Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-PR07
M. Terry, K. Phillips, Y. Liao, R. MacInnis, G. Dite, M. Daly, E. John, I. Andrulis, S. Buys, R. Buchsbaum, J. Hopper
Background: Clinical guidelines for classifying women as high-risk for breast cancer when considering chemoprevention and/or MRI screening options include thresholds of remaining lifetime risk (RLR) of 20% or more and/or a fixed time interval (e.g., 5-year risk of 1.67 or higher, 10-year risk of 3.34 or higher). Although clinicians have noted differences in risk estimates from the existing risk models, there have been few prospective validations using large cohorts to describe the magnitude of the discordancies between these models. Methods: We prospectively followed 16,285 women without breast cancer at baseline for an average of 10.2 years to compare the RLR and 10-year risk assigned by two commonly used risk estimation models for high risk women: 1) The International Breast Cancer Intervention Study tool (IBIS); and 2) the Breast Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). We compared the model-assigned 10-year risks with subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. Results: We observed differences between risk models in terms of the proportion of women classified as high-risk based on 20% or more RLR (IBIS=56% vs BOADICEA=23%). Only 21% of women were classified as high risk by both models, 35% of women were classified as high risk by IBIS only and 2% of women were classified as high risk by BOADICEA only. The difference was not evident (IBIS=52% vs BOADICEA=51%) when using a 10-year risk threshold of 3.34%. Using this 10-year threshold, 43% of women were classified as high risk by both models, 9% of women were classified as high risk by IBIS only and 8% of women were classified as high risk by BOADICEA only. IBIS risk predictions (mean=4.9%) were better calibrated to observed breast cancer incidence (5.8%, 95% confidence interval (CI)=5.4% to 6.2%) than were those based on BOADICEA (mean=4.2%). When we compared the magnitude of the discordancy between IBIS and BOADICEA by age, race/ethnicity, and number of relatives affected, we observed the extent of discordancy (e.g. one model resulted in a woman being above the clinical threshold when the other did not) depended on age. Specifically, for women under the age of 40 years, only 3.1% of women were high risk with IBIS but not BOADICEA compared with 7.5% classified as high risk by BOADICEA but not IBIS. Both models gave similar predictions of high risk with same proportion discordant for women over 50, and the same proportion discordant by race/ethnicity. When we compared the discordancy by those unaffected and affected with breast cancer after ten years of follow-up, 51% of unaffected women were high risk by IBIS using the 10-year threshold and 50% by BOADICEA with only 8% discordant (high risk on only one model). For women who were diagnosed with breast cancer prospectively after baseline, 75% were clas
背景:在考虑化学预防和/或MRI筛查选择时，将女性划分为乳腺癌高危人群的临床指南包括剩余终生风险阈值(RLR)为20%或以上和/或固定时间间隔(例如，5年风险为1.67或更高，10年风险为3.34或更高)。尽管临床医生已经注意到风险估计与现有风险模型之间的差异，但很少有使用大型队列来描述这些模型之间不一致程度的前瞻性验证。方法:前瞻性随访16,285名基线时无乳腺癌的女性，平均随访10.2年，比较两种常用的高风险女性风险估计模型的RLR和10年风险:1)国际乳腺癌干预研究工具(IBIS);2)乳腺卵巢疾病发病率分析及载体估计算法(BOADICEA)。我们将模型分配的10年风险与队列中随后的乳腺癌发病率进行了比较。我们用卡方统计来评估校准，用受试者工作特征曲线下面积(AUC)来评估鉴别。结果:我们观察到基于20%或以上RLR的高风险女性比例在不同风险模型之间存在差异(IBIS=56% vs BOADICEA=23%)。只有21%的妇女被两种模型归为高风险，35%的妇女仅被IBIS归为高风险，2%的妇女仅被BOADICEA归为高风险。当使用3.34%的10年风险阈值时，差异不明显(IBIS=52% vs BOADICEA=51%)。使用这个10年阈值，43%的妇女被两种模型归为高风险，9%的妇女仅被IBIS归为高风险，8%的妇女仅被BOADICEA归为高风险。IBIS风险预测(平均=4.9%)比基于BOADICEA的预测(平均=4.2%)更好地校准到观察到的乳腺癌发病率(5.8%，95%置信区间(CI)=5.4%至6.2%)。当我们比较IBIS和BOADICEA在年龄、种族/民族和受影响亲属数量上的不一致程度时，我们观察到不一致的程度(例如，一个模型导致女性高于临床阈值，而另一个模型没有)取决于年龄。具体来说，对于40岁以下的女性，只有3.1%的女性患有IBIS而不是BOADICEA，而7.5%的女性被BOADICEA列为高风险，而不是IBIS。两种模型都给出了类似的高风险预测，对50岁以上的女性有相同比例的不一致，对种族/民族也有相同比例的不一致。当我们比较未受影响和受影响的乳腺癌患者在10年随访后的不一致性时，51%的未受影响的女性在IBIS使用10年阈值时为高风险，50%的BOADICEA为高风险，只有8%的不一致性(只有一种模型的高风险)。基线后被诊断为乳腺癌的女性，75%在基线时被IBIS分类为高风险，72%被BOADICEA分类为高风险，仅IBIS分类为8%高风险，仅BOADICEA分类为5%高风险。结论:这些结果表明，在确定MRI和化学预防的高危分类时，两种常用的风险模型之间存在相当大的不一致性。在较短的时间范围内，特别是50岁以上的妇女，这两种模式之间的一致性更大。然而，由于高风险女性的MRI和化学预防通常需要在50岁之前开始，因此非常需要加强对这些年轻高风险女性的风险评估。引文格式:Mary Beth Terry, Kelly-Anne Phillips, Yuyan Liao, Robert J. MacInnis, Gillian S. Dite, Mary B. Daly, Esther M. John, Irene L. Andrulis, Saundra S. Buys, Richard Buchsbaum, John L. Hopper。使用前瞻性家庭研究队列(profo - sc)比较基于临床阈值的高危女性乳腺癌风险模型建议[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;癌症流行病学生物标志物pre2017;26(5增刊):摘要nr PR07。
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-IA05
A. Antoniou
Several breast cancer genetic susceptibility variants have been identified to date. These include mutations in the high risk BRCA1 and BRCA2 genes, other rare genetic variants conferring intermediate to high risks (e.g. PALB2, CHEK2, ATM and others) and >150 common alleles (SNPs) conferring low risks. The presentation will provide an overview of the latest developments and challenges in understanding the penetrance of mutations in BRCA1, BRCA2 and PALB2. Genetic counseling of women with BRCA1 and BRCA2 mutations currently relies on average cancer risk estimates obtained from retrospective penetrance studies. The talk will present penetrance estimates from ongoing prospective analyses, based on data from the International BRCA1/2 Carrier Cohort Study, the largest prospective cohort of BRCA1/2 mutation carriers that includes ~10,000 BRCA1/2 mutation carriers with follow-up information. The talk will also review the latest efforts and results from the Consortium of Investigators of Modifiers of BRCA1/2 to identify and characterise genetic modifiers of cancer risks for BRCA1 and BRCA2 mutation carriers and to provide individualized cancer risks for women with BRCA1 and BRCA2 mutations. Finally, the presentation will describe the efforts to develop a comprehensive risk prediction model for breast cancer, specifically the BOADICEA model that includes the explicit effects of mutations in BRCA1, BRCA2, PALB2, CHEK2, ATM, and of the common breast cancer susceptibility variants identified through genome-wide association studies. Citation Format: Antonis C. Antoniou. Modeling genetic susceptibility to breast cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA05.
到目前为止，已经确定了几种乳腺癌遗传易感性变异。这些突变包括高风险BRCA1和BRCA2基因的突变，其他具有中高风险的罕见遗传变异(例如PALB2、CHEK2、ATM等)和具有低风险的>150个常见等位基因(snp)。报告将概述了解BRCA1、BRCA2和PALB2突变外显率的最新发展和挑战。目前，BRCA1和BRCA2突变女性的遗传咨询依赖于从回顾性外显率研究中获得的平均癌症风险估计。本次演讲将根据国际BRCA1/2携带者队列研究的数据，介绍正在进行的前瞻性分析的外显率估计，该研究是BRCA1/2突变携带者的最大前瞻性队列，包括约10,000名BRCA1/2突变携带者，并提供随访信息。讲座还将回顾BRCA1/2修饰因子研究联盟的最新努力和结果，以确定和表征BRCA1和BRCA2突变携带者的癌症风险基因修饰因子，并为BRCA1和BRCA2突变女性提供个体化的癌症风险。最后，报告将描述开发乳腺癌综合风险预测模型的努力，特别是BOADICEA模型，该模型包括BRCA1、BRCA2、PALB2、CHEK2、ATM突变的明确影响，以及通过全基因组关联研究确定的常见乳腺癌易感性变异。引文格式:Antonis C. Antoniou。乳腺癌的遗传易感性模型。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr IA05。
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-B14
I. Hakim, S. Aldaham, J. Foote, H. Chow
Background/Purpose: Epidemiologic data implies that there are gender differences in lung cancer pathogenesis and possibly increased susceptibility to lung cancer in women. Chronic inflammation has been implicated as important modulator of human health by playing a significant role in both disease prevention and disease development. Several studies have demonstrated increased interleukin 6 (IL-6) and C-reactive protein (CRP) in the blood of smokers. The overall goal of this study was to develop a feasible tea intervention that will serve as a model for the chemoprevention of a wide range of tobacco-related diseases. Our immediate goal, that was addressed over a 4-year study period, was to determine the effects of high tea consumption on biological markers of chronic inflammation that mediate lung cancer risk, including, IL-6, CRP and antioxidant enzyme activities. Methods: We completed a 6-month randomized, controlled, double-blinded trial in a group of current and former smokers. Participants were stratified on smoking status and gender, and were randomized to green or black tea preparations or a control intervention (matching placebo). Levels of urinary iIL-6 and CRP are used to measure chronic inflammation and levels of superoxide dismutase (SOD) in red blood cells are used to measure antioxidant enzymes. Results: The study protocol was approved by all parties. A total of 138 participants (78 females and 60 males) completed the study. Our data show that il6 is significantly correlated with years of smoking and pack/year among smokers and former smokers. At the end of the 6-month intervention, female smokers in the green tea group showed a significant decrease in IL-6 (P=0.036) while male former smokers in the black tea group showed a significant decrease in CRP levels (P=0.012). There were no significant changes in dietary and serum antioxidant levels between the different groups. Conclusion: This data implies that smokers are more likely to benefit from green tea intake while the beneficial effects of black tea are observed among former smokers. Note: This abstract was not presented at the conference. Citation Format: Iman A. Hakim, Sami A. Aldaham, Janet Foote, H-H Sherry Chow. Modulating effects of green and black tea on biomarkers of chronic inflammation by gender and smoking status. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B14.
背景/目的:流行病学资料提示肺癌的发病机制存在性别差异，女性对肺癌的易感性可能增加。慢性炎症已被认为是人类健康的重要调节因子，在疾病预防和疾病发展中都起着重要作用。几项研究表明，吸烟者血液中的白细胞介素6 (IL-6)和c反应蛋白(CRP)增加。这项研究的总体目标是开发一种可行的茶叶干预方法，作为化学预防多种烟草相关疾病的模型。我们的近期目标是通过4年的研究，确定高茶摄入对介导肺癌风险的慢性炎症生物标志物的影响，包括IL-6、CRP和抗氧化酶活性。方法:我们在一组吸烟者和戒烟者中完成了一项为期6个月的随机、对照、双盲试验。参与者根据吸烟状况和性别进行分层，并随机分配到绿茶或红茶制剂或对照干预(与安慰剂相匹配)。尿il -6和CRP水平用于测量慢性炎症，红细胞超氧化物歧化酶(SOD)水平用于测量抗氧化酶。结果:研究方案得到了各方的认可。共有138名参与者(78名女性和60名男性)完成了这项研究。我们的数据显示，在吸烟者和前吸烟者中，il6与吸烟年数和吸烟包/年显著相关。在6个月的干预结束时，绿茶组的女性吸烟者IL-6显著降低(P=0.036)，而红茶组的男性前吸烟者CRP水平显著降低(P=0.012)。各组间饲料和血清抗氧化水平无显著变化。结论:这些数据表明，吸烟者更有可能从绿茶摄入中受益，而红茶的有益效果在前吸烟者中观察到。注:本摘要未在会议上发表。引用格式:Iman A. Hakim, Sami A. Aldaham, Janet Foote, H-H Sherry Chow。绿茶和红茶对慢性炎症生物标志物的调节作用与性别和吸烟状况有关。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr B14。
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-B26
C. Zhou, S. Daugherty, A. Black, L. Liao, N. Freedman, C. Abnet, R. Pfeiffer, M. Cook
Background: Prostate cancer is the second leading cause of cancer death in American men, but few modifiable risk factors have been established for prostate cancer progression and survival. Experimental studies have suggested that nonsteroidal anti-inflammatory drugs (NSAIDs) may improve prostate cancer survival through anti-thrombotic and anti-inflammation mechanisms. However, previous observational studies have shown mixed results. No study has examined over-the-counter non-aspirin NSAIDs in relation to prostate cancer survival. Few studies have assessed aspirin use before prostate cancer diagnosis in relation to prostate cancer survival, and whether any etiologically relevant time window of exposure exists remains unclear. Methods: We assessed two cohorts of prostate cancer cases from two large prospective studies in the United States NIH-AARP Diet and Health Study and Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to investigate associations of aspirin and other nonselective non-aspirin NSAID use before and after prostate cancer diagnosis with prostate cancer-specific and all-cause mortality. Cox proportional hazards models with age as the time metric were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results across the two studies were meta-analyzed in a fixed effects model if consistent associations were observed. Results: We did not find statistically significant associations of pre- or post-diagnostic NSAID use with prostate cancer-specific mortality. However, aspirin users versus nonusers five years or more before prostate cancer diagnosis had a 14% (95%CI=0.74 to 1.00) and a 16% (95%CI=0.78 to 0.89) reduced prostate cancer-specific and all-cause mortality when combining the two studies. Post-diagnostic occasional (less than once per day) and daily aspirin use were associated with 17% (95%CI=0.72 to 0.95) and 25% (95%CI=0.66 to 0.86) reductions in all-cause mortality independent of pre-diagnostic use, comparing with no use. Conclusions: This analysis suggests a modest delayed survival benefit of aspirin use before prostate cancer diagnosis and highlights the importance of comorbidity prevention among prostate cancer survivors. Citation Format: Cindy Ke Zhou, Sarah E. Daugherty, Amanda Black, Linda M. Liao, Neal D. Freedman, Christian C. Abnet, Ruth Pfeiffer, Michael B. Cook. Pre- and post-diagnostic use of nonsteroidal anti-inflammatory drugs and prostate cancer mortality among men diagnosed with prostate cancer in the NIH-AARP and PLCO cohorts. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B26.
背景:前列腺癌是美国男性癌症死亡的第二大原因，但很少有可以改变前列腺癌进展和生存的危险因素。实验研究表明，非甾体抗炎药(NSAIDs)可能通过抗血栓和抗炎症机制改善前列腺癌的生存。然而，之前的观察性研究显示了不同的结果。没有研究检查非阿司匹林非甾体抗炎药与前列腺癌存活的关系。很少有研究评估前列腺癌诊断前阿司匹林的使用与前列腺癌生存的关系，以及是否存在任何病因相关的暴露时间窗仍不清楚。方法:我们评估了来自美国NIH-AARP饮食与健康研究和前列腺、肺、结直肠和卵巢(PLCO)癌症筛查试验两项大型前瞻性研究的两组前列腺癌病例，以调查前列腺癌诊断前后阿司匹林和其他非选择性非阿司匹林非甾体抗炎药使用与前列腺癌特异性和全因死亡率的关系。以年龄为时间指标的Cox比例风险模型用于估计风险比(hr)和95%置信区间(ci)。如果观察到一致的关联，则在固定效应模型中对两项研究的结果进行meta分析。结果:我们没有发现诊断前或诊断后使用非甾体抗炎药与前列腺癌特异性死亡率有统计学意义的关联。然而，在前列腺癌诊断前5年或更长的时间内，阿司匹林服用者与非服用者相比，前列腺癌特异性和全因死亡率分别降低14% (95%CI=0.74至1.00)和16% (95%CI=0.78至0.89)。诊断后偶尔(每天少于一次)和每日使用阿司匹林与不使用相比，独立于诊断前使用的全因死亡率分别降低17% (95%CI=0.72至0.95)和25% (95%CI=0.66至0.86)。结论:该分析表明，前列腺癌诊断前使用阿司匹林有一定的延迟生存益处，并强调了前列腺癌幸存者预防合并症的重要性。引文格式:Cindy Ke Zhou, Sarah E. Daugherty, Amanda Black, Linda M. Liao, Neal D. Freedman, Christian C. Abnet, Ruth Pfeiffer, Michael B. Cook。在NIH-AARP和PLCO队列中诊断为前列腺癌的男性诊断前和诊断后使用非甾体抗炎药和前列腺癌死亡率[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr B26。
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-B20
S. Sen, Manoel Horta Ribeiro, M. Nygård
Background: Cervical cancer incidence rate has significantly decreased in countries that established organized screening programs. The program invites women in the age group 25 to 69 years for screening exams based on a set of guidelines. The guidelines aim to reduce over-screening of individuals at a very low-risk while effectively detecting and treating individuals at a high-risk of developing cervical cancer. However, risk determined by the screening program for a woman is often different from perception of their own risk. This contributes to a wide range of screening behavior as seen in the data collected by the Cancer Registry of Norway from 1992 to 2014. Some women get screened very frequently while some others consider themselves to be at a low-risk. Women make their own choices and we can see many patterns in their screening trajectories. Furthermore, implementation of new biomarkers may improve efficiency of the screening and requires adjustment of the guidelines. Therefore, we ask, can we use the complete set of cervical cancer screening related data collected from 1.8 million women in Norway to communicate personalized risk and concurrently evaluate performance of existing screening guidelines? Objective: Development and demonstration of a data exploration tool Portinari to communicate personalized risk of a patient based on historical data of a population. Methods and Results: We developed Portinari, a web-based, user friendly, data exploration tool for (non-)experts to query and visualize personalized risk of patients who have undergone a specific sequence of exams S. The sequence of exams of a patient is specified using a user-friendly visual editor. This visual representation is automatically transformed to a graph query. The query is executed on a graph database of screening data, which is an intuitive data structure to store and query trajectories of exams and their respective diagnosis taken over 22 years from the entire Norwegian female population. Matches for the graph query of a specific individual9s exams is a collection of identical sequences found in other patients in the database which forms the basis for risk visualization. The patient9s personal prognosis is presented by summarizing the future of all matching patients found in the database. The summary is presented as a Sankey diagram that shows arrows, representing patients flowing from one diagnosis to another with the origin being the last exam in S. The width of the arrows is proportional to the size of the represented flow which is the number of patients in our case. The Sankey diagram allows a patient to visualize both frequently taken paths taken by patients and also outliers to help them make an informed choice. We will demonstrate the use of Portinari to a) evaluate screening guidelines b) communicate personalized risk using various example scenarios. Citation Format: Sagar Sen, Manoel Horta Ribeiro, Mari Nygard. Portinari: Communicating personalized risk in ce
背景:在建立有组织的筛查项目的国家，宫颈癌发病率显著下降。该项目邀请年龄在25岁至69岁之间的女性根据一套指导方针进行筛查检查。该指引旨在减少对极低风险人士的过度筛检，同时有效地发现和治疗易患子宫颈癌的人士。然而，筛查项目为女性确定的风险往往与她们对自身风险的感知不同。从1992年至2014年挪威癌症登记处收集的数据可以看出，这有助于广泛的筛查行为。一些女性经常接受筛查，而另一些女性则认为自己的风险很低。女性做出自己的选择，我们可以在她们的筛查轨迹中看到许多模式。此外，实施新的生物标志物可能会提高筛选的效率，并需要调整指南。因此，我们的问题是，我们是否可以使用从挪威180万妇女中收集的完整的宫颈癌筛查相关数据来传达个性化的风险，同时评估现有筛查指南的表现?目的:开发和演示数据探索工具Portinari，以根据人群的历史数据传达患者的个性化风险。方法和结果:我们开发了Portinari，这是一个基于网络的、用户友好的数据探索工具，供(非)专家查询和可视化接受特定检查顺序s的患者的个性化风险。患者的检查顺序使用用户友好的可视化编辑器指定。这种可视化表示会自动转换为图形查询。查询是在筛选数据的图形数据库上执行的，这是一个直观的数据结构，用于存储和查询整个挪威女性人口22年来的检查轨迹及其各自的诊断。特定个体检查的图形查询的匹配是数据库中其他患者中发现的相同序列的集合，这构成了风险可视化的基础。患者的个人预后是通过汇总数据库中所有匹配患者的未来来呈现的。摘要以Sankey图的形式呈现，其中显示箭头，表示患者从一个诊断流向另一个诊断，原点是s的最后一次检查。箭头的宽度与表示流的大小成正比，在我们的情况下是患者的数量。桑基图可以让病人看到病人经常走的路和异常值，帮助他们做出明智的选择。我们将演示使用Portinari来a)评估筛选指南b)通过各种示例场景传达个性化风险。引用格式:Sagar Sen, Manoel Horta Ribeiro, Mari Nygard。Portinari:使用数据探索交流宫颈癌筛查中的个性化风险。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr B20。
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-A12
Sangmi Kim, Jeff Campbell, Wonsuk Yoo, Jack A. Taylor, D. Sandler
Prostaglandin E 2 (PGE 2 ) induces aromatase expression in adipose tissue leading to increased estrogen production that may promote the development and progression of breast cancer. However, few studies have simultaneously investigated systemic levels of PGE 2 and estrogen in relation to postmenopausal breast cancer risk. In a case-cohort study of postmenopausal women (295 cases and 294 subcohort) we previously reported that high levels of PGE-M, a major metabolite of PGE 2 , were associated with an increased risk of breast cancer among postmenopausal women who did not regularly use nonsteroidal anti-inflammatory drugs (NSAIDs). Here we determined urinary estrogen metabolites (EMs) using mass spectrometry in the same case-cohort set and using linear regression estimated the effect of PGE-M on EMs. Hazard ratios (HRs) for the risk of developing breast cancer in relation to PGE-M and EMs were evaluated in Cox regression models with and without mutual adjustment. PGE-M was a significant predictor of estrone (E1), but not estradiol (E2) levels in multivariable analysis. Elevated E2 levels were associated with an increased risk of developing breast cancer (HR Q5vs.Q1 =1.54, 95% CI: 1.01-2.35), and this association remained unchanged after adjustment for PGE-M (HR Q5vs.Q1 =1.52, 95% CI: 0.99-2.33). Similarly, elevated levels of PGE-M were associated with increased risk of developing breast cancer (HR Q4vs.Q1 =2.01, 95% CI: 1.01-4.29), and this association was only nominally changed after consideration of E1 or E2 levels. Urinary levels of PGE-M and parent estrogens were independently associated with future risk of developing breast cancer among these postmenopausal women. Increased breast cancer risk associated with PGE-M might be attributable both to PGE 2 -mediated increases in estrogens, and to additional effects related to inflammation. Note: This abstract was not presented at the conference. Citation Format: Sangmi Kim, Jeff Campbell, Wonsuk Yoo, Jack A. Taylor, Dale P. Sandler. Urinary levels of PGE-M and estrogens are independently associated with postmenopausal breast cancer risk. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A12.
前列腺素e2 (pge2)诱导脂肪组织中芳香化酶的表达，导致雌激素分泌增加，可能促进乳腺癌的发生和发展。然而，很少有研究同时调查pge2和雌激素的全身水平与绝经后乳腺癌风险的关系。在一项绝经后妇女的病例队列研究(295例和294个亚队列)中，我们之前报道过，在不定期使用非甾体抗炎药(NSAIDs)的绝经后妇女中，高水平的PGE- m (PGE- 2的主要代谢物)与乳腺癌风险增加相关。在这里，我们使用质谱法在同一病例队列中测定尿雌激素代谢物(EMs)，并使用线性回归估计PGE-M对EMs的影响。在有和没有相互调整的Cox回归模型中评估与PGE-M和EMs相关的乳腺癌风险风险比(hr)。在多变量分析中，PGE-M是雌酮(E1)水平的显著预测因子，而不是雌二醇(E2)水平的显著预测因子。E2水平升高与患乳腺癌的风险增加有关(HR Q5vs)。Q1 =1.54, 95% CI: 1.01-2.35)，调整PGE-M后，这种关联保持不变(HR Q5vs. 2.35)。Q1 =1.52, 95% ci: 0.99-2.33)。同样，PGE-M水平升高与患乳腺癌的风险增加相关(HR Q4vs)。Q1 =2.01, 95% CI: 1.01-4.29)，在考虑E1或E2水平后，这种关联只是名义上发生了变化。在这些绝经后妇女中，尿中PGE-M和母体雌激素水平与未来患乳腺癌的风险独立相关。与PGE- m相关的乳腺癌风险增加可能归因于PGE- 2介导的雌激素增加，以及与炎症相关的其他影响。注:本摘要未在会议上发表。引文格式:Sangmi Kim, Jeff Campbell, Wonsuk Yoo, Jack A. Taylor, Dale P. Sandler。尿中PGE-M和雌激素水平与绝经后乳腺癌风险独立相关。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr A12。
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-B29
A. Rahman, Aunik K. Rahman, B. Rao
A terahertz diagnosis tool has been developed to identify early stage skin cancer at the cellular level. Here, three different techniques are used where each technique independently identifies a given disease condition compared to healthy skin specimen; thus, collectively forms a diagnostic procedure with minimal falls alarm. Namely, terahertz sub-surface spectral imaging, terahertz absorbance spectroscopy and skin thickness profiling have been used. Terahertz radiation is non-ionizing, therefore, save for in-vivo investigations. It is also more sensitive than other forms of probing energies. In the present work, biopsies from three skin disease conditions have been compared with a healthy skin sample. It was found that the terahertz images clearly visualize healthy skin cells where a regular cellular pattern is visible. In contrast, cancerous skin specimen images exhibit deterioration from regular cellular pattern indicating abnormal conditions. For example, the skin specimen excised by Mohs microsurgery and diagnosed for basal cell carcinoma exhibits cell agglomeration indicating the onset of tumor formation. Similarly, other skin conditions such as squamous cell carcinoma and lentigo maligna exhibit their characteristic images without a regular cell pattern. Since the skin is a layered structure, a thickness profile of the healthy skin clearly exhibits this layering pattern while the layering is significantly diminished for cancerous skin samples. Thus a diminished layering profile is an indication of skin abnormalities. In addition, spectral analyses also exhibit distinguishable differences between different cancer conditions compared to healthy skin spectrum. Details of the methodology and results will be discussed. Note: This abstract was not presented at the conference. Citation Format: Anis Rahman, Aunik K. Rahman, Babar Rao. Terahertz spectral imaging and scanning for early detection of skin cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B29.
{"title":"Abstract B29: Terahertz spectral imaging and scanning for early detection of skin cancer","authors":"A. Rahman, Aunik K. Rahman, B. Rao","doi":"10.1158/1538-7755.CARISK16-B29","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B29","url":null,"abstract":"A terahertz diagnosis tool has been developed to identify early stage skin cancer at the cellular level. Here, three different techniques are used where each technique independently identifies a given disease condition compared to healthy skin specimen; thus, collectively forms a diagnostic procedure with minimal falls alarm. Namely, terahertz sub-surface spectral imaging, terahertz absorbance spectroscopy and skin thickness profiling have been used. Terahertz radiation is non-ionizing, therefore, save for in-vivo investigations. It is also more sensitive than other forms of probing energies. In the present work, biopsies from three skin disease conditions have been compared with a healthy skin sample. It was found that the terahertz images clearly visualize healthy skin cells where a regular cellular pattern is visible. In contrast, cancerous skin specimen images exhibit deterioration from regular cellular pattern indicating abnormal conditions. For example, the skin specimen excised by Mohs microsurgery and diagnosed for basal cell carcinoma exhibits cell agglomeration indicating the onset of tumor formation. Similarly, other skin conditions such as squamous cell carcinoma and lentigo maligna exhibit their characteristic images without a regular cell pattern. Since the skin is a layered structure, a thickness profile of the healthy skin clearly exhibits this layering pattern while the layering is significantly diminished for cancerous skin samples. Thus a diminished layering profile is an indication of skin abnormalities. In addition, spectral analyses also exhibit distinguishable differences between different cancer conditions compared to healthy skin spectrum. Details of the methodology and results will be discussed. Note: This abstract was not presented at the conference. Citation Format: Anis Rahman, Aunik K. Rahman, Babar Rao. Terahertz spectral imaging and scanning for early detection of skin cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B29.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85459127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-IA24
J. Emery
Numerous risk assessment tools have been developed which predict either current or future risk of a cancer diagnosis yet very few are used in routine clinical practice. These tools could be used for tailored disease prevention, more efficient use of cancer screening tests and to promote behavioural change to reduce cancer risk. We have a growing number of cancer risk-prediction models which incorporate phenotypic, behavioural and, increasingly, genomic variables; these models require simple-to-use risk assessment tools for their implementation into clinical practice, and in particular ones which can be incorporated into primary care. In this presentation I will present a recent systematic review of RCTs in primary care of cancer risk assessment tools. This will highlight some of the key issues which remain for successful implementation of these tools into primary care practice. Selecting which cancer risk prediction model to incorporate into a tool will depend not only the predictive utility of the model but also the feasibility of collecting more complex predictive variables in clinical practice. We should design tools that can be incorporated into the clinical consultation, and which present cancer risks in meaningful ways that are more likely to lead to appropriate behaviour change. I will present research on the development of the CRISP tool to demonstrate how we are applying these principles and trialing its effect on risk-stratified colorectal cancer screening in Australian primary care. Citation Format: Jon Emery. Developing and evaluating cancer risk assessment tools for primary care. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA24.
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-A10
Madhuri G. S. Aithal, R. Narayanappa
In cancer, DNA methylation affects important signal transduction pathways leading to altered receptor function, disruption of normal cell-cell interaction, etc. Since methylation occurs at a very early stage, hypermethylated promoters hold great promise as biomarkers for early detection and an effective drug target for gene reactivation. The Notch signaling pathway is one such developmental pathway governing cell fate decisions. Dysregulated Notch signaling is found to have a prominent role in the development of various cancers. Glioblastoma is the most common primary brain tumor with a very poor prognosis. Therefore it is important to study genetic and epigenetic events leading to gliomagenesis and to guide new treatment strategies. The aim of this study was to detect Notch pathway genes potentially regulated by promoter methylation in human glioblastoma. We used real-time PCR and methylation-specific PCR to study gene expression and methylation status of seven Notch pathway genes (Notch1, Notch2, Notch3, Notch4, JAG1, JAG2 and DLL3) from human glioblastoma formalin fixed paraffin embedded sections. We identified Notch3 and JAG2 promoters as methylated and Notch4 with both methylated and unmethylated promoter. Despite methylation, Notch3 gene showed robust gene expression suggesting its partial dependency on promoter methylation and the presence of alternative regulatory mechanisms. However, low gene expression of JAG2 and the absence of Notch4 gene expression suggest a possibility of epigenetic silencing. This study for the first time provides gene expression and DNA methylation profiles of Notch pathway genes from glioblastoma patient samples. We have identified genes whose expression may be regulated by epigenetic mechanisms and thus can be used as markers that may guide treatment decisions. Note: This abstract was not presented at the conference. Citation Format: Madhuri G S Aithal, Rajeswari Narayanappa. Analysis of gene expression and DNA methylation profiles of Notch signaling pathway genes in human glioblastoma. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A10.
在癌症中，DNA甲基化影响重要的信号转导通路，导致受体功能改变，破坏正常的细胞-细胞相互作用等。由于甲基化发生在非常早期的阶段，超甲基化启动子作为早期检测的生物标志物和基因再激活的有效药物靶点具有很大的前景。Notch信号通路是控制细胞命运决定的发育途径之一。失调的Notch信号被发现在各种癌症的发展中具有突出的作用。胶质母细胞瘤是最常见的原发性脑肿瘤，预后很差。因此，研究导致胶质瘤形成的遗传和表观遗传事件并指导新的治疗策略具有重要意义。本研究的目的是检测人类胶质母细胞瘤中可能受启动子甲基化调控的Notch通路基因。我们采用实时荧光定量PCR和甲基化特异性PCR技术研究了人胶质母细胞瘤福尔马林固定石蜡包埋切片中7个Notch通路基因(Notch1、Notch2、Notch3、Notch4、JAG1、JAG2和DLL3)的基因表达和甲基化状态。我们发现Notch3和JAG2启动子是甲基化的，Notch4启动子是甲基化的和未甲基化的。尽管甲基化，Notch3基因仍显示出强劲的基因表达，表明其部分依赖于启动子甲基化，并存在其他调控机制。然而，JAG2基因的低表达和Notch4基因的缺失提示了表观遗传沉默的可能性。本研究首次提供了胶质母细胞瘤患者样本中Notch通路基因的基因表达和DNA甲基化谱。我们已经确定了表达可能受表观遗传机制调节的基因，因此可以用作指导治疗决策的标记。注:本摘要未在会议上发表。引文格式:Madhuri G S Aithal, Rajeswari Narayanappa。人胶质母细胞瘤Notch信号通路基因表达及DNA甲基化谱分析。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr - A10。
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Pub Date : 2017-05-01DOI: 10.1158/1538-7755.CARISK16-PR02
Chi Gao, P. Choudhury, P. Maas, R. Tamimi, H. Eliassen, N. Chatterjee, M. García-Closas, P. Kraft
Background: Adding genetic and other biomarkers to breast cancer risk prediction models could markedly improve model discrimination; however, these expanded models have not been validated in a range of populations. In particular, the calibration of these new models how well the predicted absolute risks match observed risks has not been established. Good calibration is essential to confirm the utility of these risk models in precision prevention and treatment programs. Large cohort studies provide an ideal setting to validate risk models, as they can be used to validate both relative and absolute risks. However, in practice, genetic and biomarker data are often not available in the full cohort, but only on a sub sample of cases and controls. When the rules for sampling cases and controls into the sub sample are known, inverse-probability-of-sampling (IPW) weights can be used to estimate empirical absolute risks. When the sampling rules are unknown or complicated, the IPW weights can be estimated by regressing selection into the sub sample on matching and other inclusion criteria. Methods: We evaluated the performance of recently published breast cancer risk prediction models [Maas et al. JAMA Oncol 2016] in the Nurses Health Study (NHS) and Nurses Health Study II (NHSII). We first assess a prediction model that only includes questionnaire data (BMI, hormone replacement therapy (HRT), alcohol consumption, smoking status, height, parity, age at menarche and menopause, age at first birth, and family history of breast cancer). These data are available on all subjects in the NHS and NHSII blood subcohorts: 32,826 women in NHS (with disease follow-up from 1990-2012) and 29,611 women in NHS II (1999-2013). We will then validate a model that includes both questionnaire data and a polygenic risk score based on 92 established risk SNPs. Genetic data are available on case-control samples nested within the blood subcohorts: 2308 breast cancer cases and 3344 controls from NHS and 612 breast cancer cases and 933 controls from NHSII. We estimated IPW weights among controls using logistic regression in the blood subcohorts, with sampling as control being the outcome and the following predictors: age at baseline, menopausal status, HRT, length of HRT use for premenopausal women at baseline, and length of follow up time. We used the iCARE software package (Maas P, Chatterjee N, Wheeler W et al. 2015) to calculate predicted 5 and 10-year absolute risks of breast cancer based on the published models, empirical 5 and 10-year incidence across deciles of predicted risk, and Hosmer-Lemeshow goodness of fit and AUC statistics. Results: For the risk model without genetic information, predicted risks in the blood subcohorts ranged from 6.5/1,000 (1st decile) to 20.1/1,000 (10th decile) for NHS. Although empirical risks increased across deciles at approximately the same rate as predicted rates, empirical risks were higher than predicted (Hosmer-Lemeshow p Due to matching and
背景:在乳腺癌风险预测模型中加入遗传和其他生物标志物可以显著提高模型的辨别能力;然而，这些扩展的模型尚未在一系列人群中得到验证。特别是，尚未确定这些新模型的校准，预测的绝对风险与观察到的风险在多大程度上匹配。良好的校准对于确认这些风险模型在精确预防和治疗方案中的效用至关重要。大型队列研究为验证风险模型提供了理想的环境，因为它们可用于验证相对和绝对风险。然而，在实践中，遗传和生物标志物数据往往不能在整个队列中获得，而只能在病例和对照的子样本中获得。当子样本的抽样情况和控制规则已知时，可以使用逆抽样概率(IPW)权值来估计经验绝对风险。当采样规则未知或复杂时，可以根据匹配和其他包含标准将选择回归到子样本中，从而估计IPW权重。方法:我们评估了最近发表的乳腺癌风险预测模型的性能[Maas等]。JAMA Oncol 2016]护士健康研究(NHS)和护士健康研究II (NHSII)。我们首先评估了一个仅包括问卷数据(BMI、激素替代疗法(HRT)、饮酒、吸烟状况、身高、胎次、初潮和绝经年龄、初产年龄和乳腺癌家族史)的预测模型。这些数据可用于NHS和NHSII血液亚群的所有受试者:32,826名NHS女性(1990-2012年随访)和29,611名NHSII女性(1999-2013年)。然后，我们将验证一个模型，该模型包括问卷数据和基于92个已建立的风险snp的多基因风险评分。在血液亚群内嵌套的病例对照样本中可获得遗传数据:来自NHS的2308例乳腺癌病例和3344例对照，以及来自NHSII的612例乳腺癌病例和933例对照。我们在血液亚群中使用逻辑回归来估计对照组的IPW权重，以抽样作为对照作为结果和以下预测因子:基线年龄、绝经状态、HRT、绝经前妇女基线HRT使用时间和随访时间。我们使用iCARE软件包(Maas P, Chatterjee N, Wheeler W et al. 2015)，根据已发表的模型、预测风险十分位数的5年和10年经验发病率、Hosmer-Lemeshow拟合优度和AUC统计，计算预测的5年和10年乳腺癌绝对风险。结果:对于没有遗传信息的风险模型，NHS血液亚群的预测风险范围为6.5/ 1000(第1十分位数)至20.1/ 1000(第10十分位数)。尽管经验风险以与预测率大致相同的速率在十分位数上增加，但经验风险高于预测(Hosmer-Lemeshow p)。由于对照状态的匹配和选择，问卷风险因素的基线分布在血液亚群和巢式病例对照样本的对照组之间存在差异。对照组的ipw加权分布与完整亚群的分布密切匹配，表明权重计算良好。我们将在嵌套的病例对照样本中展示基于ipw的风险模型验证(工作正在进行中)。结论:这些结果证实了乳腺癌风险预测模型可以区分高风险和低风险女性，但它们也强调了绝对风险估计的准确性在不同人群中存在差异。本研究结果可为模型的改进和应用提供参考。此外，使用IPW权重近似全队列分析的方法为在未来的验证分析中使用嵌套病例对照研究提供了一个潜在的解决方案。此摘要也以海报A05的形式呈现。引文格式:Chi Gao, Parichoy Pal Choudhury, Paige Maas, Rulla Tamimi, Heather Eliassen, Nilanjan Chatterjee, Montserrat Garcia-Closas, Peter Kraft。利用护士健康和护士健康II研究验证乳腺癌风险预测模型。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;癌症流行病学生物标志物pre2017;26(5增刊):摘要nr PR02。
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