WO2023240725A1 - 一组nk/t细胞淋巴瘤预后相关基因、基因组预后模型及其用途 - Google Patents
一组nk/t细胞淋巴瘤预后相关基因、基因组预后模型及其用途 Download PDFInfo
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- NK/T cell lymphoma is a type of non-Hodgkin lymphoma. NK cells and cytotoxic T cells derived from lymphocytes often invade the lymphoid organs around the nose and are more common in young men.
- prognostic scoring systems for NK/T cell lymphoma. Among them, the International Prognostic Index (IPI), NK/T-cell lymphoma prognostic index (Prognostic Index of Natural Killer Cell Lymphoma, PINK) and the improved PINK-E have all been fully verified. And a series of corresponding treatment measures are proposed for NK/T cell lymphoma patients differentiated into high-risk and low-risk groups.
- the risk factors included in these prognostic scoring systems are mainly patients' clinical data, including advanced age, advanced stage patients, more than 1 extranodal involvement site, and non-nasal type.
- the purpose of the present invention is to provide a technical solution that can achieve more accurate and effective prognosis judgment for NK/T cell lymphoma patients in view of the above technical problems to be solved.
- the present invention provides a group of NK/T cell lymphoma prognosis-related genes, including: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B and NRG1.
- Step 2 Combined with the patient clinical data corresponding to the sequencing sample, use LASSO Cox regression to screen out a group of prognosis-related genes that are significantly and frequently associated with the prognosis of NK/T cell lymphoma tumors from all genes with mutations.
- the group of prognosis-related genes is Prognosis-related genes include BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B and NRG1;
- Step 3 Construct the genomic prognostic model using the set of prognosis-related genes and perform survival curve analysis to evaluate the survival rate of NK/T cell lymphoma patients and the efficacy of the genomic prognostic model;
- Step 5 Use multivariable Cox regression analysis to compare the efficacy of the genomic prognostic model and other risk factors for the prognosis of NK/T cell lymphoma.
- a new genomic prognostic model constructed with 13 genes with somatic mutations has significant prognosis for progression-free survival (PFS) and overall survival (OS) of patients with NK/T cell lymphoma.
- PFS progression-free survival
- OS overall survival
- Predictive value can achieve more accurate and effective prognosis judgments for NK/T cell lymphoma patients, improve poor treatment prognosis in a targeted manner, improve medical standards, and improve quality of life.
- the genomic prognostic model of the present invention can be used to improve the existing NK/T cell lymphoma prognostic scoring system, with better prediction effect, thereby further improving the risk stratification and prognosis assessment of NK/T cell lymphoma patients, with Good clinical promotion prospects.
- Prediction or “prognosis” as used herein refers to the process or outcome of predicting a patient's condition and does not mean that the process or outcome of a patient's condition can be predicted with 100% accuracy. "Prediction” or “prognosis” means determining whether there is an increased likelihood of certain processes or outcomes, and does not mean determining the likelihood that certain processes or outcomes will occur by comparing them to those in which they do not occur . For the purposes of the present invention, a particular process or outcome is more likely to be observed in patients identified as having a mutation by a genomic prognostic model than in those who do not display the trait.
- LASSO Cox regression was used to screen out 13 mutated genes that are frequently related to the prognosis of NK/T-cell lymphoma patients, namely BCOR.
- NCBI Gene ID: 54880 JAK3 (NCBI Gene ID: 3718), KRAS (NCBI Gene ID: 3845), MYH11 (NCBI Gene ID: 4629), DCC (NCBI Gene ID: 1630), ITK (NCBI Gene ID: 3702), NOTCH1 (NCBI Gene ID: 4851), FAS (NCBI Gene ID: 355), RET (NCBI Gene ID: 5979), BIRC3 (NCBI Gene ID: 330), MLLT1 (NCBI Gene ID: 4298), LRP1B ( NCBI Gene ID: 53353) and NRG1 (NCBI Gene ID: 3084).
- the specific somatic mutations of the aforementioned 13 genes in 100 NK/T cell lymphoma tumor samples with mutations identified black means that the gene in the sample has mutations, white means not
- the percentage marked on the right side of the figure is the mutation frequency of the corresponding gene in 100 samples.
- genomic prognostic model was weakly correlated with other risk factors ( Figure 4), indicating that the genomic prognostic model of the present invention is independent and can be used together with other risk factors for risk stratification and prognosis assessment of NK/T cell lymphoma patients.
- genomic prognostic model of the present invention is the risk factor most significantly related to the prognosis of PFS and OS in NK/T cell lymphoma patients.
- genomic prognostic models can be combined with existing NK/T cell lymphoma prognostic scoring systems.
- each scoring system combines the genomic prognostic model with IPI-G.
- the prognostic effects of , PINK-G and PINK-EG are significantly improved (P ⁇ 0.001, ⁇ 2 test), which shows that the genomic prognostic model of the present invention can be used to improve Current NK/T-cell lymphoma prognostic scoring system to achieve more accurate prognostic assessment.
- the present invention can also utilize the above 13 target genes to provide a detection kit for NK/T cell lymphoma prognosis-related genes, wherein the detection kit contains a probe for capturing the target gene, and the target gene Includes: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B and NRG1.
- the detection kit contains a probe for capturing the target gene
- the target gene Includes: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B and NRG1.
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Abstract
本发明公开了一组NK/T细胞淋巴瘤预后相关基因,其包括:BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。本发明还提供了一种NK/T细胞淋巴瘤的基因组预后模型、一种用于构建NK/T细胞淋巴瘤的基因组预后模型的方法、所述NK/T细胞淋巴瘤预后相关基因用于构建基因组预后模型的用途以及一种NK/T细胞淋巴瘤预后相关基因的检测试剂盒。本发明可用于准确预测NK/T细胞淋巴瘤患者的预后,而且可以结合并改进现有的NK/T细胞淋巴瘤预后评分系统,使其具有更好的评估效果。
Description
本发明涉及基因突变检测领域,具体地,涉及一组NK/T细胞淋巴瘤预后相关基因、以此构建的基因组预后模型及其用途。
NK/T细胞淋巴瘤是一种非霍奇金淋巴瘤,来源于淋巴细胞的NK细胞和细胞毒性T细胞,经常侵犯到鼻部周围的淋巴器官,多发于青年男性。目前已存在多种应用于NK/T细胞淋巴瘤的预后评分系统。其中,淋巴瘤国际预后评分国际预后指数(International Prognostic Index,IPI)、NK/T细胞淋巴瘤预后指数(Prognostic Index of Natural Killer Cell Lymphoma,PINK)和改进后的PINK-E都经历了充分验证,且对其所区分出的高风险和低风险组的NK/T细胞淋巴瘤患者提出了一系列相对应的治疗措施。这些预后评分系统所纳入的风险因素主要为患者的临床数据,包括了高龄、晚期患者、结外累及部位数量大于1和非鼻型等。
然而,目前部分患者缺乏足够有效的风险分层以指导临床治疗,因此对NK/T细胞淋巴瘤患者的风险分层和预后评估仍需要进一步改善。
随着二代测序技术的日趋便利和遗传分析手段的不断提升,近年来的一些研究揭示了涉及NK/T细胞淋巴瘤患病风险的潜在驱动损伤,包括了有关JAK-STAT/NF-κB/MAPK通路、表观遗传修饰分子、RNA解旋酶的体细胞突变以及有关HLA-DPB1、IL18RAP和HLA-DRB1这几个基因的单核苷酸位点的遗传多态性。这些研究皆为基因组预后模型的建立和完善打下了坚实的基础。
发明内容
本发明的目的是针对以上要解决的技术问题,提供一种能够对NK/T细胞淋巴瘤患者实现更精准有效的预后判断的技术方案。
为实现以上目的,本发明提供了一组NK/T细胞淋巴瘤预后相关基因,其包括:BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。
另一方面,本发明还提供了一种NK/T细胞淋巴瘤的基因组预后模型,其特征在于,所述基因组预后模型基于以下一组目的基因而构建:BCOR、JAK3、KRAS、MYH11、DCC、ITK、 NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。
另一方面,本发明还提供了一种用于构建NK/T细胞淋巴瘤的基因组预后模型的方法,其包括以下步骤:
步骤一、对NK/T细胞淋巴瘤肿瘤样本进行基因组测序,经分析得到存在体细胞突变的全部基因;
步骤二、结合与测序样本对应的患者临床数据,使用LASSO Cox回归从存在突变的全部基因中筛选出与NK/T细胞淋巴瘤肿瘤预后显著高频关联的一组预后相关基因,所述一组预后相关基因包括BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1;
步骤三、以所述一组预后相关基因构建所述基因组预后模型并进行生存曲线分析,评估NK/T细胞淋巴瘤患者的生存率及所述基因组预后模型的效能;
步骤四、将所述基因组预后模型与现有的NK/T细胞淋巴瘤预后评分系统所包含的风险因素进行相关性分析,检验所述基因组预后模型的独立性;
步骤五、利用多变量Cox回归分析比较所述基因组预后模型与其他风险因素对于NK/T细胞淋巴瘤预后的效能的优劣。
根据本发明的基因组预后模型的构建方法可用于非疾病诊断目的,也可用于疾病诊断目的。
优选地,根据本发明所述的方法,其中,所述步骤二中,当NK/T细胞淋巴瘤患者存在所述基因组预后模型的13个基因中的任意基因的突变,该患者会被赋予1分的风险分数,并作为突变型组,无突变的为野生型组。
优选地,根据本发明所述的方法,其中,所述步骤四中,纳入分析的现有的NK/T细胞淋巴瘤预后评分系统包括但不限于IPI、PINK或PINK-E。
优选地,根据本发明所述的方法,其中,在所述步骤五之后,还包括将所述基因组预后模型结合到现有的NK/T细胞淋巴瘤预后评分系统中,得到改进后的NK/T细胞淋巴瘤预后评分系统。
另一方面,本发明还提供了根据本发明所述的NK/T细胞淋巴瘤预后相关基因用于构建NK/T细胞淋巴瘤基因组预后模型的用途。
另一方面,本发明还提供了根据本发明所述的NK/T细胞淋巴瘤预后相关基因用于结合并改进现有NK/T细胞淋巴瘤预后评分系统的用途。
另一方面,本发明还提供了一种NK/T细胞淋巴瘤预后相关基因的检测试剂盒,其中,所 述检测试剂盒中含有用于捕获目的基因的探针,所述目的基因包括:BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。
本发明具备以下有益效果:
1.以13个存在体细胞突变的基因构建的新型基因组预后模型对于NK/T细胞淋巴瘤患者的无进展生存(progression-free survival,PFS)和总生存(overall survival,OS)预后具有显著的预测价值,从而可以实现对NK/T细胞淋巴瘤患者更精准有效的预后判断,有针对性的改善不良的治疗预后,提高医疗水平,提高生活质量。
2.本发明的基因组预后模型可用于改进现有的NK/T细胞淋巴瘤预后评分系统,具有更佳的预测效果,从而进一步改善NK/T细胞淋巴瘤患者的风险分层和预后评估,具有良好的临床推广前景。
图1为构建基因组预后模型所用的13个基因在100例被鉴定出存在突变的NK/T细胞淋巴瘤肿瘤样本中的具体体细胞突变情况(黑色指该样本的该基因存在突变,白色则无),图右侧所标的百分比为相应基因在100例样本中所出现的突变频率。
图2为以基因组预后模型将训练集中的NK/T细胞淋巴瘤患者划分为野生型组和突变型组,做生存曲线分析展示了两组患者间的无进展生存PFS(上图,A)和总生存OS(下图,B)的差异。
图3为基因组预后模型将验证集中的NK/T细胞淋巴瘤患者划分为野生型组和突变型组,做生存曲线分析展示了两组患者间的无进展生存PFS(上图,A)和总生存OS(下图,B)的差异。
图4为包括基因组预后模型在内的8个风险因素以及包括基因组预后模型和样本国家来源在内的9个因素两两之间的相关性热图,只有显著的Cramér's V相关系数(P<0.05)才在图中展示,系数大小与右侧图例中的颜色相对应。
图5为现有的IPI(A)、PINK(B)以及PINK-E(C)预后评分系统与分别加入基因组预后模型进行改进后的IPI-G(D)、PINK-G(E)及PINK-E-G(F)的预后评分系统的无进展生存PFS的生存曲线图,Harrell's C指数(C-index)的数值越大,表明预测模型具有更高的效能。
为了能够更清楚地理解本发明的上述目的、特征和优点,下面结合附图和具体实施例对本发明进行进一步的详细描述。本领域技术人员应该明了,所述实施例仅仅是帮助理解本发明,不应视为对本发明的具体限制;如无特殊说明,下述实施例中所使用的测序和分析方法均为常规方法。
本文所述的“预测”或“预后”是指预测患者状况的过程或结果,并不意味着能以100%的准确度预测患者状况的过程或结果。“预测”或“预后”是指确定某些过程或结果的可能性是否增加,而并不意味着通过与某些过程或结果不发生的情况进行比较来确定发生某些过程或结果的可能性。如本发明而言,由基因组预后模型鉴定为突变型的患者中,与不显示该特征的人相比,更有可能观察到特定过程或结果。
一共收集了来自中山大学肿瘤防治中心以及新加坡总医院的一共260例NK/T细胞淋巴瘤肿瘤样本的基因组测序数据,由50例全基因组测序数据和210例靶向捕获测序数据组成。经分析得到了NK/T细胞淋巴瘤中存在体细胞突变的全部基因。
结合其中的具有对应生存数据的212例NK/T细胞淋巴瘤患者作为训练集,使用LASSO Cox回归筛选出与NK/T细胞淋巴瘤患者预后高频相关的13个发生突变的基因,分别为BCOR(NCBI Gene ID:54880)、JAK3(NCBI Gene ID:3718)、KRAS(NCBI Gene ID:3845)、MYH11(NCBI Gene ID:4629)、DCC(NCBI Gene ID:1630)、ITK(NCBI Gene ID:3702)、NOTCH1(NCBI Gene ID:4851)、FAS(NCBI Gene ID:355)、RET(NCBI Gene ID:5979)、BIRC3(NCBI Gene ID:330)、MLLT1(NCBI Gene ID:4298)、LRP1B(NCBI Gene ID:53353)和NRG1(NCBI Gene ID:3084)。如图1所示,为前述13个基因在100例被鉴定出存在突变的NK/T细胞淋巴瘤肿瘤样本中的具体体细胞突变情况(黑色指该样本的该基因存在突变,白色则无),图右侧所标的百分比为相应基因在100例样本中所出现的突变频率。
以上述13个目的基因构建预测NK/T细胞淋巴瘤预后的基因组预后模型,当NK/T细胞淋巴瘤患者存在这13个目的基因中任意基因的突变时,该患者会被赋予1分的风险分数,并作为突变型组,不存在突变的为野生型组。换言之,当NK/T细胞淋巴瘤患者存在这13个目的基因中任意基因的突变时,不论存在多少个基因突变,都是1分,所以突变型患者分数是1,野生型(非突变型)患者分数是0,以此分组。
经统计学检验,应用基因组预后模型于NK/T细胞淋巴瘤患者的风险分组可以显著地预测患者的PFS和OS预后(PFS和OS:P<0.0001,对数秩检验;图2),并可在独立的验证集里实现重复(PFS:P=0.041,OS:P=0.011;图3),表明本发明的基因组预后模型具有很 高的预后效能。
现有的NK/T细胞淋巴瘤预后评分系统主要为IPI、PINK和PINK-E,其所纳入的风险因素包括高龄、晚期患者、ECOG体能状态评分大于1、乳酸脱氢酶升高、结外累及部位数量大于1、非鼻型和血清EB病毒(Epstein-Barr Virus,EBV)拷贝数高。将基因组预后模型与这些风险因素进行相关性分析来计算基因组预后模型与这些风险因素的Cramér's V相关系数,以检验基因组预后模型是否具有独立性。
此外,经多变量Cox回归分析比较了基因组预后模型与其他风险因素在预测NK/T细胞淋巴瘤患者预后上的效能,基因组预后模型相较于其他风险因素具有最大的风险比(表1),表明本发明的基因组预后模型是与NK/T细胞淋巴瘤患者的PFS和OS预后最显著相关的风险因素。
表1.Cox回归模型检验各风险因素
对于NK/T细胞淋巴瘤预后作为单变量/多变量的风险比、95%置信区间和显著性
此外,可将基因组预后模型同现有的NK/T细胞淋巴瘤预后评分系统相结合。
计算基因组预后模型的风险分数与现有的NK/T细胞淋巴瘤预后评分系统(IPI、PINK和 PINK-E)的评分结果之和,以此作为改进后的预测NK/T细胞淋巴瘤患者预后的评分系统(IPI-G、PINK-G和PINK-E-G)。
通过Harrell's C指数进行统计检验,相较于原先的IPI、PINK和PINK-E的预后效果(分别对应图5中的A、B、C),各评分系统结合了基因组预后模型后的IPI-G、PINK-G和PINK-E-G的预后效果(分别对应图5中的A、B、C)均有显著提升(P<0.001,χ
2检验),这表明,本发明的基因组预后模型可用于改进目前的NK/T细胞淋巴瘤预后评分系统,以实现更准确的预后评估。
本发明还可以利用上述13个目的基因,提供一种NK/T细胞淋巴瘤预后相关基因的检测试剂盒,其中,所述检测试剂盒中含有用于捕获目的基因的探针,所述目的基因包括:BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。从而,实现对NK/T细胞淋巴瘤的预后评估。
值得注意的是,上述说明并非是对本发明的限制,本发明也并不仅限于上述举例,本技术领域的技术人员在本发明的实质范围内所做出的变化、改型、添加或替换,也应属于本发明的保护范围。
Claims (9)
- 一组NK/T细胞淋巴瘤预后相关基因,其特征在于包括:BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。
- 一种NK/T细胞淋巴瘤的基因组预后模型,其特征在于,所述基因组预后模型基于以下一组目的基因而构建:BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。
- 一种用于构建NK/T细胞淋巴瘤的基因组预后模型的方法,其特征在于包括以下步骤:步骤一、对NK/T细胞淋巴瘤肿瘤样本进行基因组测序,经分析得到存在体细胞突变的全部基因;步骤二、结合与测序样本对应的患者临床数据,使用LASSO Cox回归从存在突变的全部基因中筛选出与NK/T细胞淋巴瘤肿瘤预后显著高频关联的一组预后相关基因,所述一组预后相关基因包括BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1;步骤三、以所述一组预后相关基因构建所述基因组预后模型并进行生存曲线分析,评估NK/T细胞淋巴瘤患者的生存率及所述基因组预后模型的效能;步骤四、将所述基因组预后模型与现有的NK/T细胞淋巴瘤预后评分系统所包含的风险因素进行相关性分析,检验所述基因组预后模型的独立性;步骤五、利用多变量Cox回归分析比较所述基因组预后模型与其他风险因素对于NK/T细胞淋巴瘤预后的效能的优劣。
- 根据权利要求3所述的方法,其特征在于,所述步骤二中,当NK/T细胞淋巴瘤患者存在所述基因组预后模型的13个基因中的任意基因的突变,该患者会被赋予1分的风险分数,并作为突变型组,无突变的为野生型组。
- 根据权利要求3所述的方法,其特征在于,所述步骤四中,纳入分析的所述现有的NK/T细胞淋巴瘤预后评分系统包括但不限于IPI、PINK或PINK-E。
- 根据权利要求3所述的方法,其特征在于,在所述步骤五之后,还包括将所述基因组预后模型结合到现有的NK/T细胞淋巴瘤预后评分系统中,得到改进后的NK/T细胞淋巴瘤预后评分系统。
- 权利要求1所述的NK/T细胞淋巴瘤预后相关基因用于构建NK/T细胞淋巴瘤基因组预后模型的用途。
- 权利要求1所述的NK/T细胞淋巴瘤预后相关基因用于结合并改进现有NK/T细胞淋巴 瘤预后评分系统的用途。
- 一种NK/T细胞淋巴瘤预后相关基因的检测试剂盒,其特征在于,所述检测试剂盒中含有用于捕获目的基因的探针,所述目的基因包括:BCOR、JAK3、KRAS、MYH11、DCC、ITK、NOTCH1、FAS、RET、BIRC3、MLLT1、LRP1B和NRG1。
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Non-Patent Citations (2)
Title |
---|
QIONG LI;WEI ZHANG;JIALI LI;JINGKANG XIONG;JIA LIU;TING CHEN;QIN WEN;YUNJING ZENG;LI GAO;LEI GAO;CHENG ZHANG;PEIYAN KONG;XIANGUI P: "Plasma circulating tumor DNA assessment reveals as a potential poor prognostic factor in extranodal NK/T-cell lymphoma", BIOMARKER RESEARCH, BIOMED CENTRAL LTD, LONDON, UK, vol. 8, no. 1, 17 July 2020 (2020-07-17), London, UK , pages 1 - 12, XP021279475, DOI: 10.1186/s40364-020-00205-4 * |
XINYANG LI, BIN WU, WEI YANG: "Research progress of NK/T-cell lymphoma related gene abnormities ", JOURNAL OF MODERN ONCOLOGY, vol. 25, no. 13, 29 May 2017 (2017-05-29), pages 2174 - 2178, XP093117610, ISSN: 1672-4992, DOI: 10.3969/j.issn.1672-4992.2017.13.042 * |
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