of the Hawaii Breast Cancer Detection Project. LennE. Goodman D. Phil.is professor of philosophy University of Hawaii. His most recent book isMonotheism: A Philosophic Inquiry into the Foundations of Natural Theology and Ethics.

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