Randomized trial of universal vs. guideline-directed germline testing among young adults with cancer

癌症是年轻人的主要非创伤性死亡原因。在40岁以下的人中，癌症 有独特的生物学，通常有潜在的遗传病因学。然而，协商一致的指导方针是由 表型特征未能识别出许多有遗传风险的年轻成年患者，部分原因是 它们的复杂性以及缺乏关于突变频率的数据。我们很可能大大低估了这种频率 年轻癌症患者的生殖系易感性谱，了解这一点将具有深远的意义 这对他们的治疗和后续护理以及对亲属的诊断和管理都有影响。 因此，需要更好的策略来诊断患有癌症的年轻人的遗传风险。此外， 在那些被确认为遗传性癌症风险的人的亲属中，基因测试率从50%到60%不等； 采取干预措施，克服患者和亲属面临的障碍，以便他们能够进行适当的筛查 以及降低风险的措施，必须加以开发和测试。最后，迫切需要将基因 将评估和测试结果写入电子病历(EMR)，以便于进行量身定制的临床决策 为临床医生和患者提供支持。本提案力求克服现有数据的局限性 以及通过两个具体目标进行护理的模式。首先，我们将在1421名患者中进行随机对照试验 患有癌症的年轻人，其中三分之一将是少数族裔或医学上的成员 服务不足的群体，比较指南驱动的、表型- 定向基因检测(目前的护理标准)和通用癌症小组基因检测。使用 宾夕法尼亚大学医学推动股和信息服务，我们将开发基于EMR的自动算法 患者转介和临床决策支持，由通过EMR导入的离散基因测试结果驱动 HL7，其中将包括“主动选择”提示、直接向患者发出警报以及最小化医生控制面板 医生的负担。我们将比较参与者对筛查建议的遵守情况 在历史控制中。第二，我们将比较两种前期测试策略的影响 患者，通过一种新的战略，在确定基因的基础上，直接与高危亲属进行团队接触 家庭成员之间的风险。我们还将对不同样本的患者进行定性访谈， 亲属和家庭团体，描述促进或阻碍沟通的关键互动 在家庭内进行风险和级联测试，并探索直接临床团队外展到- 有风险的亲戚。这项拟议的研究承诺立即改变国家的护理标准和付款人政策 通过确定评估年轻成年癌症患者遗传风险的首选方法 严格的随机试验，同时测量患者及其亲属之间的确认性。此外, 除了改变护理标准的潜力之外，这项研究还将生成基于EMR的可共享代码， 算法和模型，这将进一步增强拟议方法的可持续性。

Cancer is the leading nontraumatic cause of death among young adults. In individuals under age 40, cancer has a distinct biology and often has an underlying genetic etiology. However, consensus guidelines driven by phenotypic characteristics fail to identify many young adult patients with inherited genetic risk, in part due to their complexity and to lack of data on mutation frequency. We likely vastly underestimate the frequency and spectrum of germline susceptibility in young adults with cancer, knowledge of which would have far-reaching implications both for their treatment and follow-up care and for the diagnosis and management of relatives. Thus, better strategies for diagnosing inherited risk among young adults with cancer are needed. Further, genetic testing rates among relatives of those identified with inherited cancer risk range from 50-60%; interventions to overcome the barriers that patients and relatives face, so they can take appropriate screening and risk-reducing measures, must be developed and tested. Finally, there is a critical need to integrate genetic evaluation and test results into the electronic medical record (EMR) to facilitate tailored clinical decision support for both clinicians and patients. The present proposal seeks to overcome the limitations of current data and models of care through two Specific Aims. First, we will conduct a randomized controlled trial among 1421 young adults with cancer, one-third of whom will be members of racial or ethnic minorities or medically underserved groups, to compare rates of ascertainment of genetic risk between guideline-driven, phenotype- directed genetic testing (current standard of care) and universal cancer panel genetic testing. Working with the Penn Medicine Nudge Unit and Information Services, we will develop EMR-based algorithms for automatic patient referral and clinical decision support, driven by discrete genetic test results ported into the EMR via HL7, that will include ‘active choice’ nudges, direct-to-patient alerts, and physician dashboards that minimize physician burden. We will compare adherence to screening recommendations among participants to that among historical controls. Second, we will compare the impact of the two up-front testing strategies among patients, enhanced by a novel strategy of direct team outreach to at-risk relatives, on ascertainment of genetic risk among family members. We also will conduct qualitative interviews with a diverse sample of patients, relatives, and family groups to describe the critical interactions that facilitate or impede communication about risk and cascade testing within families and to explore the acceptability of direct clinical team outreach to at- risk relatives. The proposed study promises to immediately alter national standards of care and payer policies by identifying the preferred approach to evaluating young adult cancer patients for genetic risk through a rigorous randomized trial, while measuring ascertainment among both patients and their relatives. In addition, beyond its potential to change standards of care, the study will generate shareable EMR-based code, algorithms, and models that will further enhance the sustainability of the proposed approach.