CN117524304A - 实体瘤微小病灶残留的检测panel、探针组及其应用 - Google Patents
实体瘤微小病灶残留的检测panel、探针组及其应用 Download PDFInfo
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Abstract
本发明涉及生物信息学技术领域,特别是涉及实体瘤微小病灶残留的检测panel、探针组及其应用。本发明提供的筛选方法可以兼顾性能与成本,形成固定化小panel,在不损失性能的前提下,尽量缩减panel大小,筛选得到的检测panel对于尿路上皮癌、膀胱癌、结直肠癌、食管癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、胃癌,检出率都在90%以上,平均能检测到2个以上的阳性位点,满足检测要求。另外,本发明通过向小panel中添加个性化探针的方式,扩大追踪位点数目、提高检测灵敏度,同时检测肿瘤组织中未发现的突变位点,有能力规避肿瘤异质性,还可以检测新发突变,融合组织知情分析法和组织不知情分析法,使二者形成互补。
Description
技术领域
本发明涉及生物信息学技术领域,特别是涉及实体瘤微小病灶残留的检测panel、探针组及其应用。
背景技术
MRD(molecular/minimal residual disease)指的是经过治疗后,传统影像学或一般实验方法不能发现,但通过液体活检可以发现的微小病灶残留。MRD被认为是患者经手术治疗后肿瘤复发的主要原因。为了应对MRD造成的复发风险,临床上广泛采用术后辅助化疗的方式,进一步清除体内的残留病灶。
正常生理条件下,人体血循环中存在由凋亡和坏死的细胞释放的游离DNA(cellfree DNA,cfDNA),肿瘤患者的血循环中还可检测到游离循环肿瘤DNA(circulatingtumorDNA,ctDNA),由于ctDNA携带有与原发肿瘤组织相一致的分子遗传学改变,并且能全面的反映患者体内肿瘤细胞的遗传信息和演变进程,可以作为理想的肿瘤标志物,用于肿瘤的诊断、分子分型、疗效评价和追踪以及预后评估。
目前实体瘤MRD主流检测策略主要分为两种:组织知情分析(Tumor-informed)和组织不知情分析(Tumor-agnostic)。组织知情分析法是指先对肿瘤组织进行NGS测序,找出肿瘤特有的克隆性单核苷酸变异(SNV),然后根据这些变异设计合成定制化Panel,对患者的血浆样本进行个性化追踪,从而检测和定量ctDNA。而组织不知情分析法是指不需要肿瘤组织信息,而是直接对患者的血液样本进行NGS检测,利用预先设定的癌基因突变Panel或者机器学习算法,筛选出可能来自肿瘤的ctDNA变异。
组织知情分析法和组织不知情分析法各有优缺点。组织知情分析法方案准确度相对更高,但必须要组织样本的检测结果,特别要注意的是,由于肿瘤存在异质性,肿瘤组织检测可能无法检出所有突变位点,导致漏追踪,降低灵敏度。另外由于每次需要合成个性化panel,所以对合成能力、批次稳定性和时效性都有较大挑战。组织不知情分析法由于使用固定panel,所以开展检测更为方便,而且便于进行检测质量控制。但是固定panel的大小设置是关键问题,太大会浪费测序数据量,太小覆盖不到突变位点,而且由于二代测序平台本身检测灵敏度的局限性,在0.1%左右会有大量背景噪音突变的产生,如果没有组织突变作为参考,很难设置过滤阈值,普遍需要调高检测阈值,导致检测灵敏度性能存在不足。
发明内容
为了解决上述问题,本发明提供了实体瘤微小病灶残留的检测panel、探针组及其应用。本发明提供的筛选方法可以兼顾性能与成本,筛选得到的检测panel对于尿路上皮癌、膀胱癌、结直肠癌、食管癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、胃癌,检出率都在90%以上,平均能检测到2个以上的阳性位点,满足检测要求。
为了实现上述目的,本发明提供如下技术方案:
本发明提供了一种实体瘤微小病灶残留检测panel的筛选方法,包括以下步骤:
以泛癌种的高频驱动基因的全外显子区域为第一数据集;所述泛癌种的高频驱动基因包括EGFR、TP53、VHL、SMAD4、CDH1、PBMR1、KRAS、NRAS和BRCA2;
利用已知的肿瘤突变数据构建所述泛癌种的第二数据集;所述构建的原则包括:保留肿瘤变异等位分数/正常变异等位分数≥5、肿瘤突变深度≥8、肿瘤测序深度≥50和肿瘤变异等位分数≥1%的基因突变区域和突变位点;
将所述第一数据集和第二数据集中基因组距离低于80bp的突变位点在参考基因组下进行合并,只保留基因组的突变区域坐标,不考虑突变类型,得到突变区域数据集;
以已知的肿瘤数据库为样本集,在不同癌种水平,计算所述突变区域数据集中每个突变区域的患者阳性率,每个癌种均筛选阳性率从高到低的500个突变区域;将各癌种阳性率最高的位点进行逐步累加,得到panel区域大小与患者覆盖度的关系曲线;所述患者覆盖度=平均突变个数≥1的患者数/总患者数;所述平均突变个数=可检测出的所有突变数/总患者数;
在所述关系曲线中选择患者覆盖度为94.2%~96.5%时的区域为检测panel的区域大小;
根据所述检测panel的区域大小将第一数据集和第二数据集的突变区域任意组合,得到多种突变区域组合;
计算每种所述突变区域组合的突变平均贡献A和平均突变个数;所述突变平均贡献A=突变区域组合覆盖的泛癌种患者数/突变区域组合的区域大小;
所述突变平均贡献A×平均突变个数最大的突变区域组合为所述实体瘤微小病灶残留检测panel。
优选的,所述泛癌种包括:尿路上皮癌、膀胱癌、结直肠癌、食管癌、肾癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、***癌和胃癌;所述参考基因组包括hg19。
优选的,所述第二数据集中不含有所述第一数据集中的全外显子区域。
优选的,所述已知的肿瘤数据库包括TCGA数据库。
本发明提供了利用上述技术方案所述筛选方法得到的实体瘤微小病灶残留的检测panel,所述检测panel表1所示:
表1 检测panel的基因和外显子信息
基因 | 外显子 | 基因 | 外显子 |
ABL1 | 11 | MAP2K1 | 2、3、4、6、7 |
ACVR1 | 5、6 | MAP2K2 | 5、6、7 |
AGO2 | 13、14 | MAP2K4 | 1、3、4、5、6、8、9、10 |
AKT1 | 3、4、5、11、12 | MAP3K1 | 1、3、5、6、13、14、17、18 |
ALOX12B | 9、10 | MAPK1 | 1、6、7 |
AMER1 | 1、2 | MAX | 1、2、3 |
ANKRD11 | 8、9 | MDC1 | 8、9 |
APC | 6、7、8、9、10、11、12、13、14、15、16 | MDM2 | 11 |
AR | 1、2、3、4、5、6、7、8 | MECOM | 3 |
ARAF | 2、3、7、8、9、10 | MED12 | 1、2、3、26、27、42、43、44 |
ARID1B | 1、18、19 | MEF2B | 1 |
ARID2 | 8、9 | MEN1 | 1、2、9、10 |
ARID5B | 10 | MET | 2、3、4、6、12、13、14、15、16、17、18、19、20 |
ASXL1 | 13 | MGA | 10、11、18、19 |
ATM | 4、5、6、7、8、9、10、11、12、16、17、18、26、27 | MLH1 | 3、4、10、11、12、16、17 |
ATP9A | 1 | MRE11 | |
ATR | 3、4、8、9、10、34、35、38、39、41、42 | MSH3 | 4、5、7、8、9 |
ATRX | 8、9、13、14 | MST1 | 11、12 |
AXIN1 | 1、2、5、6、7 | MYBL1 | 9、10 |
AXIN2 | 7、8 | MYC | 2、3 |
AXL | 7、8 | MYD88 | 1、2、4、5 |
B2M | 1、2、3 | MYOD1 | 1、2 |
BAP1 | 1、2、3、4、5、6、7、8、14、15、16、17 | NAV3 | 8、9 |
BARD1 | 3、4、5、6 | NBN | 9、10、11、12、13、14 |
BCL2 | 1 | NCOR1 | 14、15 |
BCL6 | 7、8、9 | NCOR2 | 46、47 |
BCOR | 3、4、5 | NF1 | 5、6、8、9、11、12、13、15、16、17、18、19、20、21、22、27、28、29、30、31、33、34、35、36、38、39、40、41、42、43、44、45、46、47、50、51、52、54、55 |
BCORL1 | 13 | NF2 | 2、3、4、5、6、7、8、9、10、12、13 |
BLM | 7、8 | NFE2L2 | 2、3、4 |
BRAF | 2、3、7、8、9、10、11、12、13、14、15、16、17、18 | NOTCH1 | 5、6、7、8、9、10、13、14、25、26、33、34 |
BRCA1 | 1、2、3、4、5、6、7、9、10、11、13、14、15、16、18、19、22、23 | NOTCH3 | 17、18、29、30、32、33 |
BRCA2 | 2、3、4、5、6、7、8、9、10、11、12、13、14、15、16、17、18、19、20、21、22、23、24、25、26、27 | NPAP1 | 1 |
BRD3 | 2 | NPM1 | 11 |
BRIP1 | 2、3、6、7、16、17、19、20 | NSD1 | 4、5、11、12 |
BUB1B | 9、10 | NSD2 | 1 |
CALR | 9 | NTRK2 | 6、7 |
CARD11 | 1 | NUP93 | 2、3 |
CASP8 | 2、3 | OR11G2 | 1 |
CBFB | 3、4 | OR1L1 | 1 |
CBL | 1、2、8、9、10 | PAK5 | 1 |
CCND1 | 1、2、5 | PALB2 | 3、4、6、7、8、9、11、13 |
CCND3 | 4、5 | PAX5 | 1、2、7、8 |
CCNE1 | 8、9 | PBRM1 | 1、2、3、4、5、6、7、8、9、10、11、12、13、14、15、16、17、18、19、20、21、22、23、24、25、26、27、28 |
CD79A | 3、4 | PCM1 | 5、6 |
CD79B | 4、5 | PDCD1 | 1、2 |
CDH1 | 1、2、3、4、5、6、7、8、9、10、11、12、13、14、15、16 | PDGFRA | 5、6、12、13、14、15、18、19、23 |
CDK12 | 1、2、4、5、6、8、9、14 | PDGFRB | 13、14 |
CDK4 | 1、2 | PEX11A | 1 |
CDK6 | 1、2 | PGR | 6、7 |
CDKN1B | 2、3 | PHOX2B | 2、3 |
CDKN2A | 1、2、3 | PIK3C2G | 1 |
CEBPA | 1 | PIK3CA | 2、3、4、5、6、7、8、9、10、11、12、13、14、15、19、20、21 |
CEP55 | 2 | PIK3CB | 5、6、12、13、22、23 |
CHEK1 | 6、7 | PIK3CG | 2、3 |
CHEK2 | 1、2、6、7、10、11、12、13、14、15、16 | PIK3R1 | 2、3、9、10、11、12、13、14 |
CIC | 5、6、14、15、16、20 | PIK3R2 | 10、11、13、14 |
CREBBP | 15、16、24、25、26、27、29、30 | PIM1 | 1 |
CRLF2 | 3、4 | PKD1L1 | 1 |
CSMD3 | 14、15、16、24、25、54、55、56、57、64、65 | PLK2 | 6、7 |
CTCF | 3、4、5、6、7、8、12 | PMS1 | 5、6、9、10 |
CTLA4 | 3、4 | PMS2 | 10、11、12 |
CTNNB1 | 3、4、7、8、9、10、11、13、14 | POLD1 | 4、5 |
CUL3 | 1、2、3、4、5、6、7、8、9、10、11、12、13、14、15、16 | POLE | 1、8、9、12、13、35、36 |
CYLD | 14、15 | PPM1D | 6 |
CYP24A1 | 1 | PPP2R1A | 5、6、7 |
CYP2D6 | 4、5 | PPP6C | 7 |
DAXX | 4、5 | PREX2 | 2、3、16、17 |
DICER1 | 20、21、23、24、25 | PRKCI | 9、10、15、16 |
DIS3 | 7、8、9、10、15、16、17 | PRKDC | 4、5 |
DNAH3 | 1 | PTCH1 | 1、2、3、17、18、22、23 |
DNAH9 | 1、2 | PTEN | 1、2、3、4、5、6、7、8、9 |
DNMT1 | 16、17 | PTPN11 | 3、4、8、9、12、13、14 |
DNMT3B | 7、8 | PTPRD | 15、16、17 |
DOCK8 | 27、28 | PTPRS | 9、18、19 |
EGFR | 1、2、3、4、5、6、7、8、9、10、11、12、13、14、15、16、17、18、19、20、21、22、23、24、25、26、27、28 | PTPRT | 7、8、9、10、11、12、22、23 |
EIF1AX | 1、2、5、6 | QKI | 3、4 |
ELOC | 2 | RAC1 | 2、3、6 |
EP300 | 26、27、28、31 | RAD50 | 2、3、11、12、13、14、17、18 |
EPHA3 | 3、4、5、6、15、16 | RAD51 | 2、3、8、9 |
EPHA5 | 2、3、5、6 | RAD51B | 2、3、5、6、8 |
EPHA7 | 10、11、14、15 | RAD51C | 5、6、8、9 |
EPHB1 | RAD51D | 3、4 | |
ERBB2 | 3、5、6、7、8、11、12、13、14、21、22、23、24、25、26、31 | RAF1 | 6、7、16、17 |
ERBB3 | 2、3、4、6、7、8、9、10、12、13、21、22、23、24 | RARA | 9 |
ERBB4 | 2、3、11、12、13、14、15、17、18、19、20、24、25、26、27、28 | RASA1 | 19、20 |
ERCC2 | 1、2、3、4、7、8、9、10、14、15、18、19、20 | RB1 | 1、2 |
ERF | 3、4 | RBBP8 | 11、12 |
ERG | 9、10 | RBM10 | 7、8 |
ESR1 | 5、6、8 | RECQL | 2、3 |
ETV1 | 7、8 | RECQL4 | 12、13、14 |
ETV6 | 6、7 | RET | 11、12、13、14、15、16、17 |
EZH2 | 12、13、15、16 | RHEB | 2 |
FAM135B | 13、14 | RHOA | 1、2、3、4、5 |
FANCA | 3、4、7、8、28、29、36、37、39、40、41、42 | RICTOR | 30、31 |
FANCC | 6、7 | RNF10 | 3 |
FANCD2 | 3、4、13、14、34、35、42、43 | RNF43 | 2、3、4、8、9 |
FANCE | 2、3、4、5 | ROS1 | 11、12、22、23、30、31、32、33、34、35、36、37、38 |
FAT1 | 4、5、7、8、9、10 | RPS6KA4 | 17 |
FBXW7 | 1、2、3、4、5、6、7、8、9、10、11、12 | RPTOR | 4、5 |
FGF4 | 1 | RRAS2 | 1、2、3 |
FGFR1 | 3、4、11、12、13、14 | RUNX1 | 1、2、3、4、5 |
FGFR2 | 4、5、6、7、8、9、11、12、13、14 | RXRA | 12 |
FGFR3 | 6、7、8、9、10、11、12、13、14、15、16、17 | SDHA | 2、3、7、8、10、11、15 |
FGFR4 | 12、13、14 | SDHAF2 | 3、4 |
FLCN | 8、9、10、11 | SERPINB5 | 4 |
FLT1 | 6、7、15、16 | SETBP1 | 4、5 |
FLT3 | 13、14、15、16、19、20、23、24 | SETD2 | 2、3、4、5、6、7、8、9、10、13、14、16、17、18、19、20、21 |
FLT4 | 1、2、9、10 | SF3B1 | 13、14、15、16、17、18 |
FOXA1 | 1、2 | SFXN4 | 2 |
FOXL2 | 1 | SMAD2 | 4、5、7、8、10、11、 |
FOXO1 | 1 | SMAD3 | 6、7 |
FOXP1 | 14、15、17、18 | SMAD4 | 2、3、4、5、6、7、8、9、10、11、12 |
GATA3 | 3、4、5、6 | SMARCA4 | 2、3、4、5、6、7、8、9、16、17、18、19、20、21、24、25、26、27、29、30、31、32 |
GLI1 | 8、9、10、11 | SMARCB1 | 8、9 |
GNA11 | 4、5、6 | SMARCD1 | 5、6 |
GNAQ | 1、2、3、4、5 | SMO | 1、2、6、7 |
GNAS | 6、7、8、9、10 | SOX17 | 2 |
GRIN2A | 2、3、13、14 | SOX9 | 2、3 |
GTF2I | 15、16、17 | SPOP | 4、5、6 |
H1-2 | 1 | SRC | 14 |
H3C2 | 1 | SRSF2 | 1 |
HLA-A | 5、6 | STAG2 | 7、8 |
HLA-B | 3、4 | STAT3 | 12、13、19、20、21 |
HNF1A | 1、2、3、4、5 | STAT5B | 8、9、13、14 |
HRAS | 1、2、3、4 | STAT6 | 11、12 |
ICOS | 2 | STK11 | 1、2、3、4、5、6、7、8、9、10 |
IDH1 | 3、4 | STK19 | 2、3 |
IDH2 | 3、4、6、7、8 | SUFU | 1、2 |
IGF2 | 3、4 | TAP2 | 1、2 |
IKZF1 | 8 | TBX3 | 1 |
IL7R | 3、4、5、6、7、8 | TCF7L2 | 1 |
INPP4B | 24、25 | TERT | 1、2、3、4、5、13、14、15 |
INPPL1 | 26、27、28 | TET1 | 2、3、11、12 |
IRF4 | 3、4 | TET2 | 3 |
IRS1 | 1 | TGFBR1 | 1、2、4、5、9 |
IRS2 | 1 | TGFBR2 | 3、4、5、6、7 |
JAK3 | 1、2、5、6 | TMPRSS2 | 2、3 |
KAT6B | 16 | TP53 | 2、3、4、5、6、7、8、9、10、11 |
KDM5A | 22、23 | TP53BP1 | 11、12 |
KDM5C | TP63 | 8、9、10、14 | |
KDM6A | 16、17 | TRAF2 | 3 |
KDR | 7、8、20、21、22、23 | TSC1 | 2、3、4、6、7、8、9、10、11、12、14、15、18、19、20、21、22、23 |
KEAP1 | 1、2、3、4、5、6 | TSC2 | 14、15、24、25、27、28、29、35、36、39、40 |
KIT | 2、3、9、10、11、12、13、14、17、18、19、20 | TSNARE1 | 2 |
KLF4 | 1、2 | U2AF1 | 1 |
KMT2A | 1、2、3、4、11、12 | USP6 | 5、6 |
KMT2B | 3、4、27、28 | USPL1 | 2 |
KMT2C | 2、37、38、51、52 | VHL | 1、2、3 |
KMT2D | 9、10、11、30、31、32、33、34、38、39、40、41、47、48、50、51 | WSCD1 | 1 |
KNG1 | 1 | WT1 | 6、7、8、9 |
KNSTRN | 1、2 | ZFHX3 | 2、8、9、10 |
KRAS | 1、2、3、4、5 | ZNF703 | 2 |
LATS2 | 3、4 | ZNF750 | 1、2 |
LRP1B | 2、3、4、5、7、8、9、10、14、15、16、32、33、58、59、60 | ZRSR2 | 11 |
本发明提供了一种检测实体瘤微小病灶残留的探针组,所述探针组包括固定探针组和个性化探针组;所述固定探针组的靶标区域为上述技术方案所述的检测panel;
所述个性化探针组的靶标区域为待测患者肿瘤组织中突变频率从高到低排序,前20的位点。
优选的,所述固定探针组中各探针的浓度为0.2fmol/μL;所述个性化探针组中各探针的浓度为2fmol/μL。
本发明提供了上述技术方案所述的探针组在制备检测实体瘤微小病灶残留的产品中的应用。
优选的,所述实体瘤包括尿路上皮癌、膀胱癌、结直肠癌、食管癌、肾癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、***癌和胃癌。
本发明提供了一种实体瘤微小病灶残留的检测***,所述检测***包括:
样本采集模块,用于采集待测患者的外周静脉血,得到待测样本;
文库构建模块,与所述样本采集模块连接,用于提取所述待测样本的循环游离DNA构建待测文库;
检测模块,与所述文库构建模块连接,用于获取所述待测文库的突变位点检出结果;所述获取包括:利用上述技术方案所述的探针组获取所述待测文库的突变位点检出结果;
结果输出模块,与所述检测模块连接,用于输出待测患者的检测结果;所述输出包括:若所述探针组检测到突变位点,则判定为阳性,复发风险高;若所述探针组未检测到突变位点,则判定为阴性,复发风险低。
有益效果:
本发明提供的筛选方法通过panel区域大小与患者覆盖度的关系曲线确定检测panel的区域大小,根据检测panel的区域大小得到多种突变区域组合,从而确定最优突变区域组合。本发明提供的筛选方法可以兼顾性能与成本,形成固定化小panel,在不损失性能的前提下,尽量缩减panel大小,筛选得到的检测panel对于尿路上皮癌、膀胱癌、结直肠癌、食管癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、胃癌,检出率都在90%以上,平均能检测到2个以上的阳性位点,满足检测要求。
另外,本发明通过向小panel中添加个性化探针的方式,扩大追踪位点数目、提高检测灵敏度,同时检测肿瘤组织中未发现的突变位点,有能力规避肿瘤异质性,还可以检测新发突变,融合组织知情分析法和组织不知情分析法,使二者形成互补。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例中所需要使用的附图作简单地介绍。
图1为panel大小与患者覆盖度关系图;
图2为固定包平均有效深度;
图3为个性化包各位点的有效深度;
图4为掺入不同量个性化探针对检测深度的影响结果;
图5为追踪位点与检出灵敏度的关系曲线;
图6为数据量与检出性能关系图。
具体实施方式
本发明提供了一种实体瘤微小病灶残留检测panel的筛选方法,包括以下步骤:
以泛癌种的高频驱动基因的全外显子区域为第一数据集;所述泛癌种的高频驱动基因包括EGFR、TP53、VHL、SMAD4、CDH1、PBMR1、KRAS、NRAS和BRCA2;
利用已知的肿瘤突变数据构建所述泛癌种的第二数据集;所述构建的原则包括:保留肿瘤变异等位分数/正常变异等位分数≥5、肿瘤突变深度≥8、肿瘤测序深度≥50和肿瘤变异等位分数≥1%的基因突变区域和突变位点;
将所述第一数据集和第二数据集中基因组距离低于80bp的突变位点在参考基因组下进行合并,只保留基因组的突变区域坐标,不考虑突变类型,得到突变区域数据集;
以已知的肿瘤数据库为样本集,在不同癌种水平,计算所述突变区域数据集中每个突变区域的患者阳性率,每个癌种均筛选阳性率从高到低的500个突变区域;将各癌种阳性率最高的位点进行逐步累加,得到panel区域大小与患者覆盖度的关系曲线;所述患者覆盖度=平均突变个数≥1的患者数/总患者数;所述平均突变个数=可检测出的所有突变数/总患者数;
在所述关系曲线中选择患者覆盖度为94.2%~96.5%时的区域为检测panel的区域大小;
根据所述检测panel的区域大小将第一数据集和第二数据集的突变区域任意组合,得到多种突变区域组合;
计算每种所述突变区域组合的突变平均贡献A和平均突变个数;所述突变平均贡献A=突变区域组合覆盖的泛癌种患者数/突变区域组合的区域大小;
所述突变平均贡献A×平均突变个数最大的突变区域组合为所述实体瘤微小病灶残留检测panel。
在本发明中,所述泛癌种优选包括:尿路上皮癌、膀胱癌、结直肠癌、食管癌、肾癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、***癌和胃癌;所述参考基因组优选包括hg19;所述第二数据集中优选不含有所述第一数据集中的全外显子区域;所述已知的肿瘤数据库优选包括TCGA数据库。
本发明还提供了一种实体瘤微小病灶残留的检测panel,所述检测panel如表1所示。本发明所述检测panel是利用上述技术方案所述的筛选方法筛选得到的。
本发明还提供了一种检测实体瘤微小病灶残留的探针组,其所述探针组包括固定探针组和个性化探针组;所述固定探针组的靶标区域为上述技术方案所述的检测panel,即表1中的不同基因对应的外显子;
所述个性化探针组的靶标区域为待测患者肿瘤组织中突变频率从高到低排序,前20的位点。
本发明所述探针组是利用上述技术方案所述检测panel和待测患者肿瘤组织突变检测结果设计得到的。在本发明中,所述固定探针组和个性化探针组中各个探针均优选为生物素修饰的探针;设计所述固定探针组和个性化探针组的软件均优选包括XCapert,参考基因组优选包括hg19;所述固定探针组中各探针的浓度优选为0.2fmol/μL;所述个性化探针组中各探针的浓度优选为2fmol/μL。
本发明还提供了上述技术方案所述的探针组在制备检测实体瘤微小病灶残留的产品中的应用。在本发明中,所述实体瘤优选包括尿路上皮癌、膀胱癌、结直肠癌、食管癌、肾癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、***癌和胃癌中的一种或多种。
本发明还提供了一种实体瘤微小病灶残留的检测***,所述检测***包括:
样本采集模块,用于采集待测患者的外周静脉血,得到待测样本;
文库构建模块,与所述样本采集模块连接,用于提取所述待测样本的循环游离DNA构建待测文库;
检测模块,与所述文库构建模块连接,用于获取所述待测文库的突变位点检出结果;所述获取包括:利用上述技术方案所述的探针组获取所述待测文库的突变位点检出结果;
结果输出模块,与所述检测模块连接,用于输出待测患者的检测结果;所述输出包括:若所述探针组检测到突变位点,则判定为阳性,复发风险高;若所述探针组未检测到突变位点,则判定为阴性,复发风险低。
在本发明中,优选每隔3个月做一次检测,监测复发风险。
为了进一步说明本发明,下面结合附图和实施例对本发明提供的实体瘤微小病灶残留的检测panel、探针组及其应用进行详细地描述,但不能将它们理解为对本发明保护范围的限定。
实施例1
步骤一:设计基于杂交捕获法的固定化小panel
根据自有既往各个癌种临床图谱数据,构建泛癌种肿瘤突变集,确定panel总大小约为200 kb,选择最优突变位点组合,使各癌种获得最大检出阳性率及检出平均位点数(图1)。最终生成panel 208kb,包含3409个区域,308个基因(表1),具体方法如下:
设计基于杂交捕获法的固定化小panel,重点考虑肺癌、结直肠癌,胃癌,食管癌,肝癌等高发癌种,并纳入泌尿***癌种,包括肾癌,***癌,膀胱癌。
1.1 首先对组织样本的高频、多发、驱动基因,设计全外显子覆盖的数据库,基因包括EGFR、TP53、VHL、SMAD4、CDH1、PBMR1、KRAS、NRAS、BRCA2。然后根据COSMIC数据库、TCGA数据库、求臻医学database及文献[1]-[12]报道中常见的热点区域和热点突变,利用Python语言建立候选位点库,库中不包含已设计全外显子的基因区域。
参考文献:
1.Chakravarty, D. and D.B. Solit, Clinical cancer genomic profiling.Nat Rev Genet, 2021. 22(8): p. 483-501.
2.Chia, N.Y. and P. Tan, Molecular classification of gastric cancer.Ann Oncol, 2016. 27(5): p. 763-9.
3.Choi, W., et al., Genetic Alterations in the Molecular Subtypes ofBladder Cancer: Illustration in the Cancer Genome Atlas Dataset. Eur Urol,2017. 72(3): p. 354-365.
4.Di Nicolantonio, F., et al., Precision oncology in metastaticcolorectal cancer - from biology to medicine. Nat Rev Clin Oncol, 2021. 18(8): p. 506-525.
5.Dizman, N., E.J. Philip, and S.K. Pal, Genomic profiling in renalcell carcinoma. Nat Rev Nephrol, 2020. 16(8): p. 435-451.
6.Leemans, C.R., P.J.F. Snijders, and R.H. Brakenhoff, The molecularlandscape of head and neck cancer. Nat Rev Cancer, 2018. 18(5): p. 269-282.
7.Lheureux, S., M. Braunstein, and A.M. Oza, Epithelial ovariancancer: Evolution of management in the era of precision medicine. CA Cancer JClin, 2019. 69(4): p. 280-304.
8.Pich, O., et al., The translational challenges of precisiononcology. Cancer Cell, 2022. 40(5): p. 458-478.
9.Rudin, C.M., et al., Small-cell lung cancer. Nat Rev Dis Primers,2021. 7(1): p. 3.
10.Sia, D., et al., Liver Cancer Cell of Origin, Molecular Class, andEffects on Patient Prognosis. Gastroenterology, 2017. 152(4): p. 745-761.
11.Tan, A.C., et al., Management of glioblastoma: State of the artand future directions. CA Cancer J Clin, 2020. 70(4): p. 299-312.
12.Waks, A.G. and E.P. Winer, Breast Cancer Treatment: A Review.JAMA, 2019. 321(3): p. 288-300.
根据以下原则对候选位点库进行位点清洗过滤:
1)肿瘤变异等位分数(VAF tumor)/正常变异等位分数(VAFnormal)≥5;
2)肿瘤突变深度(Alt Depth)≥8,肿瘤测序深度(depth)≥50;
3)肿瘤变异等位分数(VAF)≥1%;
1)~3)没有先后顺序。
1.2 得到panel区域大小与患者覆盖度的关系曲线
对所述全外显子覆盖的数据库和清洗过滤后的候选位点数据库中基因组距离低于80bp的突变在参考基因组下进行合并,只保留基因组区域坐标,而不考虑突变具体类型。
在不同癌种水平,计算每个区域的患者阳性率,最后针对不同的癌种类型筛选阳性率从高到低的500个突变区域。将各癌种阳性率最高的位点进行逐步累加,得到panel区域大小与患者覆盖度的关系曲线,如图1所示。
从图1中可以看出,随位点的增加患者覆盖度逐渐趋于饱和,在区域到达200Kb大小的情况下,96%的患者至少有1个突变位点可以被覆盖。所以将panel总体大小控制在180~220Kb(患者覆盖度94.2%~96.5%)。并尝试不同变异区域组合,以期达到在同样覆盖度情况下,覆盖位点增加的目的。
1.3根据以下原则评估不同区域组合的综合性能,选取最优组合:
1.计算突变区域组合的突变平均贡献A,所述突变平均贡献A = 突变区域组合覆盖的泛癌种患者数/突变区域组合的大小;
2.计算突变区域组合的根据患者阳性区域M排序,患者阳性突变M = mean(每个患者的阳性突变个数);
3.突变平均贡献A×平均突变个数最大的突变区域组合为所述实体瘤微小病灶残留检测panel;
在确定最佳位点组合后,所涉及到的基因见表1。
利用设计好的小panel去TCGA数据库中进行模拟验证,对比TCGA数据中检测到的阳性点,有多少落在设计好的小panel范围内,确定中位数和阳性率,具体包括:以每例样本为单位,统计TCGA检测结果与设计好的小panel覆盖位点交集,计算每例样本可用设计好的小panel检出的位点数目,然后统计中位值和平均值;只要有1个及以上位点检出,该例样本就算作阳性,计算阳性率,结果见表2。
检测结果显示对于尿路上皮癌、膀胱癌、结直肠癌、食管癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、胃癌,检出率都在90%以上,平均能检测到2个以上的阳性位点,满足检测要求。
表2 TCGA数据模拟检测性能
步骤二:设计个性化panel
根据以上区域测试结果,最终生成的panel,包含3409个区域,308个基因,大小208Kb,针对这些区域本发明设计并合成了捕获用的生物素化长探针,作为固定包使用。在实际检测MRD时,由于固定包平均覆盖2-3个位点,整体偏少,所以本发明根据患者肿瘤组织中的位点,再设计待测患者肿瘤组织中突变频率从高到低排序,前20的位点的探针,并合成10 fmol/probe/μL浓度,作为个性化包使用。固定包和个性包的探针设计软件为XCapert,选择XCapert设计的最优探针,若区域比较难被捕获,XCapert输出多条探针时,则将输出的探针全部合成,从而加强该区域捕获效率。
步骤三:固定化+个性化检测流程
在进行检测时首先本发明对血浆cfDNA进行常规提取,然后利用KAPA试剂盒进行文库构建,包括cfDNA末端修复、接头连接、磁珠纯化去除接头、indexPCR、磁珠纯化等步骤,最后获得完整的预文库。
对于个性化探针掺入比例,进行以下稀释梯度测试,固定包0.2 fmol/μL加入4 μL,掺入0.4 fmol/probe/μL 1 μL(条件1);2 fmol/probe/μL 1 μL(条件2); 10 fmol/probe/μL 1 μL(条件3)。将探针混合后,与预文库进行杂交反应。60℃,16 h杂交;68℃,2次漂洗后48℃,3次漂洗。进行杂交后扩增,杂交产物22.5 μL,Hifi hotstart ready mix 25μL,通用引物2.5 μL,98℃ 1 min;98℃ 15s,60℃ 30s,72℃ 30s,16循环;72℃ 3 min。1倍磁珠纯化。将杂交后终文库进行二代测序上机,PE100模式10G数据量。生信质控,重点关注固定包平均有效深度及个性化包每个位点的有效深度(图2和图3)。随着个性化探针浓度升高,个性化位点深度呈上升趋势,固定化平均有效深度呈下降趋势,综合考虑优选条件2作为个性化掺入条件。
确定掺入浓度后,本发明接着测试了,将多个个性化探针混合后加入到固定化包中的效果,以达到多杂,减少试剂消耗,提升通量的作用。本发明测试掺入总大小5Kb,10Kb,20Kb,40Kb,80Kb(分别42,83,154,333,660种)探针,统计固定包平均有效深度及掺入包平均有效深度,结果见图4,其中左图代表固定包有效深度随掺入个性化探针的增多的变化趋势;右图是个性化探针有效深度随掺入个性化探针的增多的变化趋势。从图中可以看出随着外掺探针的增多,固定包和掺入包的平均有效深度呈下降趋势,再外掺不超过20Kb情况下,性能基本一致,所以可以合并最多20Kb以内的个性化探针,进行多混一杂交。
步骤四:标准品测试检测性能
测试突变频率为0.5%,0.1%,0.05%标准品(购买的twist商品化试剂,cfDNA Pan-Cancer Reference Standard)的突变检出情况。使用Twist商品化cfDNA标准品进行文库构建,分别投入10 ng,30 ng,50 ng核酸进行测序文库构建,同步骤三。然后分析突变检出情况。本发明使用0.05%标准品的性能模拟样本阳性的情况。随机选择n个突变进行追踪,如果检测到大于等于1个突变,就报MRD阳性,或检测到大于等于2个突变,才报MRD阳性;n为横坐标,Y为MRD阳性检出百分比,检测结果如图5所示。在低于20个追踪点时,1个突变就报MRD阳性,敏感性远大于2个突变MRD阳性。当位点追踪数超过20个时,特异性低于99%。因此本发明选择最多追踪20个位点,样本阳性的条件为检测到大于等于1个突变。
接着分析不同ctDNA投入量,不同ctDNA浓度下标准品的性能,通过截取数据的方式,测试不同数据量下检测的敏感性、特异性、阳性预测值、阴性预测值。
敏感性表示为:检测结果阳性且实际阳性的突变,占标准品中阳性突变的比例。
特异性表示为:检测结果阴性且实际阴性的突变,占标准品中阴性突变的比例。
阳性预测值:检测结果阳性且实际阳性的突变,占所有检测为阳性突变的比例。
阴性预测值:检测结果阴性且实际阴性的突变,占所有检测为阴性突变的比例。灵敏度与cfDNA投入量密切相关,可0.05%突变可达30%以上灵敏度,追踪20个位点,理论可检测到至少6个位点(图6)。
步骤五:实际临床样本测试
回顾性统计120例MRD样本(样本来源于医院科研样本,其中肺癌50例,肠癌50例,10例胃癌,10例食管癌),均使用固定化+个性化探针杂交实验。100例MRD阴性,20例MRD阳性;其中MRD阴性样本2年复发率5%,MRD阳性样本2年复发率70%。若不考虑个性化探针,只用固定化区域进行分析,则MRD阳性样本2年复发率70%,MRD阴性样本2年复发率11%。若不考虑固定化探针,只考虑个性化探针分析,MRD阳性样本2年复发率73%,MRD阴性样本2年复发率7.6%。固定化+个性化组合使用可以提高阳性检出率,降低阴性复发率。
尽管上述实施例对本发明做出了详尽的描述,但它仅仅是本发明一部分实施例,而不是全部实施例,人们还可以根据本实施例在不经创造性前提下获得其他实施例,这些实施例都属于本发明保护范围。
Claims (10)
1.一种实体瘤微小病灶残留检测panel的筛选方法,其特征在于,包括以下步骤:
以泛癌种的高频驱动基因的全外显子区域为第一数据集;所述泛癌种的高频驱动基因包括EGFR、TP53、VHL、SMAD4、CDH1、PBMR1、KRAS、NRAS和BRCA2;
利用已知的肿瘤突变数据构建所述泛癌种的第二数据集;所述构建的原则包括:保留肿瘤变异等位分数/正常变异等位分数≥5、肿瘤突变深度≥8、肿瘤测序深度≥50和肿瘤变异等位分数≥1%的基因突变区域和突变位点;
将所述第一数据集和第二数据集中基因组距离低于80bp的突变位点在参考基因组下进行合并,只保留基因组的突变区域坐标,不考虑突变类型,得到突变区域数据集;
以已知的肿瘤数据库为样本集,在不同癌种水平,计算所述突变区域数据集中每个突变区域的患者阳性率,每个癌种均筛选阳性率从高到低的500个突变区域;将各癌种阳性率最高的位点进行逐步累加,得到panel区域大小与患者覆盖度的关系曲线;所述患者覆盖度=平均突变个数≥1的患者数/总患者数;所述平均突变个数=可检测出的所有突变数/总患者数;
在所述关系曲线中选择患者覆盖度为94.2%~96.5%时的区域为检测panel的区域大小;
根据所述检测panel的区域大小将第一数据集和第二数据集的突变区域任意组合,得到多种突变区域组合;
计算每种所述突变区域组合的突变平均贡献A和平均突变个数;所述突变平均贡献A=突变区域组合覆盖的泛癌种患者数/突变区域组合的区域大小;
所述突变平均贡献A×平均突变个数最大的突变区域组合为所述实体瘤微小病灶残留检测panel。
2.根据权利要求1所述的筛选方法,其特征在于,所述泛癌种包括:尿路上皮癌、膀胱癌、结直肠癌、食管癌、肾癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、***癌和胃癌;所述参考基因组包括hg19。
3.根据权利要求1或2所述的筛选方法,其特征在于,所述第二数据集中不含有所述第一数据集中的全外显子区域。
4.根据权利要求1或2所述的筛选方法,其特征在于,所述已知的肿瘤数据库包括TCGA数据库。
5.利用权利要求1~4任一项所述筛选方法得到的实体瘤微小病灶残留的检测panel,其特征在于,
所述检测panel如下,
。
6.一种检测实体瘤微小病灶残留的探针组,其特征在于,所述探针组包括固定探针组和个性化探针组;所述固定探针组的靶标区域为权利要求5所述的检测panel;
所述个性化探针组的靶标区域为待测患者肿瘤组织中突变频率从高到低排序,前20的位点。
7.根据权利要求6所述的探针组,其特征在于,所述固定探针组中各探针的浓度为0.2fmol/μL;所述个性化探针组中各探针的浓度为2fmol/μL。
8.权利要求6或7所述的探针组在制备检测实体瘤微小病灶残留的产品中的应用。
9.根据权利要求8所述的应用,其特征在于,所述实体瘤包括尿路上皮癌、膀胱癌、结直肠癌、食管癌、肾癌、肝癌、肺腺癌、肺鳞癌、胰腺癌、***癌和胃癌。
10.一种实体瘤微小病灶残留的检测***,其特征在于,所述检测***包括:
样本采集模块,用于采集待测患者的外周静脉血,得到待测样本;
文库构建模块,与所述样本采集模块连接,用于提取所述待测样本的循环游离DNA构建待测文库;
检测模块,与所述文库构建模块连接,用于获取所述待测文库的突变位点检出结果;所述获取包括:利用权利要求6或7所述的探针组获取所述待测文库的突变位点检出结果;
结果输出模块,与所述检测模块连接,用于输出待测患者的检测结果;所述输出包括:若所述探针组检测到突变位点,则判定为阳性,复发风险高;若所述探针组未检测到突变位点,则判定为阴性,复发风险低。
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