The identification and validation of mechanisms and biomarkers for relapse in diffuse large B-cell lymphoma

 描述(申请人提供)：弥漫性大B细胞淋巴瘤(DLBCL)是一种侵袭性的非霍奇金淋巴瘤(NHL)，是成人最常见的淋巴瘤亚型，每年新增病例45,000例。标准疗法包括联合化疗(R-CHOP)。虽然多达60%的患者可以通过化疗治愈，但其余40%的患者通常最初看到肿瘤缩小，但最终因耐药疾病复发。一些患者复发而另一些患者不复发的原因目前尚不清楚。这些肿瘤进化和适应治疗的机制尚不清楚。目前还没有生物标志物可以预测哪些患者会复发。如果能够找到这样的生物标志物，就可以采用更积极的策略治疗风险较高的患者，和/或使用量化最小残留疾病的技术更积极地进行监测。我们假设系统生物学方法可以识别复发的机制和生物标记物。为了验证这一假设，我们将对DLBCL的临床注释诊断-复发配对活检的初始队列患者进行免疫球蛋白重链(IgH)VDJ测序、外显子测序、转录组测序和DNA甲基化分析。利用计算分析，我们将识别和验证DLBCL复发签名。我们将在更大的患者队列中验证复发特征的关键变化 使用靶向Sanger测序和靶向Massarray甲基化分析。使用我们最近发表的CRISPR-Cas9模型(Kasap等人，自然化学生物学，2014)，我们将验证复发特征中的关键基因参与复发相关表型，如化疗耐药。然后，我们将使用VDJ测序、外显子组测序、转录组测序和DNA甲基化分析，在一组在最初诊断后至少5年未复发的患者中识别新的生物标记物，以识别高复发风险的患者。我们将使用旨在识别这些生物标记的计算分析来生成候选生物标记，并使用独立的验证队列来验证它们。这项拟议的研究是对RFA“早期检测恶性血液病的生物标记物”(PA-12-221)的响应。

 DESCRIPTION (provided by applicant): Diffuse large B-Cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin Lymphoma (NHL) and is the most common lymphoma subtype in adults with 45,000 new cases per year. Standard therapy consists of combination chemotherapy (R-CHOP). While up to 60% of patients can be cured with chemotherapy, the remaining 40% usually see initial tumor size reduction but eventually relapse with chemoresistant disease. The reason why some patients relapse while others do not is currently not known. The mechanisms by which these tumors evolve and adapt to treatment are unknown. There are currently no biomarkers that can predict which patients will relapse. If such biomarkers could be found, patients at higher risk could be treated with more aggressive strategies and/or be monitored more aggressively with technologies to quantify minimal residual disease. We hypothesize that a systems biology approach can identify both mechanisms and biomarkers of relapse. To test this hypothesis, we will perform immunoglobulin heavy chain (IGH) VDJ-sequencing, exome-sequencing, transcriptome sequencing and DNA methylation profiling in an initial cohort of patients with clinically annotated diagnosis-relapse paired biopses of DLBCL. Using computational analysis, we will identify and validate the DLBCL relapse signature. We will validate key alterations in the relapse signature in a larger cohort of patients using targeted Sanger sequencing and targeted MassArray based methylation analysis. Using a CRISPR-Cas9 model we recently published (Kasap et al, Nature Chemical Biology, 2014), we will validate the involvement of key genes from the relapse signature in relapse-associated phenotypes such as chemoresistance. We will then identify novel biomarkers that identify patients at high risk of relapse using VDJ-sequencing, exome-sequencing, transcriptome sequencing and DNA methylation profiling in a cohort of patients that have not relapsed at least 5 years after initial diagnosis. We will generate candidate biomarkers using a computational analysis designed to identify such biomarkers and validate them using an independent validation cohort. The proposed study is in response to the RFA "Biomarkers for Early Detection of Hematopoietic Malignancies" (PA-12-221).