CN113053456B - Aml患者免疫分型系统、aml患者预后评分模型及其构建方法 - Google Patents
Aml患者免疫分型系统、aml患者预后评分模型及其构建方法 Download PDFInfo
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Abstract
本发明公开了AML患者免疫分型系统、AML患者预后评分模型及其构建方法。其中，免疫分型系统的构建方法，包括1)收集来自GEO数据库的AML患者免疫微环境信息；2)聚类分析得到有临床预后意义的10种免疫细胞类型；3)然后对患者进行聚类成三组：Cluster1、Cluster2、Cluster3，其中，Cluster1免疫评分和微环境评分最低，HSC浸润程度最高；Cluster3免疫评分和微环境评分最高，HSC浸润程度最低；Cluster2介于两组之间，完成免疫分型系统的建立。此外，根据免疫分型系统构建了AML患者预后模型，该模型相对于现有模型精准度更高。
Description
技术领域
本发明涉及生物技术领域，特别涉及一种AML患者免疫分型系统、AML患者预后评分模型及其构建方法。
背景技术
急性髓系白血病(AML)是骨髓祖细胞不受控的增殖引起的血液系统恶性肿瘤，其临床发病率高，复发率高，治愈率低，因此急需寻找新的治疗方案。目前AML的免疫微环境以及利用免疫原理设计新型药物的研究一直是基础医学和临床医学研究的热点。但是在AML的分型上，目前AML的分类系统主要有基于形态学的FAB分型系统，分为M0-M7；基于细胞分子遗传学的WHO分型系统，分为AML伴RUNX1-RUNX1T1、AML伴CBFB-MYH11等。尽管AML的免疫学研究越发深入，但仍未形成基于免疫微环境建立的免疫分型系统。同时欧洲白血病网(European Leukemia Net,ELN)危险度分层系统是目前临床上最为常用的分层系统，但该系统通常存在对AML患者危险度的误判，使患者危险度分层和治疗强度不匹配。随着二代测序技术和生物信息学的发展，除了正在使用的ELN系统外，目前已经出现几种预测模型，比如包含17个干细胞基因评分系统，3-microRNA预后评分系统，包含24个基因的预后评分系统，10个基因组成的预后评分系统，Clinseq-G预测模型等。尽管研究方法和结果呈现多样化，但并没有出现以免疫分型为基础构建的更优化的模型。因此，更有效的预后模型仍急需被开发。
发明内容
有鉴于此，本发明首先利用免疫大数据建立了一个AML患者免疫分型系统：
1)收集来自GEO数据库的AML患者表达谱数据，并通过表达谱预测得到免疫微环境信息；信息来着7个GEO数据库：GSE10358，GSE12417，GSE37642，GSE66525，GSE6891，GSE71014和GSE8970的1799例有效患者，其中1299例患者有总生存期数据信息。
2)归一化所有样本后，根据患者免疫细胞的相关性进行聚类分析，得到有临床预后意义的10种免疫细胞类型：CD4+T细胞、B细胞、CD8+T细胞、D8+中央记忆型T细胞、Class-switch记忆B细胞、嗜酸性粒细胞、纤维原细胞、肥大细胞、NKT细胞、造血干细胞；
3)然后将有临床预后意义的免疫细胞类型的浸润分数用R语言层次聚类包hclust依据ward.D距离对患者进行聚类成三组：Cluster 1、Cluster 2、Cluster 3，其中，Cluster1免疫评分和微环境评分最低，HSC浸润程度最高；Cluster 3免疫评分和微环境评分最高，HSC浸润程度最低；Cluster 2介于两组之间；免疫分型系统的三组患者生存具有显著差异，其中Cluster 1生存情况最差，而Cluster 3生存情况最好。
其次，本发明基于上述免疫分型建立AML患者预后评分模型：
预后评分由121个免疫相关基因的表达量经相应系数权重后的总和计算获得，y＝∑βi×Xi，其中，βi为对应基因的系数coef值；Xi为对应基因的表达量，根据病人样本测得。
建立AML患者预后评分模型具体的方法如下：
S1、筛选Cluster 1和Cluster 3两组间对AML存在预后意义的差异基因；
S2、运用LASSO-cox建立AML患者预后评分模型：y＝∑βi×Xi，其中，βi为对应基因的系数coef值；Xi为对应基因的表达量，根据病人样本测得。根据该模型，预后评分越高，预后越差。
本发明中，免疫相关基因与Cluster 1和Cluster 3两组之间存在预后意义的差异基因相同，有121个，分别为ABCC1、ACP6、ACSL4、ADA2、ADGRG1、AGA、AGFG1、ANKRD28、APBB1IP、APOBR、ARPC5L、BAX、BCAP29、BCAT1、BEX3、BPGM、CBX4、CCNA1、CCT4、CD34、CD58、CES1、CFAP70、CIAO1、CISD1、CKLF、CKM、CLIC2、CPVL、CRIPT、CTSB、CXCL2、CXorf21、CYFIP2、DEFB1、DNMT3B、DOCK1、DSG2、EFCAB2、EHD2、EIF3H、EZR、F2RL1、FAM124B、FAM30A、FCGRT、FECH、FGD2、FHL1、FSTL1、FZD6、GABRB2、GNAI1、GTF2F2、GYPC、H1F0、HAL、HDAC4、HDGFL3、HOMER3、HOPX、HSPD1、HTR1F、IDI1、IL17RA、IQGAP1、ITGA6、KCTD3、LAPTM4B、LGALS2、LILRA2、LPAR4、MAGED1、MFSD10、MN1、MS4A6A、MYL4、NBPF1、NIN、NRIP1、OPTN、P2RX7、PDE12、PF4、PGGT1B、PHLDA1、PIK3C2B、PLK4、PNMA1、PNRC2、PRKCH、RAB3GAP2、RAP1A、RBPMS、RCL1、REC8、RPS23、RUNX3、SALL2、SCRN1、SELENOP、SLC2A6、SLC38A1、SMIM10L1、SNU13、SOCS2、ST8SIA4、SV2A、TCEAL9、TCF4、TGOLN2、TMCO3、TMEM176B、TNFRSF21、TPK1、TRAPPC11、TTC28、TUBGCP5、VAMP8、ZEB2、ZNHIT1。
进一步地，上述121个基因的系数即coef值见下表：
本发明构建的新的预后评分模型不仅在GSE10358和GSE37642数据库中能将AML患者分成预后差异显著的两组，而且能够将数目庞大的IR-AML和CN-AML患者按照预后情况进一步区分。此外，与经典的ELN分层系统和已报道的其他预后评分模型相比，我们的预后评分模型预测精准度更高，分组间的预后差异更显著。总之，该预后评分模型可以使分组更精准，更有利于指导临床治疗。
因此，本发明利用AML患者免疫大数据创建免疫分型，随后在此基础上构建一个新的预后模型，帮助临床医生判断病人预后，可以一定程度上指导对病人选择何种治疗方法(化疗或骨髓移植)。
附图说明
图1为本发明的流程图；
图2为免疫细胞浸润对AML预后的影响图；
图3为AML患者免疫分型原理图；
图4为构建预后模型原理图；
图5为预后模型的验证效果图；
图6为在GSE6891和TCGA中评估121基因预后模型的效果图。
具体实施方式
为使本发明要解决的技术问题、技术方案和优点更加清楚，下面将结合具体实施例进行详细描述。
本发明利用AML患者免疫大数据创建AML患者免疫分型，随后构建一个新的AML患者预后评分模型，并探究免疫分型各组有效的潜在治疗靶点和药物，为精准治疗提供新的依据和策略。
AML患者免疫分型系统的建立过程：
(1)首先，为了构建AML患者免疫大数据，我们收集下载了7个AML相关的GEO数据库(GSE10358，GSE66525，GSE8970，GSE12417，GSE37642，GSE6891和GSE71014)，总样本数为1799例。其中，1299例样本具有总生存期(overall survival,OS)数据。患者基本信息见表1。我们通过R语言limma包的removeBatchEffect函数去除批次效应，合并了7个数据库的表达矩阵，并且对合并后的表达矩阵进行了中位数归一化后用于后续分析。
表1.AML病人基本信息汇总
(2)为了评价肿瘤微环境中的异质性，我们使用Xcell工具利用表达矩阵通过计算机模拟方法推断GEO数据集中1799例AML样本免疫微环境中33种细胞成分。相关性矩阵分析显示在AML免疫微环境中主要存在B细胞群、T细胞群和其他细胞群(图2中A)。为了评估33种细胞浸润程度的预后价值，所有患者均分别按每一种类型细胞浸润分数的中位数分为高浸润组和低浸润组，比较每一种类型细胞高浸润组和低浸润组之间的生存差异。生存分析结果显示CD4+、CD8+T细胞、B细胞、嗜酸性粒细胞、成纤维细胞、肥大细胞和NKT细胞高浸润组均有利于生存(图2中B-J；all P<0.05)；与之相反，造血干细胞(HSC)高浸润组不利于生存(图2中K；P<0.0001)。
图2为免疫细胞浸润对AML患者预后的影响。其中，A.CD4+T细胞高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.018)。B.B细胞高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.026)。C.CD8+T细胞高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.037)。D.CD8+中央记忆型T细胞高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.002)。E.Class-switch记忆B细胞高浸润AML患者与低浸润患者相比具有更高的生存期(P<0.001)。F.Eosinophils高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.042)。G.Fioroblasts高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.040)。H.Mast细胞高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.0021)。I.NKT(natural killer T)细胞高浸润AML患者与低浸润患者相比具有更高的生存期(P＝0.0017)。J.HSC高浸润AML患者与低浸润患者相比具有更低的生存期(P<0.001)。
(3)根据有预后意义的10种细胞成分的浸润水平对所有样本进行层次聚类。最终分为三组，Cluster 1免疫评分和微环境评分最低，HSC浸润程度最高；Cluster 3免疫评分和微环境评分最高，HSC浸润程度最低；Cluster 2介于两者中间(图3中A，C，D，E)。
为了确定该分组是否有意义，我们进行了生存分析。结果显示三组生存曲线差异分别存在统计学意义(P<0.001)，且Cluster 1总生存期最短，Cluster 3总生存期最长(图3中B)。为了寻找鉴定免疫分型的分子标志物，我们检测了各组间具有表达差异的CD分子，发现CD14、CD1D、CD36等在三组中表达逐渐升高，CD34、CD59、CD200等表达逐渐降低，是潜在的鉴定标志物(图3中F)。
图3为建立AML患者免疫分型。其中，A.用10种对预后有影响的浸润细胞将1799例AML患者聚类分为明显的三个Cluster，Cluster 1＝358,Cluster 2＝679,Cluster 3＝762。B.不同Cluster分组中AML患者生存期具有显著差异(P<0.001)。C.不同Cluster分组中AML患者ImmuneScore具有显著差异(P<0.001)。D.不同Cluster分组中AML患者Microenviroment Score具有显著差异(P<0.001)。E.不同Cluster分组中AML患者Stromascore具有显著差异(P<0.001)。F.探索三个Cluster存在表达差异的CD分子。CD14、CD1D、CD36等在三组中表达逐渐升高，CD34、CD59、CD200等表达逐渐降低。
(4)构建AML患者预后评分模型：
介于Cluster 1和Cluster 3差异最大，我们分析了两组中存在的差异基因。与Cluster3相比，Cluster 1存在489个上调基因，588个下调基因，共计1077个差异表达基因，其中366个基因存在预后价值(图4中A，B)。对这366个基因进行GO分析，这些基因主要作用的通路为免疫系统和免疫反应的调节、防御反应、白细胞侵袭和炎症反应等(图4中C)。采用LASSO-cox方法选择366个差异表达基因中和预后最相关的基因，计算每个基因的权重，建立了LASSO-Cox比例危险模型来计算每个患者的风险指数，y＝∑βi×Xi，其中，βi为对应基因的系数coef值；Xi为对应基因的表达量，根据病人样本测得。根据该模型，预后评分越高，预后越差。模型包含121个基因(图4中D)，分别为ABCC1、ACP6、ACSL4、ADA2、ADGRG1、AGA、AGFG1、ANKRD28、APBB1IP、APOBR、ARPC5L、BAX、BCAP29、BCAT1、BEX3、BPGM、CBX4、CCNA1、CCT4、CD34、CD58、CES1、CFAP70、CIAO1、CISD1、CKLF、CKM、CLIC2、CPVL、CRIPT、CTSB、CXCL2、CXorf21、CYFIP2、DEFB1、DNMT3B、DOCK1、DSG2、EFCAB2、EHD2、EIF3H、EZR、F2RL1、FAM124B、FAM30A、FCGRT、FECH、FGD2、FHL1、FSTL1、FZD6、GABRB2、GNAI1、GTF2F2、GYPC、H1F0、HAL、HDAC4、HDGFL3、HOMER3、HOPX、HSPD1、HTR1F、IDI1、IL17RA、IQGAP1、ITGA6、KCTD3、LAPTM4B、LGALS2、LILRA2、LPAR4、MAGED1、MFSD10、MN1、MS4A6A、MYL4、NBPF1、NIN、NRIP1、OPTN、P2RX7、PDE12、PF4、PGGT1B、PHLDA1、PIK3C2B、PLK4、PNMA1、PNRC2、PRKCH、RAB3GAP2、RAP1A、RBPMS、RCL1、REC8、RPS23、RUNX3、SALL2、SCRN1、SELENOP、SLC2A6、SLC38A1、SMIM10L1、SNU13、SOCS2、ST8SIA4、SV2A、TCEAL9、TCF4、TGOLN2、TMCO3、TMEM176B、TNFRSF21、TPK1、TRAPPC11、TTC28、TUBGCP5、VAMP8、ZEB2、ZNHIT1。
上述121个基因的权重系数即coef值分别见下表2。
表2各基因的coef值
图4基于免疫分型构建预后模型。其中，A.火山图探究Cluster 1和Cluster 3分组之间差异表达基因。Cluster 1中高表达基因有489个，Cluster 3中高表达基因有588个。合计存在1077个差异表达基因。B.用366个差异表达基因进行GO注释，发现差异表达基因主要富集在leukocyte migration、regulation of immune system process、regulation ofimmune response、defense response、inflammatory response、translationalinitiation、nuclear-transcribed mRNA catabolic process、nonsense-mediateddecay、negative regulation of gene expression、protein C-terminus binding、cytosolic small ribosomal subunit。C.预后相关差异表达基因分析。大圈表示通过分析1273例有生存预后信息的患者找到的3326个对AML预后有影响的基因，小圈表示1077个Cluster 1和Cluster 3差异表达的基因，中间交叉则为与预后相关的366个差异表达基因。D.用Lasso回归分析构建了包含121个差异基因的预后模型。
(5)接下来我们进行了预后模型的功能验证。风险评分显示cutoff中位数值在0.0097左右为患者生存和死亡临界点(图5中A)，评分越高，死亡的患者数量越多(图5中B)。为了检测模型效果，利用预测模型将所有样本评分后按照中位数分为高分组和低分组，通过Kaplan-Meier生存分析考察预后情况发现与低分组相比，高分组不利于生存(图5中C，P<0.001)，且1年AUC为0.77，2年0.79，3年0.81，5年0.77(图5中D)，表明我们的评分模型具有较高的准确性。
为了验证预测模型是否具有普适性，我们首先选择了样本库中的GSE10358(n＝91)、GSE37642(n＝553)进行分析。利用评分的中位数分为高低两组后，在两个数据库中高分组均预后较差，且差异显著，分组效果明显(图5中E，F；all P<0.001)。为了排除核型的干扰，我们选择了GSE12417数据库。该数据库有242个样本，且全部为基数最庞大的CN-AML患者。利用模型评分分组后，发现高评分显著不利于患者的预后，这与之前结果相一致(图5中G；P<0.001)；随后进一步检验了该模型在IR-AML中的作用，通过分析TCGA中92例IR-AML患者发现与低评分组患者相比，高评分组患者具有更差的预后(图5中H；P<0.001)。上述结果证明我们预测模型可成功将CN-AML和IR-AML进行危险度分层。
(6)为了探究本研究所构建的121基因模型的优劣，我们在GSE6891(n＝291)和TCGA(n＝173)中比较了121基因模型和经典的ELN分层系统外以及其他相对权威的三种风险评估模型(LSC17模型、Yang模型、Wang模型)对预后评估的准确性。多因素生存分析发现在TCGA数据集及GSE6891数据库中我们的121个基因的预后评分模型都是AML患者唯一独立预后因素(表3；P＝0.048，P<0.001)，表明该模型对患者预后风险分层具有更佳的预测效果。通过检测预后评分模型的有效性，发现在两个数据库中高分组均不利于预后，该模型能够将患者分成预后明显的两组(图6中A，D；all P<0.001)。在两个数据库中对比患者整个生存期间的AUC以及C-Index值，5种分层模式中我们的预后模型AUC和C-Index值最大，表明121基因的预后风险分层更可靠(图6中B，C，E，F)。
图6在GSE6891和TCGA中评估121基因预后模型。其中，A.在GSE6891中高评分组AML比低评分组患者具有更差的预后生存期(n＝291,P<0.001)。B.在GSE6891中AUC分析表明121基因预后模型具有更优的预后评估效果。121基因预后模型具有比LSC17，Yang,Wang以及ELN分类模型更高的AUC值。C.在GSE6891中C-Index分析表明121基因预后模型具有更优的预后评估效果。121基因预后模型具有比LSC17，Yang,Wang以及ELN分类模型更高的C-Index值(竖线表示95％置信区间)。D.在TCGA中高评分组AML比低评分组患者具有更差的预后生存期(n＝173,P<0.001)。E.在TCGA中AUC分析表明121基因预后模型具有更优的预后评估效果。121基因预后模型具有比LSC17，Yang,Wang以及ELN分类模型更高的AUC值。F.在TCGA中C-Index分析表明121基因预后模型具有更优的预后评估效果。121基因预后模型具有比LSC17，Yang,Wang以及ELN分类模型更高的C-Index值(竖线表示95％置信区间)。
表3.TCGA和GSE6891数据集中AML病人OS多因素分析
以上所述是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明所述原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。
Claims (5)
1.一种AML患者预后评分模型的构建方法，其特征在于，包括如下步骤：
S1、构建AML患者免疫分型系统
1）收集来自GEO数据库的AML 患者表达谱数据，并通过表达谱预测得到免疫微环境信息；
2）归一化所有样本后，根据患者免疫细胞的相关性进行聚类分析，得到有临床预后意义的10种免疫细胞类型：CD4+T细胞、B细胞、CD8+T细胞、D8+中央记忆型T细胞、Class-switch记忆B细胞、嗜酸性粒细胞、纤维原细胞、肥大细胞、NKT细胞、造血干细胞；
3）然后将有临床预后意义的免疫细胞类型的浸润分数用R语言层次聚类包hclust依据ward.D距离对患者进行聚类成三组：Cluster 1、Cluster 2、Cluster 3，建立免疫分型系统；其中，Cluster 1免疫评分和微环境评分最低，HSC 浸润程度最高；Cluster 3 免疫评分和微环境评分最高，HSC 浸润程度最低；Cluster 2 介于两组之间；对应地，Cluster 1组生存时间最短，Cluster 3组生存时间最长，Cluster 2介于两组之间；
S2、筛选Cluster 1和Cluster 3间对AML患者存在预后意义的差异基因；
S3、运用LASSO-cox方法选择差异基因中和预后最相关的基因建立AML患者预后评分模型：y=∑βi×Xi，其中，βi为对应基因的系数；Xi为对应基因的表达量，根据病人样本测得。
2.根据权利要求1所述的AML患者预后评分模型的构建方法，其特征在于，
所述步骤1）中，AML患者免疫微环境信息来自7个GEO数据库：GSE10358，GSE12417，GSE37642，GSE66525，GSE6891，GSE71014和 GSE8970的1799例有效患者，其中1229例患者拥有总生存期数据信息。
3.根据权利要求1所述的AML患者预后评分模型的构建方法，其特征在于，
预后评分为121个免疫相关基因的表达量与相应系数的乘积的总和计算获得。
4.根据权利要求3所述的AML患者预后评分模型的构建方法，其特征在于，
121个免疫相关基因分别为：ABCC1、ACP6、ACSL4、ADA2、ADGRG1、AGA、AGFG1、ANKRD28、APBB1IP、APOBR、ARPC5L、BAX、BCAP29、BCAT1、BEX3、BPGM、CBX4、CCNA1、CCT4、CD34、CD58、CES1、CFAP70、CIAO1、CISD1、CKLF、CKM、CLIC2、CPVL、CRIPT、CTSB、CXCL2、CXorf21、CYFIP2、DEFB1、DNMT3B、DOCK1、DSG2、EFCAB2、EHD2、EIF3H、EZR、F2RL1、FAM124B、FAM30A、FCGRT、FECH、FGD2、FHL1、FSTL1、FZD6、GABRB2、GNAI1、GTF2F2、GYPC、H1F0、HAL、HDAC4、HDGFL3、HOMER3、HOPX、HSPD1、HTR1F、IDI1、IL17RA、IQGAP1、ITGA6、KCTD3、LAPTM4B、LGALS2、LILRA2、LPAR4、MAGED1、MFSD10、MN1、MS4A6A、MYL4、NBPF1、NIN、NRIP1、OPTN、P2RX7、PDE12、PF4、PGGT1B、PHLDA1、PIK3C2B、PLK4、PNMA1、PNRC2、PRKCH、RAB3GAP2、RAP1A、RBPMS、RCL1、REC8、RPS23、RUNX3、SALL2、SCRN1、SELENOP、SLC2A6、SLC38A1、SMIM10L1、SNU13、SOCS2、ST8SIA4、SV2A、TCEAL9、TCF4、TGOLN2、TMCO3、TMEM176B、TNFRSF21、TPK1、TRAPPC11、TTC28、TUBGCP5、VAMP8、ZEB2、ZNHIT1。
5.根据权利要求4所述的AML患者预后评分模型的构建方法，其特征在于，
上述121个基因的系数即coef值分别见下表:
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