BMT Survivor Study-2 (BMTSS-2)

2017年，估计有130万人患有血液系统恶性肿瘤(HM：白血病、骨髓瘤 或淋巴瘤)。前线使用全身高强度化疗加或不加放疗 这是HM管理的特点。治疗进展性疾病或复发风险高的患者 使用更高强度的化疗/放射和血液或骨髓移植(BMT)；事实上，BMT是 通常是这些患者的唯一治疗选择。结果的稳步改善导致了不断增长的 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)与高强度治疗性暴露直接相关(例如，后续肿瘤、心脏 失败)。2000年，我们建立了2333名骨髓移植受者(希望之城[COH]或大学)的回顾性队列 曾在≥2y(BMT生存者研究[BMTSS]；R01 CA78938， Bhatia)。虽然BMTSS成功地描述了发病率、加速老龄化和早产儿的高负担 在骨髓移植-HM患者中，适度样本量(n=2333)和较大的移植时代(1974-1998)的死亡率 限制了新发现的可能性，特别是在面对不断变化的治疗策略的情况下。我们提出了一个 对现有BMTSS队列的显著增强，现在包括接受BMT治疗的10,042名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 接受非骨髓移植(Non-BMT-HM)的常规治疗。此增强型基础设施(BMTSS- 2)将准备确定在以下时间内接受或不接受骨髓移植治疗的HM患者所承担的发病率负担 个人HM诊断的背景，并了解与治疗相关的健康的发病机制 在加速老化的背景下的条件。增强的BMTSS-2基础设施将使我们能够：i) 了解接受和不接受骨髓移植治疗的HM患者健康状况的长期风险；ii) 确定暴露于治疗与健康状况之间的联系；三)确定健康趋势 情况与治疗策略的变化；四)确定可预防的改良剂之间的相互作用 (合并症、健康行为)和确定健康状况风险时的治疗暴露；五)研究 利用遗传标记研究加速衰老背景下与治疗相关的健康状况的发病机制 用于将健康状况与衰老联系起来的易感性和表观遗传学标记；vi)使用HIPAA- 兼容iOS/Android/iPad/基于Web的应用程序的合规技术平台，以教育HM 并实时测量患者的健康行为。这是有史以来规模最大、最全面的一次尝试 检查接受和不接受骨髓移植治疗的HM患者的健康和幸福感。首要目标是使用 BMTSS-2用于两个方面的翻译研究：a)开发风险预测模型，以确定HM接受者 与治疗相关的健康状况风险最高；以及B)在那些被确定为风险最高的人群中，设计 并测试有针对性的干预措施，以预防/改善这些令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.