Abstract A28: Risk model for clinical management of HPV-infected women

M. Demarco, Noorie Hyun, H. Katki, B. Befano, L. Cheung, Tina Raine-Bennett, B. Fetterman, T. Lorey, N. Poitras, J. Gage, P. Castle, N. Wentzensen, M. Schiffman
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In addition to HPV genotype, multiple established co-factors can be combined to predict with unparalleled accuracy and precision the broad range of risks for the critical transition from common HPV infection to uncommon cervical precancer. Thus, there are three types of factors predicting risk of precancer: viral (e.g., HPV genotype and viral load), host (e.g., age, race/ethnicity) and behavioral (e.g., oral contraceptive use, smoking, BMI, co-infection with other sexually transmitted agents). We are building a risk prediction model for clinical use that reflects the determinants of HPV natural history. The absolute-risk based model will consider the three possible HPV outcomes: HPV progression, else HPV “clearance” (immune suppression) signifying low risk of subsequent precancer from that infection, else persistence of HPV infection without either progression or clearance (i.e., still unresolved outcome). To estimate these competing risks for all the factors, cofactors and their combinations requires very large cohorts of HPV-infected women. Methods: Our analysis makes use of data from a uniquely large cohort study of HPV-infected women, specifically, the 35,000 HPV-positive women, 30 years or older, from the NCI-Kaiser Permanente Northern California Persistence and Progression cohort study. The median time of follow-up is 3 years (maximum >7 years). Risk predictors already recorded include: woman9s age, HPV infection status, HPV genotype, viral load, concurrent cervical cytology result, and the range of behavioral cofactors. We will present at the meeting the steps leading to the final model: 1) univariate, then multivariate, absolute risks of progression, clearance, or persistence for each HPV genotype; 2) the same risks accounting for time to event and loss-to-followup; and 3) the novel statistic mean risk stratification (MRS), which measures how well the model predicts the crucial dichotomous outcome (progression vs. not). MRS identifies which combination of variables, by virtue of frequency of positive results and strength of risk stratification, is most promising in deciding risk-based clinical management (i.e., who needs colposcopic biopsy due to high risk of precancer). We present the univariate absolute risks for HPV genotypes here, but will show the full multivariate proportional hazards and MRS analyses at the conference. Results: Risk of progression (29.4% for HPV16 to 7.2% for HPV68) varied inversely with risk of clearance (60.1% for HPV16 to 81.6% for HPV68), by HPV type. Relatively few (~10%) of infections of any carcinogenic type persisted without progression. The most important univariate cofactors in preliminary analyses are viral load (for HPV16 mainly), woman9s age, and concurrent cytology. No behavioral risk factors are especially important. Time to clearance and time to progression did not vary by HPV type, with median time to events of 1.5-2 years. Conclusions: Based on our preliminary results, the fate of most HPV infections is determined within a few years of first detection, based mainly on characteristics of the virus. MRS summarizes the average risk discrimination of the prediction model compared to pre-test probability, permitting estimation of its expected benefit. We hypothesize and will test whether multivariate calculations of absolute risks and the use of mean risk stratification can lead to improved risk-based clinical management of HPV-infected women. Citation Format: Maria Demarco, Noorie Hyun, Hormuzd Katki, Brian Befano, Li Cheung, Tina R. Raine-Bennett, Barbara Fetterman, Thomas Lorey, Nancy Poitras, Julia C. Gage, Phillip E. Castle, Nicolas Wentzensen, Mark Schiffman. Risk model for clinical management of HPV-infected women. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. 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引用次数: 0

Abstract

Background: The natural history of human papillomavirus (HPV) and the steps leading to cervical cancer are well-known; the steps include infection with one of the 13 carcinogenic HPV genotypes, viral persistence, progression to precancer, and invasion. Cervical screening programs target treatable cervical precancer to prevent cancer mortality and morbidity. HPV infections are very common and only those causing precancer pose a risk of cancer. In addition to HPV genotype, multiple established co-factors can be combined to predict with unparalleled accuracy and precision the broad range of risks for the critical transition from common HPV infection to uncommon cervical precancer. Thus, there are three types of factors predicting risk of precancer: viral (e.g., HPV genotype and viral load), host (e.g., age, race/ethnicity) and behavioral (e.g., oral contraceptive use, smoking, BMI, co-infection with other sexually transmitted agents). We are building a risk prediction model for clinical use that reflects the determinants of HPV natural history. The absolute-risk based model will consider the three possible HPV outcomes: HPV progression, else HPV “clearance” (immune suppression) signifying low risk of subsequent precancer from that infection, else persistence of HPV infection without either progression or clearance (i.e., still unresolved outcome). To estimate these competing risks for all the factors, cofactors and their combinations requires very large cohorts of HPV-infected women. Methods: Our analysis makes use of data from a uniquely large cohort study of HPV-infected women, specifically, the 35,000 HPV-positive women, 30 years or older, from the NCI-Kaiser Permanente Northern California Persistence and Progression cohort study. The median time of follow-up is 3 years (maximum >7 years). Risk predictors already recorded include: woman9s age, HPV infection status, HPV genotype, viral load, concurrent cervical cytology result, and the range of behavioral cofactors. We will present at the meeting the steps leading to the final model: 1) univariate, then multivariate, absolute risks of progression, clearance, or persistence for each HPV genotype; 2) the same risks accounting for time to event and loss-to-followup; and 3) the novel statistic mean risk stratification (MRS), which measures how well the model predicts the crucial dichotomous outcome (progression vs. not). MRS identifies which combination of variables, by virtue of frequency of positive results and strength of risk stratification, is most promising in deciding risk-based clinical management (i.e., who needs colposcopic biopsy due to high risk of precancer). We present the univariate absolute risks for HPV genotypes here, but will show the full multivariate proportional hazards and MRS analyses at the conference. Results: Risk of progression (29.4% for HPV16 to 7.2% for HPV68) varied inversely with risk of clearance (60.1% for HPV16 to 81.6% for HPV68), by HPV type. Relatively few (~10%) of infections of any carcinogenic type persisted without progression. The most important univariate cofactors in preliminary analyses are viral load (for HPV16 mainly), woman9s age, and concurrent cytology. No behavioral risk factors are especially important. Time to clearance and time to progression did not vary by HPV type, with median time to events of 1.5-2 years. Conclusions: Based on our preliminary results, the fate of most HPV infections is determined within a few years of first detection, based mainly on characteristics of the virus. MRS summarizes the average risk discrimination of the prediction model compared to pre-test probability, permitting estimation of its expected benefit. We hypothesize and will test whether multivariate calculations of absolute risks and the use of mean risk stratification can lead to improved risk-based clinical management of HPV-infected women. Citation Format: Maria Demarco, Noorie Hyun, Hormuzd Katki, Brian Befano, Li Cheung, Tina R. Raine-Bennett, Barbara Fetterman, Thomas Lorey, Nancy Poitras, Julia C. Gage, Phillip E. Castle, Nicolas Wentzensen, Mark Schiffman. Risk model for clinical management of HPV-infected women. [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 A28.
摘要A28: hpv感染妇女临床管理的风险模型
背景:人类乳头瘤病毒(HPV)的自然历史和导致宫颈癌的步骤是众所周知的;步骤包括感染13种致癌HPV基因型之一,病毒持续存在,进展到癌前病变和侵袭。子宫颈筛查计划的目标是可治疗的宫颈癌前病变,以预防癌症的死亡率和发病率。HPV感染很常见,只有那些引起癌前病变的人才有患癌症的风险。除了HPV基因型外,多种已确定的辅助因素可以结合起来以无与伦比的准确性和精度预测从常见HPV感染到罕见宫颈癌前病变的关键转变的广泛风险。因此,有三种预测癌前风险的因素:病毒(例如,HPV基因型和病毒载量)、宿主(例如,年龄、种族/民族)和行为(例如,口服避孕药的使用、吸烟、体重指数、与其他性传播媒介的合并感染)。我们正在建立一个临床使用的风险预测模型,反映HPV自然史的决定因素。基于绝对风险的模型将考虑三种可能的HPV结果:HPV进展,否则HPV“清除”(免疫抑制)意味着该感染的后续癌前病变风险较低,否则HPV感染持续存在,既没有进展也没有清除(即仍未解决的结果)。为了估计所有因素、辅助因素及其组合的竞争风险,需要非常大的hpv感染妇女队列。方法:我们的分析利用了来自一项独特的大型hpv感染妇女队列研究的数据,特别是来自NCI-Kaiser Permanente北加州持续和进展队列研究的35000名30岁或以上的hpv阳性妇女。中位随访时间为3年,最长随访时间为7年。已经记录的风险预测因素包括:女性年龄、HPV感染状况、HPV基因型、病毒载量、并发宫颈细胞学结果以及行为辅助因素的范围。我们将在会议上介绍最终模型的步骤:1)单因素,然后是多因素,每个HPV基因型的进展,清除或持续的绝对风险;2)对事件发生时间和事后损失进行相同的风险核算;3)新的统计平均风险分层(MRS),它衡量模型预测关键的二分类结果(进展与不进展)的程度。MRS通过阳性结果的频率和风险分层的强度来确定哪些变量组合在决定基于风险的临床管理(即,由于癌前病变的高风险,谁需要阴道镜活检)方面最有希望。我们在这里展示了HPV基因型的单因素绝对风险,但将在会议上展示完整的多因素比例风险和MRS分析。结果:不同HPV类型的进展风险(HPV16为29.4%,HPV68为7.2%)与清除率(HPV16为60.1%,HPV68为81.6%)呈负相关。相对很少(约10%)的致癌性感染没有进展。在初步分析中,最重要的单变量辅助因素是病毒载量(主要是HPV16)、女性年龄和并发细胞学。没有特别重要的行为风险因素。清除时间和进展时间不因HPV类型而异,发生事件的中位时间为1.5-2年。结论:根据我们的初步结果,大多数HPV感染的命运是在首次检测后的几年内确定的,主要基于病毒的特征。MRS总结了预测模型的平均风险判别与试验前概率的比较,从而可以估计其预期收益。我们假设并将检验绝对风险的多变量计算和平均风险分层的使用是否可以改善hpv感染妇女的基于风险的临床管理。引文格式:Maria Demarco, Noorie Hyun, Hormuzd Katki, Brian Befano, Li b张,Tina R. Raine-Bennett, Barbara Fetterman, Thomas Lorey, Nancy Poitras, Julia C. Gage, Phillip E. Castle, Nicolas Wentzensen, Mark Schiffman。hpv感染妇女临床管理的风险模型。[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 A28。
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