The clinical course of patients with CLL is highly heterogeneous reflecting an underlying biologic heterogeneity of the disease. In the last decade, whole exome/genome sequencing and targeted next-generation sequencing (NGS) techniques have revealed several genes recurrently mutated in CLL, involving a limited number of pathways, namely microenvironment-dependent signaling through NOTCH (NOTCH1, FBXW7), inflammatory receptors (MYD88), MAPK–extracellular signal-regulated kinase (BRAF, KRAS, NRAS, MAP2K1) and NF-κB pathways (BIRC3, TRAF3, NFKBIE), as well as intracellular programs such as DNA damage and cell cycle control (ATM, TP53, SAMHD1, POT1), chromatin modification (HIST1H1E, CHD2, ZMYM3), transcription (EGR2, IRF4, BCOR, MED12) and ribosomal processing (XPO1, SF3B1, RPS15).^{1, 2}

In this study we analyzed by NGS the mutational status of 10 target genes in a homogeneous cohort of 211 patients with Binet stage A CLL diagnosed between 2002 and 2014, and correlated the mutational status with time-to-first-treatment (TTFT). Patients' characteristics are reported in Table S1.

All patients were diagnosed according to the iWCLL 2008 criteria, retrospectively applied to patients diagnosed before 2008. The study was conducted according to the Declaration of Helsinki.

Mononuclear cells (BMMNCs) were isolated by standard density gradient centrifugation (Lympholyte-H; CEDARLANE Laboratories Ltd) from peripheral blood (n = 182) or bone marrow (n = 29). Buccal cells as control tissue were available in 66 patients (31%). Genomic DNA and total RNA were extracted following standard protocols for human tissue.

Targeted mutation analysis of 10 genes (ATM, BIRC3, FBXW7, KRAS, MYD88, NOTCH1, POT1, SF3B1, TP53, XPO1) was performed using a Truseq Custom Amplicon Sequencing Panel designed using Design Studio software (Illumina, San Diego, CA, USA). The oligo pool targets five full genes (coding exons and splice sites) and exonic hotspots of additional five genes (Table S2). Dual-barcoded TSCA libraries were created according to the manufacturer's protocol. Libraries were sequenced on a MiSeq system (Illumina). The resulting average depth of coverage for the 198 amplicons was 1287x (range: 503–3219x). The NGS data analysis relied on a robust workflow that implements GATK's pre-processing Best Practices. Functionally annotated variants were filtered based on the information retrieved from public databases (dbSNP, 1000Genome, dbNSFP, ESP6500) or in a set of in-house control tissue or healthy subiects. The remaining variants were finally tagged as oncogenic, based on the information retrieved from the literature, COSMIC and in silico prediction effect, as previously described.³ The variant analysis was limited to variants with allele frequency equal or greater than 1%. The variant allele frequency (VAF) in unselected mononuclear cells was adjusted for the percentage of the CLL cells quantified by immunophenotype. Mutations were considered subclonal when VAF was <12% and clonal when VAF was ≥12%.

Methods used for the assessment of IGHV mutational status and detection of TP53 mutations with Sanger sequencing are described in the supplementary methods (Appendix S1).

Qualitative variables were described as absolute and relative frequencies, while quantitative variables were summarized as median and interquartile range (IQR). Time-to-first-treatment, defined as the time between diagnosis and date of initiation of first treatment (event) or last follow-up (censored), was estimated by Kaplan–Meier product limit method. Log-rank test was used to compare outcome between two or more groups of patients. The effect of baseline characteristics of patients on TTFT was assessed by univariable Cox Proportional Hazard models. Variables with a p value lower than 0.2 at univariable analysis were included in multivariable model. Akaike's information criterion and Harrell's C index were used to compare multivariable models. Due to the high number of missing data, CLL-IPI and cytogenetics were not included in the multivariable model. The p values < 0.05 were considered statistically significant. All statistical analyses were conducted using the Stata 16 software (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC).

Overall, 113 mutations were found in 74/211 patients (35%), co-occurrence of mutations in ≥ 2 genes was observed in 24/74 (32% of mutated cases). The median corrected VAF of all mutations was 0.18 (IQR: 0.05–0.34). The ATM mutations were observed in 20 patients (9%). Multiple mutations in the ATM gene were rather common with two ATM mutations in four patients, three ATM mutations in one patient and four ATM mutations in another one. Six of twenty patients with ATM mutations had 11q deletion by FISH. The SF3B1 mutations were detected in 14 patients (7%). Fourteen XPO1 mutations were observed in 13 patients (6%) and most of them (13/14) occurred at E571. All but one patient harboring the XPO1 mutation showed IGHV unmutated genes. The TP53 mutations were found in 11 patients (5%) and were located at the DNA-binding domain of the protein. Four of eleven patients with TP53 mutations by NGS also had a 17p deletion by FISH. All TP53 mutations found with VAF > 10% (10/11) were confirmed by Sanger sequencing. So, POT1 mutations were found in 12 patients (6%), who were IGHV unmutated in all cases but one. The NOTCH1 frame-shift mutations were found in 11 patients (5%), 10 of whom at P2514 in the PEST domain. Nine of 11 patients harboring NOTCH1 mutations at diagnosis showed unmutated IGHV. None of the patients with NOTCH1 mutation had a concomitant FBXW7 mutation. Ten FBXW7 mutations were detected in nine patients (4%), five of whom showed unmutated IGHV. Six of 11 patients with NOTCH1 mutation and five of 10 patients with FBXW7 mutations had trisomy 12 by FISH. A total of seven MYD88 missense mutations were found in seven patients (3%), all of whom were IGHV mutated. We found also two KRAS missense mutations and two BIRC3 mutations in two patients each (1%). The frequency, type and VAF of mutations are reported in Figure S1.

With a median follow-up of 96 months (IQR: 65–138), 108 patients (51%) were treated per iwCLL criteria. The median TTFT was 6 years (95% CI: 5–12). In univariate analysis, the presence of one or more mutations by NGS was associated with shorter TTFT (p < 0.001) (Figure S2).

The presence of mutations in the following genes were associated with shorter TTFT: ATM (p < 0.001), POT1 (p < 0.001), NOTCH1 (p < 0.001), XPO1 (p = 0.002), SF3B1 (p = 0.007), TP53 (p = 0.022), MYD88 (p = 0.041), FBXW7 (p = 0.045). Other variables associated with shorter TTFT were unmutated IGHV (p < 0.001), trisomy 12 (p = 0.007), 11q deletion (p = 0.004). Of note, TP53 mutations by NGS, 17p deletion and CK were not associated with significantly shorter TTFT in univariate analysis (Figure 1). A multivariable model identified unmutated IGHV (hazard ratio 2.2, 95% CI 1.4–3.6; p = 0.001), ATM mutation (hazard ratio 2.3, 95% CI 1.3–4.2; p = 0.005), FBXW7 mutation (hazard ratio, 3.1, 95% CI 1.3–7.3; p = 0.012), MYD88 mutation (hazard ratio 4.9, 95% CI 2.0–11.6; p < 0.001) and POT1 mutation (hazard ratio 3.3, 95% CI 1.6–6.6; p = 0.001) as being independently associated with shorter TTFT (Table S3).

In this study we analyzed with NGS 10 genes, selected for their relevance in CLL, in a homogeneous cohort of 211 patients with Binet stage A disease and correlated the mutational status with TTFT. TTFT has recently emerged as an important end-point for untreated CLL patients, able to identify early drivers of disease which may be masked when investigating long-term and treatment-defined outcomes, such as progression-free survival and overall survival.

We found that the presence of one or more mutations by NGS was predictive of significantly shorter TTFT, in line with a recent study reporting that both the presence and the total number of mutated genes (i.e., the tumoral mutational load) identify those individuals who are more likely to progress requiring therapy.⁴ As one could argue that the detrimental effect was mainly attributable to TP53 mutations, we evaluated the impact of having at least one mutation by NGS excluding TP53 mutations and confirmed the adverse prognostic impact on TTFT.

In our study, mutations in POT1, ATM, FBXW7 and MYD88 genes were independently associated with shorter TTFT. While the prognostic impact of some of these mutations has been already reported in previous studies,^5-7 to our knowledge this is the first study showing POT1 as an independent risk factor for progression in early stage CLL patients.

The MYD88 mutations were detected exclusively in patients with mutated IGHV, as previously reported, and were associated with a significantly shorter TTFT in our series. The role of MYD88 mutations on clinical outcome of CLL patients remains controversial. Despite previous report of favorable outcome of MYD88 mutated CLL patients, subsequently observations showed that MYD88 mutations is associated with shorter TTFT in M-IGHV patients and may counteract the survival advantage of M-IGHV.⁷

Of note, neither TP53 mutations by NGS nor 17p deletions were associated with shorter TTFT. This is in keeping with prior studies where TP53 abnormalities did not predict a shorter TTFT, suggesting that these abnormalities, who are strong prognostic factors in advanced or relapsed CLL, have limited role in early untreated disease.⁶

The main strengths of this study are the homogeneity of the study population and the prolonged follow-up which seems adequate to assess long-term outcomes of patients with early stage CLL. On the other hand, the main limitations of this study include its single-center retrospective design, and the analysis of a limited number of genes with exclusion of non-coding regions. In addition, we did not analyze purified CLL cells, however we normalized the VAF by the actual CLL cell content.

With these limitations, our study demonstrates that one third of patients with Binet stage A CLL harbor somatic mutations with prognostic relevance and confirms the value of NGS for the identification of predictors of time to first treatment in early stage CLL.

CLL 患者的临床病程具有高度异质性，反映了该疾病潜在的生物学异质性。在过去十年中，全外显子组/基因组测序和靶向下一代测序 (NGS) 技术揭示了在 CLL 中反复突变的几个基因，涉及数量有限的通路，即通过 NOTCH ( NOTCH1, FBXW7 ) 的微环境依赖性信号传导、炎症受体（MYD88）、MAPK-细胞外信号调节激酶（BRAF、KRAS、NRAS、MAP2K1）和 NF-κB 通路（BIRC3、TRAF3、NFKBIE），以及细胞内程序，如 DNA 损伤和细胞周期控制（ATM、 TP53、SAMHD1、POT1）、染色质修饰（HIST1H1E、CHD2、ZMYM3)、转录（EGR2、IRF4、BCOR、MED12）和核糖体加工（XPO1、SF3B1、RPS15）。^1、2

在这项研究中，我们通过 NGS 分析了 2002 年至 2014 年间诊断出的 211 名 Binet A 期 CLL 患者的同质队列中 10 个靶基因的突变状态，并将突变状态与首次治疗时间 (TTFT) 相关联。表 S1 报告了患者的特征。

所有患者均根据 iWCLL 2008 标准进行诊断，回顾性适用于 2008 年之前诊断的患者。本研究根据赫尔辛基宣言进行。

通过标准密度梯度离心（Lympholyte-H；CEDARLANE Laboratories Ltd）从外周血（n = 182）或骨髓（n = 29）中分离出单核细胞（BMMNC）。66 名患者 (31%) 可获得口腔细胞作为对照组织。按照人体组织的标准方案提取基因组 DNA 和总 RNA。

10个基因（ATM、BIRC3、FBXW7、KRAS、MYD88、NOTCH1、POT1、SF3B1、TP53、XPO1）的靶向突变分析) 使用设计工作室软件 (Illumina, San Diego, CA, USA) 设计的 Truseq Custom Amplicon Sequencing Panel 进行。寡核苷酸库针对五个完整基因（编码外显子和剪接位点）和另外五个基因的外显子热点（表 S2）。双条形码 TSCA 库是根据制造商的协议创建的。文库在 MiSeq 系统 (Illumina) 上测序。198 个扩增子的平均覆盖深度为 1287 倍（范围：503–3219 倍）。NGS 数据分析依赖于实施 GATK 预处理最佳实践的强大工作流程。根据从公共数据库（dbSNP、1000Genome、dbNSFP、ESP6500）或一组内部对照组织或健康受试者中检索到的信息过滤功能注释变体。³变异分析仅限于等位基因频率等于或大于 1% 的变异。未选择的单核细胞中的变异等位基因频率 (VAF) 被调整为由免疫表型量化的 CLL 细胞的百分比。当 VAF <12% 时突变被认为是亚克隆性的，当 VAF ≥12% 时被认为是克隆性突变。

用于评估 IGHV 突变状态和使用 Sanger 测序检测TP53突变的方法在补充方法（附录 S1）中进行了描述。

定性变量被描述为绝对和相对频率，而定量变量被概括为中位数和四分位距 (IQR)。首次治疗时间，定义为诊断与开始首次治疗（事件）或最后一次随访（删失）日期之间的时间，通过 Kaplan-Meier 乘积极限法估计。对数秩检验用于比较两组或更多组患者之间的结果。患者的基线特征对 TTFT 的影响通过单变量 Cox 比例风险模型进行评估。带p 的变量单变量分析中低于 0.2 的值包含在多变量模型中。使用 Akaike 的信息标准和 Harrell 的 C 指数来比较多变量模型。由于大量缺失数据，CLL-IPI 和细胞遗传学不包括在多变量模型中。的p值<0.05被认为是统计学显著。所有统计分析均使用 Stata 16 软件（StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC）进行。

总体而言，在 74/211 名患者（35%）中发现了 113 个突变，在 24/74 名患者（32% 的突变病例）中观察到 ≥ 2 个基因的突变同时发生。所有突变的中位校正 VAF 为 0.18（IQR：0.05-0.34）。所述ATM在20名患者（9％）观察到的突变。在多重突变ATM基因是相当常见的有两个ATM 4例突变，三个ATM突变在一个病人和四个ATM在一个又一个突变。通过 FISH ，20 名ATM突变患者中有 6 名有 11q 缺失。所述SF3B1 14例（7％）中检测到的突变。十四XPO1在 13 名患者 (6%) 中观察到突变，其中大多数 (13/14) 发生在E571。除一名携带XPO1突变的患者外，所有患者均显示 IGHV 未突变基因。的TP53突变11例（5％）被发现和分别位于该蛋白质的DNA结合结构域。NGS 检测到TP53突变的 11 名患者中有 4 名也有 FISH 检测的 17p 缺失。通过 Sanger 测序证实了 VAF > 10% (10/11) 的所有TP53突变。因此，在 12 名患者 (6%) 中发现了POT1突变，除 1 名外，所有患者的 IGHV 均未发生突变。该NOTCH1在 11 名患者 (5%) 中发现移码突变，其中 10 名位于 PEST 域的 P2514。诊断时携带NOTCH1突变的 11 名患者中有 9 名显示未突变的 IGHV。具有NOTCH1突变的患者均没有伴随FBXW7突变。在 9 名患者 (4%) 中检测到10 种FBXW7突变，其中 5 名显示未突变的 IGHV。通过 FISH ，11 名具有NOTCH1突变的患者中的 6 名和具有FBXW7突变的 10 名患者中的 5 名具有12 三体。在 7 名患者 (3%) 中总共发现了 7个MYD88错义突变，所有这些患者都是 IGHV 突变的。我们还发现了两个KRAS错义突变和两个BIRC32 名患者各发生突变 (1%)。图 S1 报告了突变的频率、类型和 VAF。

中位随访 96 个月（IQR：65-138），108 名患者（51%）按照 iwCLL 标准接受治疗。中位 TTFT 为 6 年（95% CI：5-12）。在单变量分析中，NGS 存在的一个或多个突变与较短的 TTFT 相关（p  < 0.001）（图 S2）。

以下基因中的突变与较短的 TTFT 相关：ATM ( p  < 0.001)、POT1 ( p  < 0.001)、NOTCH1 ( p  < 0.001)、XPO1 ( p  = 0.002)、SF3B1 ( p  = 0.007)、TP53 ( p  = 0.022)、MYD88 ( p  = 0.041)、FBXW7 ( p  = 0.045)。与较短 TTFT 相关的其他变量是未突变的 IGHV ( p  < 0.001)、12 三体 ( p  = 0.007)、11q 缺失 ( p = 0.004）。值得注意的是，NGS 引起的TP53突变、17p 缺失和 CK 与单变量分析中显着更短的 TTFT 无关（图 1）。多变量模型确定了未突变的 IGHV（风险比 2.2，95% CI 1.4-3.6；p  = 0.001），ATM突变（风险比 2.3，95% CI 1.3-4.2；p  = 0.005），FBXW7突变（风险比，3.1， 95% CI 1.3–7.3；p  = 0.012），MYD88突变（风险比 4.9，95% CI 2.0–11.6；p  < 0.001）和POT1突变（风险比 3.3，95% CI 1.6–6.6；p  = 0.001）与较短的 TTFT 独立相关（表 S3）。

在这项研究中，我们在 211 名 Binet A 期疾病患者的同质队列中分析了 NGS 10 基因，这些基因根据其与 CLL 的相关性而选择，并将突变状态与 TTFT 相关联。TTFT 最近已成为未经治疗的 CLL 患者的重要终点，能够识别疾病的早期驱动因素，在调查长期和治疗定义的结果（例如无进展生存期和总生存期）时可能会掩盖这些因素。

我们发现 NGS 的一个或多个突变的存在预示着 TTFT 显着缩短，这与最近的一项研究报告一致，即突变基因的存在和总数（即肿瘤突变负荷）可以识别那些患有更有可能进展需要治疗。⁴由于有人可能会争辩说不利影响主要归因于TP53突变，我们评估了 NGS 具有至少一种突变（不包括TP53突变）的影响，并确认了对 TTFT 的不利预后影响。

在我们的研究中，POT1、ATM、FBXW7和MYD88基因的突变与较短的 TTFT 独立相关。虽然之前的研究已经报道了其中一些突变的预后影响，但^5-7据我们所知，这是第一项显示POT1作为早期 CLL 患者进展的独立危险因素的研究。

该MYD88是在患者的突变IGHV专门检测突变，如先前报道，并在我们的系列显著较短TTFT相关。MYD88突变对 CLL 患者临床结果的作用仍存在争议。尽管先前报道MYD88突变的 CLL 患者预后良好，但随后的观察表明，MYD88突变与 M-IGHV 患者的 TTFT 较短有关，并可能抵消 M-IGHV 的生存优势。⁷

值得注意的是，NGS 的TP53突变和 17p 缺失都与较短的 TTFT 无关。这与先前的研究一致，其中TP53异常并不能预测更短的 TTFT，表明这些异常是晚期或复发性 CLL 的强预后因素，在早期未经治疗的疾病中作用有限。⁶

这项研究的主要优势是研究人群的同质性和长期随访，这似乎足以评估早期 CLL 患者的长期结果。另一方面，本研究的主要局限性包括其单中心回顾性设计，以及排除非编码区域的有限数量基因的分析。此外，我们没有分析纯化的 CLL 细胞，但是我们通过实际 CLL 细胞含量对 VAF 进行了标准化。

由于这些局限性，我们的研究表明，三分之一的 Binet A 期 CLL 患者具有与预后相关的体细胞突变，并证实了 NGS 在识别早期 CLL 首次治疗时间预测因子方面的价值。