Genomic complexity in patients with chronic lymphocytic leukaemia; Landscape, Definitions and Utility.

慢性淋巴细胞白血病(CLL)是一种在老年人中发病率越来越高的B细胞恶性肿瘤。在临床上，CLL是高度异质性的，一些患者需要立即治疗，结果令人沮丧，而另一些患者则被置于观察和等待干预。为了改善患者的预后，已经开发了许多生物标记物，允许对患者进行风险分层，并可以决定治疗计划。这些包括血清学标记，如β-2-微球蛋白表达，起源细胞标记，如IGHV突变状态和遗传改变，包括ATM突变和染色体17p的缺失。然而，这些生物标志物不足以预测整个CLL人群的患者预后。一种更全面的方法被建议是基因组复杂性(GC)，它结合了多个已建立的生物标志物。有大量证据表明，即使在慢性淋巴细胞性白血病人群的高危亚群中，GC对患者进行风险分层也是有效的。此外，证据还表明，它可以被用作预测性标记。应用这一生物标志物的主要限制是缺乏整合多个已建立的生物标志物的GC的明确定义。目前的文献使用不同的指标来研究GC，例如每个患者的拷贝数偏差(CNA)，每个患者的突变和CNA的数量，以及亚染色体丢失和获得的总长度(Mb)(剂量)。通过用于检测和测量GC的各种技术方法，从染色体显带分析到有针对性的下一代测序(NGS)，引入了进一步的变异。此外，将GC确立为有用的预测标记物的工作通常不能充分代表使用新型药物治疗的患者。在实现这种生物标志物的临床应用之前，必须建立一种统一的方法来测量GC，并且必须完成对组成CLL人群的各种患者的验证。GC的作用机制目前尚不清楚，但有证据表明，TP53突变是导致基因组不稳定的关键因素。然而，与基因组不稳定相关的其他遗传特征还没有得到充分的研究，如ATM基因功能障碍和端粒侵蚀。更好地了解GC的分子机制，可能会促进我们对疾病复发的理解，为治疗方案的选择提供更准确的理性，并导致更有利的结果和最佳的临床处理。假设：具有高GC(一种新的预后和预测性生物标记物)的CLL患者也会有其他已确定的高危生物标记物和较差的总存活率。目的1：确定准确反映慢性淋巴细胞性白血病患者生物学和临床趋势的GC指标。目的2：调查导致慢性淋巴细胞性白血病患者发生胃癌的基因驱动因素，以及这些基因驱动因素如何影响患者的临床预后。目的3：评估GC作为新药物治疗患者的预测生物标志物的有效性。

Chronic lymphocytic leukemia (CLL) is a B cell malignancy which has increasing prevalence in older people. Clinically, CLL is highly heterogenous with some patients requiring immediate treatment with dismal outcomes, whereas others are put on a watch and wait intervention. To improve patient outcome, many biomarkers have been developed which allow risk stratification of patients and can dictate treatment plans. These include serological markers such as beta-2-microglobulin expression, cell of origin markers such as IGHV mutational status and genetic alterations including ATM mutations and deletion of chromosome 17p. However, these biomarkers insufficiently predict patient outcome across the whole CLL population. A more holistic approach being suggested is genomic complexity (GC) which incorporates multiple established biomarkers. There is a great deal of evidence that indicates GC has utility to risk stratify patients, even within high risk subsets of the CLL population. Furthermore, evidence also suggests it can be used a predictive marker. The main limitation for application of this biomarker, is the lack of a definitive definition of GC that integrates multiple established biomarkers. Current literature investigates GC using various metrics such as the number of copy number aberrations (CNAs) per patient, number of mutations and CNAs per patient and the total length (Mb) of sub chromosomal losses and gains (dosage). Further variation is introduced through the variety of technology methods used to detect and measure GC, ranging from chromosomal banding analysis to targeted next generation sequencing (NGS). Furthermore, the work that has established GC as a useful predictive marker typically underrepresent patients treated with novel agents. Before the clinical application of this biomarker can be achieved, a unified approach to measure GC must be established with a validation across the wide variety of patients that make up the CLL population must be completed. The mechanism of action of GC is currently unknown but evidence indicates a critical role of TP53 mutation leading to genomic instability. However other genetic features associated with genomic instability haven't yet been fully investigated, such as ATM gene dysfunction and telomere erosion. It is likely that a better understanding of the molecular mechanisms that contribute to GC, will advance our understanding of disease relapse, provide more accurate rational for treatment selection, and result in more favorable outcomes and optimal clinical management. Hypothesis: CLL patients identified with high GC, a novel prognostic and predictive biomarker, will also have other established high-risk biomarkers and poor overall survival. Aim 1: To identify the GC metric that accurately captures biological and clinical trends within CLL patients.Aim 2: To investigate the genetic drivers that lead to GC within CLL patients and how these genetic drivers impact the clinical outcome of patients. Aim 3: To assess the validity of GC as a predictive biomarker within patients treated with novel agents.