BMT Survivor Study-2 (BMTSS-2)

2017年，估计有130万人患有血液恶性肿瘤（HM：白血病，骨髓瘤） 或淋巴瘤）。一线使用全身高强度化疗伴或不伴放疗 这是HM管理的特点。进行性疾病或复发风险高的患者接受治疗 甚至更高强度的化疗/放疗和血液或骨髓移植（BMT）;事实上， 通常是这些患者唯一的治疗选择。结果的稳步改善导致了越来越多的 BMT-HM幸存者的数量-这是一个特别容易受到长期慢性健康状况影响的人群 （CHC）与高强度治疗暴露直接相关（例如，继发性心脏肿瘤 失败）。在2000年，我们建立了一个回顾性队列的2,333骨髓移植受者（城市的希望[COH]或大学 明尼苏达州[UMN]; BMT：1974 - 1998），生存≥ 2年（BMT生存者研究[BMTSS]; R01 CA78938， Bhatia）。虽然BMTSS成功地描述了高负担的发病率，加速老化和过早 BMT-HM患者的死亡率，适度的样本量（n = 2，333）和较早的移植时代（1974 - 1998） 限制了新发现的潜力，特别是在面对不断发展的治疗策略时。我们提出了一个 现有BMTSS队列的显著增强，现在包括10，042例接受BMT治疗的HM患者 1974年至2014年在COH，UMN或UAB（BMT-HM），以及3000 HM的频率匹配队列 接受常规治疗但未接受BMT（非BMT-HM）的患者。这种增强的基础设施（BMTSS- 2)将随时准备确定HM患者接受或不接受BMT治疗的发病率负担， 个别HM诊断的背景，并了解治疗相关健康的发病机制 加速老化设置中的条件。经加强的BMTSS-2基础设施将使我们能够： 了解接受和不接受BMT治疗的HM患者所经历的健康状况的长期风险; ii） 确定治疗暴露与健康状况之间的关联; iii）确定健康趋势 改变治疗策略的条件; iv）确定可预防的修饰剂之间的相互作用 （合并症、健康行为）和治疗暴露，以确定健康状况风险; v）研究 在加速老化的背景下，使用遗传标记研究与治疗相关的健康状况的发病机制 用于健康状况与衰老的关联的易感性和表观遗传标记; vi）使用HIPAA- 兼容iOS/Android/iPad/基于Web的应用程序的技术平台，用于培训HM 病人和测量健康行为在真实的时间。这是迄今为止， 检查接受和不接受BMT治疗的HM患者的健康和福祉。总体目标是使用 BMTSS-2转化研究沿着两条轨道：A）开发风险预测模型，以确定HM接受者 治疗相关健康状况的最高风险;和B）在确定为最高风险的患者中，设计 并测试有针对性的干预措施，以预防/改善这些使人衰弱的慢性健康状况。

In 2017, an estimated 1.3 million individuals were living with a hematologic malignancy (HM: leukemia, myeloma or lymphoma) in the US. Frontline use of systemic high-intensity chemotherapy with or without radiation characterizes the management of HM. Patients with progressive disease or at high risk of relapse are treated with even higher intensity chemotherapy/radiation and blood or marrow transplantation (BMT); indeed, BMT is often the only curative option for these patients. Steady improvements in outcome have resulted in a growing number of BMT-HM survivors – a population that is uniquely vulnerable to long-term chronic health conditions (CHCs) that are directly related to the high-intensity therapeutic exposures (e.g., subsequent neoplasms, heart failure). In 2000, we constructed a retrospective cohort of 2,333 BMT recipients (City of Hope [COH] or University of Minnesota [UMN]; BMT: 1974-1998), who had survived ≥2y (BMT Survivor Study [BMTSS]; R01 CA78938, Bhatia). While BMTSS has successfully described the high burden of morbidity, accelerated aging and premature mortality in BMT-HM patients, the modest sample size (n=2,333) and the older transplant era (1974-1998) has limited the potential for new discoveries, especially in the face of evolving treatment strategies. We propose a significant enhancement of the existing BMTSS cohort to now include 10,042 HM patients treated with BMT between 1974 and 2014 at COH, UMN or UAB (BMT-HM), as well as a frequency-matched cohort of 3000 HM patients treated with conventional therapy without BMT (non-BMT-HM). This enhanced infrastructure (BMTSS- 2) will be poised to determine the burden of morbidity borne by HM patients treated with or without BMT within the context of individual HM diagnoses, and to understand the pathogenesis of treatment-related health conditions in the setting of accelerated aging. The enhanced BMTSS-2 infrastructure will enable us to: i) understand the long-term risk of health conditions experienced by HM patients treated with and without BMT; ii) determine the association between treatment exposures and health conditions; iii) determine trends in health conditions with changes in treatment strategies; iv) identify interactions between preventable modifiers (comorbidities, health behaviors) and treatment exposures when determining risk of health conditions; v) study the pathogenesis of treatment-related health conditions in the setting of accelerated aging, using genetic markers for susceptibility and epigenetic markers for the association of the health conditions with aging; vi) use HIPAA- compliant technology platform compatible with iOS/Android/iPad/Web-based applications to educate HM patients and measure health behaviors in real time. This is the largest and most comprehensive attempt at examining the health and wellbeing of HM patients treated with and without BMT. The overarching goal is to use BMTSS-2 for translational research along two tracks: A) develop risk prediction models to identify HM recipients at highest risk of treatment-related health conditions; and B) among those identified to be at highest risk, design and test targeted interventions to prevent/ ameliorate these debilitating chronic health conditions.